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When a Seizure Unmasks the Pituitary: Profound Hyponatremia Revealing Multiple Anterior Pituitary Hormone Deficiencies

DOI: 10.31038/EDMJ.20261041

Abstract

A 33-year-old woman presented to the emergency department after generalized seizure activity and was found to have profound hypotonic hyponatremia (serum sodium 107 mmol/L; serum osmolality 219 mOsm/kg). After initial stabilization, focused history-taking revealed approximately 10 years of secondary amenorrhea beginning after her second childbirth, together with chronic fatigue, weakness and reduced exercise tolerance. Morning cortisol was markedly low at 0.70 µg/dL; free T4 was low with a non-elevated TSH, and LH and FSH were low, indicating deficiencies across multiple anterior pituitary axes. MRI showed a partially empty sella without an acute intracranial lesion. The patient received controlled 3% hypertonic saline and intravenous hydrocortisone, followed by hydrocortisone and levothyroxine replacement. Neurological status improved, serum sodium normalized during hospitalization, and no further seizures were reported. The case highlights the emergency value of integrating electrolyte abnormalities with menstrual and postpartum history and early assessment of cortisol and pituitary function.

Keywords

Hyponatremia, Seizure, Hypopituitarism, Secondary adrenal insufficiency, Central hypothyroidism, Amenorrhea, Empty sella, Emergency medicine

Learning Points

  • Severe symptomatic hypotonic hyponatremia requires immediate, controlled treatment while the underlying cause is investigated in parallel.
  • Secondary adrenal insufficiency is an important reversible cause of severe hypotonic hyponatremia and may present with hypotension and nonspecific constitutional symptoms.
  • A low free T4 with a low, normal or mildly elevated TSH can indicate central hypothyroidism when interpreted in the setting of pituitary disease.
  • Long-standing secondary amenorrhea may provide an important historical clue to chronic gonadotropin deficiency and broader pituitary dysfunction.
  • A partially empty sella is a radiological finding rather than, by itself, proof of hypopituitarism or its cause; imaging must be correlated with clinical and biochemical findings.
  • When central adrenal insufficiency is suspected with central hypothyroidism, glucocorticoid replacement should be addressed before or together with thyroid hormone replacement.

Background

Severe hyponatremia is a time-critical emergency because marked reductions in plasma sodium can cause cerebral dysfunction, seizures and coma. In patients with neurological manifestations, stabilization and etiological evaluation should proceed simultaneously. Endocrine causes, particularly glucocorticoid deficiency related to hypothalamic or pituitary disease, are important reversible contributors and may be missed when chronic symptoms are nonspecific [1-3].

Pituitary dysfunction can evolve over years, with fatigue, weakness, menstrual disturbance and reduced exercise tolerance preceding recognition of the disorder. Empty or partially empty sella may be incidental, but clinically relevant pituitary hormone deficiencies occur in a subset of patients and require biochemical assessment [4,5]. This case is reported because an acute seizure-associated electrolyte emergency led to recognition of previously unintegrated chronic endocrine symptoms and abnormalities involving several anterior pituitary axes.

Case Presentation

Emergency Presentation

A 33-year-old woman presented following generalized seizure activity characterized by loss of consciousness, upward eye deviation, teeth clenching and stiffening of all four limbs, followed by postictal drowsiness. The available clinical documentation described recurrent vomiting and several seizure episodes over approximately two days. One radiology referral described an episode lasting approximately 30 minutes, whereas the clinical record described the principal generalized seizure as approximately three minutes; therefore, the event is not classified as status epilepticus in this report.

There was no documented history of head trauma, fever or central nervous system infection. The available record did not document headache, focal neurological deficit, sensory symptoms or other acute intracranial symptoms. She had a history of hypothyroidism and was taking levothyroxine 25 µg daily.

Initial Emergency Assessment

On arrival, she was postictal but arousable. Blood pressure was 92/60 mmHg, heart rate 96 beats/min, respiratory rate 18/min, temperature 36.7°C and oxygen saturation 98% on room air. No focal neurological deficit was documented.

Laboratory Evaluation

Initial testing demonstrated serum sodium of 107 mmol/L and serum osmolality of 219 mOsm/kg, confirming profound hypotonic hyponatremia. Serum potassium was 3.33 mmol/L and blood glucose was 86 mg/dL. Urine osmolality was 209 mOsm/kg and urine sodium was 67.6 mmol/L. Non-contrast CT of the brain showed no acute intracranial pathology (Table 1).

Table 1: Initial emergency laboratory investigations.

Parameter

Result

Serum sodium

107 mmol/L

Serum potassium

3.33 mmol/L

Serum osmolality

219 mOsm/kg

Blood glucose

86 mg/dL

Urine osmolality

209 mOsm/kg

Urine sodium

67.6 mmol/L

Endocrine Clues and Diagnostic Work-up

After immediate stabilization, a more detailed history changed the diagnostic direction. Menstrual cycles had previously been regular but stopped approximately 10 years earlier, after her second full-term vaginal delivery at 23 years of age. The prolonged amenorrhea had not previously been evaluated. She also reported longstanding generalized weakness, chronic fatigue, reduced exercise tolerance and intermittent dizziness.

Endocrine investigations demonstrated a morning cortisol concentration of 0.70 µg/dL, low free T4, TSH 1.37 mIU/L, and low LH and FSH. A morning cortisol below 3 µg/dL is strongly suggestive of central adrenal insufficiency in the appropriate clinical setting, and a low free T4 with a low, normal or mildly elevated TSH supports central hypothyroidism when pituitary disease is present [3]. The combination of marked cortisol deficiency, low free T4 with a non-elevated TSH, and low gonadotropins therefore supported multiple anterior pituitary-axis deficiencies. Growth hormone/IGF-1 and prolactin were not available in the supplied record; consequently, the term “panhypopituitarism” is not used as a definitive diagnosis (Table 2).

Table 2: Endocrine investigations demonstrating abnormalities in multiple anterior pituitary axes.

Endocrine parameter

Result

Morning cortisol

0.70 µg/dL

TSH

1.37 mIU/L

Free T4

Low

LH

Low

FSH

Low

Imaging

Brain MRI performed on 8 April 2026 showed no obvious focal intracranial lesion, acute infarction or hemorrhage. The report described bilateral prominent preoptic cerebrospinal-fluid sleeves with a partially empty sella and recommended ophthalmic examination to assess for papilledema. That recommendation is reproduced from the radiology report; no diagnosis of papilledema or raised intracranial pressure is made because an ophthalmic examination result was not available in the supplied record (Figures 1-3).

Figure 1: De-identified MRI brain report dated 8 April 2026. The report describes a partially empty sella with prominent bilateral preoptic CSF sleeves and recommends ophthalmic assessment for papilledema. Patient and radiologist identifiers are obscured.

Figure 2: Representative non-contrast CT brain images from the presenting evaluation. No obvious acute intracranial hemorrhage, large territorial infarction or major mass effect is evident on the supplied images.

Figure 3: Emergency diagnostic pathway emphasizing simultaneous treatment of severe symptomatic hyponatremia and evaluation for endocrine causes.

Emergency Management

The immediate priority was treatment of acute symptomatic severe hyponatremia. Controlled administration of 3% hypertonic saline was undertaken with close monitoring of serum sodium and attention to avoiding overly rapid correction, consistent with guideline-based emergency management of severe symptomatic hyponatremia [1,2]. Given the hypotension and subsequently documented severe cortisol deficiency, intravenous hydrocortisone 100 mg was administered. After stabilization, hydrocortisone and levothyroxine replacement were continued. In suspected central adrenal insufficiency, glucocorticoid treatment should precede or accompany thyroid hormone replacement because thyroid hormone can increase cortisol requirements and may precipitate adrenal decompensation [3].

Clinical Outcome and Follow-up

The patient’s neurological status improved after correction of severe hyponatremia and corticosteroid therapy. Serum sodium gradually returned toward the normal range during hospitalization, and no further seizures were reported. She was discharged in stable condition with endocrine follow-up and hormone replacement therapy.

Discussion

This case illustrates an important emergency–endocrine overlap: the seizure was the acute event that brought the patient to medical attention, while the biochemical pattern pointed toward chronic pituitary dysfunction. At a serum sodium concentration of 107 mmol/L, severe hypotonic hyponatremia can produce major neurological manifestations. Current guidance emphasizes prompt hypertonic saline therapy for severe symptoms, together with close biochemical and clinical monitoring [1,2].

The markedly low morning cortisol is a central diagnostic finding. Glucocorticoid deficiency can promote non-osmotic antidiuretic hormone activity and reduce renal free-water excretion, thereby causing or aggravating hypotonic hyponatremia [1,3]. In this patient, the low cortisol occurred alongside hypotension and years of constitutional symptoms, strengthening the clinical relevance of the result.

The thyroid profile also required a pituitary-oriented interpretation. A TSH value within the laboratory reference interval does not exclude central hypothyroidism when free T4 is low. Endocrine Society guidance recommends interpreting free T4 and TSH together in patients with suspected pituitary disease [3]. Here, low free T4 was accompanied by cortisol and gonadotropin abnormalities, making a central process clinically plausible.

The decade-long secondary amenorrhea was the most useful historical clue to chronic gonadotropin-axis dysfunction. Its onset after childbirth raises the possibility of postpartum pituitary injury, including Sheehan syndrome. However, the supplied record does not document postpartum hemorrhage, severe postpartum hypotension, shock or failure of lactation. Because severe postpartum hemorrhage is a defining historical feature of classic Sheehan syndrome, a specific postpartum etiology cannot be established from the available evidence [6].

The partially empty sella provides structural support for a pituitary disorder but is not, by itself, diagnostic of hypopituitarism or its cause. Reviews emphasize that empty sella can be an incidental radiological finding, while a subset of patients has clinically significant endocrine dysfunction [4,5]. In this patient, the imaging finding becomes more relevant because it coincides with biochemical abnormalities in several anterior pituitary axes and a long history of endocrine symptoms.

From an emergency-medicine perspective, the key lesson is not to stop the diagnostic process once a seizure has been recognized. Severe hyponatremia requires immediate treatment, but its cause must be pursued in parallel. A brief review of menstrual history, postpartum events, chronic fatigue, orthostatic symptoms and previous endocrine treatment can substantially redirect the differential diagnosis. In this case, focused history-taking connected a decade of amenorrhea with the acute electrolyte emergency and prompted targeted pituitary evaluation.

Conclusion

A neurological emergency may be the first presentation of a chronic endocrine disorder. In this patient, profound hypotonic hyponatremia with seizure led to identification of marked cortisol deficiency, central thyroid-axis dysfunction and gonadotropin deficiency, with a partially empty sella on MRI. For emergency and endocrine clinicians, severe unexplained hyponatremia accompanied by hypotension, chronic fatigue or longstanding amenorrhea should prompt early assessment of the hypothalamic–pituitary–adrenal axis and other pituitary hormones. Careful history-taking remains a high-yield diagnostic intervention.

Declarations

Consent for Publication: Taken.

Ethics Approval: Not applicable for this case report, as stated in the supplied manuscript record.

Competing Interests: The authors declare that they have no competing interests.

Funding: No specific funding was received for this case report.

Author Contributions: Dr. Minakshee Patil: clinical care, data collection and manuscript preparation; Dr. Jagdish Patil: clinical interpretation; Dr. Sanyogita Naik: manuscript preparation; Dr. Sarbari Swaika: supervision and final approval.

Data Availability: Only de-identified information is presented in the manuscript. The data supporting this case are contained within the clinical record and are not reproduced in identifiable form.

AI-assisted Writing: Generative AI was used as a language and formatting aid during manuscript preparation. The authors remain responsible for verifying all clinical facts, interpretations, references and final wording before submission.

References

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Hostages, Barricades, and Police Communication: The Contribution of AI to Societal Issues

DOI: 10.31038/MGSPE.2026621

Abstract

The paper combines generative AI (ChatGPT-3.5) with Mind Genomics principles to synthesize different types of situations in which people may barricade themselves. The study focuses on the nature of the barricade, the thoughts of the barricader, the people who may be in danger, the messages to which the barricader may respond positively, and the signals suggesting that the situation is going in the wrong direction. The objective is to incorporate Mind Genomics principles into an easy-to-use program, allowing individuals involved in such social issues to “get up to speed” in managing these situations, with the assistance of a flexible AI-based coach.

Keywords

Mind genomics, Generative AI, Barricade incidents, Hostage negotiation, Crisis response

Introduction

Barricades and hostage situations are serious and dangerous situations that can arise due to various reasons, such as political or social grievances, personal conflicts, mental health issues, or criminal intent. Potential risks associated with barricades include physical harm to hostages, escalation of violence, and emotional trauma for all involved. Law enforcement agencies typically respond to barricades by establishing communication, assessing the situation, and negotiating a peaceful resolution. Communication plays a crucial role in resolving barricades peacefully, allowing law enforcement to establish a rapport with hostile individuals or groups, and understand their demands or motivations [1-8].

AI technology can assist law enforcement by analyzing data and providing real-time information. AI can also simulate different scenarios to help them make informed decisions by predicting potential outcomes and suggesting strategic approaches to negotiation and resolution. At the same time, human judgment plays a crucial role in balancing AI technology in resolving barricade situations by providing officers with critical thinking skills needed to interpret AI-generated data, assess the situation, and make informed decisions [9-13].

The Role of Mind Genomics as a Complement to AI

Barricades have long been a symbol of resistance or protest in society, but what exactly goes on in the minds of those who participate in barricading? This is where Mind Genomics comes into play, as it allows us to understand the different mindsets people may have when it comes to barricading. Instead of focusing on the individuals carrying out the act, Mind Genomics helps us delve deeper into the underlying thoughts and motivations behind their actions. Through Mind Genomics, we can identify the various cognitive patterns and belief systems that influence people to barricade. By recognizing such cognitive patterns and belief systems, we can gain a better understanding of the social issues at play. This knowledge can then be used to inform law enforcement agencies on how to approach and handle similar situations in the future [14-18].

By using AI technology to synthesize and simulate the properties of these mindsets, a tool may be created for training and education purposes. The integration of AI with the Mind Genomics platform, BimiLeap.com, synthesizes the possible thoughts of those individuals who engage in barricading. The effort is, of course, simulation since it may not be possible to get into the mind of actual barricaders. As we go through this study, these simulations may provide a guide for communication and further recommendations. This information can be used to develop strategies for conflict resolution and de-escalation, ultimately promoting peace and understanding in society.

The remainder of this paper focuses on a set of simulations for different types of barricades. The simulations were done with the “Idea Coach” feature of BimiLeap.com.

Following the actual simulation efforts, the Mind Genomics program later provides a detailed analysis using critical thinking, which will help in understanding the topics, the different points of view, and even suggest innovations in process.

Asking AI to Deconstruct Situations Where a Person Barricades Themselves in a Building

The process begins with instructions to the AI, in the “Idea Coach” feature of BimiLeap.com. Table 1 presents the instructions given to the AI. The instructions themselves are simple and unambiguous. The instructions do not specify the number or nature of the barricade situations, but only the detailed specifics desired from AI for each barricade situation.

Table 1: Instructions to the AI to specify an unknown number of barricade situations.

In turn, Table 2 presents the five different barricades suggested by AI. The instructions may be repeated for several iterations, which may result to more than five types of barricades, but at least five will suffice for the purposes of this paper. One important thing to note in Table 2 is the sense of being educated in barricade situations. The reader may acquire a sense of understanding of the situations and possible appropriate responses. Each of the questions in Table 2 could be expanded by asking more than one option for each request, to provide the police officer with a deeper insight on the topic. For example, the reader could request four or five responses to the specific barricader, rather than asking for only one.

Table 2: The five hostage situations suggested by AI.

Moving From Providing Information to Providing Critical Thinking About the Topic

Artificial intelligence can assist in identifying patterns and trends in barricade incidents, enabling law enforcement agencies to make data-driven decisions and predictions.

AI can also analyze various perspectives and data sources to provide insights that may not have been apparent through traditional methods, enhancing overall situational awareness and response strategies.

The program’s critical thinking adds value to the discussion on barricades by providing a deeper understanding of the basic information, themes, and perspectives surrounding the topic (see Table 3). By analyzing patterns and exploring different viewpoints, AI may offer a more comprehensive view of the issue at hand, allowing users to absorb the information in a simplified manner.

Table 3: Critical thinking, part 1 – basics (key ideas, themes, perspectives).

Table 4 continues the process of AI-based critical thinking, which now focuses on the different viewpoints. The analysis is set up with three prompts: interested audiences, opposing audiences, and alternative viewpoints, respectively. Incorporating alternative viewpoints from community members, experts, and stakeholders allows law enforcement agencies to create more inclusive and comprehensive recommendations. Considering different perspectives can help identify blind spots and ensure recommendations are culturally sensitive and responsive to the needs of everyone involved in barricade situations. Recognizing individual reactions to proposed barricade solutions is essential for effective communication and implementation. Law enforcement agencies can anticipate challenges and understand community expectations by considering potential reactions and feedback, allowing them to tailor their approach for better acceptance and compliance.

Table 4: Critical thinking from the point of view of interested audiences, opposing audiences, and alternative viewpoints.

Innovations

AI-generated ideas may provide new perspectives and approaches in law enforcement. Police departments may enhance their tactics by integrating AI suggestions to meet modern policing demands. This ongoing innovation helps law enforcement stay ahead of evolving threats in society, such as barricade situation. This may lead to safer outcomes for law enforcement and those involved in such situations.

Table 5 shows how AI can generate innovative solutions in process and then apply additional analyses to these solutions. The solutions generated in Table 5 may not be effective but are quickly and efficiently produced in just five minutes. Each iteration may produce 2-5 solutions. Thus, the BimiLeap.com platform can be a source of raw ideas for innovations, which may be polished later [19-21].

Table 5: Deep analysis of four proposed AI-generated process innovations.

Afterward: Looking At Police Confronting Barricades in the Year 2027, After AI and Mind Genomics Thinking Have Been Accepted and Implemented

In 2027, two years after the widespread adoption of BimiLeap.com and Mind Genomics thinking in law enforcement, the landscape of policing has undergone a profound transformation. New police officers entering the force are being trained to use AI tools that provide real-time insights into the mind-sets of individuals involved in barricades. This information allows them to better understand the motivations and behaviors of those they encounter during such situations, leading to more effective and empathic responses. Experienced officers, on the other hand, are adapting their approach to incorporate these new technologies, enhancing their ability to handle barricade situations with greater precision and insight.

As a result of this shift, the relationship between police officers and the community has improved significantly. The use of BimiLeap.com has enhanced transparency and communication between law enforcement and the public, fostering trust and understanding on both sides. This has led to a more collaborative and proactive approach to policing, with a focus on prevention and community engagement, rather than just reacting to incidents.

However, with the increased reliance on AI in law enforcement, ethical considerations must be carefully addressed. Questions of data privacy, bias in algorithms, and the potential for misuse of technology need to be carefully monitored and regulated to ensure responsible and ethical use of these tools. Despite the challenges, the incorporation of Mind Genomics thinking and AI in policing has greatly enhanced the safety and effectiveness of police operations during barricades. By providing officers with valuable insights and guidance, they are better equipped to de-escalate situations, protect the public, and ensure a peaceful resolution to potentially volatile encounters.

The perception of law enforcement has also shifted positively, with the public recognizing the benefits of using AI to support police decision-making. Empathy plays a crucial role in this new approach, as officers are trained to see beyond the surface of a situation and understand the underlying motivations and emotions at play.

Overall, the integration of AI and Mind Genomics thinking has led to a cultural shift in policing, fostering a more data-driven, empathic, and community-oriented approach to law enforcement. As policies and regulations continue to evolve to ensure responsible use of these technologies, the future of policing looks towards a more cohesive and effective response to barricade situations and beyond.

Acknowledgment

The authors gratefully acknowledge the help of Angela A. and Therese D. in the preparation of this and other manuscripts.

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Methodological Insights from Modeling and Simulation Informed Extrapolation for Sarilumab pcJIA Pediatric Development

DOI: 10.31038/JCRM.2026922

Abstract

Pediatric drug development frequently lags behind adult approvals. Adult-to-pediatric extrapolation frameworks leverage accumulated adult efficacy and safety data to streamline the development plan and reduce the required pediatric sample size. In this commentary, we use the pediatric polyarticular-course juvenile idiopathic arthritis (pcJIA) development program for sarilumab as a case example and discuss a model-informed pharmacokinetic-pharmacodynamic (PKPD) extrapolation approach based on exposure-response consistency between adult and pediatric populations. We contrast this strategy with a Bayesian historical control borrowing framework, which can be deployed under the assumption of disease similarity. Integrating these quantitative methodologies with innovative pediatric trial designs enables accelerated, scientifically and statistically rigorous pediatric drug development.

Keywords

Pediatric extrapolation, PKPD modeling, Statistical virtual trial simulation

Introduction

Drug development typically spans 10 to 15 years with high attrition rates. The development in pediatric patients takes even longer as it frequently lags behind the development in adults. A review on pediatric oncology development cited additional average 6.5 years lag for approved drugs and with many drugs lacking any pediatric development record [1]. These long delays result in substantial unmet medical needs in pediatric population and drive off-label use without adequate efficacy and safety evidence [2]. Despite legislative mandates and incentives designed to expedite pediatric development, challenges remain. Beyond commercial condensations, pediatric population being a vulnerable population and with higher hurdle for recruitment are among the reasons for the lag. The pediatric clinical development often starts after the efficacy and safety profiles are established in adults. Therefore, when pediatric development starts to progress, the adult indication is usually approved or with enough benefit-risk evidence to inform pediatric development decisions. At the same time, even with specific pediatric needs being addressed in the study design and conduct, a pediatric study still faces more severe unwillingness or hesitation to participate due to many reasons including the potential exposure to placebo. Consequently, innovative development paradigms that leverage existing adult data to minimize pediatric patient burden are urgently needed to accelerate pediatric development and address the unmet medical needs in kids.

Faster Pediatric Development Pathway Enabled by Adult-to-Pediatric Extrapolation

The ICH E11A “Guideline on pediatric extrapolation” (adopted in August 2024 and became effective in January 2025) establishes a structured regulatory framework for leveraging adult data to streamline the pediatric drug development [3]. The adult-to-pediatric extrapolation is advocated to enable more efficient and faster pediatric development along with relevant innovative study designs. The approach takes advantage of the amount accumulated adult efficacy and safety data, frequently being sufficiently large and robust in supporting the adult indication approval. The leveraging of different types of extrapolations, i.e. full extrapolation or partial extrapolation, depends on physiology, metabolism, and disease progression similarity and difference between adult and pediatric patients. Historically available disease data (on both adults and pediatrics), specific treatment adult data (from the adult development) and treatment pediatric data (including planned in the future development) can be jointly assessed and then differentially leveraged according to targeted differential extrapolation pathways (i.e. full vs. partial). The extrapolation approach is also frequently combined with or enabled by various modeling and simulation approaches to realize robust and efficient decision making and drug approval.

Modeling & simulations are already routinely applied in pediatric development, for example to propose pediatric doses based on the adult dose(s) with scaling based on body weight, physiology etc. The proposed pediatric doses and its “adult-equivalent” exposure are then tested and confirmed in a clinical pediatric PK study. If the adult and pediatric similarity was well accepted, the so call full extrapolation from adults to pediatrics can be applied and the pediatric PK study demonstrating exposure similarity can serve as the primary approval evidence. On the other hand, if the similarity between adults and pediatrics is heavily questioned, the gold standard of a pediatric development approach is still a fully powered, randomized, controlled clinical trial (RCT) to demonstrate treatment efficacy and safety. Many extrapolation scenarios fall in the middle under “partial extrapolation” and due to different degrees of adult to pediatric similarity evidence. Innovative approaches along with innovative study designs can be applied to generate and enhance robust evidence with scientific and statistical rigor as the pediatric program progresses.

Model-informed PKPD Extrapolation with Scientific and Statistical Rigor

Polyarticular-course juvenile idiopathic arthritis (pcJIA) manifests before the age of 16 years, with pathology similar to that of adult rheumatoid arthritis (RA). Sarilumab is an interleukin-6 receptor inhibitor approved for RA and pcJIA. In sarilumab pcJIA development case, the adult RA to pcJIA disease pathophysiology similarity to enable adult-to-pediatric extrapolation was not fully established before the pcJIA development [4]. However, the observed efficacy and safety data in the pcJIA pediatric dose finding study portion provided an opportunity to demonstrate the consistency of PKPD relationship in key efficacy and safety endpoints to those of the adult program. First, the existing adult key efficacy endpoints were reconstructed to align with pediatric meaningful endpoints. Therefore, the PKPD relationships between two populations can be compared using PKPD modeling approaches. Then, considering pediatric patient data variability in the current study size, a virtual trial simulation based on the established PKPD models was conducted to determine the sample size needed in supporting the PK/PD relationship consistency assessment. Based on the virtual trial results, the current Phase 2 dose ranging study was then expanded to ensure the final PK/PD consistency assessment with necessary statistical rigor.

The strength of this extrapolation approach is that it eliminates the need of assigning pediatric patients to a control arm and avoids a traditional RCT. It can minimize the study ethical consideration and increase the potential for enrollment operational feasibility, especially the control being the placebo. The final total study sample size could be potentially smaller depending on the expected treatment effect difference compared to the control arm. The requirement of this approach is demonstrating the treatment effect similarity between the adult and pediatric patients beyond disease similarity. The treatment effect similarity can be treatment mechanism of action (MOA) specific. Therefore, sufficient treatment specific data needs to be accumulated during the pediatric development to support this similarity claim. We can design an efficient pediatric development plan optimized by modeling and simulation approaches. The resulted pediatric plan is streamlined and greatly reduces the pediatric patient burden.

Bayesian Borrowing as an Alternative Approach Under an Alternative Extrapolation Scenario

An alternative partial extrapolation approach is Bayesian borrowing of historical control arm to enable a smaller concurrent control in a pediatric RCT study design. The recent FDA draft guidance on “use of Bayesain Methodology in clinical trials of drug and biological products” outlined a clear path forward to enable this approach [5]. With the availability of historical pediatric trial control arm, the same drug adult trial control arm or relevant real-world data, the control arm borrowing can be carried out with statistical rigor if the marginal exchangeability of historical prior information and new study data can be assumed [6]. The validity of the borrowing approach further depends on the similarity of the borrowed control arm data sources (i.e. historical adult control or real-world data) and targeted pediatric population. If the assumption similarity uncertainty exists, a dynamic borrowing can be applied [7]. The dynamic borrowing automatically down-weights (or discards) historical priors if the current pediatric control is strongly different from the historical controls. However, if the marginal exchangeability cannot be assumed, for example, between current pediatric control and historical adult control arm to borrow from, a pharmacometrics-enhanced Bayesian borrowing (PEBB) was also proposed [8]. With this approach, a model predicted pediatric control can be used instead of observed historical control.

The strength of the control arm Bayesian borrowing approach is it applicability in a randomized control study design and only the similarity between the control arms (i.e. adult control vs. pediatric control) is required. The pediatric study sample size can be reduced to gain trial efficiency as a small control arm is needed. The approach limitation is it still requires a concurrent control arm (even though it is smaller than the original design). Including a concurrent control arm, especially a placebo or standard care arm, may still present an ethical issue (retain pediatric patients on placebo) and enrollment challenge. In addition, the operating characteristics and type I error inflation need to be understood managed during the study design stage. Extensive virtual trial simulations with various scenario testing need to be carried out at the design stage to ensure the proper study power planning, especially in a dynamic borrowing setting.

Conclusion and Outlook

To speed up the pediatric development and obtain robust evidence to support the decision, adult-to-pediatric extrapolation approaches serve as important methods. When partial extrapolations are needed due to lack of sufficient evidence for a full extrapolation, innovative study design and modeling & simulation approaches are critical to accumulate robust evidence and support a scientific and statistical rigorous decision. PK/PD model-based extrapolation and Bayesian borrowing with its corresponding innovative study design are two approaches that can be applied under different evidence scenarios to enhance the decision making. The data-driven quantitative evidence supported extrapolation speeds up the pediatric clinical development, addresses pediatric trial enrollment ethic concern and unmet pediatric needs for faster accessing efficacious mediation.

References

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Toxic light Chain Autoantibodies in Late-onset Diabetic Dementia: Activation by Furin

DOI: 10.31038/EDMJ.20261033

Introduction

Dementia occurrences increase significantly in older adult type 2 diabetes mellitus (T2DM) [1]., Epidemiologic studies indicate diabetes confers an ~ 1.5-fold increased risk of non-vascular dementia and a ~ 2-fold risk for vascular dementia occurrence compared to rates in age-matched non-diabetic populations [1] Because of the current world-wide epidemic of type 2 diabetes mellitus, diabetic dementia occurrences are expected to increase substantially in the coming decades. There is a need for better understanding of the underlying disease mechanisms including potentially modifiable risk factors to reduce the future medical, social and economic burdens of diabetic dementia.

Recent evidence (from autopsy studies) suggests that the underlying causes of ‘diabetic dementia’ likely differ from those in Alzheimer’s dementia (AD) [2] For example, the characteristic neuropathologic lesions found in AD brain (amyloid plaque, neurofibrillary tangles) were not evident at increased rate in brain autopsy specimens from diabetic dementia vs non-diabetic population [2]. Cerebrovascular burden was increased in diabetic dementia brain autopsy specimens including lacunar infarct, but not microinfarct or large vessel infarct [2]. Arteriolosclerosis of deep penetrating vessels was also a significant finding reported in diabetic dementia brain autopsy specimens [2].

Genome-wide association studies in dementia related to Alzheimer’s dementia have identified gene pathways involving immune function, extracellular matrix remodeling, lipoprotein metabolism and intracellular catabolism [3] as comprising several of the many (>100) genetic risk determinants. Several forms of dementia (including Alzheimer’s dementia) have a high genetic hereditability, but dementia is a complex trait and only a fraction of the overall hereditable variability could be accounted in GWAS studies [3]. Glaucoma is another late-onset neurodegenerative disease in which gene pathways involving extracellular matrix remodeling and vascular development contribute in part to overall genetic risk [4]. Yet only 9-17% of overall hereditability in late-onset glaucoma was accounted for in GWAS studies [4]. In addition, the inclusion in GWAS studies of glaucoma of multi-ethnic populations showed that different genes were significant risk determinants in different ethnic/racial populations [5].

Regarding the unknown mechanisms underlying diabetic dementia, the apoE4 gene isoform is associated with characteristic AD-like neuropathology and confers a large population attributable risk of AD [6]. Insulin resistance has been proposed to play a role in diabetic dementia, but the precise mechanisms are not well understood [7]. Taken together, novel ‘non-AD’ disease mechanisms are likely to be multifactorial and may overlap (in part) both with vascular dementia and Alzheimer’s dementia. The aim of the current review article is to summarize our prior findings from separate reports (largely from type 2 diabetic patients) having a late-onset neurodegenerative disorder, i.e. primary open angle glaucoma, or diabetic dementia, that support roles for dysregulated immunity, chronic inflammation and endothelial dysfunction in the mechanism of non-AD diabetic dementia.

One of the mediators of endothelial dysfunction and neurotoxicity in late-onset neurodegenerative diseases (occurring in subsets of diabetes) was a novel kind of anti-heparan sulfate proteoglycan autoantibody which not only interfered with the neurotrophic (and endothelial cell survival-promoting) effects of growth factors such as fibroblast growth factor 2 (FGF2) [8], but also appeared to bind to a G-protein coupled receptor, serotonin 2A receptor, causing long-lasting activation of PLC gamma/IP3R/Ca2+ intracellular signaling in neurons and endothelial cells [9]. Our studies support intracellular calcium dysregulation as another highly relevant mechanism in diabetes-associated neurodegenerative disease pathophysiology. G-protein receptor coupled signaling is pleiotropic and in electrically-excitable cells such as neurons [10], anti-HSPG autoantibodies evoked sustained depolarization as well as actin cytoskeleton rearrangement via RhoA/ROCK activation altering morphology in neurites (causing neurite retraction) and in glial cells (causing process withdrawal) [8].

In both type 1 and type 2 diabetes mellitus, loss of heparan sulfate proteoglycan (HSPG) is an early event underlying diffuse vascular injury, an idea formulated by the Steno Group and referred to as the ‘Steno hypothesis’ [11]. We later advanced the concept that chronic inflammation associated with obese type 2 diabetes mellitus or in systemic autoimmunity (type 1 DM) drives humoral immune response(s) to HSPG promoting formation of (endothelial and neuro-toxic) anti-HSPG IgG autoantibodies which we reported to be increased in association with a diverse array of diabetic microvascular complications including: retinopathy [12], nephropathy [13,14], and painful neuropathy [8]. In our prior study of anti-HSPG autoantibodies in painful diabetic neuropathy, we observed that patients suffering with painful neuropathy were significantly more likely to have co-morbid late-onset primary open angle glaucoma (POAG) [15], the latter also increases in type 2 DM. Both POAG and painful neuropathy involve damage to small unmyelinated axons, retinal ganglion cells in the case of POAG. Tezel et al. [16] reported increased circulating autoantibodies to optic nerve head heparan sulfate proteoglycans (HSPG) in subsets of normal tension or POAG. In our studies of diabetic microvascular complications, anti-HSPG AAB caused sustained activation of Rho A/Rho kinase signaling pathway in endothelial cells and neuronal cells [8,13]. Rho kinase activation affects endothelial-like trabecular meshwork cells which play a role [17] in mediating intraocular pressure changes which are an important determinant of glaucoma risk. Taken together, these findings, led us to undertake a small study in forty-four older adult type 2 diabetic patients (with and without POAG) to test for increased level of circulating anti-HSPG AAB [15].

Endothelial cell inhibitory activity in the protein A-eluate fraction was significantly increased in plasma from glaucoma or suspects (vs no glaucoma) (Table 1). There was a significant association between co-morbid painful neuropathy and glaucoma or glaucoma suspect (vs no glaucoma) (Table 1). Glaucoma patients were significantly more likely to have reported a family history of dementia in a first-degree relative compared to no glaucoma patients (Table 1). The latter observation led us to consider whether a component of the autoantibody encoded by a heavy chain, or light chain gene might contribute to a part of the unexplained hereditability in occurrences of late-onset POAG.

Table 1: Association between diabetic glaucoma or suspect and co-morbid microvascular complications or family history of dementia.

Results are proportion; (), N number of patients tested; Glc, glaucoma, susp, suspect, *Fischer’s exact test, comparison of diabetics with or without glaucoma; ME, macular edema, AMD, age-related macular degeneration, FH, family history in a 1° relative of Alzheimer’s dementia; significant inhibitory endothelial cell (EC) activity, defined as ≤90% of basal cell number after 48 hours incubation at 37 deg C in the presence of test fractions, i.e. a ~30 ug/mL concentration of the protein-A eluate. Reproduced from [15].

We first reported an unusually potent EC-inhibitory fragment having peak apparent MW 12.5kD [8] (Figure 1D) in the protein-A eluate from two prostate cancer patients’ plasma. The activity appeared following long-term storage of protein-A eluates (at 0-4 deg C). We used a combination of heparin Sepharose, and hydroxyapatite affinity chromatography to obtain N-terminal amino acid microsequencing data showing that peak active EC-inhibitory activity had significant 87% amino acid homology to a human kappa V variable light chain [8].

Peak inhibitory activity in the protein-A eluates from both diabetic and prostate cancer patients (Figure 1A-C) had apparent MW corresponding to light chain 23kD or half-light chains 12 kD [8]. Peak prostate cancer IgG EC toxicity was substantially enhanced after heating at 56 deg x 30 minutes (Figure 1E) [8] consistent with aggregation (in light chain fragment).

Figure 1: Mass spectrometry of diabetic (A and B) or prostate cancer (C) endothelial cell inhibitory autoantibodies; (D) increase in potency of inhibitory prostate cancer autoantibodies after long-term storage of protein-A eluates at 0-4 deg C and (E) unusual stability of the long-lasting antibodies. (A–C) Samples were analyzed by mass spectrometry as previously reported [8]. (D and E) Endothelial cell activity (mean, SD) is expressed as % of basal cell number in quadruplicate assays as previously reported [8].

Since prostate cancer cells elaborate proteases such as furin [18] which activate other proteases [19,20] that can degrade extracellular matrix we next tested whether exposure of protein A-eluates to recombinant furin could reproduce the effects of long-term eluate storage. Furin is a proprotein convertase which cleaves proteins at specific multi-basic amino acid residues [21] including those present in the variable-constant switch region in a limited subclass of kappa light chains. In a small sample of five diabetic dementia patients (mean age 71 ± 5.8 years; mean diabetes duration 22.4 ± 12.6 years) long-term storage of diabetic dementia protein-A eluates (at 0-4 deg C) unmasked potent EC inhibitory activity (Table 2) [15], but not in the protein-A eluates from an age-matched subset of diabetes having macular edema and/or nephropathy without dementia (Table 2). Brief exposure of the (freshly-isolated) protein-A eluate of a diabetic dementia, POAG patient sample to recombinant furin caused similar unmasking of latent potent EC inhibitory activity (Table 2) [15] perhaps consistent with cleavage at a furin-susceptible multi-basic peptide region in the mechanism of generation of highly potent EC inhibitory activity.

Table 2: Effect of long-term storage of protein A eluates or furin digestion o EC activity.

Reproduced from [15]. Furin digestion of protein-A eluate fraction was carried out as previously described [15]. Endothelial cell (EC) survival (mean, SD) was determined in quadruplicate after 48 hours incubation at 37 deg C with test fractions and is compared to survival (in EC) without added test fractions (100%)

Light chain amyloidosis is a systemic disease caused by overproduction of a monoclonal light chain or fragment by a plasma cell clone. Peripheral nerve, heart and kidney are among the most common sites of deposition of monoclonal light chains in AL disease. We observed that a patient manifesting co-occurrence of atrial fibrillation, nephropathy and a family history of dementia harbored an IgG in the protein-A eluate fraction susceptible to furin digestion resulting in a highly EC toxic fragment (Table 3). In three age-matched type 2 DM patients with atrial fibrillation alone, exposure of the protein-A eluates to furin did not generate a toxic EC fragment (Table 3). These data suggest that in diabetic patients with a family history of dementia exposure of the protein-A eluate of plasma to recombinant furin might be a simple method to identify patients at high risk for harboring a latent highly EC toxic light chain fragment capable of contributing to the later development of diabetic dementia.

Table 3: Effect of furin treatment in diabetic depression protein A eluates on endothelial cell inhibitory activity.

Endothelial cell activity (%)- endothelial cell number was determined after 48 hours incubation (at 37 deg C) with a 30 ug/mL concentration of the protein-A eluate fraction of plasma: Results (mean, SD) is expressed as a percent of basal cell number in bovine pulmonary artery endothelial cells not exposed to test fractions.
*Comparing (mean, SD) EC activity in quadruplicate samples before and after exposure to recombinant furin as previously reported [15].

It is known that kappa light chains comprise functional antibodies [22] and that certain kappa light chain genes cluster in populations that have been exposed to similar kinds of evolutionary selection pressure. For example, subclasses of kappa II gene-encoded light chains are involved in functional antibodies providing protective immunity against the life-threatening childhood infection, Hemophilus influenza meningitis [23]. Yet because these light chains harbor a furin-targetable multi-basic amino acid sequence in the variable-joining switch region, they could contribute to late-onset neurodegenerative disease (which we hypothesize) may be due in part to unusually potent toxicity in a light chain fragment.

There have been several recent reports that plasma furin was increased significantly in adult type 2 DM and was associated with an increased risk of dysmetabolism and cardiovascular morbidity and mortality risk [24,25]. Among its many potential protein targets, furin is known to inactivate endothelial and hepatic lipoprotein lipase [26] which are responsible for normal metabolism of very low-density (triglyceride-rich) lipoproteins (vLDL). Diabetic dyslipidemia is characterized by increased plasma concentration of triglyceride TG-rich lipoproteins and it is an important risk factor accounting for substantial excess CVD morbidity in adult type 2 DM. Hepatic production of TG-rich very low-density lipoproteins (VLDL) increases as a result of either insulin deficiency and/or insulin resistance, and vLDL metabolism is affected by Apo C I a recently reported genetic risk determinant in Alzheimer’s related dementia [3]. It is of interest that the metabolism of TG-rich lipoprotein was reported to increase blood brain barrier BBB permeability and contribute to neuroinflammation which are hallmarks of Alzheimer’s related dementia [27]. Taken together, diabetes associated increases in plasma furin might have a dual role in generating toxic LC fragments which can circulate in VLDL particles and contribute to enhanced endothelial cell toxicity and neurotoxicity underlying dementia.

We had previously shown that highly neurotoxic IgG fragments from three different diabetic dyslipidemia patients with dementia (mean plasma cholesterol 181 ± 51 mg/dL: and mean plasma triglyceride concentration 392 ± 164 mg/dL) displayed increased affinity for a Liposorber (dextran sulfate) column useful in lowering plasma concentrations of LDL and vLDL apolipoprotein B-100-containing particles [28]. In one of the three patients tested (Patient 5, Table 4), in whom the peak toxicity was purified to near homogeneity, N terminal amino acid sequencing revealed significant homology to a restricted subclass of kappa II light chain [28]. Peak EC toxicity in the highly-purified DM k light chain (Patient 5) (induced 70% EC apoptosis after 48hrs incubation at 37 deg C) which was nearly as potent as toxicity in an amyloidogenic monoclonal lambda light chain secreted by lymphoblasts obtained from a patient who had died from amyloid light chain (ALL) disease [28]. Patient 5 suffered from a constellation of systemic and neurovascular complications including multiple small and medium-sized cerebrovascular infarctions and died from complications related to ESRD about ten years after study entry and blood drawing for autoantibody determination [28].

Table 4: Clinical Characteristics in Five Diabetic Dementia Patients Harboring Toxic LC.

NHW-non: Hispanic white; AD: Alzheimer’s dementia; OHT: Ocular hypertension, TBI: Traumatic brain injury; FT: Frontotemporal; Vasc: Vascular; DMT: Dementia

There is no data to indicate whether or not toxic kappa LC may form insoluble, amyloid precipitates in target organs as occurs in AL disease. Heating may have induced aggregation in kappa LC half chains to form toxic soluble oligomers. Whether toxic oligomers can form under physiological conditions is not known. Light chains are comprised of a high percentage of hydrophobic amino acid residues which normally form tightly-packed beta sheets. Since vLDL particles are amphipathic, it is possible that light chains or fragments coalesce around the lipid-rich microenvironment of the particle which favors hydrophobic residue-rich peptides. Evidence of a possible permissive role for the vLDL particle in enhancing LC toxicity comes from our study of neurotoxicity in protein A eluates from patients with Parkinsons disease and/or dementia [29]. Compared to neurotoxicity in the protein A eluates from three patients with Parkinsons disease Figure 2A), when the Liposorber-retained fractions from two dyslipidemic diabetic dementia patients plasmas was purified on protein-A, the resulting IgG displayed significantly higher, unusually potent neurotoxicity (Figure 2B) [29]. The purified IgG present in diabetic dementia and dyslipidemic plasma was active in the low-ten nanomolar range of concentrations (Figure 2B) (approaching potency reported for oligomeric Abeta). The purified diabetic dementia IgG-induced neurite retraction was significantly, but not completely prevented by co-incubation with a 200 nanomolar concentration of the selective 5HT2AR antagonist M100907 (Figure 3B). A similar concentration of M100907 completely prevented PD Ig-induced neurite retraction (Figure 3A). These data suggest that hypertriglyceridemic plasma enhances the diabetic dementia autoantibody toxicity through unknown mechanisms. It is possible that heavy chain-bearing IgG components in diabetic dementia target the 5HT2AR receptor and are blockable by selective 5HT2AR antagonist, but that more potent light chain components interact with a different receptor.

Figure 2: The highly purified protein-A eluate fraction of diabetic dementia plasma (solid diamond) caused significantly greater dose-dependent suppression of N2A mouse neuroblastoma neurite outgrowth compared to the Liposorber eluate fraction (open square) from the same patient (A). Dose-dependent N2A mouse neuroblastoma cell toxicity in the protein-A eluates from type 2 diabetic dementia (solid diamond) and PD/DMT (open triangle) significantly exceeded toxicity in the corresponding Liposorber DMT eluate fraction (open square) (B). **P < 0.001;* P < 0.05 comparing neurite length-shortening or N2 mouse neuroblastoma cell survival induced by Liposorber eluate vs protein-A eluate fractions. Reproduced from [29].
PD/DMT: Parkinson’s disease with dementia.

Figure 3: Mean N2A neurite length suppression induced by three different diabetic PD autoantibodies was nearly completely prevented by co-incubation with 200 nanomolar M100907, a highly selective 5-HT2AR receptor antagonist (A). Potent, time-dependent suppression of N2A neurite outgrowth by diabetic dementia autoantibodies was only partially prevented by co-incubation with M100907 (B). Reproduced from [29].

The implications of these findings for the prevention of diabetic dementia are several-fold. First, while the inheritance of genes encoding a furin-cleavable light chain is not easily modifiable, the prevention of diabetic dyslipidemia is in part modifiable by measures such as adherence to a low-fat diet, exercise, maintenance of ideal body weight, medications that correct insulin resistance and insulin deficiency, and avoidance of alcohol and other substances that inhibit lipoprotein lipase. The regulation of extracellular furin in diabetes and how it might cleave a subset of LC is not completely understood. Furin contains a heparin-binding domain [30] and since EC and neurotoxic diabetes AAB targeted HSPG [8], anti-HSPG AAB and furin may colocalize in spaces rich in HSPG. For example, HSPG acts as a co-receptor for lipoprotein lipase and for lipoprotein-related protein LRP which is involved in the catabolism of vLDL particles [31]. Several genes involved in protein catabolism were reported to be risk factors for dementia [3]. We cannot rule out the possibility that toxic LC fragments may be generated in part during the process of vLDL intracellular catabolism, e.g. in an acidic cellular compartments which might favor spontaneous dissolution of LC harboring multi-basic amino acid residues.

In summary, the presented (published) data support our novel hypothesis that a subset of diabetic dementia may result (in part) from inheritance of specific genes encoding subclasses of kappa light chain which harbor a furin-cleavage site in the variable-constant switch region which can serve as a substrate for increased plasma furin reported in diabetes. Cleavage generates light chain autoantibody fragments highly toxic in both endothelial and neuronal cells. The LC fragments might aggregate to form soluble oligomers having enhanced cell neurotoxicity in the low-ten nanomolar range of toxic concentrations as reported for amyloid beta oligomers. More study is needed to substantiate this theory and to test whether furin cleavability in the protein-A eluates obtained from diabetic patients having other microvascular complications and a family history of dementia might be a significant predictor of subsequent occurrence of dementia compared to patients without similar family history of dementia.

The author declares no competing interests that would affect the objectivity of the presented results.

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Artificial Intelligence in Pediatric Ventricular Dysfunction and Heart Failure: A Systematic Review, Meta-Analysis, and Future Directions

DOI: 10.31038/JCRM.2026921

Abstract

Background: Early recognition of ventricular dysfunction and heart failure (HF) in children remains challenging because diagnosis often relies on specialized imaging modalities. Artificial intelligence (AI), particularly AI-enabled electrocardiography (AI-ECG), has emerged as a promising noninvasive tool for detecting ventricular dysfunction, cardiomyopathy, and adverse cardiac remodeling.

Objectives: To systematically review the evidence supporting AI and machine-learning (ML) applications for diagnosis and risk stratification of ventricular dysfunction, cardiomyopathy, and HF in children and to evaluate the diagnostic performance of pediatric AI-ECG models.

Methods: Studies evaluating AI, ML, or deep learning approaches in pediatric ventricular dysfunction, cardiomyopathy, congenital heart disease, or HF were included. Diagnostic studies reporting sensitivity and specificity were pooled using random-effects meta-analysis. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies AI.

Results: Six studies met inclusion criteria for qualitative synthesis, and two independent pediatric diagnostic cohorts comprising 56,403 children were eligible for quantitative synthesis. AI-ECG demonstrated high diagnostic performance for ventricular dysfunction and remodeling, with pooled sensitivity of 0.92 (95% CI 0.88-0.94), specificity of 0.73 (95% CI 0.58-0.84), and diagnostic odds ratio of 34.22 (95% CI 11.08-105.72). Additional studies demonstrated utility of ML for dilated cardiomyopathy phenotyping, HF mortality prediction, and chemotherapy-related cardiomyopathy surveillance.

Conclusions: AI-ECG is a promising, scalable screening tool for pediatric ventricular dysfunction and adverse remodeling. Emerging ML applications may further improve cardiomyopathy phenotyping, HF prognostication, and precision pediatric cardiovascular care.

Keywords

Artificial intelligence, Machine learning, Electrocardiography, Pediatric cardiomyopathy

Abbreviations

AI: Artificial Intelligence; AI-ECG: Artificial Intelligence-Enabled Electrocardiography; AUROC: Area Under the Receiver Operating Characteristic Curve; DL: Deep Learning; LVSD: Left Ventricular Systolic Dysfunction; ML: Machine Learning; QUADAS-AI: Quality Assessment of Diagnostic Accuracy Studies-Artificial Intelligence; RVSD: Right Ventricular Systolic Dysfunction; SHAP: SHapley Additive exPlanations; XGBoost: Extreme Gradient Boosting

Introduction

Ventricular dysfunction, cardiomyopathy, and Heart Failure (HF) remain important causes of morbidity, hospitalization, transplantation, and mortality in children with Congenital Heart Disease (CHD) and acquired heart disease [1,2]. Nearly half of children without known heart disease who later required hospitalization for systolic HF were initially misdiagnosed, especially in primary care settings, and those with longer symptom duration were at greatest risk, resulting in considerable unnecessary evaluation and treatment [3]. Early recognition of ventricular dysfunction is particularly important because timely intervention may prevent progression to irreversible remodeling, advanced HF, transplantation, or death [4,5]. However, diagnosis continues to rely primarily on echocardiography and Cardiac Magnetic Resonance (CMR) imaging, which require specialized expertise, may be resource intensive, and are not universally available. Consequently, there is growing interest in developing accessible and scalable tools for early identification of ventricular dysfunction and HF in pediatric populations.

Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a transformative approach for extracting clinically meaningful information from large and complex datasets [6,7]. In adults, AI-enabled electrocardiography (AI-ECG) models have shown diagnostic performance comparable to more advanced imaging modalities and have been increasingly integrated into cardiovascular screening, risk stratification, and HF management pathways [8]. The application of AI to pediatric automatic ECG analysis has improved over time [9,10]. AI- ECG has demonstrated the ability to detect left ventricular systolic dysfunction (LVSD), right ventricular systolic dysfunction (RVSD), hypertrophy, chamber dilation, arrhythmias, and HF [11-15]. Importantly, adult-trained AI models appear to perform less well in children, highlighting the need for pediatric-specific algorithms that account for age-related differences in cardiac anatomy, physiology, and electrophysiology [16]. This systematic review and meta-analysis evaluate the current evidence supporting AI and ML applications for the diagnosis and risk stratification of ventricular dysfunction, cardiomyopathy, and HF in children. We summarize the diagnostic performance of available pediatric AI models, review emerging applications in pediatric cardiomyopathy and HF, and discuss future directions for integrating AI into precision pediatric cardiovascular care.

Methods

Search Strategy and Study Selection

This systematic review and meta-analysis were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement (Figure 1) [17]. A comprehensive literature search was performed in PubMed/MEDLINE, Embase, Scopus, and Google Scholar from 2000, through 2026. Search terms included combinations of “artificial intelligence,” “machine learning,” “deep learning,” “neural network,” “AI-ECG,” “electrocardiography,” “ventricular dysfunction,” “left ventricular systolic dysfunction,” “right ventricular dysfunction,” “cardiomyopathy,” “heart failure,” “ventricular remodeling,” “congenital heart disease,” “pediatric,” “children,” and “adolescent.” Reference lists of eligible studies and relevant review articles were manually screened to identify additional publications. The literature search yielded 109 records. After removal of 28 duplicates, 81 studies underwent title and abstract screening. Nine articles were assessed in full text, of which six met inclusion criteria for qualitative synthesis and two studies provided sufficient diagnostic accuracy data for quantitative meta-analysis.

Figure 1: PRISMA flow diagram for AI studies for pediatric ventricular dysfunction.

PICO Framework

The review was designed using the Population-Intervention-Comparator-Outcome (PICO) framework [18].

Population: Children and adolescents (<18 years) with ventricular dysfunction, cardiomyopathy, CHD, ventricular remodeling, or HF.

Intervention: AI, ML, DL, neural network-based algorithms, AI-ECG, and imaging-based ML models.

Comparator: Conventional electrocardiography, echocardiography, clinician interpretation, or standard clinical risk stratification methods.

Outcomes: Diagnostic performance for ventricular dysfunction, ventricular remodeling, ventricular hypertrophy, ventricular dilation, cardiomyopathy, or HF, including sensitivity, specificity, diagnostic odds ratio (DOR), positive predictive value, and negative predictive value.

Eligibility Criteria

Studies were eligible if they: (1) evaluated AI, ML, or DL methods in pediatric cardiovascular populations; (2) focused on ventricular dysfunction, cardiomyopathy, ventricular remodeling, CHD, or HF; (3) reported original human data; and (4) provided diagnostic or prognostic performance metrics. Studies were excluded if they were review articles, editorials, conference abstracts without full manuscripts, animal studies, duplicate publications, or lack of sufficient outcome data for extraction. Adult-only studies were excluded unless they represented childhood disease survivorship populations or provided methodological insights directly relevant to pediatric cardiovascular care.

Data Extraction

Two reviewers (B.D, and S.D.) independently screened titles, abstracts, and full-text articles. Disagreements were resolved by consensus. Extracted variables included study design, publication year, patient population, sample size, underlying cardiac diagnosis, AI methodology, input data source, comparator methods, and diagnostic or prognostic outcomes. For diagnostic studies, sensitivity, specificity, and DOR were extracted whenever available. For prognostic studies, mortality, transplantation, HF progression, and remodeling outcomes were collected.

Risk of Bias Assessment

Risk of bias and applicability were independently assessed by the same investigators using the Quality Assessment of Diagnostic Accuracy Studies-Artificial Intelligence (QUADAS-AI) tool, an extension of QUADAS-2 specifically developed for AI-based diagnostic studies [19]. QUADAS-AI evaluates four domains: patient selection, index test, reference standard, and flow and timing, while incorporating AI-specific considerations related to dataset development, model validation, and clinical applicability. Studies were considered to have an overall low risk of bias and low concerns regarding applicability only when all domains were rated as low risk; any high or unclear rating resulted in an overall classification of potential bias or applicability concerns. Overall study quality was determined by consensus. Overall study quality was determined through consensus review.

Data Synthesis and Statistical Analysis

A quantitative synthesis was performed for pediatric AI-ECG studies reporting diagnostic performance for ventricular dysfunction. The primary diagnostic meta-analysis included studies by Mayourian et al. [11] and Anjewierden et al. [12] which provided extractable measures of sensitivity and specificity for LVSD, ventricular hypertrophy, ventricular dilation, and RVSD. Sensitivity, specificity, and diagnostic odds ratios were pooled using random-effects models after logit transformation. DOR was calculated as (True positive × True negative)/(False positive × False negative). Summary receiver operating characteristic (sROC) curves were generated to evaluate overall diagnostic performance. Studies by Garcia-Canadilla et al. [13] Güntürkün et al. [14] Lv et al. [15] and Yang et al. [20] were not included in the quantitative meta-analysis because they lack diagnostic accuracy outcomes suitable for quantitative pooling These studies were synthesized narratively.

Results

The characteristics of included studies are summarized in Table 1. Six studies met inclusion criteria for qualitative synthesis, including four diagnostic AI-ECG studies and two ML studies focused on pediatric cardiomyopathy phenotyping and HF risk prediction. Two independent pediatric diagnostic cohorts, reported by Mayourian et al. [11] and Anjewierden et al., [12] provided sufficient diagnostic accuracy data for quantitative synthesis. Studies by Garcia-Canadilla et al. [13] Güntürkün et al. [14] Lv et al. [15] and Yang et al. [20] were included in the narrative review because they focused primarily on prognostic modeling, phenotyping, mortality prediction, survivorship risk assessment, or methodological development rather than independent diagnostic accuracy.

Table 1: Characteristics of Included Studies.

AI: Artificial Intelligence; AI-ECG: Artificial Intelligence–Enabled Electrocardiography; AUROC: Area Under the Receiver Operating Characteristic Curve; DCM: Dilated Cardiomyopathy; ECG: Electrocardiogram; HF: Heart Failure; LV: Left Ventricular; LVEF: Left Ventricular Ejection Fraction; ML: Machine Learning; RV: Right Ventricular; SHAP: SHapley Additive exPlanations; XGBoost: Extreme Gradient Boosting.

Mayourian et al. [11] developed and externally validated an AI-ECG model using 92,377 ECG-echocardiogram pairs from 46,261 children and demonstrated excellent performance for detecting LVSD, hypertrophy, and dilation, with externally validated area under the receiver operating characteristic curve (AUROC) values of 0.94, 0.84, and 0.87, respectively. Anjewierden et al. [12] evaluated 10,142 pediatric patients and reported AUROC values of 0.93 for severe LVSD (LVEF ≤35%), 0.88 for moderate LVSD (LVEF <50%), and 0.90 for RVSD. Yang et al. [20] subsequently expanded these observations in a large CHD cohort using >178,000 ECGs from over 70,000 pediatric patients and demonstrated that adversarial training strategies improved AI-ECG robustness and maintained diagnostic performance under data-limited conditions, further supporting the feasibility of large-scale pediatric AI-ECG screening. Although the Yang study [20] utilized a cohort that substantially overlapped with previous Boston Children’s Hospital datasets and did not report independent diagnostic accuracy outcomes suitable for meta-analysis, it provides important methodological validation for future deployment of AI-ECG systems.

Quantitative synthesis demonstrated high overall diagnostic performance for AI-ECG detection of pediatric ventricular abnormalities (Figure 2). Using a random-effects model, pooled sensitivity was 0.92 (95% CI 0.88-0.94), pooled specificity was 0.73 (95% CI 0.58-0.84), and the pooled diagnostic odds ratio was 34.22 (95% CI 11.08-105.72). Sensitivity remained consistently high across studies and disease phenotypes, whereas specificity demonstrated greater variability. The summary sROC curve further demonstrated excellent overall discriminatory performance (Figure 3).

Figure 2: Forest Plot of AI-ECG Diagnostic Accuracy in Children.

Figure 3: Summary Receiver Operating Characteristic (sROC) Curve for Artificial Intelligence–Enabled Electrocardiography in Pediatric Ventricular Dysfunction.

Risk-of-bias assessment using QUADAS-AI demonstrated predominantly low risk across patient selection, index test, reference standard, and flow/timing domains (Figure 4). The studies by Mayourian et al. [11] and Anjewierden et al., [12] which formed the basis of the quantitative meta-analysis, demonstrated consistently low risk across most domains. Studies focused on prognostic modeling or methodological development, including Garcia-Canadilla et al., [13] Güntürkün et al., [14] Lv et al., [15] and Yang et al., [20] demonstrated low risk for index test and reference standard domains but had unclear ratings regarding AI-specific methodology, external validation, and broader clinical applicability. No study was judged to have a high risk of bias in any major QUADAS-AI domain.

Figure 4: QUADAS-AI Assessment of Risk of Bias and Applicability Concerns in Included Studies.

Discussion

This systematic review demonstrates that AI‑ECG has matured into a reliable diagnostic tool for detecting ventricular dysfunction in children, with consistently high sensitivity across independent pediatric cohorts. Only two studies, Mayourian et al. [11] and Anjewierden et al. [12], provided sufficient diagnostic accuracy data for quantitative pooling, yet both contributed large, well‑characterized cohorts and demonstrated excellent performance for identifying LVSD and RVSD. The pooled estimates from these studies showed high sensitivity (0.92) and good overall discriminatory performance, although specificity varied more widely, reflecting differences in population characteristics, disease prevalence, and model thresholds.

Several additional studies contributed important methodological and prognostic insights despite not meeting criteria for quantitative synthesis. Yang et al. expanded prior work by demonstrating that adversarial training strategies improve model robustness and maintain diagnostic performance even under data‑limited conditions, supporting the feasibility of scalable pediatric AI‑ECG deployment [20]. Other ML studies, Garcia‑Canadilla et al., Güntürkün et al., and Lv et al. focused on phenotyping, mortality prediction, and survivorship risk assessment rather than diagnostic accuracy [13-15]. These investigations highlight the broader potential of AI to characterize disease heterogeneity, identify high‑risk subgroups, and support early risk stratification in pediatric cardiomyopathy and HF.

Risk‑of‑bias assessment using QUADAS‑AI demonstrated predominantly low risk across included studies. The two diagnostic accuracy studies underpinning the meta‑analysis showed consistently low risk across patient selection, index test, reference standard, and flow/timing domains. Studies focused on prognostic modeling or methodological development were generally low risk for index test and reference standard domains but had unclear ratings related to AI‑specific methodology, external validation, and generalizability. No study was judged to have high risk in any major domain.

Future Directions

The rapid evolution of AI is expected to extend the role of ECG‑based analytics in pediatric cardiovascular care, enhancing its utility as a scalable screening tool in resource‑limited settings and expanding applications to areas such as right ventricular functional assessment, where pediatric data remain limited. Building on early pediatric resynchronization studies demonstrating the prognostic relevance of electrical and mechanical dyssynchrony [21,22], future AI‑ECG models may detect early remodeling and stratify risk before overt ventricular dysfunction emerges. As multimodal integration of ECG, echocardiography, CMR, biomarkers, genomics, and electronic health record data advances [23], larger longitudinal datasets may enable AI systems to track disease progression, quantify response to therapy, and support the incorporation of ECG‑derived metrics as meaningful endpoints in pediatric HF research [24]. Adapting these AI‑enabled approaches to children may ultimately support precision pacing strategies tailored to individual anatomic substrates, ventricular geometry, and electromechanical activation patterns, an important need given that many pediatric patients with ventricular dysfunction do not meet traditional adult CRT criteria [25]. Federated learning frameworks offer a promising path to overcome the inherent rarity and heterogeneity of pediatric HF by enabling secure, multicenter model development while preserving patient privacy and institutional data ownership, a strategy increasingly recognized as essential for robust pediatric AI research. Together, these advances position AI‑ECG analytics to shift pediatric HF management from reactive treatment toward proactive, phenotype‑guided, and precision‑based care.

Conclusions

AI is rapidly transforming the detection, phenotyping, risk stratification, and management of pediatric ventricular dysfunction, cardiomyopathy, and HF. Current evidence demonstrates that AI-enhanced ECG provides high diagnostic accuracy for ventricular dysfunction and adverse remodeling, offering a scalable, low-cost screening tool that complements conventional imaging and may expand access to early diagnosis. ML approaches also show promise for cardiomyopathy phenotyping, prognostication, and surveillance of chemotherapy-related cardiotoxicity. However, widespread clinical implementation remains constrained by limited pediatric datasets, single-center studies, and methodological heterogeneity. Future efforts should prioritize multicenter validation, standardized reporting, prospective implementation studies, and integration of multimodal clinical, imaging, genomic, and physiologic data to enable robust, equitable, and precision-based care for children with ventricular dysfunction and heart failure.

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I Feel (and See) My Imatinib Working – A Side Effect?

DOI: 10.31038/CST.20261114

Abstract

A previously never reported case of an unusual response to taking the chronic myeloid leukemia (CML) drug imatinib is presented. Tattoos became transiently palpable with each dose taken regardless of brand of imatinib. This did not occur with dasatinib and was not evident when the patient was off therapy. The mechanism which is still uncertain, is discussed in the context of dermatological side effects of imatinib and other CML drugs.

Introduction

I am writing to report a previously undescribed event that occurred with treatment for chronic myeloid leukemia (CML). CML is a myeloproliferative malignancy where patient outcome with the use of tyrosine kinase inhibitor (TKI) therapy, has gone from fatal without a stem cell allograft to what is now essentially a curable or chronic disease with near normal patient survival [1,2]. TKI therapy is associated with side effects/adverse events that vary with the individual drug and may help decide on the safest choice in patients with co-morbidities [3].

Case Report

A 54-year-old white male was diagnosed with asymptomatic chronic phase chronic myeloid leukemia on routine blood examination in 2006. Work up revealed classical Philadelphia chromosome and bcr: : abl1 translocation. He had no co-morbidities and his examination was unremarkable other than an abundance of tattoos of various ages and techniques. He was started on branded imatinib (Gleevec®) and achieved >MR4.5 (<0.0032%IS) by 18 months.

He had no adverse events with treatment but did have a very unusual and unique side effect. Within 30 minutes of taking the imatinib, the black components of his older tattoos became raised and palpable but not painful. This did not occur with the tattoos that were done more recently and not with any other colors. After a couple of hours, this disappeared and recurred with each dose. No changes were made. The patient declined photography but agreed to the report.

After about 6 years on therapy, he elected a treatment-free remission (TFR) attempt but failed at 4 months. As he was on a study to try a drug switch to dasatinib (Sprycel®) and see if a second TFR attempt could be successful [4]. After achieving <0.0032% log reduction for greater than a year, a second TFR attempt failed after 3 months. Interestingly, he had no recurrence of his tattoo issue while off therapy or while on dasatinib. Since he was now going to be on tyrosine kinase inhibitor (TKI) therapy indefinitely after two failed TKI discontinuation attempts, and was off study, he elected to restart imatinib as he really had no quality of life (QoL) issues while taking it. By this time imatinib was standardly available as a generic and his reimbursement would not allow brand. He went on to generic imatinib and again obtained undetectable molecular disease. Surprise, his raised tattoos were also back, confirming some kind of relationship between imatinib and the black ink of his older tattoos.

Discussion and Conclusion

The relationship here is hard to explain. A detailed PubMed® review yield not only no similar cases with imatinib, but none with other drugs. Overlooked or unreported similar cases would be interesting. Imatinib and other TKIs are known to have effects on pigmentation – vitiligo in blacks or lightening of dark skin in South Asians, hyperpigmentation of the temporal regions in Chinese, spotting in whites [5,6]. There is a definite ethnic/racial association that is cosmetically disturbing in many cases but on occasion have been noted to be beneficial in matrimonial choice in South Asians. These changes are not uncomfortable and reverse completely on stopping the imatinib [5,6]. These changes are felt to be due to alternative pathways or targets of the therapy [7,8]. Similar findings have been seen with other oncology medications [9].

What is particularly curious about this case is that the effects are reproducibly transient, time limited and related to older tattoos and oral ingestion of imatinib. They are not progressive or damaging and did not interfere with the efficacy of imatinib. Tattoo ink can cause permanent cutaneous issues [10-13] depending on the makeup. Cutaneous issues with imatinib or TKIs in general are not repeatedly transient. Although immunological issues with imatinib are known [14], they would not come and go like this. My only conclusion is that this is a very mild inflammatory response to imatinib triggering in old-time tattoo inks. A single newly reported side effect can generate thought [15,16]. This may help with better understanding of a process, inspire further thought and increase awareness leading to better safety and improvement in therapy not only of CML but other diseases. On some occasions this can lead to warnings in international guidelines [17]. Or more likely as one wag stated, “Cute but better discussed over cognac” [18].

Disclosure

No IRB was required. Patient consented and in fact wanted to be an author. There are no funding or conflicts to be noted.

References

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  18. Undisclosed Reviewer (2026) Leukemia.

Supercritical Enrichment of Boron and Cesium in Pegmatites Related to the Tin Mineralization of Ehrenfriedersdorf/Germany

DOI: 10.31038/GEMS.2026884

Abstract

The Ehrenfriedersdorf Variscan tin deposit contains pegmatite-related melt inclusions enriched in volatile and incompatible elements, including boron and cesium. This study examines the relationship among H2O, B2O3, and Cs to evaluate whether their enrichment records the influence of supercritical fluids and/or melts. We homogenized previously characterized melt inclusions and analyzed them for water, boron, cesium, and associated elements, then evaluated the results using polynomial, Lorendian, and three-dimensional compositional representations. Boron increases nonlinearly with water according to a polynomial relationship, whereas cesium displays a pronounced Lorendian distribution in the H O-Cs projection (R2=0.968), centered at approximately 26.0% H2O, with a width of 4.45% H2O, an offset of 0.48% Cs, and a height of 2.65% Cs. The projected Cs-B2O3 relationship is sigmoidal, indicating accelerated cesium enrichment at approximately 4% B2O3 and 0.5% Cs. These relationships suggest that boron and cesium became strongly concentrated during the development of volatile-rich, highly evolved residual melts and fluids associated with tin mineralization. Because binary projections may obscure the geometry of the compositional field, correction using the solvus curve is required when interpreting the complete H2O-B2O3-Cs system. The results support a model in which supercritical fluids and/or melts contributed to the transport and concentration of incompatible elements during late-stage magmatic differentiation. Boron and cesium may therefore serve as indicators of advanced pegmatitic evolution and transitions among silicate melt, aqueous fluid, and supercritical regimes, although further three-dimensional, experimental, and comparative studies are required.

Keywords

Supercritical fluids and melts, Boron enrichment, Cesium enrichment, Ehrenfriedersdorf tin deposit, Pegmatitic evolution, Melt inclusions, H2O-B2O3-Cs system, Lorentzian distribution, Volatile-rich residual melts, Solvus and critical point

Introduction

The author and coauthors [1-3] demonstrated that, in the Variscan tin deposit Ehrenfriedersdorf and similar deposits, enrichment of several elements besides Sn is directly related to supercritical fluids (SCF) and/or melts (SCM). Earlier analytical projections into the H2O-element plane were a first approximation; Thomas (2026b) [4] showed that a three-dimensional representation is more accurate. Here, the H2O-B2O3-Cs system illustrates the general behavior of such an element triple. In granitic rocks, the Clarke values of Cs and B are 5 and 15 ppm, respectively [5]. Cesium is a lithophile alkali metal found in minerals, residual solutions, and pegmatites; it also forms minerals such as pollucite and ramanite-Cs [6]. Boron occurs in the Earth’s crust as boric acid [H3BO3] and borates and is important in ore-forming processes. At relatively high concentrations, boron reduces the viscosity of silicate melts.

Sample Material and Methods

The sample material and methods for homogenization, water, boron, and element determination are given in Thomas (2000) [7], (2002) [8] and Thomas et al. (2003) [9] and 2006 [10]); the homogenization methods are presented in the predecessor publications [2,3] and the citations therein. Figure 1 shows a schematic of melt-inclusion behavior of water-rich silicate-melt systems during heating. The first observation about it dates to a presentation at the ECROFI XV meeting in 1999 [11]. A more detailed description and discussion is in Thomas et al. 2000) [12].

Figure 1: Schematic behavior of melt inclusions with different water content after homogenization at different temperatures. CP is the critical point of the resulting solvus curve (red).

Note that the inclusions shown in Figure 1 are immediately after quenching in a fully homogenized state, proved by the hydrothermal rapid-quench method or, in the same cases, the quenching in liquid nitrogen. Due to the high water content in the inclusions, they heterogenized after some hours like the schematic types shown in Figure 1.

Results

Figures 2 to 4 show the key results, which are combined into Figure 5 as a room plot, demonstrating that the triple H2O-B2O3-Cs are strongly related.

Figure 2 presents the simplified water-boron plot for the silicate melts. The following equation represents the boron curve (X=H2O; Y=B2O3):

Figure 2: Water versus boron concentration in melt inclusions on the Ehrenfriedersdorf pegmatite-related quartz. This plot represents the system’s X-Y plane.

Y=1.45255 + 0.19272 ∗ X – 0.00348 ∗ X2 – 6.38965 ∗ 10-7 ∗ X3 (1)

Figure 3 shows the concentration of Cs versus H2O and shows, in a good approximation, a Lorentzian distribution. The H2O-Cs plane projected Lorentzian data are in Table 1.

Figure 3: Lorentzian distribution of Cs versus the water content of silicate melt inclusions.

Figure 4 shows the projection of the Cs content of silicate melt inclusions in the B2O3-Cs-plane.

Figure 4: Sigmoidal Cs-B2O3 correlation in the B2O3-Cs plane.

Figure 4 shows clearly that the Lorentzian distribution of Cs begins at about 0.5% Cs, corresponding to the offset (see Table 1) and a boron concentration of about 4% B2O3. Figure 5 shows the room diagram of all three phases: H2O-B2O3-Cs, showing that all three components are well correlated, and the maximum of the Cs concentration is related to the critical point of the water-boron solvus.

Table 1: Results of the Lorentzian distribution (projection to the H2O-Cs plane (R2=0.968).

Area

Center Width Offset

Height

18.53 26.0% H2O 4.45% H2O 0.48% Cs 2.65% Cs

In the CP region, by contrast, the greatest differences occur. That could indicate that the composition there is particularly sensitive to changes in the thermodynamic conditions or phase composition.

Because the Lorentzian plot of Cs vs. H2O (Figure 3) is a projection on the H2O-Cs plane, some of the data in Table 1 must be corrected using the arc segment of the solvus curve. However, according to Figure 4, the Lorentzian curve starts, in a first approximation, at a B2O3 concentration of about 4%; we can use the uncorrected data in Table 1.

Figure 5: Room diagram H2O-B2O3-Cs. CP is the critical point.

Discussion

The results demonstrate that boron and cesium enrichment in the Ehrenfriedersdorf pegmatite-related melt inclusions is closely associated with increasing water content and, consequently, with the evolution of a volatile-rich silicate melt. The boron-water relationship follows a polynomial curve rather than a simple linear trend, indicating that boron incorporation changes as the melt becomes progressively enriched in H2O. This behavior is consistent with boron’s role as a fluxing component that can modify melt structure, reduce viscosity, and increase the capacity of residual melts to transport metals and other incompatible elements. The cesium data provide an additional indication of strong enrichment during late-stage magmatic evolution. The Lorentzian distribution of Cs versus H2O, with a coefficient of determination of R2=0.968, describes a pronounced concentration maximum near 26.0% H2O. The fitted width of 4.45% H2O, offset of 0.48% Cs, and height of 2.65% Cs indicate that cesium enrichment was not gradual throughout the entire compositional range. Instead, it became especially significant after the system reached a threshold represented by the offset. In geological terms, this threshold may mark the development of a highly evolved, water-rich fluid or melt domain in which Cs was preferentially concentrated. The projected Cs-B2O3 relationship further supports this interpretation. The sigmoidal correlation suggests that cesium enrichment accelerated once the boron concentration reached approximately 4% B2O3, corresponding to the beginning of the Lorentzian increase at about 0.5% Cs. Boron and cesium therefore appear to be linked through the progressive development of a volatile-rich residual melt rather than behaving as independent components. Their association is significant because both elements can indicate advanced fractionation and conditions favorable for concentrating and transporting tin in granite-pegmatitic systems.

The three-dimensional H2O-B2O3-Cs representation is important because two-dimensional projections can conceal the geometry of the underlying compositional field. In particular, the Lorentzian parameters obtained from the H2O-Cs projection cannot necessarily be applied directly to the complete three-component system. Correction using the arc segment of the solvus curve is therefore required. This observation emphasizes that apparent correlations in projected plots may partly reflect the projection of a curved three-dimensional surface, and it provides a methodological reason for treating binary diagrams as approximations rather than complete descriptions of melt evolution. These findings broaden the interpretation of the Ehrenfriedersdorf deposit by showing that Cs and B enrichment is not merely a chemical peculiarity of individual inclusions. Instead, it records interactions among volatile enrichment, melt structure, and phase relations during the late stages of magmatic differentiation. The results are compatible with a model in which supercritical fluids and/or melts contribute to the redistribution and concentration of incompatible elements, including components relevant to tin mineralization. They also suggest that B and Cs concentrations may be useful geochemical indicators for recognizing highly evolved pegmatitic environments and for tracing transitions between silicate melt, aqueous fluid, and supercritical regimes. Several limitations should nevertheless be considered. The boron relationship is based on a simplified projection, and interpreting the cesium distribution depends on the quality and representativeness of the melt-inclusion data. A high R2 value shows that the Lorentzian function fits the available projected data well, but it does not, by itself, establish a unique genetic mechanism. The effects of pressure, temperature, inclusion re-equilibration, post-entrapment modification, analytical uncertainty, and possible loss or redistribution of volatile components must also be evaluated. In addition, the cited Clarke values provide a useful crustal reference but do not represent the composition of the specific parental magma or the full range of processes affecting the deposit. We already know that the Lorentzian element distribution curves can vary from place to place in the Sauberg deposit. At some places, the Cs concentration in the critical region can reach 8.88% (indicated by the appearance of Ramanite-(Cs) in melt inclusions trapped near the critical point of the evolved H2O-silicate system; see Thomas and Rericha, 2023 [13].

For this case, we present the data in Table 2. Further work using calibrated three-dimensional phase relations, independent fluid-inclusion constraints, and broader sampling would help test whether the observed thresholds and correlations are characteristic of Ehrenfriedersdorf alone or apply to comparable Variscan tin-pegmatite systems. First results on the Variscan Zinnwald tin deposits point to this possibility. However, boron is in the E-Erzgebirge only weak enriched. Here, fluorine has greater meaning. Boron increases westward and reaches its maximum around Ehrenfriedersdorf.

Table 2: Results of the Lorentzian distribution (projection to the H2O-Cs plane (R2=0.994) for extreme Cs- and H2O-rich silicate melts.

Area

Center Width Offset

Height

13.309 30.0% H2O 0.9% H2O 5.36% Cs 8.88% Cs

Figure 6 shows a boron-rich silicate melt drop in granite quartz from Ehrenfriedersdorf. Thomas and Rericha (2023) [13] provide more information on boron in melt inclusions in granite quartz.

Figure 6: BSE image of a B-rich melt inclusion in granite quartz from Ehrenfriedersdorf. The silicate melt contains 10.6% B2O3.

Conclusions

In this contribution, together with the precede work [1-4,14], we see clearly that a row of elements like Li, Be, B, P, Cl, Zn, As, Sn, Sb, Cs, Ta, W are highly enriched and show a general Lorentzian distribution which cannot be explained with a conventional enrichment process. The Lorentzian distribution clearly indicates a strong or moderate input of water and some critical elements via supercritical fluids (SCF) and/or supercritical melts (SCM) from mantle regions. Indicators are the sporadic appearance of diamond, lonsdaleite, moissanite, coesite, cristobalite X-I, α- and γ-AlB12, as well as the orthorhombic cassiterites of the CaCl2– and cotunnite-types (e.g., Thomas, 2024) [15]. According to Schröcke’s Figures C-D (1954) [16,17], the Sauberg mine (e.g., Prinzler-West) contains many pegmatites and pegmatite-like bodies that cannot be explained by water from the surrounding granites. These figures also show that the mineral-forming media are not homogeneous; the source is non-uniform and varies strongly in composition and consistency. Swarms of SCF and SCM of different thickness and power come from different depths. At the transition between the supercritical, critical, and subcritical stages, processes occur which cannot be reduced to well-known pieces.

Acknowledgment

The contribution is dedicated to Dr. Adolf Rericha from Falkensee/ Germany, who, despite his health problems, was always open to discussions.

References

  1. Thomas R (2026a) Attempt to interpret the exceptional element enrichment and distribution in pegmatite related to the Variscan tin deposit at the example of Ehrenfriedersdorf, Geol Earth Mar Sci 8: 1-8.
  2. Thomas R, Davidson P, Appel K (2019) The enhanced element enrichment in the supercritical states of granite-pegmatite systems. Acta Geochim 38: 335-349.
  3. Thomas R, Davidson P, Rericha A, Voznyak DK (2022) Water-rich melt inclusions as “frozen” samples of the supercritical state in granites and pegmatites reveal extreme element enrichment resulting under non-equilibrium Mineral J (Ukraine) 44: 3-15.
  4. Thomas R (2026b) Lorentzian distribution of Sn in the Ehrenfriedersdorf Geol Earth Mar Sci 8: 1-4.
  5. Rösler HJ, Lange H (1975) Geochemische VEB Deutscher Verlag für Grundstoffindustrie Leipzig. Pg: 675.
  6. Thomas R, Davidson P, Hahn A (2008) Ramanite-(Cs) and ramanite-(Pb): new cesium and rubidium pentaborate tetrahydrate minerals identified with Raman American Mineralogist 93: 1034-1042.
  7. Thomas R, Webster JD, Heinrich W (2000) Melt inclusions in pegmatite quartz: complete miscibility between silicate melts and hydrous fluids at low Contrib Mineral Petrol 139: 394-401.
  8. Thomas R (2002) Determination of the H3BO3 concentration in fluid and melt inclusions in granite pegmatites by laser Raman microprobe American Mineralogist 87: 56-68.
  9. Thomas R, Förster H-J, Heinrich W (2003) The behavior of boron in a peralumonous granote-pegmatite system and associated hydrothermal solutions: a melt and fluid-inclusion study. Contrib Mineral Petrol 144: 457-472.
  10. Thomas R, Kamenetsky VS, Davidson P (2006) Laser Raman spectroscopic measurements of water in unexposed glass inclusions. Am Mineralogist 91: 467-470.
  11. Thomas R, Webster JD, Heinrich W (1999) Melt inclusions in pegmatite quartz: Complete miscibility between silicate melts and hydrous Terra Nostra 99: 305-307.
  12. Thomas R (2000) Determination of water contents of granite melt inclusions by confocal laser Raman microprobe American Mineralogist 85: 868-872.
  13. Thomas R, Rericha A (2023) Extreme enrichment of Cs during the crystallization of the Ehrenfriedersdorf pegmatite melt related ti the Variscan tin mineralization. Geol Earth Mar Sci 5: 1-4.
  14. Thomas R (2025) Interpretation of the Lorentzian distribution of tin in the Variscan Ehrenfriedersdorf deposit, Geol Earth Mar Sci 7: 1-5.
  15. Thomas R (2024) The CaCl2-to rutile phase transition in SnO2 from high to low pressure in Geol Earth Mar Sci 6: 1-4.
  16. Schröcke H (1954) Zur Paragenese erzgebirgischer Zinnlagerstätten. Neues Mineral. Abh 87: 33-109.
  17. Thomas R (2023a) Unusual cassiterite mineralization related to the Variscan tin mineralization of the Ehrenfriedersdorf deposit, Aspects Min Miner Sci 11: 1233-1236.

Diamond-like Carbon (DLC) in Herderite from Namibia

DOI: 10.31038/GEMS.2026883

Abstract

Raman spectroscopy offers a nondestructive approach to differentiate and characterize sp2– and sp3-bonded carbon. In sp2-rich materials, the defect-activated D band generally appears near 1350 cm-1, whereas the graphite G band occurs near 1580 cm-1. Enhanced graphitic ordering may additionally generate a second-order 2D band near 2700 cm-1. Diamond exhibits a characteristic first-order Raman line at approximately 1332 cm-1, corresponding to the zone-center optical phonons of its sp3-bonded lattice. Band position, linewidth, and relative intensity provide valuable information on bonding configuration, crystallinity, defects, and structural disorder. Mechanical stress shifts Raman bands by altering lattice strain; compressive and tensile stresses generally induce opposite frequency changes, although the precise response depends on crystallographic orientation and bonding structure. Temperature modifies the spectra through phonon anharmonicity and thermal expansion, commonly producing redshift and linewidth broadening as temperature increases. Accordingly, analysis of peak positions, linewidths, and intensity ratios supports assessment of carbon-phase composition, structural quality, stress state, and thermal behavior. Prerequisites are strong reducing conditions.

Keywords

Raman spectroscopy, DLC in herderite, sp2– bonded carbon, sp3– bonded carbon, D band, G band, Reducing conditions, Supercritical fluids

Introduction

During Raman studies of herderite and hydroxylherderite samples, we found that many herderite samples from Namibia contain numerous small black flats (some micrometer thick) over wide areas. Raman studies show that these black, mostly irregularly shaped areas correspond to diamond-like carbon (DLC), but not in the sense of Irmer and Dorner-Reisel (2005) [1]. In industrial applications, DLC is a class of amorphous carbon materials that contain significant amounts of sp³-hybridized carbon atoms, giving it diamond-like hardness, while also incorporating sp² carbon for flexibility and lubricity. The purest form, tetrahedral amorphous carbon (ta-C), consists almost entirely of sp³ bonds, whereas other forms may include hydrogen, different metals, or graphitic carbon depending on the production process. In this context, DLC refers to a carbon-based coating with diamond-like properties, such as high hardness, low friction, and good wear and chemical resistance. A typical Raman spectrum of diamond-like carbon (DLC) shows two broad bands: the D band around 1350 cm⁻¹ associated with disorder and defects in sp²-bonded carbon. The G band is around 1580 cm⁻¹ associated with the stretching of sp² carbon bonds. Diamond-rich material may additionally show a sharp diamond peak near 1332 cm⁻¹, which can overlap with the DLC D band. Because crystallization in nature is fast and more chaotic, the black areas are not expected to occupy very large, undisturbed areas, as in the technique. However, the underlying processes are probably the same. In topaz from the Klein Spitzkoppe, Namibia, similar black areas are also present. They are generally rarer. However, a large part consists of moissanite [SiC], and a smaller part consists of DLC.

Sample and Methods

Herderite [CaBe(PO4]F from Namibia, Erongo Mountains, farm Davib-Ost

Cairncross and Bahmann (2006) [2,3] described the mineral as hydroxylherderite. Thomas (2026) [4] identified the material as herderite using Raman spectroscopy. However, there are water-clear, small areas with a tendency toward hydroxylherderite, particularly at the crystal rims. The herderite sample (about 2 x 1.5 x 0.5 cm) comes from miarolitic cavities in the Erongo Mountains. According to Cairncross and Bahmann (2006) [2,3], herderite is associated with small (less than 2 cm) schorl crystals, muscovite, and orthoclase. A single pocket opened in July 2003 yielded a small amount of the herderite material. Most specimens are thumbnail- to small-cabinet-size clusters composed of individual crystals measuring less than 1 cm. The largest crystal found thus far measures 6.2 cm and is studded by small schorl crystals.

Two different colors of hydroxyl-herderite were described from Erongo: pale green and off-white. X-ray diffraction analysis of crystals of both colors at the University of Johannesburg revealed that they are the same species; the cause of the color difference is not known. While working on herderite samples from the Erongo, the author was surprised to find that many crystals (the white type) contain relatively large DLC aggregates. Figures 1 to 4 show such aggregates as examples. Single diamonds with the same characteristic as the large DLC areas are also present.

Microscopy and Raman Spectroscopy

All microscopic and Raman spectroscopic investigations were conducted using a petrographic polarization microscope (BX 43) equipped with a rotating stage and coupled to an EnSpectr Raman spectrometer R532 (Enhanced Spectrometry, Inc., Mountain View, CA, USA). Measurements were performed in both reflection and transmission modes. All measurements were carried out with the EnSpectr R532. Measurements covered the spectral range from 0 to 4000 cm⁻¹ and were conducted with a single-mode 532 nm laser operating at a maximum output of 50 mW. Analytical settings included a 20 µm entrance aperture, a holographic grating of 1800 g mm⁻¹, and a spectral resolution of approximately 4 cm⁻¹ across the measured range. For most analyses, we used a long-working-distance Olympus LMPlanFL 100× objective, which allows a measuring field about 1 µm in diameter on the sample. Laser power at the sample surface was continuously adjustable down to 0.02 mW. Higher powers, up to 50 mW, were used only for overview measurements. For measurements over the range 0–4000 cm-1, we generally set the laser power to 30 mW at the sample. For minerals located deep under the polished surface, we generally also used 30 mW laser power; however, we used a significantly longer measurement time. We calibrated Raman-band positions before and after each measurement series using the Si band of a semiconductor-grade single-crystal silicon chip. Based on 20 repeated measurements, run-to-run reproducibility was ± 0.2 cm⁻¹ for silicon (520.2 ± 0.2 cm⁻¹) in the range from 0 to 2000 cm⁻¹. As a second reference, we used a water-clear diamond crystal from Brazil. We measured the first-order diamond line at 1333.4 ± 0.6 cm⁻¹, with a FWHM of 5.1 ± 0.1 cm⁻¹ (see also Solin and Ramdas, 1970) [5]. FWHM is the full width at half maximum. However, scrutiny of the Brazil diamond shows small heterogeneities (different ¹³C content).

Results

During the determination of the fluorine content of hydroxylherderite/herderite [4], we observed many irregular black areas (for example, Figures 1 to 4). Examination with Raman spectroscopy indicated diamond-like carbon (DLC). Figure 5 shows such a Raman spectrum from DLC. Crystalline diamond and graphite show very low FWHM values (~5 cm-1), opposite to DLC.

Figure 1: Herderite (white) fragment from a miarolitic cavity from Namibia. The black parts are DLC-like material. Light brown parts are hydroxylherderite.

Figure 2: Herderite (Hrd) with DLC and small hydroxylherderite (h-Hrd) from Namibia.

Figure 3: Diamond-like carbon (DLC) aggregate in herderite (Hrd) from Namibia.

Figure 4: DLC in herderite (Hrd) from Namibia.

Figure 5a: Raman spectrum of diamond-like carbon (DLC) in herderite from Namibia. Hrd – herderite from the matrix.

This spectrum (Figure 5b) shows a broad diamond line at 1328.2 cm-1; broad carbon D-lines at 1357.1 and 1379.7 cm-1 with FWHM = 28.3 and 46.3 cm-1; and a broad G-band at about 1585 cm-1. Figure 5b is an example of the decomposition of the first-order region of the Raman spectrum of DLC. The diamond band at 1328.2 cm-1 has a FWHM of 46.9 cm-1, and the G-band has two components at 1567.3 and 1594.1 cm-1 with FWHMs of 65.2 and 53.2 cm-1, respectively.

Figure 5b: Decomposition of a DLC Raman spectrum. D1 at about 1350 cm-1 is typical for poorly ordered carbons. The same applies to the D2 and D3 bands. The G-band, or graphite band, occurs at 1580 cm-1. Laser heating causes a partial shift downward to 1567 cm-1 [6]. According to Zaitsev (2001) [7], the band around 1567 cm-1 is typical for low-quality CVD diamond films.

To confirm whether the black material is real DCL, we recorded Raman spectra in the first-order range using different laser powers (5 to 50 mW). We found a “plateau-like” range (15-35 mW) with a more or less identical position of the diamond line at ≤ 1324 cm-1 (see Figure 6). Across the entire laser-energy range, the diamond-line position forms a sigmoid curve, probably produced by the interaction of sp2 and sp3 species in different carbon phases diamond, carbon, amorphous carbon).

Figure 6: Shift of the diamond line of first order with the laser power on the DLC in the herderite sample from Namibia.

The sigmoid trend shows that the position of the first-order diamond line depends nonlinearly on the applied laser power. The line position changes most strongly within an intermediate power range and tends toward a limiting value at higher power, indicating a saturation-like response. The power-dependent shift of the first-order diamond Raman line is attributed primarily to laser-induced heating. sp²- rich regions may enhance laser absorption and facilitate heat transfer to the diamond phase. The sigmoid response suggests a nonlinear, saturating thermal process, possibly caused by changes in absorption and heat dissipation as the local temperature increases. Further temperature calibration or comparison with samples having different sp² contents would be required to confirm this mechanism. Natural diamond crystals (e.g., the reference diamond from Brazil) show a nearby linear course from 4.3 to 50 mW: 1333.4 ± 0.6 cm-1, differ strongly from DLC in herderite from Namibia. The lonsdaleite-like carbon from fluorite from Zinnwald [8] shows a clear linear decline of the 1312 cm-1 line from 4.3 to 1296.8 at 50 mW laser power on the sample. Figure 7 shows the Raman shift of the first-order diamond line as a function of the used laser energy [9,10].

Figure 7: Raman shift (cm-1) versus laser (532 nm) energy (mW) obtained using the Brazil reference diamond. The sigmoid (Boltzmann) fit starts at 1302.5 cm-1, 0.8 mW and ends at 1333.6 cm-1 and 50 mW. The correlation coefficient is R2 = 0.99914.

Discussion

Raman spectroscopy responds differently to sp2– and sp3-bonded carbon: sp2 carbon produces strong, broad Raman features. The G band appears near 1580 cm-1 and is associated with in-plane stretching of sp2 bonds. The D band appears near 1350 cm-1 and indicates disorder, defects, or finite-sized sp2 clusters. The sp3 carbon in crystalline diamond. produces a sharp first-order Raman line near 1332 cm-1. In amorphous or DLC materials, sp3 bonding usually does not produce a distinct peak; instead, it affects the position, width, and intensity of the broad D and G bands. For DLC, the Raman spectrum is therefore mainly used to analyze the sp2 network. The ID/IG intensity ratio, along with the G-band position and width, can indicate changes in disorder and sp2/sp3 bonding. However, Raman alone does not provide a fully reliable quantitative sp3 concentration; techniques such as XPS, EELS, or NEXAFS are often used to confirm it [9,10].

Conclusion

In conclusion, Raman spectroscopy effectively distinguishes sp2-and sp3-bonded carbon by identifying their characteristic vibration features. In diamond, the dominant signature is a sharp line near 1332 cm-1, corresponding to the ordered sp3-bonded crystal lattice. DLC, by contrast, generally exhibits broader D and G bands: the D band is associated with disordered or defect-rich carbon structures, while the G band reflects graphitic sp2 binding. Temperature can broaden, shift, or reduce the intensity of these features by increasing phonon interactions and promoting structural rearrangement, whereas Raman spectroscopy provides a sensitive means of distinguishing sp2-and sp3-bonded carbon and evaluating their structural environments. In sp2-rich carbon, the characteristic G band, typically near 1580 cm-1, arises from in-plane bond-stretching vibrations of graphitic carbon, whereas the defect-activated D-band, commonly near 1350 cm-1, reflects disorder, finite crystallite size, and defects. In sp3-bonded diamond, the Raman spectrum is dominated by the first-order line near 1332 cm-1, corresponding to the zone-center optical phonon. This line is highly responsive to external conditions: increasing temperature generally causes phonon softening, producing a shift toward lower wavenumber and often linewidth broadening, while compressive stress shifts the line upward and tensile stress shifts it downward. The magnitude and direction of the shift therefore provide information about the thermal and mechanical state of the diamond lattice. Together, the relative intensities and positions of the sp2 G and D bands and the diamond Raman line enable assessment of bonding configuration, structural disorder, temperature, and stress in carbon-based materials. Mechanical stress commonly shifts the Raman peaks and may alter their widths. Thus, the position, intensity, and shape of the D, G, and diamond-related bands provide a practical means of evaluating bonding configuration, disorder, crystallinity, and environmental effects in carbon-based materials. The presence of DLC in pegmatitic herderite suggests that strong reducing conditions are a prerequisite for its formation. The input of supercritical fluids is therefore probable.

References

  1. Irmer G, Dorner-Reisel A (2005) Micro-Raman studies on DNC Advanced Engineering Materials 7: 694-705.
  2. Cairncross B, Bahmann U (2006) Famous mineral localities: the Erongo Mountains The Mineralogical Record 37: 361-470.
  3. Cairncross B (2005) Klein Spitzkoppe The Mineralogical Record 36: 317-335.
  4. Thomas R (2026) Remarks on herderite [CaBe(PO4)F] and hydroxylherderite [CaBe(PO4)(OH)]. Geology, Earth and Marine Sciences 8: 1-4.
  5. Solin SA, Ramdas AK (1970) Raman spectrum of Physical Review B 1: 1687-1698.
  6. Beyssac O, Goffe C, Chopin C, Rouzaud JN (2002) Raman spectra of carbonaceous material in metasediments: a new geothermometer. Metamorphic Geol 20: 859-871.
  7. Zaitsev AM (2001) Optical Properties of Diamond – A Data Berlin, Heidelberg, New York. Springer. Pg: 502.
  8. Thomas R, Trinkler M (2024) Monocrystalline lonsdaleite in REE-rich fluorite from Sadisdorf and Zinnwald/E-Erzgebirge, GEMS 6: 1-5.
  9. Lafuente B, Downs RT, Yang H, Stone N (2015) The power of databases: the RRUFF In: Armbruster T, Danisi RM (eds). Highlights in Mineralogical Crystallography. Berlin, Germany, Walter de Gruyter: 1-30.
  10. Thomas R (1982) Ergebnisse der thermobarogeochemischen Untersuchungen an Flüssigkeitseinschlüssen in Mineralen der postmagmatischen Zinn-Wolfram. Mineralisation der Freiberger Forschungshefte FFH C 370. Pg: 85.

Simplifying Neuroanatomical Diagnosis in Dogs and Cats Based Twelve Functional Regions and Associated Clinical Signs

DOI: 10.31038/IJVB.20261015

Abstract

Neuroanatomical diagnosis is a crucial clinical assessment in animals with neurological disease. Given the complexity of the nervous system, however, it can be challenging for the primary care practitioner to recall and clinically integrate the plethora of neuroanatomical structures and their functions to form this important assessment. While it is sometimes necessary to have advanced knowledge of how discrete neuroanatomical elements of the nervous system as isolated entities, it is pragmatically important for the practicing clinician to have a “functional” understanding of how discrete neuroanatomical structures are integrated, interrelated, and coordinated into a functional unit. These functional units can then be used for neuroanatomical localization. In this review the important functional regions of the nervous system that are responsible for animals performing various normal functions will be discussed in the context of making a neuroanatomical diagnosis. Concurrently, classic clinical abnormalities associated with dysfunction of these functional units will be provided. Using this functional system, the clinician can make practical decisions about disease location with the nervous system. With accurate neuroanatomical diagnosis, the clinician can subsequently formulate an accurate differential diagnosis list followed by an appropriate diagnostic plan to ultimately identify the primary neurologic disease process.

Keywords

Clinical neurology, Clinical evaluation, Neuroanatomical diagnosis, Clinical signs

Introduction

During performance of the neurological examination in animals, abnormalities of nervous system function are identified and categorized. Each clinical abnormality provides a “clue” to the location of the dysfunction within nervous system. Fundamentally, an understanding of the neuroanatomic structures that are responsible for a particular neurologic examination finding are necessary when attempting to determine the location of the lesion resulting in the neurologic dysfunction. Most importantly, however, the practicing clinician needs to understand how these neuroanatomical structures coordinate as functional units to create clinically detectable outputs. Abnormalities found during the neurologic examination help to determine 1) that an abnormality exists in the nervous system and 2) the location of this abnormality within the nervous system. While the neuroanatomical structures are foundational, it is most important that the functional outputs from these anatomical structures which are evaluated clinically are identified and collated.

The determination of where the nervous system is abnormal is referred to as the neuroanatomic diagnosis. This assessment is the fundamental process that underpins clinical neurologic assessment in animals [1-13]. Determining the location of any abnormality in the nervous system is arguably the most important aspect of appropriate clinical evaluation of animals with neurologic disease. Only after determining the location of any abnormality can an appropriate differential diagnosis of disease processes affecting that region of the nervous system be logically generated. Once a list of possible disease processes is formulated, appropriate diagnostic tests to establish the specific disease processes can logically be decided upon, performed, and interpreted in this context.

Recalling specific locations of discrete neuroanatomical structures in the day-to-day clinical evaluations of affected patients, however, can be challenging. This difficulty in making neuroanatomical connections may be a component of a known neurophobia among medical professionals [14-17]. While the nervous system is exceedingly complex, the practicing clinician ultimately identifies the functional outputs of the nervous system through clinically detectable signs such as postures and movements. The astute clinician, then, needs to have a “functional” understanding of how the nervous system is organized and integrated to subsequently understand how disease manifests in clinical dysfunction. While it is sometimes necessary to possess knowledge of how discrete neuroanatomical elements of the nervous system function as isolated entities, it is most important for the practicing clinician to have an operational understanding of how these separate neuroanatomical entities are integrated, interrelated, and interacted as a functional unit. These functional units are interconnected forming functional relationships which are responsible for clinically detectable body functions. An understanding of neurologic functionality promotes an understanding of how information is transmitted or “flows” back and forth between structures within the nervous system and expressed as clinically observable activities. When abnormalities exist within the nervous system, these functional relationships are disrupted or disconnected resulting in clinical signs as evidence of this dysfunction.

Diseases involving individual functional units within the nervous system tend to result in a “classic” set of corresponding clinical signs [1-13]. One, some, or all these classic clinical signs may be present in any individual animal. For this discussion, disease may also be referred to as a lesion (a pathologic or traumatic discontinuity of tissue or loss of function of a part) or an abnormality (any deviation from or interruption of the normal structure or function of a body part). These terms are not to be taken to designate any specific disease process or etiology. Based on regions with similar functionality and the associated clinical signs, nervous system abnormalities can be pragmatically localized into one of twelve main anatomical/functional regions (Figure 1).

Figure 1: Image depicting the 12 Functional Regions (Zones) that can be used for neuroanatomical localization in dogs and cats.

For simplicity, these anatomical/functional regions will be referred to as “Zones” for this review. While attempting to be as specific as possible with a neuroanatomical diagnosis, a practical initial goal for most clinicians is to determine if disease within the nervous system is present in either the intracranial region (and associated cranial nerves), spinal cord segments, or the peripheral/neuromuscular junction/muscle complex. Answering a series of prompted questions is helpful in determining the neuroanatomical diagnosis during a clinical evaluation (Table 1).

Table 1: Clinical Questions associated with neuroanatomical localization.

Important clinical questions relative to neuroanatomical diagnosis
1 Is the abnormality rostral to, or caudal to, the foramen magnum?
2 If the abnormality is rostral to the foramen magnum, is the disease affecting the rostrotentorial (“forebrain”) structures, the brain stem, the cerebellum, or individual cranial nerves?
3 If the abnormality is caudal to the foramen magnum, does this disease involve the spinal cord, the peripheral nerve, neuromuscular junction, or muscle?
4 If the disease involves the spinal cord, which segment (s) (C1-C5, C6-T2, T3-L3, L4-S3, Caudal segments)
5 If the abnormality involves the peripheral nervous system, which nerve (s)?
6 Does the abnormality primarily/exclusively involve the neuromuscular junction?
7 Does the abnormality primarily/exclusively involve the muscle?
8 Do all the clinical signs localize to multiple regions within the nervous system?

Question 1. Is the Abnormality Rostral to or Caudal to the Foramen Magnum?

Anatomical structures rostral to the foramen magnum include the intracranial nervous system (rostrotentorial, caudotentorial (brain stem, cerebellum)) and the specific cranial nerves [18]. Structures caudal to the foramen magnum include the spinal cord segments, specific peripheral nerves, neuromuscular junction, or muscular systems.

Clinical Signs Reflecting Intracranial Dysfunction

Clinical signs suggestive of intracranial disease include, but are not limited to, alterations in mental acuity, behavior abnormalities, cranial nerve abnormalities, seizures, various head posture abnormalities, propulsive circling movements, and conscious proprioceptive deficits in the limbs. Once it has been determined that an intracranial abnormality exists, further delineation of the location of the lesion may be possible based on the specific clinical signs present.

Question 2. Is the Abnormality Affecting the Rostrotentorial Structures, Brain Stem Structures, Cerebellum, or Individual Cranial Nerves?

The rostrotentorial (often referred to as the “forebrain”) structures (Zone 1) include the cerebral hemispheres, basal nuclei, diencephalon, rostral portion of the mesencephalon, and associated structures such as the meninges, blood vessels, and ventricles (lateral, third, and mesencephalic aqueduct). These structures are located rostral to an imaginary transverse plane extending from the tentorium cerebelli ventrally separating the intracranial nervous system into two components. The rostrotentorial nervous system structures are responsible for many functions requiring consciousness, awareness, and associated responses. Classic clinical signs of an abnormality of rostrotentorial structures are found in Table 2. The most common clinical signs of a rostrotentorial abnormality include alterations in their mental acuity or behavior, a menace response abnormality from poor or absent vision, facial sensation recognition abnormalities, true seizure activity, proprioceptive abnormalities in the limbs, a tendency to walk propulsively in a circular direction, “hemi-inattention” or “hemi-neglect”, head pressing, and or head turning. Unilateral lesions involving the rostrotentorial region predominantly result in clinical signs on the side of the body opposite (contralateral) to the abnormality.

Table 2: Classic Clinical Signs of Rostrotentorial Abnormalities.

Classic Clinical Signs of Rostrotentorial (“Forebrain”) Abnormalities
Alterations in mental acuity and behavior (listless, lethargy, somnolence, stupor, coma)
“Head Pressing”
Hemi-inattention or Hem-neglect
Contralateral Menace Response Deficit with concurrent normal Facial Nerve Function and Pupillary Light Reflex (PLR) (unilateral abnormality)
Contralateral Facial Sensation Deficit (unilateral abnormality)
Contralateral Hemiparesis/Hemiplegia (unilateral abnormality)
Tetraparesis/Tetraplegia (bilateral abnormality)
Seizures
Head turning (usually in the direction of a unilateral abnormality)
Circling (usually in the direction of a unilateral abnormality)

Importantly, while there is a constellation of clinical signs that may be present in any individual animal with a rostrotentorial disease, all these clinical signs may not be present in every affected animal. In some instances, for example, only a single clinical sign may be present to indicate an abnormality in these neuroanatomical structures. Additionally, because of the somatotopic organization of the nervous system, very discrete abnormalities may result in focal or localized clinical signs (for example, dysfunction of a single limb). The type and extent of any disease process ultimately will determine the specific associated clinical signs.

Clinical Signs of Caudotentorial (Brain Stem) Disease

The caudotentorial structures include the commonly defined brain stem (Zone 2), the cerebellum (Zone 3), and associated structures such as the meninges, blood vessels, and ventricles (mesencephalic aqueduct and fourth) [18]. The predominate clinical signs of a brain stem abnormality are found in Table 3. These include abnormal mental acuity and awareness, proprioceptive abnormalities in the limbs, abnormalities of Cranial Nerves III through XII, decerebrate posturing (midbrain abnormalities), and, in severe instances, respiratory irregularities or failure of respiratory function.

Table 3: Classic Clinical Signs of Caudotentorial Abnormalities (Brain Stem).

Classic Clinical Signs of Brain Stem Abnormalities
Alterations in mental acuity and behavior (listless, lethargy, somnolence, stupor, coma)
Cranial nerve abnormalities (Cranial nerves III through XII)
Vestibular signs – head tilting, ataxia, wide-based posturing, nystagmus, positional strabismus
Ipsilateral Hemiparesis/Hemiplegia (unilateral abnormality)
Tetraparesis/Tetraplegia (bilateral abnormality)
Abnormalities of respiration

Clinical Signs of Caudotentorial Disease Involving the Cerebellum

The cerebellum (Zone 3) is a unique structure of the central nervous system anatomically and functionally. Because of the important proprioceptive functions of the cerebellum, clinical signs of cerebellar disease are often characteristic as described as “uncoordinated” because of dysfunction of unconscious proprioception [1-13]. Unilateral lesions of the cerebellum result in ipsilateral clinical signs. Common clinical signs of cerebellar disease are found in Table 4. These include ataxia, dysmetria, Intention tremors, lack of balance, head tilting, nystagmus, broad or wide-based stance postures, a menace response deficit (ipsilateral to a unilateral cerebellar lesion) with normal vision, and, in severe instances, decerebellate posturing.

Table 4: Classic Clinical Signs of Caudotentorial Abnormalities (Cerebellum).

Classic Clinical Signs of Cerebellar Abnormalities
Uncoordinated movements
Ataxia
Hypermetria
Falling
Intention tremor
Absent menace response with normal vision, normal palpebral reflex, and pupillary light reflex (ipsilateral to a unilateral abnormality)
Pupillary abnormalities (rare)

Clinical Signs of Individual Cranial Nerves

Common clinical signs of individual cranial nerve abnormalities (Zone 4) are found in Table 5. The challenge with cranial nerve abnormalities is determining if the abnormality involves the central (primarily the cranial nerve nuclei) or peripheral components (ganglion and peripheral nerve axons) of the cranial nerve. If the central nervous system elements of the cranial nerves are abnormal, these are a component of the rostrotentorial (CN I and II) and brain stem (CN III – XII) localization schemata.

Table 5: Classic Clinical Signs of Cranial Nerve Abnormalities.

Classic Clinical Signs of Cranial nerve Abnormalities
CN I Anosmia or dysosmia
CN II Visual abnormalities; menace response abnormalities
CN III Strabismus; ocular movement abnormalities (medial direction); pupillary dilation
CN IV Strabismus; ocular movement abnormalities (rotational)
CN V Facial sensation abnormalities; ocular sensation abnormalities; temporalis and masseter muscle abnormalities
CN VI Strabismus; ocular movement abnormalities (lateral direction)
CN VII Abnormalities of the muscles of facial expression; keratoconjunctivitis sicca
CN VIII Head tilting; falling; nystagmus; positional strabismus
CN IX Swallowing abnormalities; stridor
CN X Swallowing abnormalities; stridor; abnormalities of gastrointestinal functionality
CN XI Neck positional abnormalities (lateral direction)
CN XII Tongue movement abnormalities; tongue atrophy

Question 3. If the Abnormality is Caudal to the Foramen Magnum, is this Abnormality Affecting the Spinal Cord, Peripheral Nerve, Neuromuscular Junction, and or Muscle?

Neuroanatomical structures caudal to the foramen magnum include the spinal cord and the peripheral nerves as well as the associated neuromuscular junction and muscles [18]. Based on clinical signs such as proprioceptive abnormalities of limbs, spinal reflex function, and painful areas, the clinician can differentiate the abnormality into one of these two general functional systems.

Question 4. If the Abnormality Involves the Spinal Cord, Which Segments?

If the clinical signs suggest an abnormality within the spine or spinal cord, the next step is to determine the location relative to the various spinal segments of that abnormality. Based on the associated clinical signs, the clinician should be able to localize the abnormality to one or more of five functional regions within the spinal cord. Importantly, spinal cord localization schemata are based on spinal cord segments, not vertebral segments as these two separate anatomical entities do not align in all regions of the spine.

Clinical Signs and Localization of Spinal Cord Disease

Clinical signs of spinal cord disease vary with the location of the disease within the spinal cord. As pathways to and from the intracranial nervous system structures travel cranially and caudally through the spinal cord to influence limb function, abnormalities of limb conscious or general proprioception are often found with spinal cord disease. Once the affected limb (s) are identified, spinal reflex assessments are performed to determine if the abnormality involves the central or peripheral nervous system structures (historically referred to as upper motor neuron (UMN) or lower motor neuron (LMN) systems) [1-13]. After collecting this information, the examiner should be able to localize the lesion into one of five spinal cord areas.

The spinal cord is divided into functional areas based primarily on the presence of either central (historically “UMN”) or peripheral (historically “LMN”) clinical signs to the limbs. The presence or absence of these characteristic signs is based upon the concept of a local spinal reflex (that is, a function that does not require conscious control) and the normal control of these reflex functions from intracranial and central spinal cord nervous system. A central (“UMN”) is a neuron or group of neurons that does not exit the central nervous system, and influences, either positively or negatively, peripheral nerve (“LMN”) functionality. Clinical signs of central nervous system disease include proprioceptive abnormalities, increased or normal spinal reflexes, increased or normal limb muscle tone, and, if present, disuse atrophy (Tables 6 through 10).

Table 6: Classic Clinical Signs of Cervical Segment 1 to Cervical Segment 5 Abnormalities.

Classic Clinical Signs of a Cervical Segment 1 to Cervical Segment 5 Abnormality
Limb movement abnormalities
Proprioceptive abnormalities when moving (ataxia, hypermetria, limb dragging)
Ipsilateral Hemiparesis/Hemiplegia (unilateral abnormality)
Tetraparesis/Tetraplegia (bilateral abnormalities)
Painful reactions (cervical region)
With severe abnormalities, impairment of respiration

The peripheral nervous system and the effector organs (muscles) are components of what is commonly referred to as “the final common pathway.” This functional unit, however, also includes the cell bodies of the peripheral nervous system in the grey matter of the spinal cord. Clinical signs of peripheral nerve (“LMN”) disease include proprioceptive abnormalities, decreased or absent spinal reflexes, decreased or absent limb muscle tone, and neurogenic atrophy and may result from a spinal cord abnormality that involves the gray matter or reflex interneurons within the local spinal cord segments. As both central and peripheral nervous system abnormalities can result in proprioceptive deficits, evaluation of spinal reflex function becomes important in separating abnormalities of these two functional systems.

Lesions of the Cervical (C)1-C5 spinal segments (Zone 5) classically result in limb dysfunction such as ataxia, hypermetria, and tetraparesis or, if severe, tetraplegia (Table 6). Caudally projecting pathways from the respiratory nuclei of the brain stem to the peripheral nerves for respiration (phrenic nerve to the diaphragm and the intercostal nerves to the intercostal muscles) are located in the cervical spinal cord. Therefore, disease that interrupts these pathways may compromise respiratory functions. Respiratory function is usually only severely compromised in animals that lose voluntary movement (i.e., are tetraplegic). Often, animals will have a concurrent “whispery” bark due to inadequate tidal volumes and decreased projection of that air mass through the larynx and pharynx.

If the disease occurs unilaterally within the cervical spinal cord, hemiparesis may be found on the side ipsilateral to the abnormality as the spinal cord is functionally located caudal to the functional crossover within the intracranial nervous system. Cervical hyperesthesia is often present with many cervical spinal cord diseases but not, for example, in diseases associated with primary nervous system degeneration. One caveat: some animals with intracranial diseases may also have cervical hyperesthesia and this feature of intracranial disease should be considered when localizing an abnormality exclusively to the cervical spinal cord. Components of Horner’s syndrome (sympathetic dysregulation) may be present due to involvement of the caudally projecting sympathetic fibers in the lateral tectotegmental spinal tract from the intracranial central nervous system structures. With unilateral lesions, components of Horner’s syndrome can be found in the eye ipsilateral to the abnormality. Spinal reflex function should be normal or potentially exaggerated, as the peripheral nerves in the cervical and lumbar intumescence are not affected with an exclusive C1-C5 spinal cord segment abnormality.

Lesions of the C6- Thoracic (T)2 spinal segments (cervical intumescence – Zone 6) may also result in abnormalities of limb movements including ataxia, tetraparesis or tetraplegia similarly as with lesions of the C1-C5 spinal segments (Table 7). Spinal reflexes in the pelvic limb will be normal or exaggerated, while those in the thoracic limbs should be decreased to absent due to involvement of the motor neuron cell bodies and axons that make up the nerves to the thoracic limb. This decrease in thoracic limb reflex function is an important clinical feature for localization in these spinal segments.

Table 7: Classic Clinical Signs of Cervical Segment 6 to Thoracic Segment 2 Abnormalities.

Classic Clinical Signs of a Cervical Segment 6 to Thoracic Segment 2 Abnormality
Limb movement abnormalities
Proprioceptive abnormalities when moving (ataxia, hypermetria, limb dragging)
Ipsilateral Hemiparesis/Hemiplegia (unilateral abnormality)
Tetraparesis/Tetraplegia (bilateral abnormalities)
Painful reactions (cervical; cranial thoracic regions)
Decreased to absent thoracic limb spinal reflexes
Decreased to absent thoracic limb muscle tone
Neurogenic Muscle atrophy of thoracic limb muscles
With severe abnormalities, impairment of respiration
Horner’s syndrome (miosis, ptosis, enophthalmos, elevated nictitating membrane) ipsilateral to a unilateral abnormality
Abnormalities of the cutaneous trunci reflex (due to a lateral thoracic nerve abnormality)

If the abnormality is unilateral, hemiparesis is seen with similar reflex changes (decreased to absent thoracic limb spinal reflexes and normal to increased pelvic limb reflexes). Horner’s syndrome (sympathetic denervation) or only a component such as a miotic pupil may be present as the caudal cervical intumescence (T1 to T3 spinal segments) is the location of the preganglionic cell bodies lie and concurrently where the corresponding nerves exit the spinal cord and vertebral canal. With unilateral lesions, Horner’s syndrome will be found in the eye ipsilateral to the abnormality.

If the abnormality involves the C8-T1 spinal segments or nerves, the cutaneous trunci reflex may be abnormal. If the abnormality is unilateral, the ipsilateral cutaneous trunci muscle will not contract with stimulation on either side of the body. The nonaffected side, however, should still contract with stimulation on either side confirming the integrity of the T3-Lumbar (L)3 spinal region.

Abnormalities of the T3-L3 spinal segments (Zone 7) may result in abnormalities of limb movements including ataxia paraparesis or, if severe, paraplegia (Table 8). With unilateral abnormalities, a central nervous system monoparesis of the pelvic limb may be found. While the pelvic limbs are dysfunctional, spinal reflex function in the pelvic limbs will be normal or in some instances exaggerated. The thoracic limbs, however, should function normally.

Table 8: Classic Clinical Signs of Thoracic Segment 3 to Lumbar Segment 3 Abnormalities.

Classic Clinical Signs of a Thoracic Segment 3 to Lumbar Segment 3 Abnormality
Limb movement abnormalities
Proprioceptive abnormalities when moving (ataxia, hypermetria, limb dragging)
Ipsilateral monoparesis/monoplegia (unilateral abnormality)
Paraparesis/Paraplegia (bilateral abnormalities)
Painful reactions (thoracolumbar region)
Abnormalities of the cutaneous trunci reflex
Urination (larger bladder; “firm” feeling; difficult to express; incontinence) and defection abnormalities (incontinence; normal anal tone)

The cutaneous trunci reflex may be absent caudal to the location of the abnormality, especially with more severe spinal lesions. The level where the cutaneous trunci reflex cannot be elicited is usually within one to three spinal segments of the spinal cord abnormality. With severe spinal cord abnormalities in this location, Schiff-Sherrington posture (that is, hyper-extended thoracic limbs) may be seen. Hyperesthesia may be present on palpation or manipulation of the vertebral segments in this region. Focal hyperesthesia is a helpful neuroanatomical localizing sign in all regions of the spinal cord.

Urinary function may be abnormal with T3 to L3 spinal segment lesions. This is more common with abnormalities that impair voluntary movement. In these instances, the animal similarly cannot urinate voluntarily. The bladder is often large, has appropriate muscle tone on palpation, and can be difficult to manually express due to excessive tone in the urinary sphincters.

Abnormalities of the L4-Sacral (S) S3 spinal segments (lumbar intumescence – Zone 8) may also result in paraparesis/plegia (Table 9). Pelvic limb reflexes, however, will be decreased or absent which is the most important clinical distinction compared to spinal cord abnormalities occurring cranial to these segments. Depending upon which area of the intumescence is involved, specific reflexes may be abnormal. With an abnormality of the L4-L6 spinal segments (femoral nerve), the patella reflex is reduced or absent. Quadriceps muscle atrophy may be present from neurogenic atrophy or a combination of neurogenic and disuse atrophy if the animal has not been using the pelvic limbs for an extended period. Sensation on the distal medial side of the pelvic limb may be reduced or absent due to involvement of the sensory functions of the femoral nerve.

Table 9: Classic Clinical Signs of Lumbar Segment 4 to Sacral Segment 3 Abnormalities.

Classic Clinical Signs of a Lumbar Segment 4 to Sacral Segment 3 Abnormality
Limb movement abnormalities
Ipsilateral monoparesis/monoplegia (unilateral abnormality)
Paraparesis/Paraplegia (bilateral abnormalities)
Proprioceptive abnormalities when moving pelvic limbs (ataxia, hypermetria, limb dragging)
Painful reactions (lumbosacral region)
Decreased to absent pelvic limb spinal reflexes
Decreased to absent pelvic limb muscle tone
Neurogenic Muscle atrophy of pelvic limb muscles
Urination (larger bladder; “flaccid” feeling; easy to express; incontinence) and defection abnormalities (dilated anal sphincter; incontinence)

With abnormalities of the L6-S1,2,3 spinal segments (sciatic nerve), the withdrawal reflex is reduced or absent. The patellar reflex may appear exaggerated due to the loss of antagonist muscles to this reflex because of the sciatic involvement (referred to as “pseudohyperreflexia”). Atrophy of the muscles innervated by the sciatic nerve may be present. Atrophy can occur in any muscle innervated by the sciatic nerve but is often most obvious in the cranial tibial muscle.

Correlation between the vertebral and the spinal segments is important as well. In the lumbar area, the spinal cord segments are more cranially located compared to the corresponding vertebral segments. The spinal cord usually terminates in the cranial half of the L7 vertebrae in dogs [18]. In larger breeds of dogs, the spinal cord may terminate more cranially, and in smaller breeds, more caudally [18]. In cats, the spinal cord ends at the L7 or the sacral vertebrae [18]. In dogs, the sacral spinal cord segments tend to lie over L5 vertebra with some variability among individual breeds and individual animals [18]. In cats, the sacral spinal cord segments are found more distally (S2-S3 lie over L6 vertebrae) [18]. Generally, the L1 and L2 spinal cord segments are located within the L1 and L2 vertebrae, respectively. Spinal cord segments L3-L7 are located between L3 and L5 vertebrae.

With abnormalities of the S1-S3 spinal cord segments, bladder and colonic dysfunction may be present. The external anal sphincter may be dilated and have poor muscle tone for contraction. The bladder may be large, flaccid feeling on palpation, and easily expressed. Many animals with peripheral nerve innervation abnormalities or damage primarily to the detrusor muscle (detrusor muscle atrophy) will expel urine with minimal abdominal pressure.

With abnormalities of the caudal spinal cord segments (Zone 9), tail movement dysfunction and reduced or absent tail sensation are commonly present (Table 10). The tail often cannot be extended dorsally or is located in an abnormal position, ventrally or laterally. Hyperesthesia may be present on palpation or manipulation of the sacral or caudal vertebral segments.

Table 10: Classic Clinical Signs of Caudal Segment Abnormalities.

Classic Clinical Signs of Caudal Segment Abnormalities
Tail movement abnormalities
Abnormal tail position
Painful reactions

Clinical Signs and Localization of Peripheral Nerve, Neuromuscular Junction, and Muscle Disease

Question 5. If the abnormality involves the peripheral nerves (Zone 10), which nerves?

The peripheral nervous system includes any nerve myelinated by a Schwann cell [18]. Schwann cells begin myelinating peripheral nerves to the limbs at the intervertebral foramen. Using this strict definition, other components of the peripheral nervous system, such as the cell body of the motor neuron in the ventral grey of the spinal cord, would not be included in this strict definition of the peripheral nervous system. Functionally, however, an abnormality of the peripheral nerve or cell body, the neuromuscular junction (NMJ), an or the muscle can result in a similar constellation of clinical signs.

In animals, peripheral nerve (Zone 10), neuromuscular junction (Zone 11), and or muscle (Zone 12) disease primarily results in “weakness” of muscle function (Table 11 through 13). “Weakness” in this is used to indicate a muscle strength problem, however, often is equally used to describe movement dysfunction. If the animal can generate stepping movements, the limb movements are usually associated with shorter step distances, often referred to colloquially as “choppy”. Animals are sometimes described as “stiff” due to inability to advance an appropriate step distance and concurrently with a more extended limb posture. In other instances, the limbs may appear flaccid, have tremoring movements, and buckle primarily with weight bearing. Animals may stand with the limbs placed more centrally under the body. In some instances, this may result in a kyphotic spine or even cow-hocked posture. Animals with both peripheral nerve and muscle disease may also have abnormalities of limb posture. The limb joint angulation during the stance or swing phase of the gait cycle is abnormal, usually with an over-flexion during standing and lack of flexion during forward swing movements.

Table 11: Classic Clinical Signs of Peripheral Nerve Abnormalities.

Classic Clinical Signs of a Peripheral Nerve Abnormality
Limb movement abnormalities
Proprioceptive abnormalities when moving pelvic limbs (limb dragging)
Ipsilateral monoparesis/monoplegia (unilateral abnormality)
Paraparesis/Paraplegia (bilateral abnormalities pelvic limbs only)
Tetraparesis/Tetraplegia (bilateral abnormalities all limbs)
Short inter-step distances/Shorten strides
Abnormal flexed or extended postures of the limbs
Exercise intolerance
Kyphotic posturing when exercising
Facial muscle weakness; decreased palpebral reflexes; excessive salivation
Decreased to absent limb spinal reflexes
Decreased to absent limb muscle tone
Neurogenic Muscle atrophy of limb muscles
Lateral recumbency (when severe)
Abnormal vocalization (indicative of poor air movement by the respiratory muscles)

Abnormal muscle functionality may be seen in visceral structures such as the esophagus resulting in megaesophagus in animals with peripheral nerve disease. This may result in regurgitation, decreased swallowing, excessive salivation, gagging, or aspiration pneumonia. Peripheral nerve, neuromuscular junction, and or muscle involvement of the cranial nerves, such as the glossopharyngeal and vagus, may result in dysphagia, dysphonia, gagging, swallowing abnormalities, and coughing. Inspiratory stridor may be present with laryngeal paralysis seen in association with some peripheral nerve diseases. If the diaphragm and intercostal muscles are involved, decreases in effective respiration may result. Animals with respiratory muscle weakness tend to become tachypneic to compensate for decreased inspiratory volume. As the condition worsens, abdominal breathing, wherein the abdomen moves out during inspiration may be present.

Table 12: Classic Clinical Signs of Neuromuscular Junction Abnormalities.

Classic Clinical Signs of a Neuromuscular Junction Abnormality
Limb movement abnormalities
Ipsilateral monoparesis/monoplegia (unilateral abnormality)
Paraparesis/Paraplegia (bilateral abnormalities pelvic limbs only)
Tetraparesis/Tetraplegia (bilateral abnormalities all limbs)
Short inter-step distances/Shorten strides
Exercise intolerance
Kyphotic posturing when exercising
Facial muscle weakness; decreased palpebral reflexes; excessive salivation

Animals with peripheral nerve disease often have abnormalities of general or conscious proprioceptive testing (Table 11). When evaluating general or conscious proprioception in animals with suspected peripheral nerve, neuromuscular junction, and or muscle disease, it is important to evaluate the animal when full weight-bearing and again when the animal’s weight is supported by the examiner, as some diseases affect only the motor functionality of the nerve. With exclusive motor function abnormalities, when the animal’s weight is supported by the examiner, these animals can generate normal conscious proprioception or paw placement due to normal sensory function. With many peripheral nerve and central nervous system lesions, however, animals often have both motor and sensory weakness occurring concurrently. Conscious proprioceptive deficits will be present in these animals regardless of weight-bearing status in these animals.

Table 13: Classic Clinical Signs of Primary Muscle Abnormalities.

Classic Clinical Signs of Primary Muscle Abnormalities
Limb movement abnormalities
Short inter-step distances/Shorten strides
Exercise intolerance
“Crabbing”
Kyphotic posturing when exercising
Facial muscle weakness; decreased palpebral reflexes; excessive salivation
Primary Myopathic Muscle atrophy of limb muscles

Spinal reflexes are reduced or absent in animals with peripheral nerve disease. This is an important clue to the location of the abnormality in the peripheral nerve. Resting muscle tone and muscle resistance to passive range of motion in the limbs will be poor or absent.

With peripheral nerve disease involving motor nerve innervation, neurogenic atrophy of muscles occurs. This atrophy evolves relatively rapidly after a peripheral nerve injury (within 5 to 10 days) and results in significant amounts of muscle atrophy. Often the underlying bony structures become prominent due to the significant degree of atrophy. With single nerve disease, muscle atrophy may be in focal muscles or muscle groups corresponding to the distribution of the affected peripheral nerve innervation.

Individual peripheral nerves may be selectively involved with various pathological processes that involve the motor or sensory functional components. Primary sensory nerve disease often results in exaggerated uncoordinated movements resulting from the associated proprioceptive deficits. Animals with primary afferent or sensory neuropathies may have an awkward gait wherein they tend to “flap” the limbs and strike the ground on the more dorsal palmar and plantar regions of the paw and lower limb. Evaluation of the sensory distribution loss may be helpful in localization with sensory components of the peripheral nerve are abnormal. The sensory deficiencies can be present in the dermatomes and autonomous zones innervated by these nerves. In general, however, clinical assessment of skin innervation is inconsistent.

Question 6. Does the Abnormality Involve the Neuromuscular Junction (Zone 11)?

Animals with disease primarily involving the neuromuscular junction classically have overlapping clinical signs with peripheral nerve and primary muscle abnormalities. Short stepping is one of the more classical signs. With some neuromuscular diseases, muscle weakness is exacerbated with exercise (e.g., myasthenia gravis, hypoglycemia). Dogs and cats with neuromuscular junction, and muscle disease may lie down frequently. Cats particularly may rest their head on their thoracic limbs when lying down. Cats tend to have ventral neck flexion, or inability to lift their heads, presumably due to cervical muscle weakness. Animals with disease of the neuromuscular junction or muscle abnormalities may have proprioceptive deficits during weight bearing, but not when their weight is supported by the examiner as discussed previously. In diseases of the neuromuscular junction (e.g., myasthenia gravis) and muscle, spinal reflexes are classically normal as relatively minimal muscle strength is necessary to perform simple reflex functions.

Question 7. Does the Abnormality Primarily or Exclusively Involve the Muscle (Zone 12)?

Animals with disease primarily involving the muscle also have clinical signs overlapping with peripheral nerve and neuromuscular junction abnormalities. Weakness of muscle function is the most consistent clinical abnormality. Short stepping and muscle atrophy are clinical clues. Animals with primary muscle disease, however, should have normal conscious proprioceptive testing when the examiner supports the animal’s body weight.

Primary muscle abnormalities may result in dysfunction of visceral structures such as the esophagus resulting in megaesophagus similar to peripheral nerve disease [1-13]. This muscle weakness may result in regurgitation, swallowing abnormalities, excessive salivation, gagging, or aspiration pneumonia. Muscle involvement of muscles innervated by cranial nerves, such as the glossopharyngeal and vagus, may equally result in dysphagia, dysphonia, swallowing abnormalities, gagging, and coughing. If the diaphragm and intercostal muscles are involved, decreases in effective respiration may result.

With most primary diseases of muscle, muscle mass is classically decreased. In contrast, animals with myotonia or some other muscle disorders may have hypertrophy of their muscles. Their muscle groups may be well defined (prominent) anatomically. They similarly have short step distances when gaiting and often with extended limb postures. Equally, animals with muscle cramping or myotonia may have episodic bouts of extended limb postures. As with some neuromuscular diseases, muscle weakness may be exacerbated with exercise.

Multifocal or Diffuse Lesions of the Nervous System

Question 8. Do all the Clinical Signs Localize to a Focal Region in the Nervous System?

Some diseases can affect multiple regions of the nervous system simultaneously. Additionally, two or more different diseases can concurrently affect the nervous system in differing areas. If the clinical signs cannot be adequately explained by a single focal abnormality, then consideration should be given to a single disease with multifocal pathology or that there are multiple disease processes present affecting multiple functional units. Diffuse diseases such as inflammatory conditions would be an example.

Conclusion

The ability to make an accurate neuroanatomical diagnosis is the essential step in determining possible disease processes that affect that location within the animal’s nervous system. Correctly determining the location of any abnormality in the nervous system of animals is foundational to completing a logical differential diagnosis list and subsequently diagnostic plan. While seemingly complex, there are essentially twelve functional regions of the nervous system for neuroanatomical diagnosis. Based on the classic clinical signs associated with these twelve functional regions, an accurate neuroanatomical location of any neurologic abnormalities is possible. Once an accurate neuroanatomical diagnosis is identified, further clinical assessment can logically be performed to ultimately make a definitive diagnosis.

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Bispectral Analysis of NIRS Muscle Oxygenation in Type 2 Diabetes During Treadmill Walking

DOI: 10.31038/EDMJ.20261032

Abstract

Background: Type 2 Diabetes Mellitus (T2D) impairs peripheral microvascular reactivity and skeletal muscle oxygen consumption during locomotion causing impaired exercise tolerance in a poorly understood manner. While linear spectral measures of NIRS capture total oxygen signal power, they fail to capture the complex and chaotic dynamics of muscle oxygen control.

Aim: Using bispectral analysis to quantify non-linear harmonic interactions and quadratic phase coupling (QPC) of muscle oxygenation in T2DM participants with and without DM complications.

Methods: Participants were stratified into two distinct cohorts ( n =12 each) based on T2DM severity: Group 1 (T2D with complications: elevated HbA1c, extended disease duration, clinical neuropathy/vasculopathy) and Group 2 (Well-controlled/no complications). Time-series continuous tissue oxygenation (StO2) data (N = 1,826 frames per series at fs = 10 HZ ) were collected from participants performing a standardized treadmill walking task at self-selected usual walking pace. Higher-order spectral analysis—specifically bispectrum magnitude |B (f1 , f2)|  and normalized bicoherence b (f1 , f2) —was computed across the principal non-linear domain ( f2 ≤ f1 ,  f1+ f2 ≤ 5.0).

Results: Group 1 exhibited an 84.1-fold increase in mean total bispectral energy ( 1.28 x 1011 vs. 1.52 x 109 arbitrary units p < 0.001 ) and an 8.7-fold higher peak bispectral magnitude ( 2.42 x 105 vs. 2.79 x 104 p < 0.001 ) compared to Group 2. Quadratic phase coupling concentrated along the fundamental diagonal (f1= f2), with Group 1 peaking at 0.94 Hz ( f3= 1.88 Hz second harmonic) and Group 2 peaking at  ( second harmonic). Peak normalized bicoherence reached 0.886 in Group 1 versus 0.785 in Group 2. Group1 generated severe non-linear distortion and exaggerated low-frequency harmonic coupling in active muscle oxygenation during locomotion.

Conclusions: More advanced diabetes, or complications of T2DM, not only have direct impact on exercise capacity but also disrupt the poorly understood complex, dynamic local control of muscle oxygenation. Bispectral analysis of NIRS signals serves as a sensitive, non-invasive biomarker for evaluating the deterioration in oxygen dynamics, seen in more advanced diabetes, which is not detectable by conventional NIRS techniques and shows promise for early detection of clinical deterioration.

Introduction

Type 2 Diabetes Mellitus (T2D) is characterized by exercise intolerance (as characterised by a lower muscle oxygen consumption at VO2max compared with non-diabetes [1] although the precise mechanisms are not fully understood due to the complex interactions of local mechanisms controlling the balance of oxygen consumption and supply in local muscle tissue. During physical activity, skeletal muscle requires precise, real-time matching of local microvascular oxygen delivery to dynamic metabolic demand. In healthy individuals, local vasomotion, autonomic sympathetic tone, and endothelial nitric oxide release regulate local perfusion in a balanced, quasi-linear oscillatory manner, and each with distinct frequencies. Significant skeletal muscle deoxygenation is known to occur during exercise in type 2 diabetes [2]. Local recruitment of microvascular blood volume/hematocrit, blood flow, and VO2peak are normally all mutually correlated but uncouple in T2DM [3]. In advanced T2D—marked by prolonged disease duration, poor glycaemic control (HbA1c > 8.0%) and microvascular structural remodeling, vasodilatory responsiveness becomes severely compromised [4].

When analysing oxygen levels in muscle (often measured via Near-Infrared Spectroscopy (NIRS) as Oxygenated Haemoglobin (HbO) or tissue oxygenation index (TSI), traditional linear signal processing techniques (e.g., Fourier Transform, Power Spectral Density) quantify energy distribution across isolated frequency bands. But this only indicates which frequencies are present, not the interactions and coupling effects between (e.g., myogenic, neurogenic, endothelial rhythms). As a result, overall NIRS oxygen dynamics in exercise often appear normal in T2DM [5,6]. These linear spectrums inherently assume phase independence between frequency components and mask higher-order non-linear dynamics. In contrast, the mean bispectrum magnitude reveals how these frequencies interact with each other and therefore serves as a powerful tool to detect nonlinear physiological control mechanisms and microvascular regulation [7]. The bispectrum |B (f1 , f2)| evaluates higher-order statistics by identifying quadratic phase coupling (QPC)—a mechanism where non-linear interactions between two frequencies f1 and f2 and  generate a coupled harmonic component at f3= f1 + f2 . It extends the traditional power spectrum (which uses two-point correlations) to a three-point frequency correlation or third order statistic. It evaluates how three frequency components—f₁, f₂, and their sum f₁ + f₂—relate to one another in terms of both amplitude and phase. Because it is derived from a triple product of Fourier transforms, the bispectrum is a complex number possessing both a phase (biphase) and a magnitude. The 3 practical benefits of Bispectral compared to traditional power spectra are; Phase information to determine whether frequency components are interdependent. Quadratic phase coupling to reveal when two frequencies combine to create a third frequency. Gaussian noise suppression, making non-Gaussian or hidden signals easier to analyse.

Because the raw bispectrum magnitude is dependent on the amplitude of the underlying signal, a large peak can simply mean the oxygen level fluctuations have increased in volume, not necessarily that their coordination increased. To separate pure coordinate coupling from raw amplitude changes during muscle contraction or rest, the analysis is paired with bicoherence b (f1 , f2),. Bicoherence normalizes the mean bispectrum magnitude to a scale of 0 to 1, indicating the true strength of the phase coupling.

This study therefore applies bispectrum analysis to 24 recordings of skeletal muscle oxygenation (StO2) recorded during treadmill walking in participants with T2DM. We tested the hypothesis that advanced diabetic complications (neuropathy, vasculopathy, extended duration, high HbA1c) reduce oxygenation dynamical complexity and instead induce structural non-linear distortions manifesting as significantly higher bispectral energy and altered quadratic phase coupling compared to spectra of no complications diabetes as the control.

Materials and Methods

Study Design and Dataset

Study sample size was based on previous studies [8] where DM affected oxygenation HbO by 11%. To detect a difference between complications and no complications T2DM groups with 80% power and 5% significance requires 24 individuals (12 in each group). To allow for missing data of 10%, sample size was set at 24.

Ethical Considerations

The study was conducted in accordance with ethical guidelines for observational research within a larger research program: IRAS Project ID: 67269. An amendment for further recruitment and data collection was approved on 5 March 2024. (10002.NSA6.5.03.2024) (Appendix 1) Patient confidentiality was maintained throughout the study, and all data were fully anonymised for analysis. Only data relevant to the statistical analysis was extracted from records. (Appendix 1)

Inclusion and Exclusion Criteria

Inclusion criteria included confirmed diagnosis of type 2 diabetes mellitus and age 18 years or older. The diabetes with complications group included T2DM adults with coded diagnoses of confirmed CVD, PAD, DPN in medical records or T2DM >12years, while the comparison group comprised T2DM adults with no such codes in the records.

Exclusion criteria were applied to ensure participant safety and data quality. Patients with mobility problems that would prevent gait assessment on a treadmill were excluded, as were individuals lacking mental capacity to provide informed consent or follow testing instructions.

Group Assignment

  • Group 1 (Advanced T2D/Complications):  n = 12 participants corresponding clinically to elevated HbA1c levels, longer diabetic duration, and high prevalence of diabetic peripheral neuropathy and peripheral artery disease.
  • Group 2 (Controlled T2D/no complications): n = 12 participants corresponding clinically to well-managed glycemia and minimal vascular/neuropathic complications.

Treadmill Testing

Participants were priorly provided with a participant information sheet and if in agreement signed consent forms. Participants familiarised themselves with treadmill walking starting at a slow pace and self-adjusting the speed control up to self-perceived natural walking pace. NIRS measurements of walking at steady pace for 2minutes were recorded.

Muscle Oxygenation Testing

Throughout the exercise, continuous non-invasive measurements of muscle oxygenation/deoxygenation status were measured with near-infrared spectroscopy (NIRS) (PortaMon; Artinis Medical Systems, Gelderland, the Netherlands) at 10 Hz. The portaMon was placed on the lateral head of gastrocnemius muscle of the dominant limb, secured using a Velcro strap, and covered with a cloth bandage to exclude ambient light. The NIRS monitor emits two wavelengths (780 and 850 nm) which interact with oxyhaemoglobin and deoxyhaemoglobin. Using the Beer–Lambert law to account for scattering we determined changes of relative concentrations of oxy (HbO) and deoxygenated (HHb)

Datasets

Continuous tissue oxygenation time-series signals were acquired from N = 24 adult participants diagnosed with T2D during a standardized treadmill walking protocol (TMv3). Each participant dataset comprised 1,826 sequential temporal data points recorded at a uniform sampling frequency fs = 10.0 Hz (Δt=0.1 s), yielding a total continuous measurement window of  182.6 seconds.

Analysis

Analysis was performed using a dedicated script coded in python (Appendix 1).

Higher-Order Spectral Analysis Formulation

For a discrete zero-mean time series x(t), segment-averaged Fourier transforms X(f) were calculated using Welch’s modified periodogram approach with Hann windowing ( Nperseg = 128 points,  50% overlap). The discrete bispectrum B ( f1 , f2) measures the complex third-order correlation between three spectral components satisfying the resonant condition f1 + f2= f3 :

where IE[·] denotes the expectation operator across time segments and X*(·) represents the complex conjugate. To eliminate magnitude dependence and quantify true non-linear phase coupling independent of signal amplitude, the normalized bicoherence index b ( f1 , f2)  was calculated:

Total Bispectral Energy (EB) was evaluated over the alternating region ( f1 , f2 ≥ 0.15 Hz):

Results

Individual Participant Bispectral Profiles

Rank-ordering the 24 diabetic participants by total bispectral energy revealed a strong, multi-order-of-magnitude separation between the advanced disease cohort (Group 1) and the controlled cohort (Group 2) (Table 1) (Appendix 2).

Table 1: Demographics of the patients and of the healthy controls.

Characteristic

Group 1 T2DM+ Complications (N=12)

 Group 2-no comps (N=12)

Age (years)

63.7 ± 5.7

61.0 ± 6.2

Males

7

5

HbA1c (%)

9.1 ± 1.0

7.4 ± 0.9

HbA1c (mmol/mol)

62 ± 14

57 ± 14

BMI (kg/m2)

27.2 ± 2.2

25.5 ± 1.9

Duration diabetes (years)

19.0 ± 9.9

3.2 ± 2.1

DM peripheral neuropathy (DPN)

10

1

Group Comparative Analysis

Non-Linear Energy Disparity: Group 1 exhibits over 84 times greater total bispectral energy ( 1.28 x 1011 vs. 1.52 x 109 ) than Group 2. Harmonic Phase Coupling: Both groups display focused quadratic phase coupling along the diagonal ( f1= f2), generating strong second-harmonic interactions ( f3=f1 + f2 ≈ 2f1). (Table 2). Crucially, while the normalized bicoherence magnitude is comparable between groups ( bmax ≈ 0.52), the absolute bispectral magnitude |B| is 8.7 times higher in Group 1. The Bispectrum magnitude reflects the degree of quadratic phase coupling or nonlinear interactions between triads of frequencies of controllers of oxygenation in response to exercise. This demonstrates that while both groups display intrinsic non-linear phase coupling, the magnitude of non-linear wave interaction and dynamic amplitude distortion is vastly magnified in the group with more advanced diabetes disease.

Table 2: Comparative Group Statistics (Mean  SD).

The mean bispectrum magnitude (6.6x higher in Group 1), taken over multiple epochs, ensures that random, incoherent noise and phase fluctuations cancel out toward zero, isolating stable, phase-coupled harmonic components. The higher value indicates that fluctuations are more driven by deterministic, nonlinear physiological systems rather than random variations (Figures 1 and 2).

Figure 1: Log-Bispectrum and bicoherence contour plots averaged for Groups 1 and 2. Log scaling reveals underlying harmonically coupled structures across 0.5 Hz ≤ f1 ≤ 2.5 Hz. Group 1 shows a higher baseline magnitude ( log10 ≈ 4.5 to 7.5). Group 2 shows similar coupling but lower overall magnitude intensity ( log10 ≈ 0.75 to 6.15) indicating higher diversity/complexity in oxygenation signals.

Figure 2: 3D contour maps of the bispectrum magnitude (vertical axis) and the geometric distribution of quadratic phase coupling for a) participant in Group 1 T2DM with diabetic complications and b) participant in group 2 without complications. Note the reduction and clustering of spectral energy and peaks in (a) reflecting muscle oxygen changing in a highly predictable, inflexible and restricted pattern, compared to (b). (b) shows a wider energy dispersal, and a more complex peak signature reflecting the complex, fluid, and non-linear variations seen in healthy tissues.

Discussion

The primary finding of this study is the high increase in total bispectral energy in participants with advanced/complicated T2D (Group 1) relative to those with T2D without complications (Group 2). High bispectral energy reflects muscle oxygen levels changing to a highly predictable, rigid and strictly coupled harmonic pattern, rather than showing the complex, fluid, and chaotic variations seen in healthy tissues. This shift in frequencies is quantified by the mean bispectrum magnitude. Chaotic systems have the advantage of being rapidly adaptable to changes in local conditions without the need for central control. Therefore, while a more inflexible pattern of oxygenation appears more organised (hence higher spectral energy/lower entropy) it loses the dynamic ability to adapt to changes in local oxygen demand. As a result, oxygenation not meeting the demands of muscle, over time, leads to muscle degenerative changes and sarcopenia [9]. The results accord with those only other bispectral NIRs muscle study by Molinari, where spectral energy reduced in muscle after 1 year of exercise training in diabetes [8]. The physiological and metabolic reasons behind why more advanced T2DM exhibits high bispectral energy (and thus lower signal entropy) in muscle oxygenation patterns results from three main factors:

Microvascular Stiffness

In healthy microvasculature, vasomotion is mediated by smooth muscle cell electromechanical coupling, endothelial nitric oxide release, and sympathetic innervation operating within a flexible, linear regulation envelope. The microvasculature supplying the muscle is incredibly dynamic. It constantly adjusts to minor local changes via endothelial nitric oxide release, neural inputs, and metabolic demands. This creates a highly complex, chaotic, and “organic” oxygenation signal (low bispectral energy, high entropy). Capillary basement membrane and advanced glycation end-product (AGE) cross-linking reduce arterial compliance. When subjected to the mechanical pumping of treadmill locomotion, stiffened microvessels generate non-linear pressure-flow wave interactions The capillaries lose their ability to dynamically fluctuate. The oxygenation signal becomes strictly bound to macro-hemodynamic forces—like the mechanical, repetitive pumping of the heart or rigid breathing rates. This strong phase-locking to external cyclic rhythms drives up bispectral quadratic phase coupling (QPC), creating high bispectral energy. Loss of sympathetic nerve fibre density impairs smooth vasomotor control, transforming smooth sinusoidal vasomotion into abrupt, asymmetric flux variations that generate strong higher-order harmonics [10].

Mitochondrial Dysregulation

Healthy muscle tissue rapidly and flexibly switches between metabolic substrates (carbohydrates and fatty acids) and continuously adjusts oxygen extraction based on immediate cellular needs [11]. Diabetic myocytes become metabolically inflexible and unable to dynamically “demand” and extract oxygen in a rapid and responsive manner. [12,13]. The oxygen extraction rate becomes severely impaired and fixed. The oxygenation signal loses its internal biological complexity and flattens into a simple, repetitive cycle dictated purely by systemic delivery.

De-conditioning and Structural Muscle Changes (Sarcopenia)

T2DM causes structural changes in the muscle, including muscle wasting (sarcopenia), fatty infiltration, and a shift in muscle fibre types (often a loss of capillaries wrapping around the fibres) [14]. When muscle is structurally simplified, alongside reduced capillary density, the diffusion path for oxygen into the tissue becomes uniform and restricted [15]. Localized oxygen pools that create complex signals are replaced by homogenous tissue saturations which translate mathematically into cleaner, highly coupled harmonic waves and high bispectral energy.

Clinical Implications as a Diagnostic Biomarker

Bispectral energy analysis, combining both phase alignment and third-order amplitude variance, acts as a highly sensitive non-linear biomarker that can detect loss of microvasculature complex dynamics, not visible by standard spectral analysis of NIRS signals and likely before clinical signs of deterioration. The separation observed between Group 1 and Group 2 suggests that bispectral profiling during walking tests could provide a non-invasive diagnostic tool to screen for:

  • Subclinical diabetic peripheral neuropathy before sensory loss manifest.
  • Microvascular diabetic vasculopathy and peripheral artery risk.
  • Objective tracking of therapeutic interventions aimed at restoring vascular flexibility.
  • Early diabetic myopathy prior to diabetic sarcopenia.

NIRS already has advantages over other methods of assessing muscle oxygen content (portable, low cost, non-invasive) and research also shows that NIRS provides reliable measures of exercise-induced oxygen responses regardless of the protocol used [16].

Conclusions

Bispectral analysis of skeletal muscle tissue oxygenation during locomotion provides novel insights into muscle oxygenation dysfunction in Type 2 Diabetes. Advanced diabetic complications—including neuropathy, vasculopathy, prolonged disease duration, and poor glycemic control—are characterized by an 84-fold surge in total non-linear bispectral energy and significant phase coupling, overall indicating a loss of physiological adaptability. Higher-order spectral analysis represents a promising, non-invasive approach for identifying muscle oxygenation dysfunction in T2DM and the potential for earlier detection of disease progression.

Funding

No funding was provided for this study.

Conflict of Interest

RB, SH and LC declare no conflicts of interest.

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