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Evolving the Doctor’s Waiting Room: Applying AI to Visioning the Future, a Cartographic Approach

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DOI: 10.31038/IMROJ.2024911

Abstract

SCAS (Socrates as a Service), incorporating ChatGPT 3.5, was instructed to create ten visions for the future waiting room of the doctor, based upon positioning the waiting room as part of the continuum of healthcare, rather than just a room housing people before they are admitted to see the medical professionals. SCAS ‘fleshed out’ each of the 10 visions in paragraph form, and then generated 15 questions about aspects of implementing these visions. In the subsequent iteration, SCAS was given the 15 questions it had previously created, and instructed to provide answers to each question, as well as estimate the difficulty involved in achieving what the answer specified. At the end of the process, about 15 minutes later, the program returns with a detailed Microsoft Excel file, the Idea Book, showing each iteration, and providing additional insights generated by the AI-based ‘Summarizer.’ The process presented here shows the power of AI to help create the future by allowing easy-to-create, quick-to-run queries, and providing detailed answers and additional subsequent analysis. With the turn-around time in seconds, and with a system which is iterative, the user can explore a topic in depth, generating a strong educational experience for any topic that one can imagine. The strength of the approach is the ability to help one think and envision in a way which is absorbing, user-driven, and often filled with surprises.

Introduction

What the future will bring is always a topic of conversation. It becomes especially interesting when the focus is on something relevant to the daily lives of people, and when the evolution may improve the quality of life. This paper is an attempt to envision the doctor’s waiting room as part of a smooth transition from greeting the patient to treating the patient. The strategy uses AI to help envision the future, doing so in a way which requests specificity from AI. Furthermore, the objective is to integrate OpenAI’s freely available AI ChatGPT 3.5, a large language model (LLM), into an easy-to-use system with a limited number of queries, automatically built-in. All the work is done behind the scenes by ChatGPT 3.5 [1]. The objective is to create a system which lets the user focus on the work, not on the effort to code. As a result, the user spends time learning through iterations, the immediate feedback allowing a quick change of query, with the iterations eventually generating the information desired. The notion here is that AI becomes the collaborator in the up-front development, and in some cases a participant who estimates the difficulty of the task [2,3].

The approach used in this paper is called SCAS, an abbreviation for Socrates as a Service. The name comes from the belief that the approach presented here is a coach to creative thinking. Rather than Socrates ‘pulling out’ ideas from the mind of slaves through questioning, the idea is for people to pull out ideas from the mind of AI through questioning. The questions are not meant to be factual, but conceptual. The user is challenged to let Socrates give expansive answers, not constrained ones, while at the same time ensuring that these expansive answers are feasible and produce something tangible. [4] called this approach ‘Homo Silicus.’

Systematizing New Idea Creation

Creating new ideas is a well-accepted business objective. Business always needs to create ‘new’ for the simple reasons that business must keep up with changes, whether the changes be responses to truly new situations and their requirements, or activities to maintain the interest of fickle customers. The world changes, people get tired of what they have, the two of these factors combining to force the creation of the new as a standard part of one’s operations, one’s business.

With all the focus on ‘new,’ it should come as no surprise that there are myriad publications with their pronouncements of how to create the new, hundreds of thousands of experts who are ready to advise about the ‘new’ at a moment’s notice and for an agreed-upon fee, not to mention the multitudinous best practices which dot the horizon of business.

The introduction of AI has changed the world of new product development. With user-friendly technology such as that embedded in Open AI’s ChatGPT 3.5, it has become possible to provide AI with a specification, a request, and let AI return with the suggestions. It is from experience with AI in this format that the study reported here arose. The paper focuses on the application of AI to envision the patient’s waiting room, an integral part of the medical facility, as a room where the healing process begins.

The process presented here grew out of the effort to understand how people respond to the world. The typical process of understanding the mind of a person with respect to something like the patients’ waiting room is to ask questions about the waiting room, to identify the relevant features, and their importance. The typical approach is to use some type of scale and instruct the respondent to rate the importance of different features that the waiting room might feature. It is from this type of scaling that the designer gets a sense of what is important. It is up to the designer to then take the information and create the prototype rooms to be evaluated for acceptance, utility, and so forth. Sometimes the survey is put aside or occurs after a discussion with participants about the new patient’s waiting room. In this case a trained interviewer discusses the nature of the waiting room, either with one or two people in a so-called ‘depth interview’ or convenes a group of people called a ‘focus group’. In both cases the researcher ends up conducting a structured interview in the form of a conversation. The foregoing process is a standard part of most research approaches to understand what is needed in a new product.

One emerging problem embedded deeply in the foregoing conventional process is the failure to deal with the details. Surveys, discussions, and so forth can provide direction, but it is the execution of these ideas which is important. In research this is known as the gap between ‘strategy,’ the big idea, and ‘execution,’ making the particulars a reality and bringing that into the world. The emerging science of Mind Genomics was developed to incorporate the specifics, the executions, into the research process [5]. Rather than instructing the respondent to rate general ideas, hoping that these ideas would end being well executed, the Mind Genomics approach took its cue from nature. The approach was to create small vignettes, combinations of concrete ideas, specific descriptions, instruct respondents to rate these vignettes, and then through statistics (regression, clustering) deconstruct the response to the part-worth contribution of the individual concrete elements, the granular information shown to the respondent . The outcome was the recognition that valuable data could be obtained by focusing on the specifics of a situation, rather than focusing on large, featureless, general ideas. A good strategy was to generalize from the pattern of particulars, easier to do than particularizing from a featureless generality. It was this realization which pushed the focus of Mind Genomics to focus on the concrete, the granular.

It was the pattern of questions and answers, begun with Mind Genomics, which was to evolve to the approach presented here. In its current form, Mind Genomics requests the user to create four questions regarding a topic (see Figure 1, Panel A), and then for each of the four questions create four answers (see Figure 1, Panel B). The process thus creates 16 elements, the four sets, each comprising four answers. It was the ensuing difficulty with having a user, develop four questions which eventuated in introducing AI was used as a coach, called, not surprisingly, the Idea Coach. The user would type background to a topic in the form of an AI prompt, which Mind Genomics calls a ‘squib’ (Figure 1, Panel C). In turn, the Idea Coach would then use AI to develop the four questions. (Figure 1, Panel D). The same approach could be used to create four answers to each question. The process of using AI a system to generate questions for a specific topic, or answers for a specific question soon moved AI to a central role in Mind Genomics.

fig 1

Figure 1: The original process in the set-up of a Mind Genomics study. Panel A shows the request for four questions which ‘tell a story’. Panel B shows one question, with the request to provide four answers. Panel C shows the background to the project and the desired results, the so-called ‘squib’, typed into the Idea Coach. Panel D shows 5 of the 15 questions provided by SCAS operating within Idea Coach.

It soon became apparent that the embedded AI in Mind Genomics was being used far more to create ‘books of knowledge’. The user would create a set of questions to ‘brief’ the AI, and then run the AI several times, occasionally just iterating, but often changing the input slightly and then iterating. The emerging focus was using the embedded AI to provide a range of alternative answers to the same question. Furthermore, the AI had been programmed to provide additional summarization, viz., post-emergent analysis. The system ended up being renamed SCAS, Socrates as a Service, in recognition of this evolution from a system which recommended questions and answers into a system to educate people on a topic, using AI to help people think critically about a topic, imagine alternative realities, and create by typing descriptions of those realities.

Using AI to Drive the Future

The immediate stimulus driving this set of experiments resulted from a fortunate accident. than simply asking a question and getting the SCAS to provide 15 answers, something that was done a dozen times in the set up of a Mind Genomics study, the user this time created a more detailed briefing, by writing down both the relevant input for Idea Coach, but then additional, background information that was usually part of the thinking, but never submitted to the AI in Idea Coach. The AI was ‘briefed’ in a far more detailed way, with thoughts, requests, hypotheses, and so forth, again by accident, but an accident which proved remarkably productive as shown below.

Table 1 show the entire briefing. Table 1 is divided into two parts, shaded and unshaded, respectively. The shaded parts provide modest details, specifically the nature of the room (part of the flow, first room in a sequence of room), followed by a statement of ‘fact’, viz., that there are ten mind-sets. There is no supporting evidence given for the statement about ten mind-sets, however. The user could just have easily specified three mind-sets or 13 mind-sets. The second part of Table 1 shows the general instructions to the AI. These are to focus on the room itself, and afterwards on the reaction of people to this room. Again, Table 1 is far more extensive. We can contrast this extensive brief with the ordinary brief of that time, which would be something like: ‘What are questions to ask about the nature of the patient’s waiting room, if we think of the next decade, starting in 2030?’

Table 1: The briefing given to AI about the waiting room outside the doctor’s office

tab 1

The actual creation of the AI briefing takes a few minutes. Once the AI is provided with the briefing, the AI is invoked through SCAS, the Socrates as a Service procedure, embedded in the Mind Genomics program. Within 10-20 seconds the AI returns with preliminary answers along with an additional set of 15 questions for consideration..

Table 2 presents the 10 visions of the patient’ waiting room, attempting to follow the request to discuss the room itself as an integral part of the visit, and of the continuum from waiting room to examination room. Each of the 10 visions is labelled, followed by a description of the room itself, and then followed by the response of the patient in that room.

Table 2: The 10 visions for the patient’s waiting room as part of the continuum for health and healing. Each vision comprises a name, a description of the room, and then a description of the feelings of people towards that ‘now-evolved’ space.

tab 2

The important thing to keep in mind is that the AI was not given any information at all, other than this was to be a patient’s waiting room but was also going to be part of the spatial continuum, and thus an integral part of the ‘path’ for health and healing.

As noted above, each iteration was accompanied by 15 questions to answer. These questions are based on the topic. The questions emerge automatically. The next step was to copy the 15 questions into a new run of Idea Coach. Table 3 shows the 15 questions (underlined), two answers to each question provided by SCAS and for each question an estimate of the difficulty of achieving that answer (Easy vs Difficult).

Table 3: The 15 questions generated by SCAS when it provided information about the vision, along with two answers to each question, and the estimated difficulty of implementing the answer. The questions were ‘answered’ by a separate run of SCAS, where the questions were inserted as part of the briefing to AI.

tab 3

Summarizing and Expanding the Ideas; Themes and Perspectives

SCAS generates a great deal of information when it creates visions (Table 2) and generates questions (Table 3). One of the benefits of AI is the ability to summarize the information in a succinct way. Table 4 shows two summarizations. The top of Table 4 shows the key ideas. The bottom of Table 4 shows the four themes identified by SCAS, followed by the good (plus), bad (minus) and novel (interesting) aspects of each theme. It is in Table 4 that we begin to see a secondary analysis by AI of the information generated by AI.

Table 4: Key ideas, themes and perspectives

tab 4

Table 5 moves the analysis to who would be interested in these ideas, and who would be opposed to them. Once again we see a deeper secondary analysis of the information provided by AI.

Table 5: Interested versus opposing audience to the topic ideas

tab 5

Driving Towards Innovation

Table 6 shows the final analysis provided by AI, comprising three sets of analyses/questions which drive towards innovation. The first comprises a set of 10 questions about what is missing. This first set is meant to challenge the user. The second comprises a set of 16 alternative viewpoints, contrasting answers to a question. The third focuses on innovations, comprising a set of four general categories or topics, each with several suggestions.

Table 6: Steps toward innovation, comprising ‘what is missing,’ alternative viewpoints, and then categories or topics with several suggestions each.

tab 6(1)

tab 6(2)

tab 6(3)

Discussion and Conclusions

The focus of this paper is to present an approach to solving specific problems, once these problems are specified and provided to AI in a useful manner. The AI approach used here, SCAS, Socrates as a Service, was developed to help critical thinking, and to focus attention on potential answers. Rather than focusing the time on elaborating the question, SCAS builds on the richness of AI, especially in the world of design. The effort shown here revolves around the desire to structure a development effort, or perhaps even a research effort.

As we consider what has been presented here, it should be kept in mind that the work presented here belongs to the world of philosophy of science, specifically the topic of ‘hard to define’ problems [6]. [7] presented the issue which summarizes the thrust of this paper. In their words: “‘The formulation of a problem is often more essential than its solution’….To raise new questions, new possibilities, to regard old problems from a new angle, requires imagination’ Yet, how the creative process unfolds in framing ill-defined problems remains an open question. Indeed, untangling the interrelation between problem framing and the creative process in its lower-level aspects can inform scholars.. Problem framing entails building mental representations that simplify the problem..”

At this point, once the results of the effort have been presented, it may well be productive to contrast what has gone on in this paper versus the standard scientific or brainstorming effort which characterizes current research. Conventional research begins with observations, a hypothesis, and then establishes the validity or the falseness of that hypothesis through one or more experiments. The objective of science is to amass many hypotheses that have been shown to be correct, or more precisely, have not been falsified from experiments. The current research follows a different approach, one which ‘explores’ rather than confirms. The term ‘cartography’ is appropriate here. The approach here is ‘mapping’ the topic, almost as an explorer maps an area that is unknown. Knowledge becomes the accretion of these cartographies, and human advancement may end up through the application of these cartographies, the applications showing up in changes in the worlds of the material and of experience, respectively.

References

  1. Binz M, Schulz E (2023) Using cognitive psychology to understand GPT-3. Proceedings of the National Academy of Sciences 120(6)
  2. Kitadai A, Tsurusaki Y, Fukasawa Y, Nishino N (2023) Toward a novel methodology in economic experiments: Simulation of the Ultimatum Game with large language models. 2023 IEEE International Conference on Big Data 3168-3175.
  3. Gray K, Dillon D, Tandon N, Yuling G (2023) Can AI language models replace human participants? Science & Society. Trends Cogn Sci 27: 597-600. [crossref]
  4. Horton J.J (2023) Models as Simulated Economic Agents: What Can We Learn from Homo Silicus? National Bureau of Economic Research Working Paper 31122.
  5. Moskowitz HR (2012) ‘Mind Genomics’: The experimental, inductive science of the ordinary, and its application to aspects of food and feeding. Physiology & Behavior 606-613. [crossref]
  6. Garbuio M, Lin N (2021) Innovative idea generation in problem finding: Abductive reasoning, cognitive impediments, and the promise of artificial intelligence. Journal of Product Innovation Management 38: 701-725.
  7. Pham CTA, Magistretti S, Dell’Era C (2023) How do you frame ill-defined problems? A study on creative logics in action. Creativity and Innovation Management 32: 493-516.

Synthesis and Biological Evaluation of Chalcone Analogues as HIV Latency Reversing Agents

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DOI: 10.31038/JPPR.2024713

Abstract

The primary barrier of eradicating HIV virus is the existence of latent reservoirs. To overcome this problem, it is urgent to identify specific and effective agents that can activate HIV latent reservoirs and bind with effective anti-retroviral therapy to eventually eliminate HIV virus. In previous study, we reported a chalcone analogue called Amt-87 that can significantly reactivate the transcription of latent HIV. However, it is of limited use due to high toxicity. Here, we have synthesized a series of structurally modified analogues of Amt-87 and evaluation for their reactivation of HIV-1 latency revealed one excellent active compound 4k, which can be taken up for further studies.

Introduction

It is more than 30 years passed since HIV was first reported in 1981 [1]. There are many drugs designed by targeting enzymes which are important for replication event during different stages of HIV-1 life cycle, preventing HIV-1 virus replication, therefore decrease the virus in the host blood to an undetectable level [2,3]. However, HAART is unable to completely eliminate the virus due to the existence of latent viral reservoirs, which are formed early after host infection and undetectable by common clinical tests [4], thus the host immune system can be invaded again upon the treatment interruption. HIV latency reservoir in the host CD4 T cell is the main obstacle to cure HIV-1 as reported recent years. Effective curative strategies aiming at eradication of HIV-1 virus are being developed [5-7]. One significant therapy which is called “shock and kill” has been proposed to achieve HIV eradication, there are two steps in this approach, using latency-reversing agents (LRAs) to reactivate the latent proviruses in the “shock” phase and then combining with the HAART to make the reactivated cells be sensitive to host immune system and cytopathogenicity response in the “kill” phase [8,9]. In this strategy, finding drugs to activate HIV latency catches much attention. Currently there are several activators have been proved, for example HMAB, SAHA, prostration JQ1 and so on, but most of these drugs are toxic to host cell and have some side effects [9]. Thus, highly efficient and specific drugs for reactivating HIV latency is important for HIV-1 eradication. Chalconoids, natural phenol compounds, have an array of biological activities including anti-inflammatory, antioxidant, antitumor and antibacterial activities and inhibit angiogenesis in vivo and in vitro [10-14]. Chalcone analogues have attracted a great deal of interest due to their synthetic and biological importance in medicinal chemistry. In our previous study, we reported the synthesis and biological evaluation of a chalcone analogue Amt-87, which can significantly reactivate the transcription of latent HIV proviruses and act synergistically with known LRAs such as prostratin and JQ1 to reverse HIV latency [15]. Unfortunately, Amt-87 is less active and requires high concentrations to reactivate latent HIV. Herein, we describe the optimization and characterization of the structure−activity relationship (SAR) of chalcone scaffold to generate a series of compounds with enhanced HIV latency reactivation activity.

Results

Synthesis of Chalcone Derivatives

Chalcone derivatives 4a-4k were prepared according to a two-step procedure (Scheme 1) previously reported. Condensation of 1-(2-hydroxy-5-methylphenyl)ethanone with 2- or 3-carboxybenzaldehyde in the presence of alkali gave carboxylic acid derivatives 3a and 3b. Treatment of 3a or 3b in CH2Cl2 with various amines in the presence of 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDCI) and N-hydroxybenzotriazole (HOBt) afforded various amides 4a-4k with yields ranged from 30% to 50% (Scheme 1).

scheme 1

Scheme 1: Synthesis of chalcone amide derivatives 4a-4k. Reagents and conditions: (a) NaOH, EtOH, r.t., 24 h. (b) various amines, anhydrous HOBt, EDCI, DCM, 12 h.

Reactivation of latent HIV-1 in J-Lat A2 cells and structure–activity relationship. In order to identify compounds that can efficiently reactivate latent HIV-1, the J-LatA2 latency model was used in a screen that involved flow cytometry. This cell line was generated by infecting Jurkat T cells with viral particles containing an HIV-1 vector expressing Tat and GFP under the control of the viral 5’ LTR, with an IRES (internal ribosome entry site) separating the two open reading frames [15]. All synthesized chalcone compounds were evaluated for their activation of latent HIV-1 in J-Lat A2 cells and Amt-87 was used as the positive control. The results are presented in Table 1. The structure–activity relationship (SAR) was further explored as described below. Among these structurally related chalcone derivatives, we found that 4b, 4d, 4g were more active than Amt-87 in terms of HIV latency-reversing activity. Specifically, 4g showed the most optimal properties in the latent HIV-1 reactivation activity, inducing 14.6 ± 2.1% and 20.5 ± 2.8% GFP production in J-Lat A2 cells at 25 μg/mL and 50 μg/mL, respectively. What’s more, compound 4g has no significant cytotoxicity at a concentration of 50 μg/mL. These observations may demonstrate the potential of chalcone derivatives as LRA leads.

Table 1: Summary of HIV reactivation of Chalconoids in J-Lat cells

tab 1

Levels of HIV-1 activation and cell viability were evaluated by FACS analysis. Data are presented as mean ± SD of at least three independent experiments performed in triplicate.

Discussion

In this study, we synthesized and biologically evaluated chalcone derivatives for their HIV latency-reversing activity using HIV-LTR based cell model. Among these new derivatives, compound 4g effectively induced GFP production in J-Lat A2 cells at 25 and 50 μg/mL, while displaying no significant cytotoxicity at these concentrations. This study provides valuable data for the future development of chalcone as effective LRAs.

Materials and Methods

Chemistry

General procedure for the synthesis of Chalcones [15]. Claisene Schmidt condensation. To a solution of the corresponding aldehyde (1 equiv) and corresponding acetophenone (1 equiv) in EtOH (3 mL for 1 mmol of acetophenone) was added NaOH (5 equiv). The reaction mixture was stirred at room temperature for 24 h and neutralized with 10% HCl solution to form yellow precipitate. The yellow precipitate was filtered and washed with appropriate amount of water. The crude product was purified by chromatography using hexane/EtOAc and recrystallized by MeOH to give the desired compounds.

(E)-2-(3-(2-Hydroxy-5-Methylphenyl)-3-Oxoprop-1-en-1-yl)Benzoic Acid (3a)

Following the general procedure for the Claisen-Schmidt condensation, 1-(2-hydroxy-5-methylphenyl)ethanone 1 (1.1 mmol, 165.2 mg) and 2-carboxybenzaldehyde (1.0 mmol, 150.1 mg) were used to give 3a as a yellow solid. Yield 45.7%. 1H NMR (600 MHz, C5D5N): δ 8.41 (d, J=8. 2 Hz, 2H), 8.19 (d, J=15.5 Hz, 1H) , 8.12 (d, J=17.2 Hz, 1H), 8.07 (d, J=1.0 Hz, 1H), 7.88 (d, J=8.2 Hz, 2H), 7.31 (dd, J=1.8, 8.4 Hz, 1H), 7.1l (d, J=8.4 Hz, 1H), 2.18 (s, 3H). 13C-NMR (150 MHz, C5D5N): δ 193.9, 168.3, 161.6, 143.9, 138.7, 137.7, 137.2, 130.5 (2C), 129.0 (2C), 128.3, 126.4 (2C), 120.5, 118.2, 20.0. ESI-HRMS (-): m/z [M-H]- calcd for C17H13O4–, 281.0819, found, 281.0816.

(E)-3-(3-(2-Hydroxy-5-Methylphenyl)-3-Oxoprop-1-en-1-yl)Benzoic Acid (3b)

Following the general procedure for the Claisen-Schmidt condensation, 1-(2-hydroxy-5-methylphenyl)ethanone 1 (1.1 mmol, 165.2 mg) and 3-carboxybenzaldehyde (1.0 mmol, 150.1 mg) were used to give 3b as a yellow solid. Yield 50.5%. 1H NMR (600 MHz, DMSO-d6): δ 12.3 (s, 1H), 8.40 (s, 1H), 8.18 (d, J=7.9 Hz, 1H), 8.08-8.14 (m, 2H), 8.03 (d, J=7.7 Hz, 1H), 7.89 (d, J=15.6 Hz, 1H), 7.62 (t, J=7.7 Hz, 1H), 7.40 (dd, J=1.7, 8.3 Hz, 1H), 6.92 (d, J=8.3 Hz, 1H), 2.33 (s, 3H); 13C NMR (150 MHz, DMSO-d6): δ 193.9, 167.4, 160.3, 144.0, 137.8, 135.4, 133.5, 132.2, 131.8, 131.0, 130.2, 129.7, 128.5, 123.5, 120.8, 118.0, 20.4; ESI-HRMS (-): m/z [M-H]- calcd for C17H13O4–, 281.0819, found, 281.0814.

General Procedure for Synthesis of Amide Compounds 1a-1p. A Mixture of 3 (3a or 3b)

(1 equiv), HOBt (1.2 equiv) and EDCI (1.2 equiv) was dissolved in CH2Cl2, and stirred for 30 min. The mixture was then added with appropriate amine (2.0 equiv), and stirred at the room temperature for 12 h. After completion of the reaction, the mixture was concentrated in vacuum to give the crude product. The crude product was purified by column chromatography with hexane/EtOAc, and recrystallized with EtOAc to afford pure products.

(E)-N-(2-(1H-Indol-2-yl)Ethyl)-2-(3-(2-Hydroxy-5-Methylphenyl)-3-Oxoprop-1-en-1-yl)Benzamide (4a)

The title compound 4a was obtained by the reaction of compound 3a with 2-(1H-indol-2-yl)ethan-1-amine following the general procedure. Yellow solid, yield 57.5%, mp 182-183°C. 1H-NMR (600 MHz, CHLOROFORM-d): δ 12.49 (s, 1H), 8.08 (d, J=15.6 Hz, 1H), 7.57-7.59 (m, 1H), 7.52 (d, J=7.9 Hz, 1H), 7.45 (td, J=3.2, 8.9 Hz, 4H), 7.38 (dt, J=1.6, 7.5 Hz, 1H), 7.30-7.35 (m, 2H), 7.26 (dd, J=2.0, 8.4 Hz, 1H), 7.23 (d, J=8.1 Hz, 1H), 7.09 (t, J=7.6 Hz, 1H), 7.06 (d, J=2.2 Hz, 1H), 6.99-7.03 (m, 1H), 6.88 (d, J=8.4 Hz, 1H), 3.80 (d, J=6.2 Hz, 2H), 3.05 (t, J=6.5 Hz, 2H), 2.27 (s, 3H); 13C-NMR (151 MHz, CHLOROFORM-d): 192.6, 167.6, 166.7, 160.5, 141.7, 136.9, 136.6, 132.0, 131.3, 129.9, 129.2, 128.6, 127.8, 127.0, 126.7, 126.4, 121.9, 121.3, 121.2, 118.6, 118.5, 117.6, 117.4, 110.3, 64.6, 39.0, 29.5; HRMS calcd for C27H24N2O3 [M-H]- 423.1714, found 423.1718.

(E)-2-(3-(2-Hydroxy-5-Methylphenyl)-3-Oxoprop-1-en-1-yl)-N-(2-(Thiophen-2-yl)Ethyl)Benzamide (4b)

The title compound 4b was obtained by the reaction of compound 3a with 2-thiopheneethanamine following the general procedure. Yellow solid, yield 57.5%, mp 182-183°C. 1H-NMR (600 MHz, CHLOROFORM-d): δ 12.47 (s, 1 H), 8.08 (d, J=15.41 Hz, 1H), 7.67 (d, J=7.70 Hz, 1H), 7.58 (s, 1H), 7.46 (d, J=15.41 Hz, 1H), 7.37-7.42 (m, 2H), 7.34 (t, J=7.4 Hz, 1H), 7.22-7.25 (m, 1H), 7.04 (d, J=5.0 Hz, 1H), 6.85 (d, J=8.4 Hz, 1H), 6.81-6.83 (m, 1H), 6.79 (d, J=3.1 Hz, 1H), 5.98 (br. s., 1H), 3.68 (q, J=6.4 Hz, 2H), 3.10 (t, J=6.6 Hz, 2H), 2.26 (s, 3H); 13C-NMR (151 MHz, CHLOROFORM-d): 193.5, 168.6, 161.5, 142.6, 141.0, 137.6, 137.6, 133.3, 130.4, 130.2, 129.6, 128.0, 127.7, 127.5, 127.1, 125.6, 124.1, 123.0, 119.6, 118.4, 41.5, 29.7, 20.6; HRMS calcd for C23H21NO3S [M-H]- 390.1169, found 390.1171.

(E)-2-(3-(2-Hydroxy-5-Methylphenyl)-3-Oxoprop-1-en-1-yl)-N-Octylbenzamide (4c)

The title compound 4c was obtained by the reaction of compound 3a with octan-1-amine following the general procedure. Yellow solid, yield 57.5%, mp 182-183°C. 1H-NMR (600 MHz, CHLOROFORM-d): 12.47 (s, 1H), 8.09 (s, 1H), 7.69 (d, J=7.7 Hz, 1H), 7.59-7.61 (m, 1H), 7.50 (d, J=15.4 Hz, 1H), 7.45 (dd, J=1.3, 7.5 Hz, 1H), 7.42 (dt, J=1.2, 7.6 Hz, 1H), 7.36-7.39 (m, 1H), 7.24 (dd, J=2.0, 8.4 Hz, 1H), 7.19 (s, 1H), 6.86 (d, J=8.4 Hz, 1H), 3.38-3.42 (m, 2H), 2.27 (s, 3H), 1.14-1.33 (m, 12H), 0.80 (t, J=7.1 Hz, 3H); 13C-NMR (151 MHz, CHLOROFORM-d): 192.4, 167.6, 160.5, 141.6, 137.0, 136.6, 132.1, 129.2, 129.2, 128.5, 126.9, 126.8, 126.6, 122.0, 118.5, 117.3, 39.3, 30.8, 28.6, 28.2, 28.2, 26.0, 21.6, 19.6, 13.1; HRMS calcd for C25H31NO3 [M-H]- 392.2231, found 392.2233.

(E)-N-Benzyl-2-(3-(2-Hydroxy-5-Methylphenyl)-3-Oxoprop-1-en-1-yl)Benzamide (4d)

The title compound 4d was obtained by the reaction of compound 3a with benzylamine following the general procedure. Yellow solid, yield 57.5%, mp 182-183°C. 1H-NMR (600 MHz, CHLOROFORM-d): 12.48 (s, 1H), 8.11 (d, J=15.6 Hz, 1H), 7.65 (d, J=7.7 Hz, 1H), 7.55 (s, 1H), 7.42-7.45 (m, 2H), 7.39 (t, J=7.4 Hz, 1H), 7.31-7.34 (m, 1H), 7.21-7.25 (m, 3H), 7.16-7.19 (m, 1H), 6.83 (d, J=8.4 Hz, 1H), 6.21 (br. s., 1H), 4.54 (d, J=5.5 Hz, 2H), 2.24 (s, 3H), 1.18 (br. s., 1H); 13C-NMR (151 MHz, CHLOROFORM-d): 193.5, 168.5, 161.5, 142.6, 137.7, 137.6, 137.5, 133.2, 130.4, 130.2, 129.6, 128.9, 128.0, 128.0, 127.8, 127.7, 127.6, 123.0, 119.5, 118.3, 44.3, 20.6; HRMS calcd for C24H21NO3 [M-H]- 370.1449, found 370.1448.

(E)-N-(Tert-Butyl)-2-(3-(2-Hydroxy-5-Methylphenyl)-3-Oxoprop-1-en-1-yl)Benzamide (4e)

The title compound 4e was obtained by the reaction of compound 3a with 2-methylpropan-2-amine following the general procedure. Yellow solid, yield 57.5%, mp 182-183°C. 1H-NMR (600 MHz, CHLOROFORM-d): 12.48 (s, 1H), 8.06 (d, J=15.4 Hz, 1H), 7.64 (d, J=7.5 Hz, 1H), 7.58-7.60 (m, 1H), 7.47 (d, J=15.5 Hz, 1H), 7.32-7.41 (m, 3H), 7.22 (dd, J=2.0, 8.4 Hz, 1H), 6.83 (d, J=8.6 Hz, 1H), 5.63 (br. s., 1H), 2.26 (s, 3H), 1.41 (s, 9H); 13C-NMR (151 MHz, CHLOROFORM-d): 192.5, 167.2, 160.5, 141.7, 137.9, 136.5, 131.7, 129.2, 128.9, 128.5, 126.9, 126.7, 126.6, 121.8, 18.5, 117.3, 51.3, 28.7, 27.8, 19.5, 13.1; HRMS calcd for C21H23NO3 [M-H]- 336.1605, found 336.1607.

(E)-2-(3-(2-Hydroxy-5-Methylphenyl)-3-Oxoprop-1-en-1-yl)-N-Phenethylbenzamide (4f)

The title compound 4f was obtained by the reaction of compound 3a with 2-phenylethan-1-amine following the general procedure. Yellow solid, yield 57.5%, mp 182-183°C. 1H-NMR (600 MHz, CHLOROFORM-d) 12.48 (s, 1H), 8.09 (d, J=15.41 Hz, 1H), 7.68 (d, J=7.70 Hz, 1H), 7.59 (s, 1H), 7.47 (d, J=15.59 Hz, 1H), 7.41 (br. s., 1H), 7.33-7.36 (m, 2H), 7.25 (d, J=8.25 Hz, 1H), 7.18-7.22 (m, 3H), 7.15-7.17 (m, 2H), 7.12 (d, J=7.15 Hz, 1H), 6.87 (d, J=8.44 Hz, 1H), 3.67-3.72 (m, 2H), 2.89 (t, J=6.79 Hz, 2H), 2.27 (s, 3H)；13C NMR (151 MHz, CHLOROFORM-d) 192.5, 167.6, 160.5, 141.5, 137.5, 136.7, 136.6, 132.2, 129.3, 129.2, 128.5, 127.7, 127.7, 126.9, 126.7, 126.5, 125.6, 122.0, 118.5, 117.4, 40.1, 34.5, 19.6;HRMS calcd for C25H23NO3 [M-H]- 384.1605, found 384.1603.

(E)-3-(3-(2-Hydroxy-5-Methylphenyl)-3-Oxoprop-1-en-1-yl)-N-(2-Methoxyphenyl)Benzamide (4g)

The title compound 4g was obtained by the reaction of compound 3b with 2-methoxyaniline following the general procedure. Yellow solid, yield 57.5%, mp 182-183°C. 1H-NMR (600 MHz, CHLOROFORM-d): 12.49 (br. s., 1H), 8.49 (br. s., 1H), 8.42 (d, J=7.0 Hz, 1H), 8.12 (br. s., 1H), 7.75-7.83 (m, 2H), 7.68 (d, J=7.3 Hz, 1H), 7.61-7.65 (m, 1H), 7.59 (br. s., 1H), 7.42-7.45 (m, 1H), 7.18-7.22 (m, 1H), 7.00 (dt, J=1.7, 7.8 Hz, 1H), 6.90-6.94 (m, 1H), 6.81-6.84 (m, 2H), 3.83 (s, 3H), 2.25 (s, 3H); 13C-NMR (151 MHz, CHLOROFORM-d): 192.3, 163.4, 160.5, 147.2, 142.8, 136.7, 135.0, 134.3, 130.9, 128.4, 127.5, 127.1, 126.4, 126.1, 123.2, 120.4, 120.1, 118.9, 118.5, 117.3, 109.0, 54.8, 19.5; HRMS calcd for C24H21NO4 [M+Na]+ 410.1363, found 410.1358.

(E)-3-(3-(2-Hydroxy-5-Methylphenyl)-3-Oxoprop-1-en-1-yl)-N-Octylbenzamide (4h)

The title compound 4h was obtained by the reaction of compound 3b with octan-1-amine following the general procedure. Yellow solid, yield 57.5%, mp 182-183°C. 1H-NMR (600 MHz, CHLOROFORM-d): 12.51 (s, 1H), 8.05 (s, 1H), 7.77 (d, J=15.4 Hz, 1H), 7.68 (d, J=7.7 Hz, 1H), 7.61-7.65 (m, 2H), 7.59 (s, 1H), 7.38 (t, J=7.6 Hz, 1H), 7.22 (d, J=8.4 Hz, 1H), 6.83 (d, J=8.4 Hz, 1H), 6.41-6.49 (m, 1H), 3.39 (q, J=6.2 Hz, 2H), 2.26 (s, 3H), 1.24-1.31 (m, 3H), 1.17-1.24 (m, 9H), 0.78-0.81 (m, 3H); 13C-NMR (151 MHz, CHLOROFORM-d): 193.4, 166.9, 161.5, 144.0, 137.7, 135.7, 135.1, 131.7, 129.4, 129.2, 128.6, 128.1, 126.9, 121.3, 119.6, 118.3, 40.3, 31.8, 29.7, 29.3, 29.2, 27.1, 22.6, 20.6, 14.1; HRMS calcd for C25H31NO3 [M+Na]+ 416.2196, found 416.2193.

(E)-3-(3-(2-Hydroxyphenyl)-3-Oxoprop-1-en-1-yl)-N-(2-(Thiophen-2-yl)Ethyl)Benzamide (4i)

The title compound 4i was obtained by the reaction of compound 3b with 3-thiopheneethanamine following the general procedure. Yellow solid, yield 57.5%, mp 182-183°C. 1H-NMR (600 MHz, CHLOROFORM-d): 12.49 (br. s., 1H), 7.99 (br. s., 1H), 7.70-7.76 (m, 1H), 7.62 (t, J=8.5 Hz, 2H), 7.55-7.59 (m, 2H), 7.33-7.37 (m, 1H), 7.21 (dd, J=1.9, 8.3 Hz, 1H), 7.07-7.09 (m, 1H), 6.87 (dd, J=3.3, 5.0 Hz, 1H), 6.81-6.83 (m, 1H), 6.79 (d, J=2.8 Hz, 1H), 6.63 (br. s., 1H), 6.56 (br. s., 1H), 3.64-3.68 (m, 2H), 3.07-3.10 (m, 2H), 2.25 (s, 3H); 13C NMR (151 MHz, CHLOROFORM-d): 193.4, 167.0, 161.5, 143.9, 141.1, 137.8, 135.4, 135.1, 131.9, 129.4, 129.3, 128.7, 128.1, 127.2, 126.8, 125.5, 124.1, 121.3, 119.6, 118.3, 41.5, 29.8, 20.6; HRMS calcd for C23H21NO3S [M+Na]+ 414.1134, found 414.1129.

(E)-3-(3-(2-Hydroxyphenyl)-3-Oxoprop-1-en-1-yl)-N-Phenethylbenzamide (4j)

The title compound 4j was obtained by the reaction of compound 3b with 2-phenylethan-1-amine following the general procedure. Yellow solid, yield 57.5%, mp 182-183°C. 1H-NMR (600 MHz, CHLOROFORM-d): 12.49 (br. s., 1H), 7.96 (s, 1H), 7.69-7.75 (m, 1H), 7.57-7.60 (m, 2H), 7.53-7.57 (m, 2H), 7.31-7.35 (m, 1H), 7.22-7.25 (m, 1H), 7.19-7.22 (m, 2H), 7.14 (d, J=7.3 Hz, 3H), 6.80-6.82 (m, 1H), 6.49 (br. s., 1H), 6.44 (br. s., 1H), 3.64 (q, J=6.8 Hz, 2H), 2.86 (t, J=6.9 Hz, 2H), 2.25 (s, 3H); 13C-NMR (151 MHz, CHLOROFORM-d): 192.3, 165.9, 160.5, 142.9, 137.8, 136.7, 134.4, 134.0, 130.8, 128.4, 128.2, 127.8, 127.7, 127.6, 127.1, 125.7, 125.6, 120.2, 118.5, 117.3, 40.3, 34.6, 19.5; HRMS calcd for C25H23NO3 [M+Na]+ 408.1570, found 408.1565.

(E)-N-(2-(1H-Indol-3-yl)Ethyl)-3-(3-(2-Hydroxy-5-Methylphenyl)-3-Oxoprop-1-en-1-yl)Benzamide (4k)

The title compound 4k was obtained by the reaction of compound 3b with 2-(1H-indol-3-yl)ethan-1-amine following the general procedure. Yellow solid, yield 57.5%, mp 182-183°C. 1H-NMR (600 MHz, CHLOROFORM-d): 12.49 (s, 1H), 8.08 (d, J=15.6 Hz, 1H), 7.57-7.59 (m, 1H), 7.52 (d, J=7.9 Hz, 1H), 7.45 (td, J=3.2, 8.9 Hz, 4H), 7.38 (dt, J=1.6, 7.5 Hz, 1H), 7.30-7.35 (m, 2H), 7.26 (dd, J=2.0, 8.4 Hz, 1H), 7.23(d, J=8.1 Hz, 1H), 7.09 (t, J=7.6 Hz, 1H), 7.06 (d, J=2.2 Hz, 1H), 6.99-7.03 (m, 1H), 6.88 (d, J=8.4 Hz, 1H), 3.80 (d, J=6.2 Hz, 2H), 3.05 (t, J=6.5 Hz, 2H), 2.27 (s, 3H); 13C NMR (151 MHz, CHLOROFORM-d): 192.6, 167.6, 166.7, 160.5, 141.7, 136.9, 136.6, 132.0, 131.3, 129.9, 129.2, 128.6, 127.8, 127.0, 126.7, 126.4, 121.9, 121.3, 121.2, 118.6, 118.5, 117.6, 117.4, 110.3, 64.6, 39.0, 29.5; HRMS calcd for C27H24N2O3 [M+Na]+ 447.1679, found 447.1674.

Biochemistry

Reagents and Cell Lines

All the cell line used in this study were either purchased from American Type Culture Collection (ATCC, Manassas, VA) or described as previously [16].

Flow Cytometry Screening

The Flow cytometry screening assay was performed in Jurkat-based cells line(J-LatA2), containing LTR-Tat-IRES-GFP-LTR [15]. After incubation with Amt-87 as indicated or 0.1% dimeththyl sulfoxide (DMSO) as negative control, J-Lat A2 were harvested and washed twice with 1× phosphate buffered saline (PBS) and analyzed the expression level of GFP by flow cytometer (Epics Altra, BECKMAN COULTER). The changes of GFP expression level can indirectly reflect the transcription level of HIV.

Notes

The authors declare no competing financial interest.

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The Use of Pine Apple Juice in the Elimination of Egg Stickiness in Catfish (Clarias gariepinus), Reared in a Hatchery, Sebeta, Ethiopia

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DOI: 10.31038/CST.2021115

Abstract

This study was conducted from May to June 2018 at the National Fishery Hatchery in Ethiopia and aimed to assess the effectiveness of pineapple juice in the elimination of egg stickiness and improved hatchability rate in Clarias gariepinus. There were four different concentrations of pineapple juice tested: 0% (without juice), 1%, 3%, and 5%. The result revealed that egg stickiness was reduced dose-dependently (p < 0.05), with the 5% concentration showing the greatest reduction (59%) compared to the control group (77%). However, it was noted that higher concentrations of pineapple juice have adversely affected egg hatching and fertilization rates. The minimum observed fertilization rate (45%) was observed at the highest concentration tested (5%), while the maximum (77%) was observed at lower concentrations (1%). Similarly, the minimum hatching rate (51.5%) was observed at higher concentrations (5%), with the maximum (71.3%) at 3% pineapple concentrations. Despite these variations, no significant difference (p > 0.05) in the survival rate of juvenile catfish was observed across different concentrations of pineapple juice. This finding highlights the potential of pineapple juice as a practical and accessible approach for improving catfish hatching operations. This study helps to optimize catfish breeding procedures and demonstrates the efficacy of natural treatments in aquaculture practices.

Keyword

Clarias gariepinus, Pineapple juice, Egg stickiness, Hatchability, Fertilization

Introduction

Aquaculture is crucial for meeting the world’s expanding food demand. Sustainable aquaculture output is strongly dependent on good reproduction and efficient hatching methods. Fish seed mass propagation, whether artificial or semi-artificial, is essential to modern fish farming. Due to seasoned breeding habits in most culturable species and the fact that fish must be caught at a time that may not coincide with ideal production conditions, the natural supply of fingerlings is erratic, unstable, and insufficient. Of these species, the African catfish (C. gariepinus) reproduces according to its seasonal breeding behavior at particular periods of the year [1-3].

The African catfish is a valuable tropical aquaculture fish in Africa, Asia, and Europe. This is because the species is suitable for aquaculture due to its fast growth rate, tolerance of high stocking density and poor water quality, acceptance of both artificial and natural feeds, and high market demand, and high production in the pond, 2.5 times higher than tilapia. It is also the second most important commercial fish species in Ethiopia’s capture fishery next to the Nile tilapia. Despite this, the lack of good quality and quantity of seeds has been a major constraint in aquaculture development. Thus, at larvae and fry stages, low survival rates and poor growth performance are the major challenges in catfish production in Ethiopia. This is most probably related to egg stickiness, which needs to be reduced before eggs can be incubated successfully [3-7].

Chemical treatments like tannic acid, urea, and acylase enzyme are frequently used in traditional ways for reducing egg stickiness. However, the extensive use of such chemicals raises concerns about potential detrimental implications on water quality, fish health, and the general ecological balance of the aquaculture system. This procedure also takes a minimum of one hour and a great deal of labor and experience. As a result, this circumstance forces people to look for more eco-friendly alternatives, like pineapple juice [8-12].

In catfish hatcheries, recent studies on the use of pineapple juice as an alternate egg-sticking reduction agent have produced encouraging results. According to preliminary research, bromelain in pineapple juice significantly decreased the stickiness of catfish eggs, offering a healthy and natural alternative. Pineapple is widely available in many tropical nations, including Ethiopia. It includes several proteolytic enzymes, primarily bromalinases, which can breakdown proteins. The skin and hump of pineapple contain tannin chemicals, which belong to the phenol group. Tannins can eliminate egg adhesion because they can bind and precipitate protein compounds due to the presence of functional bond groups that interact strongly with protein molecules, resulting in large and complex cross-links, known as tannin-protein [13-17].

With sustainability becoming a top priority in the aquaculture sector, this study attempts to thoroughly explore green ways to enhance the hatchability of catfish eggs. Therefore, in order to overcome the drawbacks of chemical agents, this study aimed to evaluate pineapple juice’s potential as a natural substance to remove egg stickiness and increase the hatchability rate in catfish. This is in line with the necessity of ethical and ecologically friendly aquaculture methods, guaranteeing the sustainability of Ethiopia’s catfish aquaculture sector.

Materials and Methods

Broodstock Selection and Hormone Administration

The experiment was conducted from May to June 2018 at the National Fisheries and Aquatic Life Research Center, NFALRC, hatchery. Three males and three females of C. gariepinus broodstock, weighing between 958 and 1000 grams, were taken from the NFALRC ponds. Priority was given to individuals presenting an elongated papilla and a slightly swollen reddish urogenital organ for male brooder selection. While, criteria including a well-rounded and soft abdomen, extending anteriorly beyond the pectoral fins to the genital opening were used for female brood stock selection. The broodstocks were acclimatized in separated tanks in hatchery for 24 hours.

The female brooders were artificially stimulated using 0.3 ml of common carp pituitary gland per kilogram of body weight, as described by Olanrewaju et al. The injection was done intramuscularly above the lateral line just below the dorsal fin. The female brooder was given light abdominal pressure twelve hours following the injection, at which point the ovulated eggs were gathered and weighed to the closest 0.01 g in a dry, clean plastic dish. The milt was collected by sacrificing the male broodstock. The milt and eggs were mixed and two drops of saline water were added to activate the milt and stirred gently with a feather to fertilize the eggs. The eggs were left for one minute to provide sufficient contact with the milt [18,19].

Pineapple Juice Preparation and Application

Pineapple juice was prepared by squeezing freshly peeled fruit from a local market in Sebeta, Ethiopia. Three pineapple juice concentrations, including 1% (1 ml of juice to 99 ml of clean water), 3% (3 ml of juice to 97 ml of clean water), and 5% (5 ml of juice to 95 ml of clean water), as well as a control group without pineapple juice, were prepared to investigate dose-dependent effects. In order to represent each concentration of pineapple juice, the fertilized eggs were divided into four equal parts weighing 800 g each. These portions were then divided in triplicate. After pouring the juice solution over the eggs, the mixture was constantly swirled for approximately three minutes using a feather.

Experimental Design and Water Management

For egg hatching, a total of twelve 200-liter plastic bowls were utilized, one for each of the four treatments in triplicate. A mesh was placed above the water surface within the bowl to provide a platform for placing fertilized catfish eggs treated with pineapple juice. The eggs were gently poured over the mesh, ensuring uniform distribution above the water surface. In order to guarantee sufficient oxygenation, a constant water flow was maintained during the trial. The ideal conditions for embryonic development were preserved by controlling the flow rate. In order to facilitate the incubation of catfish eggs, the water temperature was carefully monitored and changed to 24 to 27°C. Water quality indicators such as temperature, pH, and dissolved oxygen levels were carefully monitored at regular intervals throughout the 36-hour experiment. The hatching success criterion was defined as the emergence of larvae from egg capsules. Dead eggs, which are white and transparent, are removed immediately.

Data Collection

Egg adhesiveness was assessed by determining the percentage of eggs that formed clumps. After 15 minutes of incubation, the eggs that had formed clumps within each container were counted. The percentage of adhesion, fertilization, hatching, and survival rate were determined using the standard formula (Eq1 to Eq4) [20,21].

FOR

Statistical Analysis

The collected data on egg stickiness and hatchability rates for each concentration was statistically analyzed using R statistical software, with a one-way ANOVA used determine the significant differences between the treatment and different variables. Pearson’s correlation was used to assess the relationship between pineapple juice concentrations and fertilization and hatchability parameters [22].

Results and Discussion

Physicochemical Parameters

The result of major physicochemical parameters is presented in Table 1. Our study indicated that water temperature (24-27°C), dissolved oxygen (>5 mgL-1), pH (7.12-7.16), and ammonia levels (< 0.1 mgL-1) remained within optimal ranges for effective hatching in catfis. Effective water quality management strategies are critical for providing an optimal environment for catfish embryogenesis [23,24].

Table 1: Physicochemical water quality parameters with mean ± SD

Parameters

Range

Mean ± SD
DO (mgL-1)

5.8 – 6.6

6.2 ± 0.4
pH

7.12 – 7.16

7.1 ± 0.2
Temperature (°C)

24 – 27.5

25 ± 0.3
Ammonia (mgL-1)

0.1 – 0.15

0.1 ± 0.05

Effect of Pineapple Juice Concentrations on Cat Fish Egg Stickiness

Supplementing catfish eggs with pineapple juice resulted in a significant reduction in stickiness (ANOVA, p < 0.05) at varied doses (Figure 1). It was found that all of the treatments reduced the adhesiveness of C. gariepinus eggs to varying degrees. The control group, without pineapple juice, had the highest adhesion value (78.6%) showing the least reduction in egg stickiness. Interestingly, the greatest concentration, 5%, showed the highest reduction in egg adhesion (61%). The 3% pineapple juice concentration resulted in a medium reduction (67.3%). Subsequent post-hoc Tukey’s tests indicated a significant decrease in egg stickiness for the 3% and 5% concentrations compared to the control. This could be explained by the different concentrations of the enzyme bromelain in pineapple juice, which breaks down protein in a sticky fish egg layer. Fish eggs are sticky when they come into touch with water because of their high protein cortical envelope. The more easily the bromelain enzyme can break protein molecules into smaller forms, the more effective it is at eroding the protein layer found in catfish eggs. Our findings corroborate this theory because we found that the amount of egg stickiness decreased more noticeably at greater pineapple juice concentrations [25-28].

FIG 1

Figure 1: Egg adhesion across different pineapple juice concentrations. Letters a and b indicate significant differences between treatments (P < 0.05).

Our result aligns with previous studies indicating the potential of pineapple juice to affect adhesive properties with dose-dependent stickiness reduction in catfish eggs. The egg of C. gariepinus has a sticky layer, which is composed of glycoprotein. Because of its proteolytic action, bromelain may interact with the glycoproteins on the egg surface, changing their sticky characteristics and changing the way the egg clumps.

Furthermore, the slight decrease in stickiness that was seen at lower pineapple juice concentrations (1%), which points to a possible threshold effect wherein enzyme activity might not be enough to cause appreciable changes in sticky characteristics. This suggests a dose-dependent effect of pineapple juice on egg adhesiveness, with lower concentrations potentially leading to greater egg aggregation. Excessive egg clumping may result in restricted water flow around individual eggs and poor oxygenation. Furthermore, it reduces oxygen levels and increases fungal infection, leading to higher mortality rates.

Fertilization and Hatchability Rate

The result of fertilization and hatching rates are presented in Figure 2 with significant variation among treatments (ANOVA, p < 0.05). In comparison to the control group, treatment with 1% concentration resulted in a higher improvement in fertilization rates (77%), which was less than the values reported by Nwerich and Igill-Iboi (88%), Sukendi et al. (89.5%), Egwenomhe et al. (98.6%) in C. gariepinus, and Thal and Ngo (89.5%) in C. carpio. Surprisingly, the 5% concentration, despite reducing egg stickiness significantly, showed a decline in fertilization rates (45%). The highest fertilization value obtained in this study was not related to the smallest adhesion value obtained, which suggests that additional influencing elements as well as the egg adhesion value affect the fertilization value. This is in agreement with the previous studies which reported the highest fertilization rate in the lowest concentrations of pineapple juice solution. This research implies that pineapple juice improves fertilization rates at specific concentrations, while greater concentrations may have a negative impact on sperm-egg interactions [13-16].

FIG 2

Figure 2: The fertilization and hatching rate across treatments. Letters indicate a significant difference between treatments (p < 0.05).

The egg’s adhesiveness makes it more difficult for the embryo to hatch successfully in artificial breeding. This is because the eggs have become agglutinated, covering the micropyles and making it more difficult for sperm to fertilize them. As a result, there are less opportunities for sperm to come into touch with the eggs, which lowers the likelihood that the eggs will be fertilized. Other substances, such as pineapple juice, are used to crack the egg layer, which accelerates fertilization by allowing sperm to make direct contact with the eggs without being impeded by the mucus coating on the egg surface [14-16].

There was significant variation in hatching rate among treatments (p < 0.05). The highest hatching rate was obtained in the 3% pineapple juice concentrations (71.3) and showed a decrease (51.5%) in the 5% concentrations. Similar results were found in the previous studies confirming the dose-dependent trend in egg hatchability of 86.6 % at 1% concentration and decreasing to 25.3 % at 5% concentration. Similarly reported 78.1 % hatching rate at 0.75% pineapple juice concentration and decreased to 75.6% at 1% concentration [18].

This emphasizes the critical role of optimal pineapple juice concentration in influencing not only fertilization but also hatchability in catfish. This shows the effect of pineapple juice in reducing the adhesive layer of catfish eggs. In normal cases, when the eggs come in contact with water for a while, and clump reducing the prospect of the eggs hatching. In general, destocking of eggs using pineapple is quick and simple and requires three minutes only instead of the one hour for conventional desticking techniques. This technique is easy to adopt and advantageous by dramatically reducing the egg handling period.

Survival Rates of Hatched Fish

The survival rates of fish in treatment groups were extensively observed after hatching (Table 2). The larvae survival rate varied from 84% in 1% pineapple concentration to 90% in 3% pineapple concentration, with no significant difference across treatments (p > 0.05). The general survivability of the growing larvae may not be directly impacted by the enzymatic activity of bromelain, which mainly modifies protein structures and sticky characteristics. Similar results were observed in the previous studies [16,17].

Table 2: Survival rate of catfish fries at different pineapple juice concentrations

Concentrations

Number of hatchlings

Survival rate (%)

0% (control)

261

87

1%

259

84

3%

315

90

5%

291

89

The Response of Different Variables to Pineapple Concentrations

The relationship between different parameters and pineapple juice concentrations is indicated using Pearson’s correlation (Figure 3). The pineapple juice concentrations exhibited a strong negative correlation with egg adhesion (ANOVA, p < 0.001, r2=-0.93), indicating that higher concentrations of pineapple juice led to reduced egg adhesiveness. This is associated with the presence of proteolytic enzymes in the pineapple juice that can inhibit the activities of glycoprotein which could have the potential of increasing the sticky layer of fish eggs. Similar reports were found on the negative correlation between pineapple concentrations and fish egg adhesiveness. Although not statistically significant, there was a negative correlation found between the concentrations of pineapple juice and the rates of fertilization and hatching. Similarly, report state a decrease in fertilization and hatching rate with a corresponding increase in the concentration of the pineapple juice. On the other hand, hatching rates and fertilization rates exhibited a positive correlation (ANOVA, p < 0.001, r2=0.87), indicating a comparable response to the concentrations of pineapple [16,17].

FIG 3

Figure 3: Person’s correlation showed the relationship between pineapple juice concentrations and different parameters.

The findings underscore the potential of pineapple juice as a natural agent for manipulating reproductive parameters in aquatic organisms, offering insights into its application in aquaculture practices for improving breeding success and egg quality. However, pineapple juice concentration matters at a certain concentration and might be detrimental over the optimal dosage.

Conclusion and Recommendation

In conclusion, the application of pineapple juice solution has demonstrated efficacy in reducing the stickiness of Clarias gariepinus eggs, offering a practical solution for hatcheries in Ethiopia. Our findings suggest that this method not only effectively mitigates the adhesive properties of the eggs but also presents a user-friendly approach that does not necessitate specialized expertise for implementation. Moreover, while higher concentrations of pineapple juice may have adversely affected egg hatching and fertilization rates, it’s noteworthy that the positive impact on egg-destocking adhesiveness was observed. Therefore, this approach holds promise as a simple and accessible solution for hatchery operations, allowing for improved handling and management of catfish eggs without requiring extensive training or technical skills. To optimize hatchery operations and improve the management of Clarias gariepinus eggs in Ethiopia, the following recommendations are proposed:

  • Incorporate pineapple juice solution application to reduce egg stickiness effectively.
  • Utilize lower concentrations of pineapple juice to maintain optimal hatching and fertilization rates while mitigating egg adhesiveness.

Acknowledgements

We would like to acknowledge the National Fisheries and Aquatic Life Research Center (NFALRC) for financing the research work.

References

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  8. Zarski D, Krejszeff S, Kucharczyk D, Palinska-Zarska K, Targonska K (2015) The application of tannic acid to the elimination of egg stickiness at varied moments of the egg swelling process in pikeperch, Sander lucioperca (L.). Aquaculture Research 46 (2).
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Verbal Judo and Prospective Crime – How AI + Mind Genomics Can Synthesize Mind Sets for Training

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DOI: 10.31038/MGSPE.2024434

Abstract

This paper shows how the emerging technology of AI (large language models), coupled with the world-view of the importance of mind-sets contributed by Mind Genomics can help negotiators to learn how to deal with situations where violence may take place unless the hostage situation is defused. The approach technique uses artificial intelligence embedded in the Mind Genomics platform to specify the situation, and then to hypothesize the thinking and appropriate actions for the negotiator in the presence of different mind-sets. The paper shows how to interact with AI to synthesize these mind-sets, then how to recognize them, what these mind-sets might be thinking, and suggested communications. The paper focuses on educating police negotiators through the use of easy-to-access technology, and inexpensive, rapid learning by iterating the questions asked to AI. Rather than waiting for actual data, the paper uses artificial intelligence based in large language models to provide information that can be adjusted to a specific situation, providing the opportunity to look at the same issue but from different perspectives provided by the nature of the situation and the mind-set of the person who must be disarmed without causing damage.

Keywords

Artificial intelligence, Mind genomics, Mind-set, Synthesize, Training

Introduction

Look at the daily news to see how many times persons or groups turn hostile to police officers or conduct harm to property or people and must be bargained with by police negotiators. The problem is what police and other negotiators should say in specific instances, when they are immersed in the situation. Negotiations, such as family violence and criminal hostage-taking, requires talent, sensitivity to others, and understanding what to say, knowing what works, knowing what doesn’t. After years of experience, expert negotiators learn what to say and its consequences. How do we teach new hires about new situations? Can negotiation opportunities be synthesized? Artificial bodies designed to offer feedback are used in medicine. Is it possible with artificial minds to teach negotiation skills?

There are many different situations where individuals or groups become hostile to police officers, or where these groups become hostile and do hostile acts to property or to people and must be negotiated with by police negotiators. The issue is what to do in particular situations. The knowledge base of negotiating with criminals is not new, spanning centuries, a fixed part of the human comedy. The characters may change, the causes may evolve, society may change what it considered to be “out of bounds” behavior, but the problem remains [1-3]. In the end where the crime or prospective crime is under the “control” of a person, one may have the opportunity to “talk through” that situation with the person, reaching a positive outcome. What does one say, how does one say it, and when? This ability to talk through requires a skill that must be developed, a sensitivity to individuals, a sense of knowing what to say, what will work and what will not work.

Over many years of experience, professional negotiators begin to understand what they can say, what they should say, the repercussions of what they say. The question becomes, how do we transmit this information for new situations to people who are just coming on the job? Is there a way to synthesize negotiation opportunities?

Advances in artificial intelligence based on LLMs (large language models) have made AI more attractive [4,5]. Data analysis using statistics used to be challenging. Once the data were analyzed, the user faced the onerous task of interpreting the results, and then putting that interpretation into text. The result was often tortured prose rather than felicitous and well expressed. Today’s LLMs now enable an almost-human teaching interface, both at the level of the input, and at the level of the output. More important, however, is the emerging reality that the LLMs need not produce factual results, requiring statistical and factual accuracy. We can instruct the LLM to suggest how reality is structured, and then investigate the different nuances within that constructed reality. Everything offered today is assumed to be generalities presented for learning purposes.

The emerging science of Mind Genomics — which has been developing for almost 40 years — helps when it becomes the precursor to LLM simulation. Mind Genomics is empirical, working with combinations of phrases, having people read the combinations, and rate each one. Through statistical modeling (regression and clustering), Mind Genomics discovers the language which “convinces,” which makes the person answer “yes.” The process uncovers mind-sets, groups of individuals who respond differently to the same set of phrases or messages. These mind-sets are uncovered through empirical evaluations with real people [6,7]. Subjects such as law, medicine, as well as the minds of children and adults have been topics of Mind Genomics studies [8-11].

The combination of Mind Genomics thinking about mind-sets and LLMs to provide “content” generates a new world of opportunities to understand the way people think, doing so quickly by simulation. The approach taken in this paper uses LLMs to provide deep, albeit simulated content, for these mind-sets. In a sense, we use Mind Genomics “thinking” to inform and guide the start of the LLM on a topic area, with the result that we generate a system to learn quickly.

The actual system is embedded in the Mind Genomics platform (BimiLeap.com). The access to the LLM is through the embedded Idea Coach. Idea Coach, in turn, uses an entire structure of inquiry called SCAS, Socrates as a Service. With Idea Coach, the user can type in the topic, and a request to the LLM. The SCAS translates this request, giving it to the LLM. The process is straightforward, requiring only an account, and the proper information provided to Idea Coach.

Phase 1 – Setting up the scenario for the LLM

After creating a Bimileap account and studying, the user is instructed to use Idea Coach to enter ideas. The format is a “squib” similar to that shown in Table 1. One has to be certain that the syntax is correct, and that everything is closed-ended. The LLMs are powerful, but in this case not particularly forgiving. After tries, however, with feedback given in 15 seconds, one eventually understands the syntax and what to do for the specific case.

Table 1: The “squib” or query (background, specific request) given to SCAS (Socrates as a Service), the AI embedded in the Mind Genomics platform, BimiLeap.com.

tab 1

Table 1 shows the request, the aforementioned squib. Table 1 supplies very little information to the AI, Socrates as a Service, but requests a fair amount of testable, actionable information that can be validated because of their concrete nature.

There is no underlying “reason” for selecting six mind-sets, other than the desire to see what the system returns. In fact, across the different iterations, seven iterations emerged, not six. They can be discussed and even tested in real-life.

As a point of information, the brief or squib in Table 1 is the toughest aspect of the procedure and sometimes requires many iterations, viz., attempts. It takes the large language model about 15 seconds per iteration. An inexperienced user might require 20 iterations to get the “syntax” of the request “just right.” Those 20 iterations might require 5-10 minutes.

The artificial intelligence SCAS generates seven mind-sets, shown in Tables 2A-2G, one more mind-set than was specified in Table 1. This over-delivery, as well as corresponding under-delivery, often happens because the mind-sets are developed in separate iterations. Generally, each iteration produces one or two mind-sets. The user ends up requesting more iterations to complete the full number. The under-delivery of one, two, occasionally three, mind-sets in an iteration ends up being not problematic because, as noted above, each iteration requires about 10-15 seconds. The speed and ease to iterate often ends up with some mind-sets repeated in different iterations, and the happy coincidence of more mind-sets than were planned for. Finally, when a mind-set repeats in several iterations, the language surrounding the mind-set may change, and some of the finer points may change from iteration to iteration. Most of the “knowledge” will be the same, but some may be fresh ideas across iterations.

Table 2A: Results for the MACHO Mind-Set

tab 2A

Table 2B: Results for the RADICALIZED Mind-Set

tab 2B

Table 2C: Results for the MENTALLY ILL Mind-Set

tab 2C

Table 2D: Results for the EXTREMISTS Mind-Set

tab 2D

Table 2E: Results for the ANGRY INDIVIDUALS Mind-Set

tab 2E

Table 2F: Results for the CRIMINAL Mind-Set

tab 2F

Table 2G: Results for the DESPERATE INDIVIDUALS Mind-Set

tab 2G

From Tables 2A-2G, it becomes apparent that there is an education to be had. Whether any of these results represent actual mind-sets or not, whether these are the slogans or not, the results are interesting and educational. One even gets a sense of the effectiveness of the rating by artificial intelligence, which is fascinating in and of itself. We might even have put in things like, provide slogans that are not effective, and we could do that for each of these as well. In the interest of space and brevity, we have limited our exploration of the mind-set.

Deeper Analyses through SCAS

Early empirical investigations in Mind Genomics in the late 1990s and onward revealed these mind-sets in topic after topic, study after study. It was the task of the user to understand these mind-sets. Fortunately, the mind-sets were clearly demarcated, and easy to differentiate and to discuss.

In order to discover patterns, SCAS in the BimiLeap program has been set up with a set of fixed analyses in order to extract more information from each iteration. Table 3 shows a summary table about what was learned. The results were analyzed in eight ways, such as key concepts, topics, views, interested audiences, opposing audiences, and three stages toward innovation (alternate viewpoints, what is lacking, and potential innovations). For this paper, this deeper AI analysis was reduced to one summary table.

Table 3: Summarized “deeper analysis” of the results, combining the results from all seven mind-sets, although ordinarily done on a mind-set basis.

tab 3(1)

tab 3(2)

The actual deeper analysis does not occur at the time that the user goes through the iterations. Rather, for each iteration, the material is stored in an Excel workbook as a separate worksheet. Only afterwards, when the study is closed, does the second level of analysis take place. This deeper analysis is done separately for each worksheet, going through all eight steps, returning with recommendations, analyses, themes, and so forth. The seven deeper analyses were put into the “Excel Idea Book.” The Idea Book was returned by e-mail approximately 15 minutes after the project was closed.

It is important to keep in mind that despite the “hand-craft” work to combine information from seven deeper analyses, one per mind-set, the actual sentences and information shown in Table 3 are taken directly from the AI-generated output. Thus, the reality is that we have AI providing second level, deeper analysis of AI-generated ideas, those ideas originally emerging from a simple squib and a paragraph of very little detailed information, viz., Table 1.

Discussion and Conclusions

Artificial intelligence-generated results are only now becoming of interest to scientists in the world of behavior, such as psychologists. It is only now that there is a focus on the nature of ideas generated by AI. The reason may be the increasing use of AI to act as an aid-to-thought, perhaps a “technical aid to creative thought” in the words of the late Harvard professor of computer science, Anthony Gervin Oettinger [12].

In the words of the late Harvard professor may appear off-putting for a scientific publication but they should not be. We are not offering factual knowledge, but frameworks in which one may incorporate certain bits of information to obtain insight. We are not interested in factual information that can be obtained by digging into the data, such as how do the mind-sets distribute in the population, or really anything better understood empirically. This whole text has been a teaching tool to comprehend a possibly extreme situation in an hour or two. The approach presented here empowers novices and professionals alike to describe a scenario and understand its aspects, ranging from attitudes, behaviors, motives, public reactions, and far more.

Much more should be said about this realistic approach. Pragmatism will succeed. In fact, the late psychologist George Miller says that most of one’s knowledge gets “chunked,” with just the general structure maintained [13]. We might consider this approach to be a “cartography,” a mapping of the relevant features of a topic. As a cartographer explores an area, looking at its geographies, lay of the land, and flora and fauna, the professional might explore the mind of a criminal for the best way to communicate.

In the end, the world-view espoused by this paper is pragmatics. The age of enhanced learning is coming. This paper may provide in a short time a new approach, a new way of extracting insights, of synthesizing the real world. The approach demonstrated here emerges not so much by slow learning from books and the formation of concepts, but rather human thinking augmented by the “creative powers” embedded in artificial intelligence, ready to be released through the appropriate prompts. With the power of AI embedded in accessible LLMs, people can now ask more focused questions using a simple set of queries, the power of easy use through SCAS. The final output is made user-friendly, the outcome of the AI efforts returned in the form of easy-to-read material.

In the end, the goal of this project is to suggest ways to think about the topic of communicating in a situation which is potentially dangerous. To reiterate the caveat, we’re not looking for an accurate description of each mind-set, but rather interested in providing a general framework. It would be interesting to see whether or not the untutored person, inexperienced in criminology and verbal negotiations with criminals, would use this information, or perhaps even just specific parts of the information. Which parts are useful? Which parts seem useful, but are really irrelevant to practice? The approach is well worth the try, simply because the format here presented by Socrates as a Service, SCAS, using LLMs, generates the key information easily with virtually instant analyses of iteration after iteration, each iteration returned in 15 seconds, with a deeper analysis an hour later returned by email, and done for each iteration.

Acknowledgement

The authors wish to thank Vanessa Marie B. Arcenas for her assistance in preparing this manuscript.

Abbreviations

AI: Artificial Intelligence; LLM: Large Language Model; SCAS: Socrates as a Service

Competing Interests

The authors have no conflict of interest to disclose.

References

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What Israel Will Have Done to Help Gaza in the Next 30 Days – Strategic Envisioning Using AI with Mind Genomics Thinking to Look at the Future as if it were Describing the Past

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DOI: 10.31038/MGSPE.2024433

Abstract

This paper presents a continuation of the issue of Gaza and what’s next to rebuild Gaza after the debacle which has happened. The paper starts off with the issue of how to help Gaza become the Singapore of the Middle East. The paper continues by asking Artificial Intelligence to provide it with questions, provide it with a general summary, then provide it with questions, and then answer those questions. The result suggests that Artificial Intelligence can act as an aid to thinking and to springboard creative analysis and ideas for the future. The notion of having Artificial Intelligence assume that what is going to be done has already been done allows us to ask questions such as the specifics of what was done. In this respect, positioning Artificial Intelligence as a looking back of that which has not happened yet provides a new level of specificity to help the decision maker.

Artificial Intelligence as a Coach

The objective of this paper is to show what might happen when artificial intelligence is provided with a scenario and asked to specify what would happen, as well as what might be the reactions. In this respect, AI can become a colleague and a trusted advisor of a person who has to make decisions. By using different prompts for different aspects of the situation, it’s quite possible in a matter of an hour or two to get a sense of the different ramifications of a situation, what to do to ‘repair,’ and how people might react. We’re not saying of course that this is correct, but we’re simply saying that this is in the category of a best guess by AI. And if that is accepted, then we have a tool now to help understand policy. Furthermore, AI offers superior predictions over experts due to its ability to process vast data at a faster pace. AI can provide objective insights, identify unusual patterns, and mitigate human biases, resulting in more accurate and reliable predictions for future events [1-3].

The strategy used here is letting AI forecast the future by asking it to report on ‘what occurred’ in the future. By believing that artificial intelligence is looking back from the future, policymakers might acquire a unique perspective on the prospective implications of their actions and change their plans appropriately [4-6]. Predicting the future by asking artificial intelligence to describe it as the past provides a unique perspective that can uncover hidden patterns and trends. By modeling the future after the past, AI algorithms can identify connections and relationships that may not be immediately apparent to human experts. This approach can also help to eliminate bias and preconceived notions that experts may bring to the table, allowing for a more objective analysis of potential scenarios. AI’s ability to process vast amounts of data and make accurate predictions based on historical patterns makes it a valuable tool for forecasting future events.

A continuing issue in the adoption of artificial intelligence to help decision-making is the belief or the aversion to mechanical methods for creative thinking. Whenever artificial intelligence is brought up, almost always there are people who talk about the fact that artificial intelligence cannot create anything new. To them, artificial intelligence is not really new thinking, but simply going through lots of data and looking for patterns. And in fact, that is quite correct, but not relevant. To the degree that artificial intelligence can present us with ordinary, typical types of scenes, questions, and even synthesize the responses of Gazans is itself remarkable and should be used. If artificial intelligence were simply doing this randomly, we might not be able to interpret the data. But the data, the language, the meanings of that which we are reading seem to be real. All things considered, it is probably productive for society to use artificial intelligence as a coach, to suggest ideas, to be springboards to thinking. In other words, to be a coach, a consultant. Artificial intelligence need not provide the answer, as much as it gives us a sense of the alternative ideas from which to choose.

Edward Bellamy, Visioning the Future, and Its Application to Today’s Gaza

Edward Bellamy wrote about the future through Looking Backward by creating a utopian society set in the year 2000, where all resources are shared equally, work is replaced by leisure, and everyone lives in harmony. Bellamy’s style was descriptive and detailed, painting a vivid picture of this ideal society. But more than that. Bellamy’s style suggests the possibility of predicting the future but positioning it as a historical exercise of ‘looking backward’ at that which has not happened [7]. The style of ‘looking back’, attributed to Edward Bellamy, is quite similar to what we are doing here by analyzing a month of Israel trying to transform Gaza into the Singapore of the Middle East. Bellamy’s approach involved reflecting on past events and envisioning potential future scenarios, which is exactly what we are doing as we look back on Israel’s efforts and speculate on the outcome of their actions in Gaza. By examining this one month of work, we are essentially anticipating the potential consequences and impact it may have on the region, much like Bellamy did in his literary works.

One similarity between the two is the concept of idealistic visions for the future. Bellamy’s writings often portrayed a utopian society, while Israel’s goal of turning Gaza into a thriving economic hub reminiscent of Singapore also embodies a vision for a better future. Both involve the imagining of a better world based on certain actions and decisions taken in the present. However, a key difference lies in the context and the means by which these visions are pursued – Bellamy’s works were fictional narratives, whereas Israel’s efforts in Gaza are very much real and come with their own set of challenges and complexities. The act of looking back to a month of work yet to come in Today’s Israel hearkens back to Bellamy’s style as it requires a blend of reflection on past actions and anticipation of future outcomes. By analyzing the progress and developments in Israel’s project to transform Gaza, we are essentially engaging in a form of speculative thinking that mirrors Bellamy’s approach in envisioning alternative societal structures. Both involve a form of projection into the future based on current events and decisions, highlighting the interconnectedness of past, present, and future in shaping our understanding of the world around us.

Visioning the Future of a Gaza Becoming a New Singapore

In a previous paper we looked at what Israel might do to help Gaza become a Singapore [8]. Singapore of course is a remarkably successful city-nation in Asia, carved out of Malaysia. Singapore was not always well-off, Singapore was once poor, it was subject to the Japanese occupation and the damage that the Japanese did to Singapore. With a wise government, Singapore began to modernize, until today it’s held up as one of the most successful countries or really city-states in the world. With that in mind, we wanted to look at what would happen if in fact Israel had unilaterally begun work on creating or recreating Gaza as a Singapore of the Middle East. On our first work, we looked at what it should do. In this work, we are assuming that Gaza is now, we are assuming now, again, stop. In this work, we are now assuming that during the month of June, Israel unilaterally took action to do non-harmful reconstruction and restructuring of Gaza to begin its path to become Singapore. It is now August, or it is now July, and the question is, what can we report, and what do the Gazans say? The strategy here is to envision the future, not in general terms, but in specific terms, and see whether it would be possible, using artificial intelligence, to get a sense of what would be the things that one would be most proud of, and therefore this paper. The approach follows the use of Mind Genomics embedded in the platform BimiLeap.com. The AI in BimiLeap is SCAS, Socrates as a Service, based in part on ChatGPT. With SCAS, we are able to phrase a request, and many times with the proper phrasing, we’re able to get at a series of answers appropriate to the question. The objective here was to figure out what would be reported if, after the fact, people were to know that Israel did this. What exactly would have happened, and what would have been the responses of Gazans who were asked to comment on the results? Keep in mind that the Artificial Intelligence, SCAS, was never told precisely what happened, but only that which Israel was doing was unilaterally able to do.

What AI Produces When Asked to ‘Look Backward’

Table 1 shows the request to AI about what Israel did. The assumption here is that we are reporting after the fact, and we want a more or less factual report.

Table 1: Instructions to the AI (SCAS) to imagine looking background a day after Israel unilaterally began to recreate Gaza as a Singapore of the Middle East.

tab 1

Note: We requested six paragraphs, but we may not get them. One could request from SCAS a certain number of paragraphs and a certain number of sentences but that will not always be delivered. It’s a matter of repeating the questions iteration after iteration. Each iteration takes about 15 seconds. At some point, the artificial intelligence returns with what is desired.

Table 2 the result from one iteration which satisfied the request in Table 1. The important thing to look at is the fact that artificial intelligence can return with a variety of reports of what it thinks Israel did, and these in turn can be specific suggestions about what might be the obvious things to do in the future. Of course, we have to take into account the fact that this is the point of view of artificial intelligence and not necessarily representing the reality what’s possible, what’s straightforward to do. Nonetheless, by using artificial intelligence, it becomes much more possible to get a sense of what the accomplishments will look like. It is straightforward to run tens or iterations on the platform, changing the instructions to the AI until the appropriate type of information emerges. The platform, BimiLeap.com, was constructed to make the iterations sufficiently rapid (about 15 seconds per iteration), allowing the user to satisfy the objective simply through trial and error.

Table 2: The results from one iteration (of several) which produced answers from AI in the format presented in Table 1. The was generated by SCAS (Socrates as a Service).

tab 2

Continuing this approach, Table 3 shows hypothetical interviews and points of view of random Palestinians who are assumed to have seen what was going on and are asked to comment. Not all the comments are positive. There is no request on the part of the artificial intelligence, no prompt at all, to be positive but just to say what happened based upon these things. One could of course change the nature of the prompts, put in various types of information about what happened and see the results in terms of the phrasing and tonality of the ‘synthesized interview comments.’ Artificial intelligence now serves up new ‘information’, a synthesized retrospective of something that has not yet happened, among a population that will be the best ones to give the content to that respective.

Table 3: Background to the interview, request to AI, and 15 synthesized reactions to having experienced Israel’s effort

tab 3

Our final analysis looks at specific things that were done, asking for the reaction of Palestinians to those, by a quote, and then creating three slogans. It is this kind of specificity which allows artificial intelligence to become more of an aid in decision making. Table 4 presents the request to SCAS and the AI synthesis of ‘what happened.’

Table 4: Twelve questions about what was done, and for each question, respectively, the answer to that question, opinion of a local Gazan, and three slogans emblemizing the effort. All the text was synthesize by SCAS, and slightly edited for clarity.

tab 4(1)

tab 4(2)

Discussion and Conclusions

Artificial intelligence analyzing the hypothetical scenarios regarding what ‘Israel did in June 2024 unilaterally to help Gaza evolve into the Singapore of the Middle East’ can reveal surprising insights into potential strategies for economic development and peace-building in the region. By examining Israel’s actions through the lens of AI, we may uncover innovative approaches and solutions that traditional experts may not have considered. AI can also identify potential pitfalls and challenges that Israel may face in its efforts to transform Gaza into a prosperous and thriving area. This analysis can inform policymakers and stakeholders about the possible outcomes of different decisions and help to guide future actions.

Using artificial intelligence to synthesize comments and interviews about the development of Gaza into the Singapore of the Middle East can offer a comprehensive and objective overview of the public sentiment. AI can analyze a large volume of data quickly and identify common themes or concerns among the population. However, there are risks of bias or misinterpretation in AI-generated content because AI may not fully grasp the nuances of human emotions and experiences. Ultimately, the synthesized comments can provide valuable insights into the diverse perspectives and reactions towards the proposed transformation of Gaza.

Creating slogans to symbolize ideas helps to distill complex emotions and concepts into a simple and memorable phrase. These slogans can serve as a rallying cry or a unifying message for a community. Witness, for example, the power of slogans like “Free Palestine” and “End the Occupation” which reflect the deep-seated resentment and anger towards Israel. Positive slogans can be developed for Israel’s unilateral efforts to transform Gaza into a Singapore of the Middle East.

Artificial intelligence to shape a future by simulating a ‘looking back’ perspective can help countries anticipate and prepare for potential challenges and opportunities. By analyzing possible future scenarios, policymakers can develop proactive strategies to address emerging issues before they escalate, minimizing the impact on the country’s stability and prosperity. This forward-thinking approach enables countries make informed decisions which align with their long-term goals. By harnessing the power of artificial intelligence in the mode of ‘Looking Backwards’ there is the definite potential of better navigating the complex seas of geopolitics, where storms are the rule, and calm the blessed exception.

References

  1. Davis PK, Bracken P (2022) Artificial intelligence for wargaming and modeling. The Journal of Defense Modeling and Simulation.
  2. Goldfarb A, Lindsay JR (2021) Prediction and judgment: Why artificial intelligence increases the importance of humans in war. International Security 46: 7-50.
  3. Ransbotham S, Khodabandeh S, Fehling R, LaFountain B, Kiron D (2019) Winning With AI, MIT Sloan Management Review and Boston Consulting Group, October.
  4. Brunn SD, Malecki EJ (2004) Looking backwards into the future with Brian Berry. The Professional Geographer 56: 76-80.
  5. Rollier B, Turner S (1994) Planning forward by looking backward: Retrospective thinking in strategic decision making. Decision Sciences 25: 169-188.
  6. Yankoski M, Theisen W, Verdeja E, Scheirer WJ (2021) Artificial intelligence for peace: An early warning system for mass violence. In Towards an International Political Economy of Artificial Intelligence pp. 147-175.
  7. Levi AW (1945) Edward Bellamy: Utopian. Ethics 55: 131-144.
  8. Moskowitz HR, Rappaport SD, Wingert S, Moskowitz D, Braun M (2024) Gaza as a Middle East Singapore – Enhanced Visioning of Opportunities Suggested by AI.

Gaza as a Middle East Singapore – Enhanced Visioning of Opportunities Suggested by AI (Socrates as a Service)

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DOI: 10.31038/MGSPE.2024423

Abstract

The project objective was to explore the opportunity of Gaza as a new Singapore, using AI as th source of s suggestions to spark discussion and specify next steps. The results shows the ease and directness of using AI as a ‘expert’ when the materials presented here were developed through the Mind Genomics platform BimiLeap.com and SCAS (Socrates as a Service). The results show the types of ideas developed, slogans to summarize those ideas, AI-developed scales to profile the ideas on a variety of dimensions, and then five ideas expanded in depth. The paper finishes with an analysis of types of positive versus negative responses to the specific solutions recommended, allowing the user to prepare for next steps, both to secure support from interested parties, and to defend against ‘nay-sayers’.

Introduction

Europe had been devastated by Germany’s World War II invasion. The end of World War II left Germany in ruins. The Allies had deported many Germans, leveled Dresden, destroyed the German economy, and almost destroyed the nation. From such wreckage emerged a more dynamic Germany with democratic values and a well-balanced society. World War II ended 80 years ago, yet the effects are being felt today. The Israel-Hamas battle raises the issue of whether Gaza can be recreated like Germany, or perhaps more appropriately Singapore Can Gaza become Middle East Singapore? This article explores what types of suggestions might work for Gaza to become another Singapore, with Singapore used here as a metaphor.

Businesses have already used AI to solve problem [1,2]. The idea of using AI as an advisor to help with the ‘social good’ is alo not new [3-5]. What is new is the wide availability of AI tools, such as Chat GPT and other larger language models [6]. ChatGPT are instructed to provide replies to issues of social importance, specifically how to re-create Gaza as another Singapore. As we shall see, the nature of a problem’s interaction with AI also indicates its solvability, the acceptance by various parties and the estimated years to completion

The Process

First, we prompt SCAS, our AI system, to answer a basic question. The prompt appears in Table 1. The SCAS program is instructed to ask a question, respond in depth, surprise the audience, and end with a slogan. After that, SCAS was instructed to evaluated the suggestion rate its response on nine dimensions, eight of which were zero to 100, and the ninth was the length of years it would take this program or idea to succeed.

Table 1: The request given to the AI (SCAS, Socrates as a Service)

tab 1

The Mind Genomics platform on which this project was run allows the user to type in the request and press the simple button and within 15 seconds the set of answers appears. Although the request is made for 10 different solutions, usually the program returns with far fewer. To get more ideas, we simply ran the program for several iterations, meaning that we just simply pressed the button and ran the study once again. Each time the button was pressed by the user, the program returned anew with ideas, with slogans and with evaluations. Table 2 shows 37 suggestions. emerging from nine iterations, the total time taking about 10 minutes. The importance of many iterations is the AI program does all the ‘thinking,’ all the ‘heavy lifting’.

Table 2: The suggestions emerging from eight iterations of SCAS, put into a format which allows these suggestions to be compared to each other.

tab 2(1)

tab 2(2)

tab 2(3)

tab 2(4)

tab 2(5)

tab 2(6)

Table 2 is sorted by the nature of the solution, the categories for the sorting provided by the user. Each iteration ends up generating a variety of different types of suggestions, viz., some appropriate for ‘ecology’, others for ‘energy’, other for ‘governance’ and so forth. Each iteration came up with an assortment of different suggestions. Furthermore, across the many iterations, even the ‘look’ of the output change. Some outputs comprised detailed paragraphs, other outputs comprised short paragraphs. Looking at the physical format gave a sense that the machine seemed to be operated by a person whose attention to details was oscillating from iteration to iteration. Nonetheless, the AI did generate meaningful questions, seemingly meaningful answers to those questions, and assigned the ratings in a way that a person might.

The table is sorted by the types of suggestions. Thus the same topic (e.g., ecology) may come up in different iterations, and with different content. The AI program does not make any differentiation, but rather seems to behave in a way that we would call ‘does whatever comes into its mind.. It can be seen that by doing this 10, 20, 30 times and having two or three or four suggestions for each, the user can create a large number of alternative solutions for consideration. Some of these will be, of course, duplicate. Many will not. A number will be different responses, different points of view, different things to do about the same problem, such as economy.

Expanding the Ideas

The next was to selected five of the 37 ideas, and for each of these five ideas instructed AI (SCAS) to ‘flesh out’ the idea with exactly eight steps. Table 3 shows the instructions. Tables 4-7 show the four ideas, each with the eight steps (left to SCAS to determine). At the bottom of each table are ‘ideas for innovation’ provided by SCAS when it summarizes the data at the end of the study. The Mind Genomics platform ‘summarizes’ each iteration. One of the summaries is the ideas for innovation. These appear at the bottom section of each of the five sections, after the presentation of the eight steps.

Table 3: The prompts to SCAS to provide deeper information about the suggestion previously offered in an iteration

tab 3

Table 4: The eight steps to help Gaza become a tourism hotspot like Singapore

tab 4

Table 5: The eight steps to make Gaza ensure inclusive and sustainable growth for all its residents

tab 5

Table 6: The eight steps to help Gaza improve its economy and living standards

tab 6

Table 7: The eight steps to help Gaza promote entrepreneurship and small business development

tab 7

The Nature of the Audiences

Our final analysis is done by the AI, the SCAS program in the background after the studies have been complete. The final analysis gives us a sense of who the interested audiences might be for these suggestions and where we might find opposition. Again, this material is provided by the AI itself and the human prodding. Table 8 shows these two types of audiences, those interested versus those opposed, respectively.

Table 8: Comparing interested vs opposing audiences

tab 8

Relations Among the ‘Ratings’

The 37 different suggestions for many topic areas provides us with an interesting set of ratings assigned by the AI. One question which emerges is whether these ratings actually mean anything. Some ratings are high, some ratings are low. There appears to be differentiation across the different rating scales and within a rating scale across the different suggestions developed by AI. That is, across the 37 different suggestions, each rating scale shows variability. The question is whether that is random variability, which makes no sense, or whether that is meaningful variation, the meaning of which we may not necessarily know. It’s important to keep in mind that each set of ratings was generated entirely independently, so there is no way for the AI to try to be consistent. Rather, the AI is simply instructed to assign a rating. Table 8 shows the statistics for the nine scales. The top part shows the three decriptive statistics, range, arithmetic mean, and standard error of the meant. Keeping in mind that these ratings were assigned totally independently for each of the 37 proposed solutions (Table 2), it is surprising that there is such variability.

The second analysis shows something even more remarkable. A principal components factor analysis [7] enables the reduction of the nine scales to a limited set of statistically independent ‘factors’. Each original scale correlates to some degree with these newly created independent factors. Two factors emerged. The loadings of the original nine scales suggest that Factor 1 are the eight scale of performance as well as novelty, whereas Factor 2 is years to complete. The clear structure generated across independent ratings by AI of 37 suggestions is simply very clear, totally unexpected, and at some level quite remarkable. At the very least, one might say that there is hard-to-explain consistency (Table 9).

Table 9: Three statistics for the nine scales across 37 suggested solutions

tab 9

Discussion and Conclusions

This project was undertaken in a period of a few days. The objective was to determine whether AI could provide meaningful suggestions for compromises and for next steps, essentially to build a Singapore from Gaza. Whether in fact these suggestions will actually find use or not is not the purpose. Rather the challenge is to see whether artificial intelligence can become a partner in solving social problems where the mind of the person is what is important. We see from the program that we used, BimiLeap.com and its embedded AI, SCAS, Socrates as a Service, that AI can easily create suggestions, and follow up these suggestions with suggested steps, as well as ideas for innovation.

These suggestions might well have come from an expert with knowledge of the situation, but in what time period, at what cost, and with what flexibility? All too often we find that the ideation process is long and tortuous Our objective was not to repeat what an expert would do, but rather see whether we could frame a problem, Gaza as a new Singapore, and create a variety of suggestions to spark discussion and next steps, all in a matter of a few hour.

The potential of using artificial intelligence to help spark ideas is only in its infancy. There’s a good likelihood that over the years as AI becomes quote smarter and the language models become better, suggestions provided by AI will be far more novel, far more interesting. Some of the suggestions are interesting, although many suggestions are variations on the ‘pedestrian’. That reality not discouraging but rather encouraging because we have only just begun.

There’s a clear and obvious result here that with the right questioning, AI can become a colleague spurring on creative thoughts. In the vision of the late Anthony Gervin Oettinger of Harvard University, propounded 60 years ago, we have the beginnings of what he called T-A-C-T, Technical Aids to Creative Thought [8]. Oettinger was talking about the early machines like the EDSAC and programming the EDSAC to go shopping [9,10]. We can only imagine what happens when the capability shown in this paper falls into the hands of young students around the world who can then become experts in an area in a matter of days or so. Perhaps solving problems, creative thinking, and even creating a better world will become fun rather than just a tantalizing dream from which one reluctantly must awake.

References

  1. Kleinberg J, Ludwig J, Mullainathan S (2016) A guide to solving social problems with machine learning. In: Harvard Business Review.
  2. Marr B (2019) Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning To Solve Problems. John Wiley & Sons.
  3. Aradhana R, Rajagopal A, Nirmala V, Jebaduri IJ (2024) Innovating AI for humanitarian action in emergencies. Submission to: AAAI 2024 Workshop ASEA.
  4. Floridi L, Cowls J, King TC, Taddeo M (2021) How to design AI for social good: Seven essential factors. Ethics, Governance, and Policies in Artificial Intelligence, Sci Eng Ethics 125-151. [crossref]
  5. Kim Y, Cha J (2019) Artificial Intelligence Technology and Social Problem Solving. In: Koch, F, Yoshikawa, A, Wang, S, Terano, T. (eds) Evolutionary Computing and Artificial Intelligence. GEAR 2018. Communications in Computer and Information Science vol 999. Springer, [crossref]
  6. Kalyan KS (2023) A survey of GPT-3 family large language models including ChatGPT and GPT-4. Natural Language Processing Journal100048.
  7. Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics 2: 433-459.
  8. Bossert WH, Oettinger AG (1973) The Integration of Course Content, Technology and Institutional Setting. A Three-Year Report,Project TACT, Technological Aids to Creative Thought.
  9. Cordeschi R (2007) AI turns fifty: revisiting its origins. Applied Artificial Intelligence 21: 259-279.
  10. Oettinger AG (1952) CXXIV. Programming a digital computer to learn. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 43: 1243-1263.

Creating a Viable Gaza the ‘Day After’: How Mind Genomics and AI Can Suggest and Inspire

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DOI: 10.31038/MGSPE.2024432

Abstract

This paper introduces the combination of Mind Genomics thinking with AI for the solution of practical issues, focusing specifically on what to do to create a viable Gaza after the hostilities cease. The approach allows the user to specify the problem, and the type of answers required. In seconds, the AI returns with actionable suggestions. The user can iterate, either using the same problem-specification, or changing the problem specification. After the user finishes the iterations and receives the initial results. The system returns within 30 minutes with a detailed summarization of each iteration. The summarization shows the key ideas, the reactions of audiences (acceptors vs rejectors), and ideas for innovative solutions. The approach is proposed as a way to think of new solutions, doing so at the level of the granular.

Introduction

The 1993 and 1995 Oslo Peace Accords between Palestinian and Israeli leaders negotiated for Israel’s withdrawal from Gaza and other key areas. This happened in 2005 under Prime Minister Ariel Sharon. An Islamist political group called Hamas won elections and took control of Gaza in 2006. Since then, Hamas has occupied the strip, which has become a site for protests, bombings, land assaults and other acts of violence. Israel and the United States, as well as several other countries, consider Hamas a terrorist organization [1].

The Hamas charter called for the abolition of Israel, and death to Israelis and Jews, world-wide. The Hamas charter did not have any proviso for co-existence, but rather called for a radical form of Islam. Hamas became the de facto government of Gaza, creating a massive military infrastructure. On October 7, 2023, during the Jewish holiday of Succoth, a Rave music festival was held in Israel, in areas abutting Gaza. An attack by Hamas terrorists ended up killing 1200 Israelis, and the abduction of more than, 200 many of whom were later killed. The Israeli response was justifiably furious, resulting in the wholesale destruction of Hamas, the destruction of the infrastructure of Gaza in a way resembling the destruction of Nazi Germany by the Allies. The academic literature is filled with the background to these issues, the public press filled with accusations, counter-accusations, and the brouhaha of seeming irreconcilable differences rooted in politics, education and Islam.

With the foregoing as a background, the question arose as what to do on the ‘day after,’ when Hamas would be declared ‘gone.’ What creative ideas about Gaza could emerge. The same problems occurred 80+ years ago upon the occasion of the Allied victory. What would happen the day after? Would the Allies follow the Morgenthau Plan of returning Germany to a more primitive country to punish it for the horrible crimes the Nazi’s had committed? Or would other plans for reconstruction be adopted, plans which guided Germany towards democracy, and towards a renewed place in ‘civil society.’ Fortunately for the world as well as for Germany, it was the latter plan that was adopted.

Using AI to Provide Suggestions about Rebuilding Gaza

The origins of this work come from at least three distinct sources. It is the combination of these sources which provides the specifics about what to do ‘the day after.’ These sources are Mind Genomics [2], then collaboration with Professor Peter Coleman at Columbia University [3], and finally the introduction of artificial intelligence into Mind Genomics and now the use of that AI technology to suggest ideas for rebuilding Gaza.

The first source is the emerging science of Mind Genomics [4]. Mind Genomics can best be thought of as an approach to understanding what the important factors are driving attitudes and decisions, the focus being the granular quotidian world, th world of the ordinary, the world of the everyday. Studies of decision making are the daily bread of those involved in consumer research, political polling, and so forth. Studies of decision making use a variety of techniques, such an observation, interpersonal discussions with individuals or groups, surveys, and even experiments creating artificial situations in which the pattern of behaviors gives an idea of what rules of decision making are being used. Within this framework, Mind Genomics provides a simple but powerful process, best described as presenting the respondent (survey taker) with systematically constructed combinations (vignettes), getting ratings of these vignettes, and then deconstructing the ratings into the contribution of the individual elements which constitute the building blocks the vignettes.

The second source is the recognition that quite often the research effort in Mind Genomics seems to be unduly difficult for the typical user. More often than not, a user investigating a topic may feel overwhelmed when asked to generate four questions about the topic, and then for each question, provide four answers. This is the way the science of Mind Genomics works. The problem is that ordinary people feel quite intimidated. It is the introduction of artificial intelligence a way to generate ideas (questions, answers to questions) which provides a way through the thicket, a way to do the study [5] The AI provided ere, SCAS, Socrates as a Service, becomes a tutor to the user, and in turn, a much appreciate feature of Mind Genomics.

The third source is the re-framing of the input to the AI. Rather than simply abiding by the request to provide simple questions and answers, the user can provide AI with a complete story, and ask the AI to provide appropriate answers. In most, although to be not in all the times, the change in focus ends up delighting, as the SCAS provides a far more integrated approach to solving a problem.

It is important to keep in mind that the approach presented here deals with suggestions about the practical solution of a problem, rather than with the more conventional academic approach of defining a narrow problem and seeking a testable solution.

Example – One of Many Iterations Dealing with the Reconstitution of Gaza

The remainder of this paper shows what SCAS, the artificial intelligence embedded in Mind Genomics, produces when properly queried. The process begins with the creation of a Mind Genomic study, as shown in Figure 1, Panel A. The creation of the study is templated, with Panel A showing that the user simply names the study, selects a language, and then agrees not to request nor accept private information.

fig 1

Figure 1: Panel A – Project initiation. Panel B – Request for AI help (Idea Coach), or user-provided questions. Panel C – Rectangle where the user types the relevant information to prompt SCAS. Panel D – The rectangle filled with the relevant information.

Once the study has been created, the Mind Genomics program, www.bimileap, presents the user with a screen requesting four questions. It is at this point that the user can work with SCAS, the AI embedded in the program. Rather than providing four questions, the user presses the ‘Idea Coach’ button, and is led to a screen requesting that user type in the request. That screen appears in Figure 1, Panel C. Finally, the user provides background materials and the requests, as shown in Figure 1, Panel D.

Table 1 presents the full text version of the request that the user simply created and copied into the rectangle in Panel D. It is important to note that the language in Table 1 is simple, written in the way one might talk, filled with material which is both relevant (e.g., the phrase I want to find 14 different ways to do this. For each way, give the way a name and write that name in all titles. Then, in a paragraph tell me exactly what i should do) as well as personal and not particularly germane to the actual task (e.g., How do I do this as a private citizen who only can offer them Mind Genomics as a way to help business and education).

Table 1: The prompting information provided to SCAS (Socrates as a Service), the AI in Mind Genomics

tab 1

Within a minute or so after the user presses the ‘submit’ button in Figure 1, Panel D, the embedded AI generates the first response. The first response is a set of 15 questions. These questions are assigned sequential numbers and are shown in Table 2. Afterwards, material such as that in Table 3 will appear.

Table 2: Results from immediately using SCAS to answer the 15 questions provided by SCAS as the ‘first answer’ to its request (see Table 2). Each question posed by SCAS in its initial response to the user is shown as a three-component paragraph (question posed, answer provided, SCAS-estimated performance of the answer). This step is a slight detour.

tab 2

Table 3: The 14 efforts requested to SCAS in the squib, along with the elaboration of these efforts in simple-to-understand prose English.

tab 3

The Short, Momentary Detour, to Answer the Newly Presented Questions Which are Part of the Answer

Before proceeding, it is relevant to note an additional step that can be done, almost immediately. That step is to ask SCAS to ‘answer’ the 15 questions through an immediate next iteration. When the 15 questions appear, those shown in Table 2, SCAS has already done its work, responding to the request in Table 1. The program has also recorded all that needs to be recorded from the output of SCAS. The user is free now either to read all the information provided by AI, run another iteration, or take a quick detour to answer the 15 questions, before proceeding.

Table 2 shows what happens when the user copies the 15 questions, and then moves to the next iteration. The user presses requests permission to modify the input instructions to SCAS (Figure 1, Panel C). The user requests that SCAS repeat the question in text, then answer the question, and then rate the answer provided on three attributes, all on a 0–100-point scale. These three attributes are likelihood of success, degree of difficulty implementing the answer, and originality of the answer.

It is at Table 2 that one can see the power of the AI to help the user. It is not clear whether the user could have come up with these 15 questions, and certainly not in the time of 30 seconds. Should the user not like the questions, the user can continue to iterate until the user finds 15 questions of interest as outputs to the request made in Table 1. Once the set of questions are identified, it is straightforward to copy the set of questions, and mov in another direction by requesting SCAS to answer the 15 questions, and ‘scale the answer’ on the three dimensions. Finally, when that action, Table 2 is produced, the user now returns to the main study.

Once the first set of materials have been delivered, SCAS can be re-rerun for a second iteration. The user provided ‘squib’ or information about the topic and request to the AI, either remain the same, or can be edited ‘on the fly’ by the user. SCAS is now ready for a second run, and so forth. Iterations can be done in periods of about 30 seconds (excluding editing the squib to change the information to SCAS). Thus, the system becomes a tool for immediate iteration, learning, and fine tuning.

Once the user finishes the iterations and closes the study, either by working with respondents or simply by ‘logging off’, as was done here, the program puts each iteration into a separate Excel tab, and for that iteration performs a number of ‘summarizations’ explained and demonstrated below. Each iteration is analyzed thoroughly by its own ‘summarization,’ meaning that SCAS both generated the information from a minimal input shown in Table 1, and then created deep analyses of that information (Tables 2-6). For each study, the user receives the information in the ‘Idea Book,’ the aforementioned Excel book. For example, when the user decides to work with the program 15 times, iterating and then changing some of the squib, or even simply re-running the squib without changing it at all, the program will return within 30 minutes with the fully summarized material.

The remainder of this paper presents the results, discussing the nature of the results, the implications of the analyses presented, and so forth. It is important to keep in mind that for this particular study, it was possible to run the SCAS module more than 15 times, each iteration requiring less than 30 seconds, unless the squib was manually changed to shift direction in the effort to understand.

Ideas Presented – Answering the Statement ‘Mind Genomics for Business and Education

The initial request to SCAS was to provide 14 ideas for initiatives and programs, and then to explicate them. Again, the input information was minimal, focusing on business and education. Table 3 shows the results in detail, with the level of detail sufficient to ‘paint the picture’.

The summarization further proceeds with a variety of analyses about the ideas themselves. Table 4 shows three summarizations, as follows:

  1. Key ideas in the topic questions. As an aid to thinking, the summarization restates each of the questions in a simple manner.
  2. Further summarization occurs by distilling the ideas into general themes, and then for each theme showing the specific ideas relevant to that theme.
  3. Perspectives: For each theme, listing the positive and the negatives,

Table 4: Summarization of ideas into key questions, themes, and perspectives relevant to those themes

tab 4

The expansion of the ideas continues, with the ‘summarizer’ considering which groups would be positive to the ideas (interested audiences) and which groups with be against the ideas (opposing audiences). Once again it is important to stress that these groups emerged from the AI further ‘working up’ the material that it generated before. Table 5 shows these two groups, interested audiences versus opposing audiences, emerging from this one iteration presented in depth in this paper.

Table 5: Responses to the suggestions, divided into interested audiences and opposing audiences

tab 5

The final summarization presents the basis for new ideas, new strategies, and new products. Table 6 shows different ‘steps’ towards creating the ‘new’. The first comprises ‘alternative viewpoints,’ about the need for other perspectives. The second comprises ‘what is missing?’ focuses on additions to the 14 suggestions. The third comprises ‘innovations,’ first presenting the innovation and then providing some detail.

Table 6: Steps towards innovation; Alternative viewpoints, What is missing, and Innovations

tab 6(1)

tab 6(2)

Discussion and Conclusions

As emphasized in the introduction to this paper, the focus here is to find so-called actionable solutions to the issue of what to do ‘the day after.’ There are many studies about the problems, their history, and their manifestations. The academic literature is replete with such analyses, and with suggested solutions, although the stark reality is that analysis often leads to its own implicit paralysis because the focus is on the ‘why,’ and not on the ‘what to do’.

The inspiration for the Mind Genomics work and its evolution presented here came from the world of consumer psychology, whose academic goal of ‘knowledge’ was deeply intertwined with the ultimate desire by some to improve business by understanding the mind of people. It was from that beginning and from the experience in medicine, and actually changing people’s behavior for the better [6] that the approach presented here evolved. The notion that SCAS (Socrates as a Service) could produce actionable results further motivated us, once it became apparent that one could challenge AI with issues, and have AI first provide solutions given minimal input, and then ‘work up’ those minimal solutions into far more profound results.

Finally, it is important to close with the realization that the information presented here required no more than a minute to create, or perhaps more correctly, nor more than 30 seconds to create. The important thing about that short time is that it permitted the user, whether researcher or policy maker, to explore many different alternatives with on-the-fly modifications of the input squib shown in Table 1. In effort after effort author HRM has discovered that one iteration did not suffice. Rather, natural curiosity promoted many iterations, almost in a way that could be called ‘results-addiction.’ The immediate information returning with 15 seconds, and then the receipt of the Idea Book by email within 30 minutes made the process almost irresistible, similar to consuming dessert for those who are so addicted to sweet things. Eventually the process stops, the Idea Book arrives, and the ideas contained therein take over, to be put into practice.

References

  1. https://www.history.com/news/gaza-conflict-history-israel-palestine (accessed February 13, 2024)
  2. Papajorgji P, Moskowitz H (2023) The ‘average person’ thinking about radicalization: A Mind Genomics cartography. Journal of Police and Criminal Psychology 38: 369-380. [crossref]
  3. Coleman PT (2021) The Way Out: How to Overcome Toxic Polarization. Columbia University Press.
  4. Moskowitz HR (2012) ‘Mind genomics’: The experimental, inductive science of the ordinary, and its application to aspects of food and feeding. Physiology & Behavior 107: 606-613. [crossref]
  5. Wu Y (2023) Integrating Generative AI in Education: How ChatGPT Brings Challenges for Future Learning and Teaching. Journal of Advanced Research in Education 2: 6-10.
  6. Nikolić E, Masnic J, Brandmajer T, Nikolic A (2022) Chronic pain control using Mind Genomics in patients with chronic obstructive pulmonary disease knowledge. International Journal 52: 455-459.

How Open Education Can Facilitate Digital Competence Development

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DOI: 10.31038/PSYJ.2024631

Abstract

In today’s digitalized world, proficiency in digital skills is crucial for employability and academic success in higher education. However, there is a gap between students’ perceived and actual digital competence , which is rather limited.This paper explores how open education can foster the development of digital competence in higher education, employing theoretical frameworks alongside a practical example. The article examines theoretical approaches that combine open education concepts, specifically Open Educational Practices (OEP), with the principle of constructive alignment. Open education aims to democratize knowledge access and promote collaboration, making it conducive to digital competence development through OEPs. The principle of constructive alignment emphasizes aligning learning goals, assessments, and activities to foster competence development. One example illustrates the effectiveness of this approach, showing a significant improvement in students’ digital competence through participatory Open Educational Resource (OER) production. In conclusion, the paper emphasizes the significance of integrating OEPs into higher education pedagogy to assist students in acquiring essential digital competencies.

Keywords

Digital competence, Open educational practices, Open educational resources, Open education, Higher education

Introduction

Digital Competence in Higher Education

In the age of digitalization, digital competence is becoming increasingly important. Many jobs today require some level of digital competence, particularly in communication and collaboration. Digital competence enables individuals to effectively navigate online platforms, search for information, and distinguish credible sources from misinformation. In the digital age, civic engagement increasingly relies on digital platforms for activities such as voting, accessing government services, and participating in public discourse. Technology is constantly evolving, and new digital tools and platforms emerge regularly. The European Commission [1] describes digital competence in five areas. The five key areas of digital literacy are: “(1) Information and data literacy, including management of content; (2) Communication and collaboration, and participation in society; (3) Digital content creation, including ethical principles; (4) Safety; and (5) Problem solving.”

Higher education institutions should support students in developing their competencies in employability, academia, and personal responsibility [2]. Despite students’ high self-perception of their digital competence [3], their actual digital competence is inadequate [4]. This paper aims to explore how open education can promote the development of digital competence in higher education. To achieve this, we take a theoretical and practical approach. Firstly, we review literature on open education. Secondly, we provide an example of teaching and learning in open education.

Theoretical Approaches to Open Education and Digital Competence Development

To answer this question, we need to combine two theoretical approaches to higher education: conceptualizations and frameworks of open education and its design in higher education, and the principle of constructive alignment in the case of digital competence development.

There are several definitions of open education, with two major strands in the discussion [5]. First, there are those who discuss Open Educational Practices (OEP) in the context of open educational resources (OER). The most influential in this group are Wiley and Hilton [6], who focus their discussion of OER-enabled pedagogy on the different open educational practices enabled by the 5Rs (i.e., retain, reuse, revise, remix, and redistribute). Second, there are those who discuss OEP in relation to open scholarship, open learning, open teaching or pedagogy, open systems and architectures, and open-source software. Exemplary, DeRosa, and Robison [7] focus on open pedagogy and learner-driven practices. In the present article we focus on Cronins’ definition, which combines both aspects regarding OERs and learning processes in OEPs, that she defines as “collaborative practices that include the creation, use, and reuse of Open Educational Resources, as well as pedagogical practices employing participatory technologies and social networks for interaction, peer-learning, knowledge creation, and empowerment of learners” [8]. Open education aims to democratize access to knowledge, promote collaboration and innovation in teaching and learning, and address barriers to education such as cost, geography, and institutional constraints [8]. Regarding the research question above, the implementation of OEPs with a focus on student activity in OER production may facilitate digital competence development in higher education.

One theoretical approach for designing education towards competence development is the principle of constructive alignment [9]. This principle is fulfilled when learning outcomes are communicated in advance, performance assessments measure students’ achievement of those outcomes, and teaching and learning activities help students to achieve them. Consequently, the development of digital competence can be achieved by defining and communicating the learning goal in advance, assessing digital competence development, and supporting digital learning through activities that focus on information and data literacy, communication and collaboration, participation in society, digital content creation, safety, and problem-solving.

Based on the conceptualizations of open education and the principle of constructive alignment, educators can facilitate the development of digital competence in higher education by implementing OEPs that enable students to construct their own learning through engagement in relevant digital activities. The application of the principle of constructive alignment in OEP design could enhance digital competence, provided that the learning goal addressing digital competence is achieved through appropriate open learning activities and assessment formats. Therefore, students can acquire digital competencies if they are informed of this objective beforehand, if their learning activities concentrate on creating digital content, collaborating, and solving problems, and if the chosen assessment format also covers these learning areas. To enhance students’ digital competence, educators and students concentrate on generating digital OERs, and the assessment is based on this production.

Open Education and Digital Competence Development: A Practical Example

To answer the question on a practical level, we draw on an example study by Braßler [10]. The study presents a teaching-learning arrangement that implements OEPs to enable students to co-produce OER. The planning and implementation of the OER production course followed the principle of constructive alignment. To improve students’ digital competence development, the course educators and students focused on creating digital OERs in the form of videos and scripts covering various topics related to sustainability. The implemented learning activities were designed to develop the students’ digital competence. These activities included interdisciplinary peer learning, empowering learners in self-directed problem-solving, providing discipline-based expertise on demand by educators, technical expertise in shooting and editing videos on demand, and several feedback loops on the OER product by students and educators. The assessment was also based on the production of OERs. All interdisciplinary student teams were graded on their OER products. The study indicates a significant increase in digital competence over time among students who produced OERs in the production course, compared to their peers enrolled in courses unrelated to OER content development. In summary, the implementation of OEPs can enable students to co-construct their own learning towards digital content creation, with a clearly defined learning goal of digital competence, and assessment on digital content. This can lead to the development of digital competence through open education.

Conclusion

The purpose of this article is to analyze how open education can facilitate the development of digital competence in higher education. The key points of the theoretical and practical results are summarized below:

  • Implementing OEPs that enable students to actively participate in teamwork and co-creation.
  • Implementing OEPs that empower students in digital content creation.
  • Defining digital competence development as a learning goal and communicating this to students.
  • Implementing OEPs that enable students to co-create digital OERs as a product of their learning process.
  • Implementing OEPs that support students in their OERs production.

Consequently, educators should familiarize themselves with the opportunities for implementing OEPs in their teaching to support students’ development of digital competence.

Conflict of Interest

The author claims no conflict of interest.

Funding

No funding was received for this paper.

References

  1. European Commission (2018) Recommendation on Key Competences for Lifelong Learning. Proceedings of the Council on Key Competences for Lifelong Learning, Brussels, Belgium.
  2. Kolmos A, Hadgraft RG, Holgaard JE (2016) Response strategies for curriculum change in engineering. International Journal of Technology and Design Education 26: 391-411.
  3. Zhao Y, Sánchez Gómez MC, Pinto Llorente AM, Zhao L (2021) Digital Competence in Higher Education: Students’ Perception and Personal Factors. Sustainability 13: 12184.
  4. Marrero-Sánchez O, Vergara-Romero A (2023) Digital competence of the university student. A systematic and bibliographic update. Amazonia Investiga 12: 9-18.
  5. Koseoglu S, Bozkurt A (2018) An exploratory literature review on open educational practices. Distance Education 39: 441-461.
  6. Wiley D, Hilton J (2018) Defining OER-enabled pedagogy. International Review of Research in Open and Distributed Learning 19: 133-147.
  7. DeRosa R, Robison S (2017) From OER to open pedagogy: Harnessing the power of open. In R. S. Jhangiani & R. Biswas-Diener (Eds.), Open: The philosophy and practices that are revolutionizing education and science Ubiquity Press.
  8. Cronin C (2017) Openness and praxis: Exploring the use of open educational practices in higher education. International Review of Research in Open and Distributed Learning 18: 15-34.
  9. Biggs J, Tang C (2011) Teaching for Quality Learning at University. The Society for Research into Higher Education & Open University Press.
  10. Braßler M (2024) Students’ Digital Competence Development in the Production of Open Educational Resources in Education for Sustainable Development. Sustainability 16: 1674.

Experiments in Mind Genomics + Artificial Intelligence: Helping “College Towns” Deal with the Natural Rebelliousness of the Students

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DOI: 10.31038/MGSPE.2024431

Abstract

Using a combination of Mind Genomics thinking and artificial intelligence through LLMs (Large Language Models), the paper shows how police officers can understand the different mind-sets of students and others in college towns. The paper shows how to deal with a specific mind-set, INDIFFERENT, in order to encourage law-abiding behavior. The approach is generalizable, easy to use anywhere and anytime, with the ability for the user to incorporate situation-specific information as deemed relevant.

Keywords

Artificial intelligence, Authority, College towns, Mind genomics, Mind-set, Students

Introduction

Police officers in college towns often face unique challenges due to a diminished respect for the local police force. With the presence of a large student population, many young adults may have negative perceptions of law enforcement based on their own experiences or the influence of peers. This lack of respect can lead to conflicts and tensions between students and police officers, making it difficult for law enforcement to effectively serve and protect the community [1-3].

One potential solution to the issue of diminished respect for the local police force in college towns is to prioritize community engagement and outreach. By fostering positive relationships with students, law enforcement can work to build trust and mutual respect. This may involve hosting events, providing educational opportunities, and creating open lines of communication between police officers and the community [4,5].

It is also important to address the influence of leftist agendas on college campuses, which may promote anti-authoritarian attitudes and encourage students to resist or protest against law enforcement. Creating dialogue and promoting understanding between students with diverse backgrounds and beliefs can help bridge the gap between different mind-sets and foster a culture of respect for local authority [6-8].

Additionally, addressing systemic issues such as inequality and discrimination within the criminal justice system can help improve perceptions of law enforcement in college towns. By promoting policies and practices that prioritize fairness and accountability, police officers can work to earn the respect and trust of the community. Ultimately, finding effective strategies to promote respect for local authority among high school and college-aged students requires a multifaceted approach. By addressing societal attitudes, promoting community engagement, and fostering understanding between diverse groups, law enforcement can work to create a safer and more cohesive community for all residents.

Using Mind Genomics Thinking Coupled With AI (LLM, Large Language Models)

Mind Genomics is a new way of looking at how people think and how they make decisions. It helps us understand the different mind-sets of students in a college and high school town. By using Mind Genomics, we can learn more about what makes students think, and how we can better help them succeed. When we do a Mind Genomics analysis of the mind-sets of students in a college and high school town, we can see patterns in how they think about certain things. For example, we might find that high school students are more likely to be motivated by competition, while college students are more interested in collaboration. This information can help us cater our teaching methods to better meet the needs of each group.

The research strategy of Mind Genomics creates a set of messages about a topic, mixes these messages together to create vignettes, presents these vignettes to respondents, survey takers, obtains their ratings, and identifies the contribution to the rating of each messaging using OLS (Ordinary Least Squares) regression. The approach sounds more convoluted than conventional rating scales, but Mind Genomics ends up being far more productive and far less subject to biases. It is impossible to game the Mind Genomics system. The respondents end up evaluating the vignettes, the systematic variations, with disinterest, allowing real feelings to come through in the ratings. The result is far more actionable insights into the way people think and to the way people react [9-12].

Mind Genomics may give us a deeper understanding of the different mind-sets of students in a college and high school town. By analyzing these mind-sets, we can tailor our approaches to teaching and learning to better meet the needs of our students. This can lead to improved academic outcomes and a more positive learning environment for everyone involved. By understanding the different mind-sets of students in a college and high school town, we can create programs and initiatives that address their unique needs. For example, we might offer different types of study materials or extracurricular activities based on what we know about how students think and learn. This can lead to more engaged and successful students overall [13].

Mind Genomics Thinking and Artificial Intelligence

The evolving interaction between Mind Genomics and artificial intelligence (AI) is revolutionizing the way we understand human thinking patterns and behavior. Mind Genomics to identify mind-sets or ways people think about a particular topic, can be enhanced by the use of AI, specifically Large Language Models (LLMs), to provide content and insights. This collaboration allows for a deeper exploration of the nuances and variations in how individuals perceive and process information [14]. Through the use of AI, researchers can specify a topic and have LLMs generate expanded content on that topic based on the identified mind-sets. This capability enables a more comprehensive understanding of the various perspectives and thought processes which exist within a given population. By being able to delve into the intricacies of different mind-sets, researchers can gain valuable insights into how people approach and engage with specific subjects.

One of the key benefits of integrating Mind Genomics with AI is the ability to identify and analyze patterns in human thinking at a scale and speed that were previously unattainable. This advanced technology allows for the exploration of a wide range of mind-sets and thought processes, leading to a more holistic view of human cognition and behavior. By instructing the LLM to expand on topics and explore different mind-sets, researchers can uncover new connections and patterns that may have previously been overlooked.

The ultimate benefit to society when Mind Genomics thinking is linked with generative AI is the potential for greater innovation and understanding in various fields, such as psychology, marketing, and education. By gaining a more in-depth understanding of how people think and approach different topics, researchers can develop more targeted and effective strategies for communication and problem-solving. This advancement could lead to the development of more personalized services and products to better meet the needs and preferences of individuals within a population.

Directing AI (LLM) to Identify Student Mind-Sets in a Town, and How to Deal With Them

The remaining paper is given over to showing how to use Mind Genomics thinking and AI to synthesize mind-sets and to understand what to do in the town, given those synthesized mind-sets. We will focus specifically on one mind-set, the INDIFFERENT mind-set, knowing that what we present here can be done easily for every other mind-set.

Step 1: Write the Prompt (Table 1) and Receive a Preliminary Group of Mind-sets

Table 1 shows the prompt provided to the Mind Genomics program, www.BimiLeap.com. The program allows the user to interact with the LLM (ChatGPT 3.5) in the section called Idea Coach. The prompt in bold letters gives background, requesting the name and nature of each mind-set. The mind-sets themselves are not specified.

The bottom of Table 1 shows 15 mind-sets generated by the LLM in response to the request. The mind-sets are sorted alphabetically, although they did not emerge as such. The Idea Coach is programmed to provide a maximum of 15 options, doing so in the interest of cost and space. It is important to keep in mind that there might be many more mind-sets that LLM could generate.

Table 1: 15 mind-sets generated by the LLM

TAB 1

Step 2: Instruct the LLM to Provide Deeper Information About One Mind-set (INDIFFERENT).

Table 2 shows a more complete prompt requesting six pieces of information about each mind-set. Once again, keep in mind that no mind-sets are specified in Table 2. The LLM will return with different mind-sets. Each mind-set will be dealt with in detail following steps 1-6 in Table 2. In the interest of space, we look only at one of these mind-sets, INDIFFERENT.

Table 2: The prompt to provide six answers to each mind-set. The prompt does not specify the mind-set

tab 2

Table 3 shows the information immediately returned by the LLM for the mind-set INDIFFERENT. The important thing about Table 3 is the completeness of the information provided by the LLM. That is, with virtually no input information whatsoever, the artificial intelligence is able to synthesize a great deal of information about this so-called indifferent mind-set and provide it to us in a way that we can use it. The output is firstly informational, such as name, nature, how it came to develop, and how it thinks about authority, respectfully. Secondly, the output is actionable, first in terms of how to change the mind-set to become more respectful, and then providing slogans to use with this mind-set to get it to respect the police and the local authority.

Table 3: Information immediately returned by the LLM about the INDIFFERENT mind-set

tab 3

Using slogans to emphasize ideas is a smart idea because they are easy to remember and catchy. When a slogan is repeated over and over, it sticks in people’s minds and helps to reinforce the message being communicated. Slogans are special because they are short and to the point, making them easy for people to understand. They can also create a sense of unity and belonging among a group of people who share the same beliefs or ideas. In addition, slogans can be used to motivate and inspire people to take action or make a change. Overall, slogans are a powerful tool for getting a message across and can have a lasting impact on the way people think and act.

Step 3: Receive Deeper Analysis of Results from Each “Iteration,” Using the Summarizer Function Built Into the Mind Genomics Program

Understanding key ideas, themes, and perspectives is important because it helps us make sense of the world around us (see Table 4). When we take the time to explore different ideas and perspectives, we gain a deeper understanding of how things relate to each other and why people think and act the way they do. This type of learning helps us develop critical thinking skills, empathy, and a more open-minded mind-set. One way to think about understanding ideas is like solving a puzzle. Each idea is like a piece of the puzzle and when you put them all together, the bigger picture emerges more clearly. Themes are like the patterns and colors in the puzzle which help tie everything together. Perspectives are like looking at the puzzle from different angles to get a better view of the whole picture. By understanding key ideas, themes, and perspectives, we gain a better appreciation for diversity and different ways of thinking.

Table 4: Summarization of Key Ideas, Themes, and Perspectives

tab 4

When we think about interested audiences versus opposing audiences (Table 5), we can learn different perspectives and ideas about a topic. Interested audiences are people who are already interested in the subject, so they may have more knowledge and positive opinions. Opposing audiences are people who have different views and may disagree with what is being discussed. By considering both, we can get a complete picture of the issue and understand all sides. The benefit to thinking about this is that it helps us see the full picture and make informed decisions.

Table 5: Summarization of Interested Audiences versus Opposing Audiences

tab 5

For the police force in Millersville requesting this information, the benefit is that they can gather a wide range of opinions and ideas about a problem they are facing. By looking at both interested and opposing audiences, they can get a better understanding of the issue and find potential solutions that consider all perspectives. A deeper education into the problem can be achieved by considering opposing points of view because it allows for a more thorough analysis and consideration of different angles. By looking at different opinions, the police force can uncover new insights and strategies for addressing the problem effectively.

Alternative viewpoints provided by the LLM are important because they help us see things from different perspectives (Table 6). Just like how looking at a picture from different angles can give us a better understanding of what it is, listening to different viewpoints can help us understand a topic better. For example, if one person thinks that chocolate is the best flavor of ice cream, but someone else thinks that vanilla is the best, hearing both of their opinions can help us think about what flavor we might like the best. Having different viewpoints can also make our discussions more interesting, because we can learn new things and hear different ideas that we might not have thought of on our own.

Table 6: Summarization of Alternative Viewpoints

tab 6

When trying to figure out what is missing from a topic (Table 7), it is productive to combine critical thinking and generative AI. Start by reviewing the existing data and findings related to the topic at hand. Look for patterns, trends, and common themes that emerge from the data. This will identify gaps or missing pieces of information that may not have been explored or considered in previous research. Additionally, consider the potential implications and applications of the existing findings – are there any unanswered questions or unexplored areas that could provide valuable insights?

Table 7: Summarization of What is Missing

tab 7

Afterwards, use LLM (generative AI) to pose new questions or ideas to further expand the existing knowledge base. Use AI algorithms to analyze the data and identify potential areas of interest that have not been fully explored. This may generate innovative research questions or hypotheses which will drive new discoveries in the field of Mind Genomics and LLM experiments. By combining critical thinking skills with the power of generative AI, it may be possible to uncover hidden insights and overlooked perspectives, leading to a more comprehensive understanding of the topic.

To generate innovative ideas from a topic, it is essential to ask thought-provoking questions which challenge existing assumptions and push boundaries. Questions should be exploratory in nature, aiming to uncover hidden opportunities or unmet needs within the topic. For example, questions could focus on questioning the status quo, exploring unconventional perspectives, and considering the implications of emerging technologies or trends. Through a combination of critical thinking and generative AI tools, it is possible to generate a wide range of questions that spark creative thinking and lead to innovative solutions. In this way, the process of asking and answering questions can serve as a powerful tool for uncovering innovations from a topic (Table 8).

Table 8: Summarization of Innovations

tab 8

Discussion and Conclusions

AI may assist officers in college towns understand how students and “townies” think by analyzing data and identifying trends in behavior. This allows the police and other local authorities to “foresee” issues before they occur and take measures to safeguard the community. The use of artificial intelligence allows police departments in college towns to handle various situations more quickly and effectively. This might make the community safer for everyone, especially children, and allow the police to get along better with the residents.

Using AI to teach courses may help new police officers learn more rapidly by providing compelling training materials and video games. This may educate students about the many circumstances they may encounter at work and how to manage them effectively. In actual situations, the ability of AI to process large quantities of data makes it possible for officers to make better decisions and respond to situations more quickly.

Officers with greater experience may utilize AI to get fresh perspectives by evaluating data and generating predictions. This may help them identify patterns and trends in crime rates and behavior, allowing them to devise more effective approaches to prevent and solve crimes.

The most important thing, however, is the ability of the process described here to “teach” on virtually any topic. The LLM contains a wealth of information. The ability to extract that information through easy to create prompts in the “Idea Coach” feature in the www.BimiLeap.com platform is an educational tool with as many uses as there are situations to deal with, mind-sets to understand.

Acknowledgment

The authors thank our clerical professional, Vanessa Marie B. Arcenas, for continuing help in preparing these manuscripts.

References

  1. Ruddel R, Thomas MO and Way LB (2005) Breaking the chain: Confronting issueless college town disturbances and riots. Journal of Criminal Justice 33(6): 549-560.
  2. Williams LS and Nofziger S (2003) Cops and the college crowd: Young adults and perceptions of police in a college town. Journal of Crime and Justice 26(2): 125-151.
  3. Woldoff RA and Weiss KG (2018) Studentification and disorder in a college town. City & Community 17(1): 259-275.
  4. Cardarelli AP, McDevitt J and Baum K (1998) The rhetoric and reality of community policing in small and medium‐sized cities and towns. Policing: An International Journal of Police Strategies & Management 21(3): 397-415.
  5. Patten R, Alward L, Thomas M and Wada J (2016) The continued marginalization of campus police. Policing: An International Journal of Police Strategies & Management 39(3): 566-583.
  6. Baldwin DL (2021) In the shadow of the ivory tower: How universities are plundering our cities. Bold Type Books.
  7. Kamen S (2020) The People’s Republic of Ann Arbor: The Human Rights Party and College Town Liberalism. Michigan Historical Review 46(2): 31-69.
  8. Marginson S (2011) Higher education and public good. Higher education quarterly 65(4): 411-433.
  9. Gofman A and Moskowitz H (2010) Isomorphic permuted experimental designs and their application in conjoint analysis. Journal of sensory studies 25(1): 127-145.
  10. Moskowitz HR, Gofman A, Beckley J and Ashman H (2006) Founding a new science: Mind genomics. Journal of sensory studies 21(3): 266-307.
  11. Moskowitz HR, Wren J and Papajorgji P (2020) Mind genomics and the law. LAP LAMBERT Academic Publishing.
  12. Porretta S, Gere A, Radványi D and Moskowitz H (2019) Mind Genomics (Conjoint Analysis): The new concept research in the analysis of consumer behaviour and choice. Trends in food science & technology 84: 29-33.
  13. Moskowitz H, Kover A and Papajorgji P (2022) Applying mind genomics to social sciences. IGI Global.
  14. Rane NL, Tawde A, Choudhary SP and Rane J (2023) Contribution and performance of ChatGPT and other Large Language Models (LLM) for scientific and research advancements: a double-edged sword. International Research Journal of Modernization in Engineering Technology and Science 5(10): 875-899.

Enhancing Patient-Centered Care in Leukemia Treatment: Insights Generated by a Mind-Set Framework Co-developed with AI

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DOI: 10.31038/CST.2024914

Abstract

Leukemia is a challenging and complex cancer which significantly impacts patients’ lives. Understanding patient perspectives and needs is crucial for providing effective care and support. This study develops a framework for understanding patient mind-sets and their implications for leukemia care with the assistance of AI. Through the identification of five key mind-sets (Proactive, Anxious, Acceptance, Emotional, and Uncertain) and the mapping of the leukemia journey stages, we analyze patient needs and perspectives at each stage. The findings reveal critical points for intervention and support and suggest strategies for tailoring communication and care to patient mind-sets. We also propose a set of sample questions and tools for assessing patient mind-sets in clinical practice. The mind-set framework offers valuable insights for improving patient-provider communication, enhancing psychosocial support, and optimizing treatment adherence and outcomes. This study contributes to the growing body of knowledge on patient-centered leukemia care and provides a foundation for future research and practice. This framework provides a valuable tool for healthcare providers to deliver more personalized, effective patient care and support in leukemia.

Introduction

Leukemia is a life-altering cancer which poses significant physical, emotional, and social challenges for patients. As they navigate the complex journey of diagnosis, treatment, and survivorship, patients may experience a wide range of emotions, uncertainties, and coping strategies. Understanding patient perspectives and needs is essential for providing effective, compassionate, and patient-centered care. The importance of patient-centered care in oncology has been increasingly recognized in recent years. Studies have shown that incorporating patient perspectives and preferences into treatment planning can lead to improved patient satisfaction, treatment adherence, and health outcomes [1,2]. In the context of leukemia, research has highlighted the diverse psychosocial needs of patients and the importance of tailored support throughout the cancer journey [3,4]. The concept of patient mind-sets, or the cognitive and emotional frameworks through which individuals approach their health experiences, has gained attention as a valuable tool for understanding and addressing patient needs. The work of Howard R. Moskowitz, a pioneer in the field of consumer psychology, has demonstrated the power of mind-set segmentation in developing targeted marketing strategies [5]. Moskowitz’s approach involves identifying distinct consumer mind-sets based on their attitudes, beliefs, and preferences, and tailoring product offerings and communication strategies to each segment. While originally developed in the context of consumer behavior, the mind-set segmentation approach has since been applied to various domains, including healthcare [6], and has since been updated to incorporate contributions from AI [7]. Building on this foundation, we developed an AI-assisted methodology to develop a detailed patient mind-set framework for leukemia care. In this study, we aim to develop a framework for understanding patient mind-sets and their implications for leukemia care and support. By identifying key mind-sets, mapping the leukemia journey stages, and proposing tools for assessing patient mind-sets, we seek to provide insights into patient experiences and inform strategies for enhancing care and support throughout the leukemia journey. Our approach draws upon the principles of mind-set segmentation, as well as the growing body of literature on patient-centered care in oncology.

It is important to note that whereas the concept of patient mind-sets offers a valuable lens for understanding patient experiences, it should not be used to stereotype or pigeonhole individuals. Patients may exhibit characteristics of multiple mind-sets, and their perspectives and needs may evolve throughout the cancer journey. The mind-set framework is intended to serve as a guide for tailoring care and support, rather than a rigid classification system. Healthcare providers should use the framework in conjunction with other patient-centered assessment tools and engage in ongoing dialogue with patients to ensure that their individual needs and preferences are met.

Method

Developing the Mind-set Framework

The mind-set framework was developed through an iterative process of conversational prompting and analysis using the Claude.ai Opus language model [8]. Five key mind-sets were identified: Proactive, Anxious, Acceptance, Emotional, and Uncertain. Each mind-set was further elaborated upon, with descriptions of their characteristics, thought processes, and needs.

Mapping the Leukemia Journey Stages

The mapping of the leukemia journey stages was accomplished through the AI-assisted conversation. A comprehensive map of the leukemia journey was developed, including 11 key stages: Initial Diagnosis, Further Testing and Classification, Treatment Planning, Transplant Consideration (if applicable), Induction Therapy, Hospital Discharge, Consolidation Therapy, Maintenance Therapy, Monitoring and Follow-up, Supportive Care, and Survivorship and Long-term Care.

Analyzing Patient Needs and Perspectives

To explore patient needs and perspectives at each stage of the leukemia journey, a series of stage-specific questions was developed through the AI-assisted conversation. The resulting analysis of patient needs and perspectives was synthesized from this AI-generated content, providing a comprehensive profile of the key concerns, emotions, and support needs at each stage of the leukemia journey for each of the five identified mind-sets.

Results

Patient Mind-sets and Their Implications

The analysis revealed five distinct patient mind-sets, each with specific characteristics, needs, and implications for care (Table 1). Mapping these mind-sets to the stages of the leukemia journey provided a framework for understanding patient experiences and tailoring support strategies. The five identified mind-sets (Proactive, Anxious, Acceptance, Emotional, and Uncertain) represent distinct approaches to the leukemia journey, with significant implications for patient needs, preferences, and coping strategies.

Table 1: Patient mindsets and their implications

Mind-Set

Key Characteristics

Implications for Care and Support
Proactive Information-seeking, active decision-making, problem-solving Benefit from detailed explanations, collaborative care planning, and resources for self-management
Anxious Worry, fear, need for reassurance and support Require frequent reassurance, emotional support, and guidance in managing fears and uncertainties
Acceptance Realistic, action-oriented, focus on normalcy Benefit from clear, direct communication and support in maintaining a sense of normalcy
Emotional Strong need for emotional support, validation, coping Require extensive validation, empathy, and resources for coping with the psychological impact of the journey
Uncertain Doubt, indecision, need for clarity and guidance Benefit from guidance, decision support, and help in navigating complex decisions and adapting to changing circumstances

The Proactive mind-set is characterized by a desire for information, active involvement in decision-making, and a focus on problem-solving. Patients with this mind-set may benefit from detailed explanations of their diagnosis and treatment options, collaborative care planning, and resources for self-management. Healthcare providers should engage these patients in shared decision-making and provide them with the tools and information they need to take an active role in their care. In contrast, patients with an Anxious mind-set may struggle with worry, fear, and a need for frequent reassurance and support. These patients require a high level of emotional support and guidance in managing their fears and uncertainties. Healthcare providers should prioritize clear, empathetic communication and connect these patients with resources for mental health support and stress management. The Acceptance mind-set is characterized by a realistic, action-oriented approach to the leukemia journey, with a focus on maintaining a sense of normalcy. Patients with this mind-set may benefit from clear, direct communication about their diagnosis and treatment plan, as well as support in adapting to the challenges of cancer while maintaining their daily routines and activities. Patients with an Emotional mind-set have a strong need for validation, empathy, and emotional support throughout their journey. They may struggle with the psychological impact of cancer and require extensive resources for coping and self-care. Healthcare providers should prioritize empathetic, non-judgmental communication and connect these patients with counseling and support services. Finally, the Uncertain mind-set is characterized by doubt, indecision, and a need for clarity and guidance. Patients with this mind-set may struggle to navigate the complex decisions and challenges of the leukemia journey and may benefit from decision support tools, clear explanations of their options, and ongoing guidance from their healthcare team.

The Leukemia Journey and Patient Experiences

The mapping of the leukemia journey stages reveals key medical and social challenges at each point in the patient experience (Table 2).

Table 2: The Leukemia journey and corresponding patient experiences

Stage

Description

Key Challenges

Support Needs
Initial Diagnosis Receiving the news of a leukemia diagnosis Shock, fear, uncertainty, complex decisions about testing and treatment Emotional support, clear information, guidance in decision-making
Further Testing and Classification Undergoing additional tests to determine the specific type and subtype of leukemia Anxiety about test results, understanding implications of diagnosis Clear explanations of tests and results, emotional support, guidance in understanding diagnosis
Treatment Planning Discussing and deciding on the best course of treatment based on the type of leukemia and individual factors Emotional impact of diagnosis, communication with loved ones, weighing treatment options and side effects Psychosocial support, resources for communication and decision-making, detailed information on treatment options
Transplant Consideration (if applicable) Evaluating the need for and feasibility of a stem cell transplant Complex decision-making, fear and uncertainty about transplant process Detailed information about transplant options and process, emotional support, decision-making tools
Induction Therapy Receiving intensive chemotherapy to achieve remission Intense physical and emotional challenges, managing side effects, maintaining normalcy Support in managing side effects, emotional coping strategies, resources for maintaining a sense of normalcy
Hospital Discharge Transitioning from inpatient to outpatient care Uncertainties, transition to home care, new support needs Guidance in transitioning to home care, resources for managing challenges, ongoing support
Consolidation Therapy Receiving additional chemotherapy to prevent relapse Ongoing management of side effects, emotional coping, lifestyle adjustments Strategies for managing side effects, emotional support, resources for adapting to lifestyle changes
Maintenance Therapy Receiving long-term, low-dose chemotherapy to maintain remission Long-term management of side effects, emotional coping, adapting to a “new normal” Ongoing support for managing side effects and emotional challenges, resources for maintaining quality of life
Monitoring and Follow-up Undergoing regular check-ups and tests to monitor for signs of relapse Fear of recurrence, need for vigilance, redefinition of normalcy Psychosocial support, guidance in managing fear and uncertainty, resources for redefining normalcy
Supportive Care Receiving comprehensive care to address physical, emotional, and practical needs throughout the journey Navigation of complex physical, emotional, and social needs Comprehensive support for physical, emotional, and social needs, coordination of care across multiple providers
Survivorship and Long-term Care Adjusting to life after treatment and managing long-term effects Integration of cancer experience into identity and purpose, ongoing physical and emotional challenges, fear of long-term effects Resources for finding meaning and purpose, ongoing support for physical and emotional well-being, guidance in managing long-term effects of treatment

The leukemia journey is marked by a series of medical and social challenges which evolve over time, from the initial shock and uncertainty of diagnosis to the long-term implications of survivorship. At each stage, patients face a unique set of physical, emotional, and practical concerns which require targeted support and intervention. The AI-discovered stages not only make intuitive sense but resemble stages published in IQVIA’s global patient and carer experience survey (2013), funded by a consortium of three leukemia patient advocacy networks. Table 3 shows that the IQVIA framework covers the same stages in a more summary form [9]. The AI-generated stages map perfectly to the IQVIA stages while offering greater specificity. From a clinical perspective, both maps “work” but the AI-generated stages seem be able to guide the patient experience in a more granular fashion throughout the leukemia journey.

Table 3: Leukemia Stages, IQVIA and Study Stages Mapped

IQVIA Framework

Current Study Stages Developed by AI
Diagnosis Initial Diagnosis; Further Testing and Classification
Watch and wait Treatment Planning (note: primarily for CLL patients)
Treatment Treatment Planning; Transplant Consideration (if applicable); Induction Therapy; Hospital Discharge; Consolidation Therapy; Maintenance Therapy
Ongoing Monitoring Monitoring and Follow-up
Living with Leukemia Survivorship and Long-term Care; Supportive Care

The initial stages of diagnosis and treatment planning are often characterized by intense emotions, complex decision-making, and a need for clear, compassionate communication from the healthcare team. As patients progress through treatment, they may struggle with the physical and emotional toll of therapy, as well as the challenges of maintaining a sense of normalcy in their daily lives. The transition to post-treatment survivorship brings its own set of uncertainties and support needs, as patients grapple with the fear of recurrence, the need for ongoing monitoring, and the task of integrating the cancer experience into their identities and life narratives. Across all stages of the journey, patients require a comprehensive, patient-centered approach to care that addresses their medical, emotional, and social needs. This may include providing clear, understandable information about diagnosis and treatment options, offering psychosocial support and resources for coping, and facilitating communication and decision-making with loved ones and the healthcare team. By understanding the unique challenges and support needs at each stage of the journey, healthcare providers can tailor their interventions and resources to better meet the needs of individual patients and families.

Enhancing Patient Care and Support

The mind-set framework offers valuable insights for enhancing patient care and support throughout the leukemia journey (Table 4).

Table 4: Strategies for Enhancing Patient Care and Support

Mind-Set

Communication Strategies

Support Strategies

Critical Points for Intervention
Proactive Provide detailed explanations, engage in collaborative decision-making, offer resources for self-management Encourage active participation, provide tools for tracking and managing care, connect with peer support and information resources Initial diagnosis, treatment planning, transitioning to survivorship
Anxious Offer frequent reassurance, validate emotions, provide clear and concise information Provide emotional support, connect with mental health resources, offer relaxation and stress-management techniques Initial diagnosis, treatment planning, induction therapy, hospital discharge, monitoring and follow-up
Acceptance Use clear, direct communication, focus on actionable steps and realistic expectations Support in maintaining a sense of normalcy, provide practical resources for managing challenges, encourage a focus on the present Treatment planning, induction therapy, hospital discharge, consolidation and maintenance therapy
Emotional Provide empathy and validation, allow ample time for emotional expression, offer coping strategies Connect with emotional support resources, provide counseling referrals, encourage journaling and other expressive outlets Initial diagnosis, treatment planning, induction therapy, hospital discharge, monitoring and follow-up, supportive care and survivorship
Uncertain Offer guidance and decision support, provide clear information, explore options and alternatives Provide decision-making tools, connect with peer support and information resources, offer ongoing guidance and support Initial diagnosis, treatment planning, transplant consideration, hospital discharge, monitoring and follow-up, supportive care and survivorship

By understanding the unique needs and concerns of each mind-set, healthcare providers can tailor their communication and support strategies to better meet the needs of individual patients. This may involve providing detailed explanations and resources for Proactive patients, offering reassurance and emotional support for Anxious patients, using clear and direct communication for Acceptance patients, providing empathy and coping strategies for Emotional patients, and offering guidance and decision support for Uncertain patients. As patients progress through treatment, their needs may shift depending on their mind-set and the challenges they face. For instance, Anxious patients may require ongoing reassurance and emotional support during induction therapy and hospital discharge, whereas Acceptance patients may benefit from practical resources for managing side effects and maintaining a sense of normalcy during consolidation and maintenance therapy. The framework also highlights the importance of supporting patients during the transition to survivorship, when they may face new challenges related to long-term side effects, emotional adjustment, and redefining their sense of normalcy. Healthcare providers should be attuned to the unique needs of each mind-set during this stage and provide appropriate resources and support, such as counseling referrals for Emotional patients and peer support connections for Uncertain patients. By using the mind-set framework to guide patient care and support strategies at these critical points, healthcare providers can ensure that patients receive the targeted, personalized support they need to navigate the challenges of the leukemia journey and achieve the best possible outcomes. It is important to note that patients’ needs and preferences may evolve over time. Healthcare providers should use the mind-set framework as a starting point for understanding and addressing patient needs but should also remain attuned to the unique experiences and perspectives of each individual. Regular check-ins and ongoing communication with patients can help providers adapt their support strategies as needed and ensure that patients feel heard, understood, and supported throughout their journey. Tools for assessing patient mind-sets at any point in the journey can make valuable contributions to ensuring that care is patient-centered. These tools are presented next in part IV.

Tools to Assess Patient Mind-sets and Communicate Properly

This section provides a set of tools and resources to help clinicians assess and respond to patient mind-sets throughout the leukemia journey. The tools are designed to complement the mind-set framework and journey mapping discussed above, offering practical guidance for tailoring communication and support to the unique needs and perspectives of each patient. Section 1 presents the Patient Mind-Set Questionnaire, a simple tool for identifying a patient’s primary mind-set at the beginning of their leukemia journey. By asking patients to select the statement that most closely aligns with their thoughts and feelings, clinicians can quickly gain insight into the patient’s overall approach to coping with their diagnosis and treatment. The questionnaire is accompanied by a set of instructions for patients and clinicians, as well as a discussion of the potential benefits and limitations of assigning patients to a single primary mind-set (Table 5). Section 2 introduces a stage-specific approach to assessing patient mind-sets throughout the leukemia journey. For each of the 11 stages identified in the journey map, a key question and set of keywords are provided to help clinicians identify the secondary mind-sets that may emerge in response to the unique challenges and priorities of that stage. By listening for these keywords and themes in patient responses, clinicians can gain a more nuanced understanding of each patient’s needs and adapt their communication and support strategies accordingly (Table 6).

Table 5: The Patient Mind-Set Questionnaire (PMQ) to discover the patient’s mind-set

Patient Mind-Set Assessment Questionnaire

Which Statement Do You Most Agree With? (Patient)

Mind-Set Assignment Key (Healthcare Staff)
1.  I try to focus on the present and take things one day at a time. (Acceptance)

Acceptance
2.  I often feel anxious or worried about my leukemia and treatment. (Anxious)

Anxious
3.  I need a lot of emotional support to cope with my leukemia journey. (Emotional)

Emotional
4.  I prefer to be actively involved in my care and treatment decisions. (Proactive)

Proactive
5.  I often feel unsure about my treatment options and what to expect. (Uncertain)

Uncertain
©2024. Stephen D. Rappaport and Howard R. Moskowitz

It is important to note that whereas the tools presented here offer a structured approach to assessing patient mind-sets, they should be used as part of a broader, holistic assessment that takes into account each patient’s unique background, experiences, and goals. In addition to these mind-set-specific tools, clinicians may also benefit from using more general patient-reported outcome measures and quality of life assessments to gain a comprehensive understanding of each patient’s well-being and support needs.

Patient Mind-Set Assessment Questionnaire and Assignment Key

The Patient Mind-Set Questionnaire (PMQ) presented in Table 5 can be used during the initial consultation or early in the patient’s leukemia journey to help clinicians understand their primary mind-set and tailor their communication and support strategies accordingly. The PMQ can be incorporated into EHR systems as a standard component of the patient intake/onboarding process and/or as a tool for assessing the patient’s mind-set as they go through stages of the journey from diagnosis to survivorship. By take a patient’s mind-set “pulse” at various times, healthcare staff can detect mind-set shifts and adjust accordingly to the patient.

Note that during administration of the Questionnaire, the patient sees only the statements. The mind-set assignment is done afterwards, either automatically or manually depending on the mode of administration.

Patient Instructions:

“Please read the following statements carefully and select the one that most closely resembles your thoughts and feelings about your leukemia journey. If you feel that more than one statement applies to you, please choose the one that you identify with the most.”

Doctor Instructions:

“Provide the Patient Mind-Set Questionnaire to your patient during the initial consultation or early in their leukemia journey. Encourage them to read each statement carefully and select the one that most closely aligns with their thoughts and feelings. If a patient expresses difficulty choosing just one statement, guide them to select the one they identify with the most. After the patient has selected their statement, use the Mind-Set Key to assign the mind-set.”

Patient Mind-Set Interview

The Patient Mind-Set Interview (PMI) presented in Table 6 is a second approach to mind-set identification, envisioned to be used in clinical settings with the patient, and as an adjunct tool with the PMQ. Here the medical professional asks an empathic question directly to the patient. The table provides a sample patient response, keywords and non-verbal cues that can assist medical staff with assigning the patient to a mind-set. The PMI can be used at any time throughout the patient’s journey.

Table 6: Mind-set interview assigner with sample patient reply, keywords important to listen for, and non-verbal cues emerging from observation.

Mind-Set

Sample Patient Reply

Keywords to Listen For

Non-verbal Cues
Proactive “I like to research my options, ask questions, and work closely with my healthcare team to develop a plan that feels right for me.” research, options, questions, plan, involved, decide, work together Actively takes notes, asks questions, engages in shared decision-making, maintains eye contact, confident posture
Anxious “To be honest, I often feel quite overwhelmed and worried. I really need a lot of reassurance and support from my healthcare team and loved ones.” overwhelmed, worried, reassurance, support, anxious, afraid, uncertain Fidgets, appears restless, seeks frequent reassurance, tense, furrowed brows, worried expression, shaky voice, on the verge of tears
Acceptance “I try to accept the situation and focus on taking things one day at a time. I trust my healthcare team to guide me through this.” accept, focus, present, day at a time, trust, guide, cope Appears calm, attentive, nods in understanding, makes statements reflecting acceptance or focus on the present, neutral or slightly positive facial expression
Emotional “I experience a wide range of emotions and really need help processing my feelings and finding healthy ways to cope with the challenges.” emotions, help, processing, feelings, cope, challenges, support Cries, expresses strong emotions, seeks physical comfort, expressive, animated facial expressions and hand gestures, difficulty focusing due to emotional state
Uncertain “To be honest, I often feel quite unsure about what to do. I would really appreciate more information and guidance to help me make decisions about my care.” unsure, information, guidance, decisions, options, clarify, explain Appears hesitant, indecisive, frequently asks for clarification, puzzled or contemplative facial expression, processes information slowly, struggles to understand

Question: “As we navigate your leukemia journey together, it would be helpful for me to understand how you typically cope with challenging situations. Could you share with me how you usually respond when faced with difficult news or decisions related to your health?”

Independent of the method used, PMQ or PMI, assigning a patient to their primary mind-set based on the questionnaire can be valuable for clinicians in several ways:

  1. Tailored Communication: Understanding a patient’s primary mind-set allows clinicians to adapt their communication style to best meet the patient’s needs. For example, a proactive patient may appreciate detailed information and a collaborative approach, whereas an anxious patient may benefit from reassurance and clear, concise explanations.
  2. Personalized Support: By identifying a patient’s primary mind-set, clinicians can provide targeted support and resources which align with the patient’s coping style and emotional needs. This may include connecting patients with relevant support groups, offering tailored coping strategies, or providing additional resources for self-management, respectively.
  3. Anticipating Challenges: Knowing a patient’s primary mind-set can help clinicians anticipate potential challenges or barriers to treatment adherence and enable these to be actively addressed. For instance, an uncertain patient may require more guidance and decision-making support, whereas an emotional patient may need additional emotional care throughout their journey.
  4. Building Trust and Rapport: By demonstrating an understanding of a patient’s primary mind-set and adapting the approach accordingly, clinicians can foster a stronger therapeutic alliance and build trust with their patients. This can lead to improved communication, shared decision-making, and better overall patient satisfaction.

Suggestions for Clinical Practice

  1. Incorporate the Patient Mind-Set Assessment Questionnaire into initial patient consultations. The benefits are identified primary mind-sets and tailored communication and support strategies accordingly.
  2. Use the stage-specific Patient Mind-Set Assessment Questions and associated keywords to assess secondary mind-sets throughout the leukemia journey, and adapt care plans as appropriate.
  3. Provide training for healthcare providers on recognizing and responding to different patient mind-sets to enhance patient-centered care and support.
  4. Develop a comprehensive resource library with mind-set-specific support materials, such as coping strategies, educational resources, and referrals to support services.
  5. Integrate the mind-set framework into multidisciplinary care team discussions to ensure a consistent, patient-centered approach across all aspects of leukemia care.

Discussion

Implications of the Mind-set Framework for Leukemia Care

The mind-set framework has significant implications for improving patient-provider communication, shared decision-making, and psychosocial support in leukemia care. By understanding patient mind-sets and their associated needs and preferences, healthcare providers can engage in more effective, patient-centered communication and collaborate with patients to develop personalized care plans. The framework highlights the importance of tailoring communication and support strategies to the unique needs and perspectives of each patient. For example, patients with a Proactive mind-set may benefit from detailed explanations and collaborative decision-making, wherease those with an Anxious mind-set may require frequent reassurance and emotional support. By adapting their approach to the individual patient, healthcare providers can foster a stronger therapeutic alliance, improve patient satisfaction, and optimize treatment adherence and outcomes. The mind-set framework also emphasizes the importance of providing comprehensive, multidisciplinary support throughout the leukemia journey. This includes addressing patients’ medical, emotional, and social needs, and connecting them with appropriate resources and support services. By taking a holistic, patient-centered approach to care, healthcare providers can help patients navigate the complex challenges of the leukemia journey and maintain the best possible quality of life.

Limitations and Future Research Directions

Whereas this study provides a valuable foundation for understanding patient mind-sets and their implications for leukemia care, it is not without limitations. The mind-set framework was developed through a qualitative, AI-assisted analysis of patient experiences and may require further validation through larger, more diverse patient samples. Future research could explore the application of the mind-set framework to other cancer types and chronic illnesses, as well as the development of specific interventions and tools for assessing and addressing patient mind-sets in clinical practice. Another limitation of this study is its reliance on a single AI language model for the generation and analysis of patient perspectives. Whereas the Claude.ai model provided valuable insights and suggestions, it is important to acknowledge that AI-generated content may not fully capture the complexity and nuance of real patient experiences. Future research could incorporate data from patient interviews, focus groups, surveys or experiments to further refine and validate the mind-set framework. Additionally, the mind-set framework presented in this study is intended as a conceptual tool to understand and address patient needs, rather than a prescriptive or exhaustive classification system. Further research is needed to explore the ways in which patient mind-sets may intersect with other factors such as age, gender, cultural background, and socioeconomic status, as well as develop more nuanced and inclusive approaches to patient-centered care. Despite these limitations, the mind-set framework offers a promising avenue for future research and practice in leukemia care and beyond. By providing a structured approach to understanding and addressing patient needs, the framework can inform the development of targeted interventions, resources, and support services which optimize patient experiences and outcomes. Addressing these limitations through further research will strengthen the framework and its practical applications.

Conclusions

This study offers a novel framework to understand patient mind-sets and their implications for leukemia care and support. By identifying five key mind-sets (Proactive, Anxious, Acceptance, Emotional, and Uncertain) and mapping the leukemia journey stages, we provide actionable insights into patient experiences, needs, and preferences throughout the leukemia journey. The mind-set framework suggests strategies to tailor communication, support, and care to the unique needs of each mind-set, and highlights critical points for intervention and support. We also propose a set of sample questions and tools for assessing patient mind-sets in clinical practice which can help healthcare providers better understand and address individual patient needs. These findings have significant implications for improving patient-provider communication, enhancing psychosocial support, and optimizing treatment adherence and outcomes in leukemia care. We call for further study to gauge the value of integrating mind-set-based approaches into leukemia care and envision a future in which all patients receive personalized, compassionate, and effective support throughout their cancer journey.

We close with a suggestion for five future directions:

  1. Validate the mind-set framework through larger, more diverse patient samples and explore its applicability to other cancer types and chronic illnesses.
  2. Develop and test specific interventions and tools for assessing and addressing patient mind-sets in clinical practice, such as mind-set-based communication training programs for healthcare providers.
  3. Investigate the impact of mind-set-tailored care on patient outcomes, including treatment adherence, quality of life, and overall satisfaction with care.
  4. Explore the potential for technology-based solutions, such as mobile apps or web-based platforms, to support the assessment and management of patient mind-sets throughout the leukemia journey.
  5. Conduct longitudinal studies to examine how patient mind-sets may evolve over time and in response to different stages of the leukemia journey and identify factors that contribute to mind-set transitions and adaptations.

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