Monthly Archives: November 2019

Migraine, A Review of Basic, Clinical, and Translational Approaches to New Treatment

DOI: 10.31038/AWHC.2019263

Abstract

Migraine is a debilitating neurological primary headache disorder characterized by recurring unipolar headaches lasting 4–72 hours with accompaniment of nausea and sensory sensitivities. Migraine is the most common headache disorder resulting in seeking of medical care [1, 2], in addition to being one of the most debilitating chronic disease conditions in terms of both morbidity and lost economic productivity. Migraine incidence has been observed since ancient times to disproportionately affect women, and most current epidemiological assessments put current incidence estimates at 12% overall for US populations, with an incidence of 18% in women and 6% in men when stratified by sex. This dimorphism of incidence is crucial when assessing overall health of a community, specifically when concerning women’s health and therefore must be taken into consideration when developing both clinical and basic models of migraine to enact the best possible outcomes of combined translational research efforts. While major recent advances have been made in the field of pharmacologic intervention for migraine with the recent approval of the anti CGRP and anti CGRP receptor antibody medications, prohibitive cost and limited access have made older treatments, such as the triptans and NSAIDs, still the most commonly utilized medications to combat migraine attacks. Current preclinical research models are heavily interested in modulation of the neuropeptide CGRP, and the phenomenon of cortical spreading depression (CSD), believed to be the underlying trigger of migraine with aura, via pharmacological intervention. Modulations of these phenomena has found to be correlated with menstrual events in women, tying back to the overarching theme of higher morbidity in women. With the advent of pharmacogenomics and personalized medicine, a new epoch of potential customizable treatments looms on the horizon, endearing those afflicted with this severely debilitating condition a new glimmer of hope as research progresses into its next phase.

Acronyms: AMPP: American Migraine Prevalence and Prevention study. NIH: National Institute of Health. ICHD-3: International Classification of Headache Disorders, 3rd edition. NHIS: National Health Interview Survey. NSAID: Non-Steroidal Anti-Inflammatory Drug. CGRP: Calcitonin Gene Related Peptide. 5HT1: 5-Hydroxytryptamine (Serotonin) Receptor, Subfamily 1. FDA: Food and Drug Administration. CNS: Central Nervous System. MOH: Medication Overuse Headache. PGE2: Prostaglandin E2. CSD: Cortical Spreading depression. fMRI: Functional Magnetic Resonance Imaging. GWAS: Genome Wide Association Study. SNP: Single Nucleotide Polymorphism.

Background

Migraine is a highly debilitating neurological disorder characterized by recurring unipolar headaches with a duration of 4–72 hours, accompanied by nausea and sensory sensitivities [3]. Migraine is classified as a primary headache disorder, indicating no known underlying cause, and is the most common of all the headache disorders to result in patients seeking medical care [1, 2]. Migraine is also one of the most prevalent and disabling chronic disease conditions, in terms of both individual morbidity and lost economic productivity, with 18% of women and 6% of men in the US suffering from some form of migraine [1], with an incidence in women nearly triple that of men according to the National Health Survey. The economic burden of migraine has been assessed by the American Migraine Prevalence and Prevention study (AMPP) and estimates a mean direct annual healthcare cost burden of $4,144 per chronic migraineur in addition to an average of $5,392.03 of lost economic productivity annually [4]. When quantifying the high degree of morbidity associated with migraine, it quickly becomes evident that the need for better understanding of the underlying pathophysiology be accomplished through basic and clinical research models to aid in ablating the impact of this public health issue.

Migraine Types/Epidemiology

Migraine has been classified into several categories depending on the clinical presentation and description of the patient, these are 1) migraine without aura, 2) migraine with aura, this class includes hemiplegic migraine (including familial and sporadic), migraine with brainstem aura, retinal migraine and aura without headache. 3) chronic migraine (CM), which is defined by 15 or more “headache days” per month, for three months, in absence of medication overuse. Aura is described as a temporary visual disturbance appearing as zigzag lines, flashing lights, or temporary visual loss according to the NIH. Episodic migraine (EM) is classified as having less than 15 headache days per month for three months according to the ICHD-3. While precise estimates of migraine incidence can be difficult to ascertain due to differences in study methodology, the most recent and largest of these was the AMPP, a longitudinal study focused on 120,000 surveyed US households with recipients based on US census data. Overall incidence of migraine was reported as 12%, with 18% of women and 6% of men reporting experiencing at least one migraine attack in the previous year. Upon stratifying by gender, 17.4% of women and 5.7% of men had EM, while 1.29% of women and 0.48% of men meet diagnostic criteria for CM [5]. The disease burden of migraine is carried much more heavily by women.

Sexual dimorphism in migraine presentation: Often, migraine begins at menarche for girls, and continues until approximately age 40, at which symptomology levels off [1, 2]. It was also noted that women experience more severe pain intensity and associated disability when compared to men [6, 7]. This finding is highly significant as these are the most economically productive years for an individual woman and instantiates an even higher degree of cost burden when factoring in the higher observed incidence rate of migraine in women. Coupled to this phenomenon is the degree to which female sex hormone fluctuation during the menstrual cycle, pregnancy, and post-partum periods has been positively observed to impact not only incidence of migraines attacks, but also perceived severity [6, 7]. This chronic level of unpredictability can cause a severe amount of stress and disability at a time in a woman’s life when she is expected to be at her peak performance, both economically and in terms of social and familial commitments. For those women with children, the crippling effect of chronic or episodic migraine attacks is a cost burden most simply not afford, and those suffering under the severe duress from a typical migraine attack will still be expected to perform career duties, child care duties, social duties etc. The crippling morbidity of this condition cannot be understated, as the buildup of chronic stress due to migraine results in lost economic productivity, lost time to care for offspring, lost ability to invest in pleasurable activities, which can all contribute to a noticeable decline in long term mental health. It doesn’t take long to connect the dots here and realize the agonizingly debilitating effect this condition has on the nearly one fifth of women who suffer from it, and why addressing it is a grave public health concern to society. While studies in the US have found an inverse relationship between income and migraine [8], European studies have been more nebulous about this association, failing to replicate the results seen in the US [6].

Migraine and Race: Migraine incidence varies by race in the US, according to the AMPP study white women and men had the highest incidence (20.4%, 8.4%), followed by African Americans (16.2%, 7.2%), and lowest among Asian Americans (9.2%, 4.2%) [9]. Further surveys of underrepresented populations undertaken by the NHIS indicate highest prevalence in American Indians and Alaska Natives (19.2%), followed by whites (15.5%), African Americans (15%), Hispanic and Latinos (14.9%), Native Hawaiians and Pacific Islanders (13.2%) and Asians (10.1%) [6]. Overall, migraine is a functional pain disorder that disproportionally impacts women during their most productive years, regardless of race.

Clinical Aspects

Diagnostic criteria for the several subtypes of migraine are laid out in the ICHD-3 manual. For use as a type example, the diagnostic criteria for migraine with aura is as follows: Recurrent attacks, lasting minutes, of unilateral fully-reversible visual, sensory or other central nervous system symptoms that usually develop gradually and are usually followed by headache and associated migraine symptoms. Diagnoses of migraine invariably depend on self report by patients, and therefore accurate estimation of true disease prevalence can be difficult [10]. Patients suffering from migraine are currently treated with a host of pharmaceutical agents developed to either abort an acute migraine attack, or act as a prophylactic treatment against future attacks [11].

Treatment and sex sensitivity: Currently, the US headache consortium has outlined a 6 point objective plan to help guide physicians in treating acute migraine attacks. These are 1) treat attacks rapidly and consistently without recurrence; 2) restore patient’s ability to function; 3) minimize the use of back-up and rescue medications; 4) optimize self-care and reduce subsequent use of resources; 5) to be cost-effective for overall management; 6) have minimal or no adverse events, however achieving all of these is typically not possible with available treatments. Treatment criteria also differs by region, as some pharmaceuticals are approved in one are while others are not (ergotamines). NSAIDs and acetaminophen, with or without caffeine, are typically first line treatment for a migraine attack [12], due to their easy availability and lack of harmful side effects from frequent use. These are typically used for a mild to moderate migraine sufferer. Triptans are the current first line medications for treating moderate to severe migraine [12], they have agonist activity at the 5HT1 receptors and are believed to work through suppression of CGRP release. They are well-tolerated and demonstrate efficacy in 68% of patients in one meta-analysis [13, 14]. Use of triptans, however, are limited to a maximum use limit of 8–9 doses per month, due to fear of medication overuse headache and the potential of serotonin syndrome [15]. Some first line combination agents exist, such as sumatriptan-naproxen to maximize efficacy of both compounds. Recently on the market are the anti CGRP monoclonal antibodies and anti CGRP receptor monoclonal antibodies. These drugs have yet to be classified in this hierarchy of use, due to their prophylactic nature (once monthly/quarterly administration) and prohibitive cost (estimated cost for erenumab, $6900 annually). While considered safe by the FDA, utilization of any monoclonal antibody does carry some degree of risk of autoimmune induction. This is a relevant point here as CGRP has been shown to have immune function in both the CNS and periphery [16]. However due to the very recent availability of these drugs some time must pass before an in depth analyses can be made. A more cost effective and longer standing approach to migraine prophylaxis lies in the application of the beta blocker drug class. Being the first in their class in terms of application towards migraine treatment, they are still extensively utilized as a prophylactic treatment with the benefit of a much lower cost and tolerable side effect profile [17]. Second line drugs for acute migraine treatment include those of the anti-emetic class, such as promethazine and chlorpromazine. Last resort agents include opiates, barbiturates, ergotamines and valproate, due to either contradicting results, abuse potential, risk of medication overuse headache, or lack of current approval. Medication overuse headache (MOH) is a phenomenon observed from overuse of migraine and pain disorder medications [13, 14]. the ICHD-3 defines MOH as headache occurring on 15 or more days per month, for three months due to over-usage of acute or symptomatic headache medication. The prevalence of MOH is 1–2% globally, however it is one of the costliest neurological disorders known due to its extremely debilitating effects and treatment resistance [18]. A variety of medication have been observed to cause MOH, however findings have specifically found analgesics such as opioids to be the highest risk class for causing MOH, with triptans being at most equal to opiates for relative risk of developing MOH [13, 14]. Demonstration of not only lack of efficacy of classic analgesics, but the ability for them to increase relative risk for MOH demonstrates the pertinent need for new therapeutics.

Divergence in Female Treatment Response Sensitivity: While the current literature is somewhat lacking in this study metric, fitting within the narrative of this review it would be prudent to assess relevant clinical observational data that is currently available. While many studies have been performed assaying treatment efficacy, there seems to be an overall dearth of results stratified on gender in respect to observed efficacy of pharmaceutical migraine treatment. However, studies have observed a higher female preponderance to developing medication overuse headache; this could be an artifact of the higher overall incidence of migraine in women, higher usage of medication by women, and higher seeking of medical care by women vs men, and underdiagnosing of migraine in men [10]. Moreover, specific agents that induce MOH were not discussed. A recent study has outlined differences in pharmacokinetics in women vs men for the triptan drug class, particularly noting the substantially higher peak plasma concentration of triptans observed in women, which can have far reaching effects on the differences observed in treatment response in men vs women [10]. Regarding the new anti CGRP drugs, studies are under way to assess different response in women and men when treated. However, the few studies that have been done have not been able to definitively state a difference in response to the monoclonal antibodies [19].

Preclinical Research Models

While the precise mechanisms of Migraine are yet to be elucidated, numerous preclinical and basic research models are available, including in vitro, in vivo, and ex vivo models. While the complete understanding of migraine pathophysiology is beyond the scope of this review, a major recent success has arisen from focusing on the interplay of CGRP, the neurovascular unit, and the trigeminal nerve complex [20]. This has led to the development of several new treatments based on inhibiting activity of CGRP. Current preclinical models include a multitude of models to mirror physiological phenomena believed to be impacted in contributing, all or in part, to overall cellular and neurobiological states resulting in a migraine episode. The inflammatory soup model is one such example. This model is based upon the hypothesis that migraine progression is based upon abnormal functioning of neurons in several brain regions [21]. It is essentially an animal model upon which a cocktail of proinflammatory compounds are introduced into the brain, and fMRI imaging is utilized to map the alterations in brain response, cellularly and chemically, to the introduced disruption [21]. The cocktail itself is an acidic mix of bradykinin, serotonin, histamine and prostaglandin PGE2. This paradigm was conceptualized from samples of inflamed human tissue, utilized to induce a state of allodynia and hyperalgesia in an animal model [22]. Migraine has been observed to be induced by a host of triggers, and in clinical settings it was noticed cardiac angina patients undergoing nitroglycerin therapy demonstrated a high degree of headache incidence from this treatment. This observation led to the development of the use of the NO donor in preclinical studies to serve as a migraine attack trigger in animal and ex vivo models [23], due to the documented vasodilative properties of NO and NO donor chemicals. An interesting observation made in the clinic also found that over 50% of migraine without aura is highly correlated with the menstrual cycle [24]. This has translated into application of progesterone treatment in basic research models, in vitro and animal models to simulate this phenomenon in the laboratory to corroborate possible application of contraceptive medications in pursuit of alleviating menstrual associated migraine without aura with these readily available medications. In addition to many other factors, a major mechanism of action of progesterone only contraceptives are believed to down regulate expression of estrogen receptors in the trigeminal vascular system, thereby reducing nociceptive response to elevated estrogen levels associated with menstrual cycles [24]. in further exploring the myriad of possible triggers producing a migraine response, it would be prudent of the preclinical researcher to investigate inroads into possible environmental triggers of a migraine episode. One such tool developed for this purpose is umbellone, an environmental irritant that has found recent application in studying possible activation of transient receptor potential ankyrin-1 (TRPA1) channels and possible contribution to induction of a migraine event [25].

While a notable amount of current research is being focused on modeling and understanding the cortical spreading depression (CSD) event, it must be noted that it has been observed that not all CSD events result in triggering of migraine event, or any type of headache for that matter. This duality is important to note, as CSD events are hypothesized to be an underlying mechanism for migraine with aura10, direct evidence of this has not yet been fully elucidated and ongoing efforts to model it are being pursued to fully tease out the full impact of a CSD event, as pathological brain conditions other than migraine also demonstrate association with CSD [26]. all of these models and study paradigms have been essential in advancing the field of migraine research in basic and preclinical laboratories in institutions across the globe. As more research is clearly needed to elucidate the sex specific reasons women are affected at a much higher rate with migraine, sex specific models are being developed to further investigate this phenomenon. One immediate and simple method of accomplishing this is by simply including female animals, tissue, or female animal derived primary cell cultures for use in experiments. This paradigm can also be carried further into the clinic for translational studies by the usage of female participants for IRB approved studies. The Dussor research group has developed several models for investigating sex-based differences in progesterone signaling leading to higher incidence of migraines observed in females. An animal model has been developed and utilized by this group to explore the relationship between elevated estrogen levels and specific response patterns to fluctuations of these female sex hormones, further relating to the translational application of progesterone as a treatment [27]. The Dussor and Russo labs have also investigated the differences of CGRP expression in a female model. Due to the hypothesized impact of CGRP on development of migraine, it would be prudent to assess if a difference in expression patterns of this neuropeptide could be contributing to the observed difference in migraine incidence [28]. while this research is still in its infancy, the new avenues being opened by pharmacogenomic technology and the approach of personalized medicine will potentially allow for a new zenith of breakthroughs, as the apocryphal working hidden within our DNA becomes available for study.

Future Direction of Field and Precision Medicine

The recent development of the new class of anti CGRP and anti CGRP receptor antibodies has been an exciting advance in the field of migraine research, however their high cost makes access to all who could benefit from their use impractical. With the emerging concept of precision medicine and pharmacogenomics becoming more and more optimized and readily available, the possibility of applying these technologies to new treatments looms ever closer on the horizon. Due to the high degree of genetic variation within each migraineur, different variants of the enzymes, transporters, and receptors will be more or less responsive to a unique blend of polytherapy, as coding variants for each of these proteins will respond ever so slightly different to each blend of agent utilized to treat migraine [29]. GWAS analyses is proving to be an extremely powerful tool in analyzing single nucleotide polymorphism (SNP) variants across populations [30, 31]. Emerging research has indicated familial migraine contains a higher pathologic gene load associated with migraine than sporadic cases [32], while another study has begun to map possible loci containing genes involved in migraine pathology, specifically locating 38 new loci [15]. In addition to physiologic aspects, applications of high end computing are being utilized to analyze high volumes of drug safety data [33]. This approach utilizing personalized medicine has already been put into translational studies for cardiovascular anti-coagulant drugs, such as warfarin, which has highly variable therapeutic windows depending on the DNA variants encoding enzymes in its metabolic pathway. Moving forward it is hoped to be able to adapt this individual tailoring approach to create a treatment plan specifically optimized for a given patient. The urgency for this approach is highlighted by the fact that only 50% of migraineurs respond to acute or prophylactic treatment [29]. It is hoped that by moving forward with entrenched research tools in the laboratory, best practices observed in the clinic, and the wealth of knowledge and potential unlocked by pharmacogenomic technology, a new synergistic approach to migraine treatment may be made in order to alleviate this horrifically debilitating condition.

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Disseminated Tumor Cells in Bone Marrow In gastric Cancer Patients with Obesity

DOI: 10.31038/CST.2019452

Background

Obesity is a risk factor for cancer development and is associated with poor prognosis in multiple tumor types. There is emerging evidence of a strong association between obesity and gastrointestinal cancer. The molecular mechanism underlying gastric cancer invasion and metastasis is still poorly understood. Problem of Disseminated Tumor Cells (DTCs) in gastric cancer remains to be relevant for clinics and less is known concerning this problem for patients with obesity.

Aim

This study was aimed to evaluate how incidence of DTCs in bone marrow is conditioned by excess of adipocites in tumor microenvironment of patients with gastric cancer and obesity.

Results

There was not found the associations between availability of DTCs in BM as well CXCR4-positive cells in tumor and body mass index (BMI) but incidence of DTC in BM was associated with high density of Cancer-Associated Adipocytes (CAAs) as well with high number of CXCR4-positive cells in tumor of patients with BMI<25 and BMI>25<30 but it was not true for patients with BMI>30 where frequency of DTCs finding in BM was significantly decreased and that was statistically significant.

Conclusion

In patients with BMI>30 high density of CAAs and high number of CXCR4-positive cells in tumor may create specific tumor microenvironment that prevent tumor cells to leave primary lesion.

Obesity is associated with poor prognosis in multiple tumor types [1]. There is emerging evidence of a strong association between obesity and gastrointestinal cancer [2]. In contrast to the convincing evidence that obesity (measured by body mass index, BMI) increases the risk of many different types of cancer, there is an ambiguity in the role of obesity in survival among cancer patients [3, 4]. Some studies suggested that higher BMI decreased mortality risk in cancer patients, a phenomenon called the obesity paradox [1]. Changes that occur in the obese state and the biologic mechanisms underlying the connections of these changes to increased cancer risk are poorly understood [5–6]. Many types of solid tumors grow in proximate or direct contact with adipocytes and adipose-associated stromal and vascular components. During interaction with cancer cells adipocytes dedifferentiate into pre-adipocytes or are reprogrammed into Cancer-Associated Adipocytes (CAAs) that modulate the tumor microenvironment by promoting angiogenesis, affecting immune cells and altering metabolism to support growth and survival of metastatic cancer cells [7]. Quail D et al. indicate that special consideration of the obese patient population is critical for effective management of cancer progression [8].

During tumor progression, cells can acquire the capability for invasion and metastasis to escape the primary tumor, first of all, from breast, lung, colorectal and prostate, and colonize new organs [9, 10]. Tumor cells leaving primary site can settle mainly in Bone Marrow (BM) as a common homing-organ for Disseminated Tumor Cells (DTCs) with potency to form the metastases [11–13]. The most important factors controlling cellular migration are chemokines and their receptors. Stem cell receptor CXCR4 as a transmembrane chemokine receptor and its specific ligand CXCL12 (Stromal Cell-Derived Factor 1, SDF-1α) play a vital role in dissemination of tumor cells from primary sites, transendothelial migration as well as homing of cancer stem cells. In the tumor microenvironment under hypoxic condition cells of a growing tumor are reprogrammed to express the CXCR4 receptor thereby enhancing the metastatic potential of the tumor cells. [14–17]. the molecular mechanism underlying gastric cancer invasion and metastasis is still poorly understood. Problem of DTCs in gastric cancer remains to be relevant for clinics [18] and less is known concerning this problem for patients with overweight and obesity [19]. Therefore our study was aimed to evaluate how of CAA density, CXCR4 expression in primary tumor affect presence of DTCs in BM of patients with gastric cancer according to the Body Mass Index (BMI).

Patients and Methods

Patients

A total of 94 patients (60 men and 34 women) with primary gastric cancer were diagnosed and treated at the City Clinical Oncological Center (Kiev). No patient received any pre-operative anti-cancer therapy. Tumors were classified and staged according to the 2002 version of the UICC staging system [20]. Histological types of tumor were evaluated by WHO histological classification (2000) [21]. Tissue samples were taken immediately after tumor excision. Preoperatively, 2.0–3.0 ml of BM aspirates from the sternum with conventional cautions to avoid the hit of skin epithelial cells into the sample were obtained. All patients were thoroughly informed about the study that was approved by the local ethics committee.

Immunocytochemical Examination of Bone Marrow

Detection of tumor cells (cytokeratin-positive cells, CK-positive cells) in BM cytospin preparations fixed in acetone was provided by APAAP method (alkaline phosphataseantialkaline phosphatase) and visualization system EnVision G/2 System/AP Rabbit/Mouse (Permanent Red) (Dako Cytomaiton, Denmark). Monoclonal mouse antibodies against panCK (clone AE1/AE3, Dako Cytomation, Denmark) were used as primary antibodies. Each assay was controlled negatively by staining of one cytospin preparation with nonspecific IgG1 (MOPC21, Sigma). Number of tumor cells (CK-positive cells) was expressed on 106 BM mononuclear cells. BM samples were scored “positive” if the presence of two or more CK-positive cells per 106 mononuclear cells were detected (from 6 to 12 slides per patient were screened).

Immunohistochemical Examination of Tumor Tissue

Expression Perilipin (Plin5+) as a marker for viable adipocytes as well expression of CXCR4 were provided on deparaffinized slides using specific polyclonal rabid antibodies (Perilipin-5/OXPAT Antibody, Termoscientific, USA) dilution 1:200 and specific monoclonal mouse antibodies: clone AB2074 (Abcam, UK), respectively. Slides for evaluation of Plin5+ were covered with 1% of Bovine Serum Albumin (BCA) and incubated with polyclonal antibodies during for 1hour and then washed in Phosphate-Buffered Saline (PBS). Immunoreactions were detected and visualized with the polymer-peroxidase method (EnVision+/HRP and 3, 3-diaminobenzidine; DakoCytomation, Denmark) followed by counterstaining with Mayer hematoxylin. Negative control was employed in which the primary antibody was replaced by Phosphate-Buffered Solution (PBS). Immunopositive cells were counted per 1000 cells in each slide and the number of positive cells was reported as percent. When the tumor consisted of more than 10% of CXCR4-positive cells, the case was scored as positive.

Body Mass Index (BMI, Kg M−2)

Patients were classified according to BMI, following the WHO definitions, as underweight, normal (18.5–<25.0 kg/m2), overweight (25.0–<30.0 kg/m2) or grade 1 obesity (30.0–<35.0 kg/m2).

Statistical Analysis

All statistical analyses were conducted using the NCSS 2000/PASS 2000 and Prism, version 4.03 software packages. Prognostic values of relevant variables were analyzed by means of the Cox proportional hazards model using Odds ratio and χ2 test. Two-tailed p values <0.05 were considered statistically significant.

Results

Caas in Tumors of Patients According To BMI

Individual patient data from a total 94 histological confirmed gastric cancer patients were included in this study. Median number of CAAs in tumors was 26.5%. We defined this number as the cut-off value and classified all cases into high- or low-density groups. Overall, 48.4% of tumors were characterized by a low density of CAAs and 51.6% by high CAAs during follow-up. 39.5%, 46.4%, 89.5% of patients with BMI<25, BMI>25<30, BMI>30, respectively, had high CAAs in tumors. The probability of availability of high density of CAAs in tumor of patients with BMI>30 is increased by a factor of almost 9 (OR 8.84, χ2 = 13.47, 95%CI 16.777–4.665, P<0.01) as compared with BMI<30. Data obtained demonstrate that adipocytes are as major component of the microenvironment of gastric cancer, especially under obesity.

CK-Positive Cells in Bone Marrow

 Overall, 88.3% of patients have been with M0 category. It was determined that CK-positive cells were detected in BM of 50.1% gastric cancer patients among all investigated. There was no association between DTCs in BM and clinicopathological characteristics. It makes no difference between of groups of patients according to BMI concerning the availability of DTCs in BM: 47.6%, 56.2% and 41.2% of patients with BMI<25, BMI>25<30, BMI>30 had DTCs in BM, respectively. Meanwhile, it was found the association between the presence of DTCs in BM and density of CAAs in tumors: DTCs in BM were detected in 41.3% and in 58.7% of patients when tumors characterized by low and high density of CAAs, respectively. When tumors characterized by high density of CAAs appearance of tumor cells in BM has been found in 35.7% of patients with BMI>30 as compare with 70.6% and 62.5% of patients with BMI<25 and BMI>25<30. In patients with obesity frequency of DTCs finding in BM was significantly decreased and it was statistically significant (OR 4.33, χ2 = 3.82, 95%CI 9.341–2.007, P<0.05) as compare with patients with BMI<25. It may be suggested that adipocites, namely CAAs, playing an essential role in the regulation of metabolic functions in the variety of processes involved in metastatic spread of tumor cells.

CXCR4-Positive Cells in Tumor Tissue

Overall, 83.1% of patients had tumors with CXCR4-positive cells. Statistically significant correlation between CXCR4-positivity of tumors and clinical characteristics was not found. The median number of CXCR4+ cells was 24.2% (range of 13.4–81.0%). It makes no difference between of groups of patients according to BMI concerning the CXCR4-positive cells: 66.7%, 65% and 60% of patients with BMI<25, BMI>25<30, BMI>30 had high number of CXCR4-positive cells in tumor, respectively. Meanwhile, it was found the association between high number of CXCR4-positive cells and density of CAAs in tumors. High number of CXCR4-positive cells were detected in 35.3% and in 72.7% of patients when tumors characterized by low and high density of CAAs, respectively (OR 7.3, χ2 = 12.45, 95%CI 13.654–4.208, P<0.01).

The mean number of CXCR4-cells in tumors with high density of CAAs was 47.4±1.9%, 37.8±4.1% and 48.5±2.9% in patients with BMI<25, BMI>25<30, BMI>30, respectively.When tumors characterized by high density of CAAs presence of high number of CXCR4-positive cells have been found in 77.8%, 62.5% and 71.4% of patients with BMI<25 and BMI>25<30 and. BMI>30, respectively, and presence of DTCs in BM in these groups of patients was the following: 88.9% of patients with BMI<25, in 58.3% of patients with BMI>25<30 and in 18.2% of patients with BMI>30.It is notably important to note that in patients with BMI>30 having high density of CAAs and high number of CXCR4-positive cells in primary tumor the incidence of DTCs in BM was rather low. It may be supposed that under obesity additional mechanisms may be switched off in tumor microenvironment modulated by excess of adipocites to prevent cells escaping.When tumors characterized by low density of CAAs low number of CXCR4-positive cells was detected in 33.3%, 51% and 44.1% of patients with BMI<25 BMI>25<30 and. BMI>30, respectively. Presence of DTC in BM in these groups of patients was the following: in 31.5% of patients with BMI<25, in 44.6% of patients with BMI>25<30 and in 41.1% of patients with BMI>30.

Conclusion

There was not found the associations between availability of DTCs in BM as well CXCR4-positive cells in tumor and BMI but incidence of DTC in BM and high number of CXCR4-positive cells in tumor associated with high density of CAAs of patients with BMI<25 and BMI>25<30 but it is not true for patients with BMI>30 where frequency of DTCs finding in BM was significantly decreased and that is statistically significant.In patients with BMI>30 high density of CAAs and high number of CXCR4-positive cells in tumor may create specific tumor microenvironment that prevent tumor cells to leave primary lesion. Understanding the metabolic changes that occur in obese individuals may also help to elucidate more effective treatment options for these patients when they develop cancer.

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Synthesis of Nanolignin Following Ozonation of Lignocellulosic Biomass

DOI: 10.31038/NAMS.2019244

Introduction

The non-food valorization of biomass represents a major axis of research currently animating a large number of scientists. Whether for energy purposes, replacing fossil fuels such as oil, or as innovative strategies for access to new bio-sourced products, modern valorization approaches are above all respectful of the principles of green chemistry, and generally refer to processes or to the use of eco-compatible products. Renewable polymers have emerged as an attractive alternative to conventional metallic and organic materials for a variety of different applications, due to their biocompatibility, biodegradability and low cost of production [1].Renewable biomass can provide many industrial solutions as it is composed primarily of cellulose (30–35%), lignin (15–30%) and hemicellulose (20–35%).Among them, lignin is known as one of the main bio-resource raw material that can be used for the synthesis of environmentally friendly polymers, thus a good candidate for replacing regular industrial aromatic polymers and fine chemicals. Due to its chemical and structural diversity, lignin valorization remains a major challenge for achieving a viable biomass-based economy [2]. Lignins are composed of polymerized monolignols and their derivatives. Particularly grass lignins contain important amounts of alkenes yielding up to 10–15% by weight aldehydes [3].

Lignin extraction from lignocellulosic biomass

The lignocellulosic biomass has to be treated before any widespread utilization of its components. As a decrease in the lignin content in plants results in an increase in biodegradability, lignin removal from this biomass is a crucial pretreatment step [4,5]. A large number of chemophysical pretreatment approaches has been investigated on a wide variety of feedstock [6]. These methods require the use of hazardous materials such as acids, alkalis and/or organic solvents. They are currently four industrial processes to extract pure lignin: sulfite, kraft, organosolv and soda processes [7, 8]. Kraft pulping accounts for approximately 85% of the produced lignin. The delignification process is performed at high temperatures (170°C) and high pH 13 or 14, during which the lignin is dissolved in sodium hydroxide and sodium sulfide (white liquor) [9].

The sulfite process involves the reaction between lignin and a metal sulfite and sulfur dioxide, with calcium, magnesium or sodium acting as counter ions; the pH can vary between 2 and 12, and the temperature between 120 and 180 °C, with a digestion time of 1–5 h [10]. The soda process is typically used for the treatment of grass, straw and sugarcane bagasse, which accounts for 5% of the total pulp production [11]. The biomass is digested at temperatures that vary between 140 and 170 °C in the presence of 13–16% by weight of aqueous solution of sodium hydroxide. The soda lignin contains no sulfur which is not the case of the kraft and sulfite processes.

The organosolv process is based on the treatment of the biomass using organic solvents including ethanol, methanol, acetic and formic acid that are usually mixed with water at temperatures that range from 170 to 190°C [12]. The recovery and separation of the dissolved lignin and hemicelluloses can be done by precipitation of lignin or evaporation of the organic solvent, after adjusting the temperature, pH and concentration of the organic solvent. Organosolv pulping is one of the most efficient options for the further valorization of lignin and it also preserves the native structure of the lignin [13]. The obtained lignin is sulfur-free, with lower ash-content, and it has higher purity.

The removal of lignin by different technologies originates different product streams. Beyond the production costs and the environmental impact the lignin extraction method has to be selected depending on the use that will be given to the extracted lignin. In general, the kraft and sulfite processes allow extracting lignin at reasonable costs, while the organosolv method continues to be an expensive technology but still with high quality extracted lignin. However, the soda extraction process generates lower production costs and low environmental impact.

Ozonation of lignocellulosic biomass

Ozonation can circumvent the different issues of the above extraction processes that require the use of hazardous materials (acids, alkalis and/or organic solvents), as it is considered as a green process. Ozone (O3) is a powerful oxidizing agent (E° = 2.07 V). It is one of the most promising lignocellulosic biomass oxidative pretreatment for selective lignin degradation with minimal effects on the hemicellulose and cellulose contents [14]. It provides low production of inhibitory compounds such as furfural and HMF (Hydroxymethylfurfural), and more importantly it requires no chemical additives during all the pretreatment process. However, ozonation demands high energy generation costs but this aspect can be avoided by optimizing the ozonation process. Ozone has a high affinity for phenol and polyphenols such as lignin and tannic acid. During ozonation lignin is converted to soluble products which to a great extent are biodegradable and thus yield a useful byproduct [15].

It has been reported the ozonolysis of grass lignin to selectively cleave aromatic aldehydes by limiting the reaction residence time to a few minutes for preventing over oxidation of targeted products [16], the ozonation was done in acidic media to avoid the production of secondary ozonides usually done at neutral pH media [17]. Ozone has been used to remove lignin from different biomass such as wheat and rye straw [18], cotton stalk [19], magazine pulps [20], among others.

Lignin presents a very complex assembly which limits enormously their interaction with host polymer matrices for industrial applications. It has been reported that only 2% of the annually extracted lignin from paper and pulp industry is used for applications such as fillers, adhesives and dispersants; the remaining lignin is burned as industrial waste for energy generation [21].

Synthesis of lignin nanoparticles

One way to overcome the limitations of lignin pointed out in the above section is to reduce the size of lignin particles until the nanometric size (less than 100 nm). At this scale, new functionalities and properties of materials are observed and used for a wide range of novel applications. As the size of the particles is reduced to the nanoscale range, the surface to volume ratio of the particles gradually increases which in turn increases the reactivity of the particles and changes their mechanical, electrical and optical properties [22] (Adusei-Gyamfi and Acha, 2016).These lignin nanoparticles hold huge potential for downstream valorization due to their unique morphology and abundant multifunctional groups. Thus the idea is to prepare lignin nanoparticles, which will greatly improve their reactivity and solubility with host matrices, and will provide a morphological and structural control of these structures for different high-value applications [21].

Several different methods have been published to synthesize nanolignin. Frangville et al. reported nanolignin obtained by precipitation in HCl. The resulting nanoparticles were crosslinked with glutaraldehyde, getting good stability over a wide range of pH [23]. Gilca et al. prepared nanolignin by sonication, they identified two main reaction patterns resulting in chain cleavage (depolymerization) and oxidative coupling (polymerization), both probably promoted by the hydroxyl and superoxy radicals generated by ultrasound [24]. Hydroxypropyl lignin nanoparticles were reported to be prepared by reacting an alkaline lignin solution with propylene oxide, then acidifying and centrifuging the mixture to precipitate the nanoparticles [25]. It has been reported also the use of a solution precipitation from alkaline lignin with either ethylene glycol or alkaline solution, which resulted in smaller nanoparticles [26]. Of all the methods mentioned above, the precipitation method in HCl seems to be the easiest for the synthesis of lignin nanoparticles.

Applications of lignin nanoparticles

As for the applications for lignin nonmaterial’s, lignin shows unique properties such as antioxidant and antibacterial properties, ultraviolet absorption, and high toughness [27]. To produce novel materials with improved properties, nanolignin particles can be incorporated in polymers for use in food packaging with the properties mentioned above, such as polyvinyl alcohol/Chitosan (providing UV absorbance, antioxidant and antibacterial properties), glycidyl methacrylate grafted polylactic acid (providing UV-absorbance and antibacterial properties), polylactic acid(providing UV-absorbance and antibacterial properties) [28], among others.

Lignins are known by their free radical scavenging activity due to their complex phenolic structure, which make them recognized as efficient natural antioxidants [29]. The incorporation of natural antioxidants to food packaging materials has been widely studied in order to improve protection of light and/or oxygen sensitive products [30]. Lignins have been proposed as antioxidants for polylactic acid films [31]. Lignin nanoparticles have been reported to exhibit higher antioxidant activity than neat lignin [26, 32, 33].

Nanolignin particles have also been involved in production of antimicrobial materials. Richter et al. [34] produced nanolignin loaded with silver ions and coated with a cationic polyelectrolyte layer capable of adhering to bacterial cell membranes. The nanoparticles killed both Gram-negative and Gram-positive bacteria while using at least 10 times less silver than conventional silver nanoparticles, thus reducing the environmental impact produced by silver nanoparticles. Films based on polylactic acid [35], chitosan, and/or polyvinyl alcohol [36] added with nanoparticles of lignin presented activity against Gram-negative bacteria, indicating that the films containing nanolignin particles could be used as antibacterial food packaging.

UV radiation accelerates oxidation rates in food [37] and also photodegradation of organic polymers [38]. The UV-absorbing capacity of lignin was tested by Yearla and Padmasree [32] by monitoring survival rates of UV-irradiated E. coli. In the absence of lignin compounds, the mortality of E. Coli was 100% after 5 minutes of UV exposure, while their survival was improved in the presence of lignin in proportion to their concentration. This study was done based on the fact that Escherichia coli suffer intracellular oxidative damage and death caused by UV radiation. In addition, because lignin nanoparticles have superior UV protection over ordinary lignin, the survival rate results were much better when nanolignin was used. Bionanocomposite films made of gluten-lignin nanoparticles have been reported to absorb UV radiation [99], thus these materials could be applied in food packaging with UV protective features, such as nuts and other food products that are prone to lipid oxidation.

Conclusion

Lignin as a renewable polymer is an attractive alternative to conventional metallic and organic materials due to its biocompatibility and biodegradability. Ozone has proved its efficiency as pretreatment method of lignocellulosic biomass for removing lignin. Ozonation is a green method requiring no hazardous compounds such as acids or alkalis, and needs moderate reaction conditions such as room temperature and atmospheric pressure. Lignin is a very complex polymer limiting its applications. One way to solve this problem is to reduce the size of ordinary lignin particles at the nanoscale, where new properties are being harnessed for novel applications. Lignin nanoparticles are good candidates for the next generation functional nanocomposites as they present very interesting properties such as UV light blockers, radical scavengers, and antioxidants very promising for potential applications in the food sector such as packaging.

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Structural Elucidation of Natural Polymeric Materials Treated by Ball Milling: A Mass Spectrometrist View

DOI: 10.31038/NAMS.2019243

 

It is well known to synthetic organic chemists that ball milling can be used for the synthesis of new compounds [1]. On the other hand, it is well known that ball milling of complex natural materials such as lignin and pollen grains can lead to alteration of their original structure and may produce new compounds that originally do not exist [2]. For example, Milled wood Lignin (MWL) contains more hydroxyl groups than the native one due to the extensive depolymerization during the ball milling process [2]. The presence of more hydroxyl groups indicated the homolytic bond cleavage between lignin monomers, which in turn produce reactive radicals that can create new compounds [2].

It is well known that sporopollenin is composed of carbon, hydrogen, and oxygen in the form of a cross-linked polymer that is amazingly stable [3]. It makes up the outer wall of pollen grains, and when extracted it is in the form of an empty exine or microcapsule [3]. During the late past century sporopollenin exine resistance against chemical treatments has been well documented [3]. It was found that the rigidity of sporopollenin restricted its practical analytical analysis to a limited number of techniques, such as Fourier transform infrared (FTIR) spectroscopy and analytical pyrolysis combined with gas chromatography–electron ionization-mass spectrometry (GC–EI-MS) [4,5]. Based on those outdated and old studies, there is still the belief that sporopollenin contains an aromatic identified as p-coumaric acid and, to a lesser extent, ferulic acid [4,5]. Some recent studies, which still champion this outdated theory was published in nature plants implying a resemblance between sporopollenin and lignin. In this study, Li et al.[6] reported the structure elucidation of sporopollenin extracted from ball-milled pollen grains. It should be noted that in 1966, Gordon Shaw, one of the earliest pioneers in sporopollenin, withdraw his proposal that sporopollenin contains lignin because it does not give any positive test for lignin’s [7].

Needless to say that the pollen grain contains proteins and genetic material that is enclosed by the intine composed of carbohydrates followed by the exine composed of sporopollenin [8]. Logically, high energy ball milling of the pollen grains could produce new artefactual compounds through the reaction between all these pollen grains components together, which in turn could alter the structure of the studied target material sporopollenin.

The effect of ball milling on the structure of these complex natural polymeric biomaterials is perhaps comparable to using specifically pyrolysis GC-MS for their structural analysis [9,10]. Pyrolysis GC-Ms can lead to the identification of compounds that initially does not exist in your sample [9,10]. As an example, pyrolysis GC-MS of unsaturated fatty acids leads to the production of aromatic compounds and, even linear saturated polymers such as polyethylene can produce aromatics during pyrolysis GC-MS analysis [9,10]. Overall,  as a mass spectrometrist, firstly, it is recommended to avoid any procedures that could alter the structure of these complex natural materials before using any mass spectrometric techniques for structure elucidation and/or sequencing purposes. Secondly, using soft ionization methods such as electrospray ionization (ESI-MS) and matrix-assisted laser desorption/ionization (MALDI-MS) is more advantageous than pyrolysis GC-MS. The use of these soft ionization methods allows the analysis of the native form of these complex materials without any alteration in their structure that could lead to misleading results [4,5,9,10]. We are in the process of reporting new finding on the structure of the hollow empty clean sporopollenin exine using state of the art analytical experiments, such as high-resolution X-ray-photoelectron spectroscopy, TOF-SIMS, TOF-SIMS, MALDI-TOF-MS, OF-SIMS (FIB) MALDI-Tandem Mass spectrometry analysis and Solid-State 1H and 13C-NMR (1D and 2D experiments) [11]. We can state that sporopollenin exine does not contain any aromatics and bear no resemblance to lignin [11].

References

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  2. Sjöström E, Alén R (Eds.) (2013) Analytical methods in wood chemistry, pulping, and papermaking. Springer Science & Business Media Pg No: 104.
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Recent Research Efforts in Improving Lithium-Ion Battery Safety

DOI: 10.31038/NAMS.2019242

Abstract

The Lithium-ion battery (LIB) has been utilized in many applications for thirty years, from personal electronics to electric vehicles. Many developments during the past decades resulted in high energy density and capacity in LIBs. However, battery safety still remains an issue alongside the continuous growth in the LIB market. Recent high-profile hazardous incidents gained considerable attention from the public as well as among researchers. This short review summarizes the recent efforts in improving LIB safety on three fronts: (1) materials advancement, (2) early monitoring and detection of thermal runaway events, and (3) fault diagnosis and fault-tolerance controls.

Keywords

lithium-ion battery; battery safety; battery management system; fault diagnosis; fault-tolerance control

Introduction

Lithium-Ion Battery (LIB) technology and the industry experienced rapid development in the past three decades, due to the advantages of LIBs over other energy storage systems, including: high energy density, strong stability, low maintenance, and low self-discharge. The LIB is the predominant power source in both consumer electronics and Electric Vehicles (EVs). The global market of LIBs (Figure 1) increased from $18.8 billion in 2014 to $28.5 billion in 2018. It is expected to increase by 11.1% (compound annual growth rate, CAGR) to $53.3 billion by 2024 [1]. The continuous growth in the global EV market from 2014 to 2024, at a rate of 21.1% (CAGR), is anticipated to contribute to the growth in the LIB market [2]. Although the LIB offers many advantages, safety still remains as an issue. According to a report issued by the Federal Aviation Administration (FAA), the number of air/airline incidents involving the battery – lithium-battery-induced smoke, fire, extreme heat, or explosion – increased from 9 to 50 incidents annually from 2014 to 2018 [3]. There are many reports about EV fire accidents caused by LIBs. For example, an all-electric compact car BYD e6 (BYD Auto company, China) caught fire after being hit by a Nissan GTR, an accident that led to the deaths of three passengers in Shenzhen, China, in May 2012 [4]. The battery pack of a Tesla Model S immediately caught fire after the driver hit debris on a highway in Washington State in 2013 [5]. Improving LIB safety is an important issue that requires research into new materials for the device, as well as structure optimization, and system design.

A typical EV battery is a three-level assembly: battery cell, battery module, and battery pack (Figure 2). The battery cell is a basic unit, consisting of an anode, a cathode, a separator, and liquid electrolyte. Multiple battery cells are connected and placed into a frame, called a battery module. Finally, the several battery modules are assembled into a battery pack, along with a control system, and system protection. The battery pack is a complete system that can be installed in an EV. Fires in a battery cell occur because of physical and electrical faults [6]. A chain reaction fire in the battery cells results in an explosion of the battery pack, called a cascading thermal runaway event. As mentioned in a review by Mauger et al., a gasoline fire can only be ignited when the gas tank’s air level is between 1.4 and 7.6%, and temperature is above 200oC [7]. The gas tank design of the vehicle ensures that gasoline cannot self-ignite in a tank. Unlike gasoline, the cascading thermal runaway event can happen in the LIB. This review summarizes the recent research efforts to improve LIB safety on three fronts: (1) materials advancement, (2) early monitoring and detection of thermal runaway events, and (3) fault diagnosis and fault-tolerance controls.

Current efforts in battery safety improvements

1 . Materials advancements

Cathode (positive electrode) material can be categorized into three groups, based on crystal geometry:

  • Lamellar compounds (lithium cobalt oxide – LCO; lithium nickel oxide – LNO; lithium nickel cobalt aluminum oxide – NCA; and lithium nickel manganese cobalt oxide – NMC)
  • Spinel lithium manganese oxide – LMO
  • Olivine lithium iron phosphate – LFP

NAMS-2019_Jianyu Liang_F1

Figure 1. Global history and forecast data for lithium battery, including lithium battery global market size; electric vehicle sales globally; and numbers of problem incidents related to lithium battery reported by FAA [1–3].

NAMS-2019_Jianyu Liang_F2

Figure 2. A typical battery pack assembly for EV.

LCO is the conventional cathode material used in the invention of LIBs by Sony. Because of materials shortages, cost, and the toxicity of cobalt (Co), transition metals (Mn and Ni) are used to partially substitute for Co. However, Huggins’ study showed that the partial pressure of oxygen at equilibrium varies exponentially, with a redox potential of the transition metal oxide vs lithium [8]. At 25oC, the equilibrium oxygen pressure for cathode materials is 1 atm, at a potential of about 3V. This oxygen pressure increases to greater than 50 atm at higher potentials. The lamellar compounds tend to lose oxygen (migrating to the counter-electrode, graphite) and produce carbon dioxide (exothermic reaction), which results in a thermal runaway and release of the emission gases.

LFP exhibits remarkable thermal stability because the oxygen is covalently bonded with the phosphorous atoms. Despite the fact that the operating voltage of the LFP with a graphite anode (3.2V) is lower than that of LCO (3.7–3.9V) and LMO (4.0V), the LFP cell passes the mechanical stress tests and short circuit tests without thermal runaway [7]. Hence, the LFP is commonly used for safety purposes in applications such as EVs and in industrial applications. Although the demand for the LFP cathode reached ~100 Gg (100,000 tons) in 2017, the forecast for LFP’s market share (along with LCO, NMC, NCA, and LMO) will decrease from 38% in 2017 to 15% in 2025 because of its low energy density [9]. Further developments of cathode materials with moderate to high energy density and safety are still needed.

Anode (negative electrode): spinel lithium titanate -LTO (theoretical capacity ~175 mAh/g) has been studied as an alternative to graphite (theoretical capacity ~372 mAh/g) [10–11]. Belharouak’s and Chen’s studies of lithiated LTO and lithiated graphite by differential scanning calorimetry showed that LTO exhibits higher exothermic onset temperature than that of graphite (130oC vs 100oC, respectively) and generates much less heat (383 J/g vs 2,750 J/g, respectively) [12–13]. In nail penetration tests, the temperature of the cells after penetration increased by only 5oC in a LIB with the LTO anode but rapidly increased by 130oC with the graphite anode [13]. Although the LTO has much lower theoretical capacity, it shows high thermal stability and capability to delay a thermal runaway event and may serve as a potentially safer alternative to graphite.

Separator is a porous polymeric membrane that prevents internal short-circuit events between the anode and cathode. Typical commercial separators are polypropylene PP (Tm ~165oC) and polyethylene PE (Tm ~130oC), configured as a single layer, or bi- and tri-layers [14]. Uniform and appropriate porosity (40–60%) is necessary to have uniform current density, retain a sufficient amount of liquid electrolyte, and maintain mechanical strength [15]. High porosity separators tend to shrink, which results in pore distortion and leads to an internal short circuit as the temperature increases. Zhang’s study showed that the mechanical properties of the PP and PE separators exhibited low punch strength and anisotropy in uniaxial tensile tests [16]. The separator failed easily under tension and punch. Recent separator improvements include surface coating of polydopamine (PDA), to inhibit Li-dendrite growth, and gamma irradiation, to enhance cross-linking of the PE polymer chains and enhance thermal stability of the PE separator, in order to improve safety [15].

Table 1. Typical materials in lithium-ion battery technology [7].

Cathode

Anode

Cell voltage (V)

Energy density (Wh/kg)

LiCoO2 (LCO)

Graphite

3.7–3.9

140

LiNiO2 (LNO)

Graphite

3.6

150

LiNi0.8Co0.15Al0.05O2 (NCA)

Graphite

3.65

130

LiNixMnyCo1-x-yO2 (NMC)

Graphite

3.8–4.0

170

LiMn2O4 (LMO)

Graphite

4.0

120

LiNi1/2Mn3/2O4 (LNM)

Graphite

4.8

140

LiFePO4 (LFP)

Li4Ti5O12 (LTO)

2.3–2.5

100

Liquid electrolyte includes ethylene carbonate (EC), diethyl carbonate (DEC), ethyl-methyl carbonate (EMC), and dimethyl carbonate (DMC). Different additives are added to improve safety, reduce gas generation, provide overcharge protection, and serve as fire-retardant. One drawback of liquid electrolyte is its flammable nature, due to the low flash point of each component. To overcome this hazard, ionic liquid (IL) has been added to reduce the flammability. However, the use of IL is limited in the commercial electrolyte because of its high cost. Current liquid electrolyte developments aim for low flammability, fire suppression, electrochemical stability, and performance. For examples, Zeng et al. reported a stable and electrode-compatible, non-flammable phosphate electrolyte by adjusting the Li salts-to-solvent molar ratio [17]. Wang et al. demonstrated that the concentrated electrolyte contains lithium salt and a flame-retardant solvent to suppress fires and permit stable charge-discharge cycling [18].

Solid-state electrolyte (SSE) is considered as a safe alternative to the organic liquid electrolyte and separator. SSE must demonstrate high ionic conductivity (greater than 10–4 S/cm at room temperature RT), negligible electron conductivity, and a broad electrochemical stability window [19]. There are three types of inorganic oxide-based SSE: a sodium super ion conductor (NASICON), with Li+ replacement; a garnet type; and a perovskite type. They have comparable bulk ionic conductivity (10–5-10–3 S/cm at RT) with the organic liquid electrolyte and show good chemical stability. However, there are several problems with the SSE: poor electrolyte/electrode interface in the NASICON-type; Li dendrite problems in the garnet type; and high interfacial resistance in the perovskite type. Sulfide-based SSE is another inorganic SSE with an ionic conductivity (10–2 S/cm) higher than that of the oxide-based SSE. However, it suffers from the generation of flammable hydrogen sulfide (H2S) upon exposure to the ambient atmosphere. The solid-state, hybrid electrolyte consists of a soft and flexible polymer electrolyte and a rigid inorganic SSE. It demonstrates good compatibility with the anode because of the intimate contacts. It is safer than the rigid inorganic SSE, because the uniform interfacial Li+ distribution also inhibits lithium dendrite formation [20]. Although the SSE has attractive properties, especially superior thermal stability and low flammability, problems such as lower bulk ionic conductivity at RT and interfacial mismatch between the solid-state electrolyte and the electrode materials still prevent widespread commercial application.

Beyond LIB: other battery chemistries – several non-lithium chemistries (Na, K, Mg, Ca, and Al) have been studied as potential alternative batteries to the LIB [21]. The Na+ and K+ in propylene carbonate (PC) exhibit higher mobility and ionic conductivity than that of Li+ because the Stoke’s radii in PC are in the order sequence of K+ < Na+ < Li+. Na-ion and K-ion batteries are expected to be a low-cost alternative to the LIB due to the abundance of Na and K resources, in addition to a similar energy density, similar battery design, and the same production process as the LIB [22]. Currently, studies of Na- and K-ion batteries and their safety are still in the development stage at research institutes.

2. Early monitoring and detection of thermal runaway events

Four different methods to monitor and detect thermal runaway events include (1) monitoring terminal voltage and surface temperatures, (2) an embedded optical fiber sensor, (3) electrochemical impedance analysis (EIS), and (4) a gas sensor monitor [23].

Terminal voltage and surface temperature monitoring method – This method uses the voltage and temperature sensors in real-time measurements of state of health (SOH), state of charge (SOC), and location of the faulty battery. Disadvantages of this method include high cost, low accuracy in thermal runaway prediction, and complexity of voltage sensor setup [23].

Embedded optical fiber sensor method – In this method, several types of fiber are used to set up optical sensors. For example, fiber Bragg grating arrays are attached on the surface of the cathode to record temperature and strain [24–26]. A nickel-coated fluorescent fiber is used for fluorescence lifetime measurements [23]. This technique can predict a thermal runaway event with high accuracy and directly monitor the internal temperature of the battery. However, the cost to set up the optical fibers and modify battery packaging is significant.

EIS method – The EIS technique uses an electrochemical impedance meter and a frequency response analyzer to determine the relationships between internal temperature and impedance phase shift or ohmic resistance [23, 27]. This method is able to predict the state of battery and thermal runaway temperatures with high accuracy. A drawback is the complex calibration process due to the fact that different battery systems have variant impedance parameters.

Gas sensing method – This simple and inexpensive method offers high accuracy and rapid detection, and functions by detecting vented gas concentration, because air flows faster than the speed of heat propagation in solid materials [23, 28]. Recently, Cai et al. validated the gas-sensing-based method by simulations [29]. In the simulation, the detection of the thermal runaway can be made at 85 seconds by the gas-sensing method, while the surface temperature measurement detected the thermal runaway propagation to neighboring cells at 710 seconds. Although the gas sensing-based method has many advantages, the sensor faults such as gas-sensor poisoning and gas cross-interference still persist.

3. Fault diagnosis and fault-tolerance control

A battery management system (BMS) consists of sensors, controllers, and computational algorithms. The BMS is designed to function in several ways: detect malfunctions and ensure battery safety, maintain accuracy and reliability, and predict and maximize battery life [30]. Battery faults are typically detected by data-driven approaches. Sensor faults are commonly diagnosed by model-based approaches, i.e. comparing the actual outputs to the estimated or nominal outputs (residual generation). A statistical cumulative-sum test is applied, rather than the selection of a fixed threshold, for high accuracy. The detected faults are then distinguished and monitored by fault-tolerant control (FTC) [31]. Most current and voltage sensors use Hall effect sensors and are usually subjected to bias and gain faults [32]. Many FTC methods for both the battery and sensors have been proposed and validated, including: re-arranging voltage measurement topology to distinguish between sensor and cell faults, without false detection and additional sensor [33–34]; nonlinear observability analysis for the sensor-biased fault-tolerance [35]; and active FTC to maintain battery temperature and deenergize the cell under faulty conditions [36].

Conclusion

Safety of LIBs has attracted considerable attention of researchers worldwide, as the incidence of LIB fires and explosions increases. SSE is a safe alternative but exhibits low ionic conductivity. Other battery chemistries have been studied but still remain in the development stage. BMS, the sensor system, and FTC are the most suitable measures to monitor and detect malfunctions of LIBs. Further developments of diagnostic schemes for fault detection and FTC for battery and sensors, together with novel materials developments, are needed to improve the safety of lithium-ion batteries.

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The plastic age – where and what is the future?

DOI: 10.31038/NAMS.2019241

 

We are currently living in the “Plastic age” as rightfully predicted in the classic movie “The graduate” in the 60’s where main character young Benjamin, just graduated and asking himself what to do next gets the famous advice: “I just say one word – Plastics. There is a great future in Plastics!” This was certainly correct; our daily life relies on synthetic polymers for all aspects of modern life. Going back in history, one of the first synthetic resins, a thermoset produced by a catalyzed reaction of phenol and formaldehyde, was introduced already 1909 under the name Bakelite. The creation of a synthetic plastic was revolutionary for its electrical non conductivity and chemical, solvent and heat-resistant properties in electrical insulators, radio and telephone casings and such diverse products as kitchenware, jewelry, pipe stems, children’s toys, and firearms. Bakelite was particularly suitable as a molding compound, an adhesive or binding agent, a varnish, and a protective coating and as such extremely useful for the emerging electrical and automobile industries. In the following years a number of new polymeric materials, thermoplastics and thermosets were developed, mainly formed by linking small molecules – monomers – together in a repetitive formation. Plastics are today available with extremely versatile properties ranging from, resistance to corrosion, low density, high strength, transparency, low toxicity, durability and a remarkable affordability and therefore used by almost every industry in the world, from food packaging to space exploration. Plastic is the ultimate commodity of convenience.

Especially hydrocarbon polymers like polyethylene and polypropylene, which account for more than half of the world plastics production and more than 90% of packaging materials are ubiquitous in single-use and short-term applications because their starting materials are abundant and inexpensive. In addition, we have learned to vary the chemical structure of the polymer chains (such as branching, molecular weight, and dispersity) through catalysis, and in such to alter their physical properties. The single-use nature of plastics is essential in sterile packaging for foods, strong-but in expensive materials for transportation and storage, and safe and disposable components in medical devices, leading to their manufacture in tremendous quantities. Three hundred and eighty million tons (380 Mt) of plastics are created worldwide each year, which corresponds to roughly 7% of crude oil and natural gas produced. Moreover, the plastic market is currently increasing, and some analysts predict quadrupled production by 2050 (~1100 to 1500 Mt per year).

However, the good properties of plastics, namely the mechanical performance connected with low density, are intrinsically connected with the existence of large molecules in the material. This long chain molecular nature turned out to be a major draw-back in the circularity and recycling. Mixtures of polymers are thermodynamically not stable; in fact only a few polymers are miscible at all and thus small impurities (not speaking of non-polymeric ones) deteriorate the functional properties significantly! Processing of plastic waste is limited by technical challenges, which include contamination from mixtures of polymers and additives as well as oxidative degradation during melt re-processing. Current recycling processes rely mainly on primary recycling (termed closed-loop recycling, reprocessing an uncontaminated, single plastic to give a product used for the same purpose as the original plastic) and secondary (mechanical) recycling –down cycling- results in lower-in-value materials with different uses compared to the original material. Both primary and secondary recycling involve sorting, grounding, washing, and extruding, which cause varying degrees of polymer degradation, resulting in a limited number of reprocessing cycles of polymers. This accounts even more for cross-linked polymers, often referred to as thermosets and another class of plastics comprising ca. 15−20% of polymers produced. The outstanding performance of conventional thermosets like the first material Bakelite originates from their covalently cross-linked networks but results directly in a limited recyclability. The available recycling techniques include mechanical, thermal, and chemical processing. These methods typically require a high energy input and do not take the recycling of the thermoset matrix itself into account but focus on retrieving the more valuable fibers, fillers, or substrates. Thermoset materials are in particular amongst the most difficult materials to recycle, and in most cases even considered impossible to recycle. Most attention is given to the recycling of fiber-reinforced composites, since the fibers are generally more valuable than the matrix material, especially when carbon fibers are used. The most favorable method of recycling, the direct re-use of components in similar or lower performance applications without any form of reprocessing is virtually impossible for the existing thermoset materials.

The downside of the plastic age is the massive quantity of waste, pollution and lost value associated with single-use plastics. Over 75% of materials produced each year, 300 Mt, are discarded after a single use. Currently, most of this waste is either lost to landfills and the environment, or inefficiently incinerated in power plants to produce electricity, generating greenhouse gases (e.g., CO2) and toxic by-products in the process. Inefficient recycling and extremely slow environmental degradation of plastics are causing increasing concern about their widespread use. After a single use, 90 % of these materials are currently treated as waste creating a global environmental crisis, despite of their inherent chemical and energy value. Plastics have got themselves a bad name, mainly for two reasons: most are made from petroleum and they persist in the environment for decades or centuries beyond their functional lifetimes and end up as litter in the environment. Durability, one of plastic’s greatest assets is now its curse. Its robustness means that plastics stay in our environment for hundreds of years turning them into a persistent part of the landscape, and more importantly of the seascape. Once discarded, bulk plastics are polluting the oceans. Converging sea currents are accumulating plastic waste in a floating island known as the Great Pacific Garbage Patch, which now covers an area larger than Greenland. The bigger bits of plastic are life-threatening to marine life and sea birds, they can strangle marine animals and birds or build up in their stomachs. More recently, the awareness of the presence and danger of micro plastics has raised concern about their presence in the food chain. If nothing changes, by 2050 there will be as much plastic in the sea as there is fish. Who wants to eat plastic then?

The switch from a linear economy with its throwaway culture to a circular economy with efficient reuse of waste plastics is therefore mandatory. An increased focus on bio-derived and degradable composites as well as recycling could lower the degree of pollution. Reduce, reuse and recycle have been embraced as the common approach to tackle the escalating plastic waste problem. The goal is to create a circular plastic economy where products are 100%recyclable, used for as long as possible, and their waste is minimized. Compared to plastics, massive recycling of much older man-made materials like iron and steel (> 70 %!), copper, glass, aluminum and paper is the current technology and already since a long time developed and optimized. For a circular economy, the vast majority of plastic materials should be recyclable and the materials entering the chain should be bio-based. Although biodegradable polymers and in particular PLA have been the focus of much research over the last decades, only ~1% of plastics are currently produced from renewable resources. Besides, polymers derived from bio-renewable resources, commonly referred to as renewable polymers, bio-based polymers, or even sustainable polymers in the literature, are not necessarily sustainable or degradable, where as degradable polymers are not necessarily recyclable. For example, 100% bio-based polythene (bio-PE) and bio-based polyethylene terephthalate (bio-PET) are not biodegradable. Dreams and reality of green polymer chemistry have no match, conflicts and competition with food production and unrealistic high CO2 production are consequences. Biopolymers are also not a realistic alternative to synthetic polymers (properties and processing). Recycling is costly, reliant on changes in human behavior and produces partially lower quality materials, in terms of both thermal and mechanical properties. Additionally, recycling does not change our plastic addiction; if we want to maintain our current lifestyles, modification to plastic manufacture needs to go hand in hand with effective recycling. So a change in mind-setting is mandatory!

A already developed way to reduce the demand for finite raw materials and to minimize the negative impact on the environment involves chemically recyclable polymers which are capable of being returned to the corresponding monomers in a depolymerization process ready for repolymerization to virgin-quality polymers. This seemingly ideal strategy has motivated the research on the exploration of chemically recyclable polymers and also the mild processes for the catalytic conversion of the recyclable polymers to monomers or new polymers, namely chemolysis (by depolymerizing or decomposing the polymer in the presence of a chemical catalyst, typically needing relatively low temperature or even ambient temperature). Consequently, to advance plastic recycling practices, improving chemical recycling selectivity and efficiency through monomer and polymer design and catalyst development is mandatory, minimizing the need for sorting and expanding recycling beyond polyesters, polyamide and polyurethanes [1]. Recent advances report on the catalytic selective hydrogenolysis of PE at moderate conditions (300 °C, 9 bar H2) on nanoparticles in a solvent free process offering an option to obtain high value re-usable materials out of waste-PE[2] showing as such an opportunity to deal with the already existing large quantities of plastic waste in an economic and ecological way. Key feature of circular economy is preventing waste by making products and materials more efficient and reusing them. The challenge in a circular economy is the development of environmentally friendly polymers with better properties and at the same time facilitating reuse of plastics. This can be e.g. realized by polymers custom designed for recycling such as e.g. all-polymer (one component) composites, self-reinforcing polymers (molecular composites) [3] or the use of reversible chemistry [4]. Starting with the already about 15 year development of instruction of self-repair functionalities [5] in plastics and as such enabling these materials to react early on a developing damage or defect and reducing over-design of systems and realizing weight and cost reduction in a new way the future for plastics and other materials is not only a design or use (self-repair) but a design for recycling. This starts with a design of products to encourage a re-use as well as in a material choice for less impacting and recyclable materials, for reduction of quantities and variations in one product and an optimalisation of process technology. In the design phase, material sorting processes, separation and dismantling considerations are as important as the introduction of extension of lifetime and preservation of functional property technologies like self-repair. Consequently, there is a need for a new generation of materials that can be reprocessed like thermoplastics that still retain the beneficial properties of high performance thermoplastic or thermoset materials4. Such a material can be realized by the incorporation of dynamic interactions and dynamic covalent bonds into linear polymers and networks. In such plastics, thermal depolymerization and solvent assisted depolymerization and especially dissociative depolymerization and adaptable cross-links can transfer the non-recyclable plastics to small thermoplastic piece and thus enabling complete recycling and re-use. Alternatively (and parallel)it is the intrinsic production of cost-, resource-, eco- and energy efficient high performance polyolefin’s using modern multisite polymerization catalysts as reported recently by Mühlhaupt et al. resulting in all polyolefin injection moldable composites with a unique combination of high toughness, stiffness and strength which show virtually no change in properties during 7 cycles of remolding one of the most promising trends3. While following these ideas, the future of Plastics and ourselves will be still great. The need is to shift now from the Design-for-Use –self repair – strategies to the Design for recycling–multiple use- strategies to guarantee a bright Future for Plastics.

References

  1. Tang X, Chen E XY (2018) Toward Infinitely Recyclable Plastics Derived From Renewable Cyclic Esters. Chem https://doi.org/10.1016/j.chempr.2018.10011
  2. GokhanCelik, Robert M Kennedy, Ryan A Hackler, MagaliFerrandon, AkalankaTennakoon,  et al. (2019) Upscaling Single-Use Polyethylene into High Quality Liquid Products. ACS Central Science DOI: 10.1021/asccentsci.9b00722.
  3. Hees T, Zhong F, StürzelM, Mühlhaupt R (2018) Tailoring Hydrocarbon Polymer and All Hydrocarbon Composites for a Circular Economy. Makrom. Rapid. Comm 1800608.
  4. Post W, Arijana Susa, Rolf Blaauw, Karin Molenveld, Rutger JIKnoop, et al. (2019) A Review on the Potential and Limitations of recyclable Thermosets for Structural Applications. Polym Rev https://doi.org/10.1080/15583724.2019.1673406.
  5. Fischer H (2010) “Self Healing Material Systems – A Dream or Reality”. Natural Science 2: 873–901.

First Case Report of Pancreatitis in Lyme disease

DOI: 10.31038/IMROJ.2019423

Short Abstract

We report a case of Lyme disease, revealed by pancreatic damage in a 49-year-old man without any medical history. The Lyme disease was revealed by repeated abdominal pain for 4 weeks, a skin lesion of quadricipital region, biological and radiological results showing pancreatic abnormalities.

Case Report

A 49-year-old man, non-alcoholic forest worker, with no past medical history, consulted to the Emergency Department for fever and persistent abdominal pain for a week. The biological results including, C – reactive protein (CRP), lipase, hepatic assessment were normal as well as contrast-enhanced abdominal Computed Tomography (CT). On the day after, the evolution was favorable under symptomatic treatment including nefopam and paracetamol and the patient was discharged from the hospital. One week later, the patient was admitted to the Emergency Department with an identical symptomatology. A posterior quadricipital peeling skin lesion, appeared two weeks earlier according to the patient, was observed (Figure 1a.) A gastroscopy, a colonoscopy, other abdominal CT and biological tests were performed. An inflammation biomarker elevation was observed (CRP: 180 mg/L and hyperleukocytosis: 13.3 G/L) without other biological abnormalities (lipase: 48 UI/L, ALAT: 48 UI/L). The endoscopic examinations and abdominal CT were normal. The patient was discharged from the hospital without any treatment. Half a month later, the patient was admitted to the Emergency Department for the third time and recurrence of the abdominal pain. The clinical examination found a hemodynamic stability, an abdominal pain of the left hypochondrium associated with a cutaneous ulcerative and non-progressive skin lesion in the same region as previously mentioned (Figure 1b.). The biological assessment found a very mild inflammatory syndrome (CRP 86 mg/L, Procalcitonin < 0.2 ng/mL, leukocytes 9.5 G/L), a high lipase level at 1714 IU/L without hepatocellular abnormalities. The third abdominal CT revealed an aspect of pancreatic necrosis with a pseudocyst (6 cm) at the tail of the pancreas, in contact with the splenic hile and the posterior wall of the stomach (Figure 1c.). The patient was hospitalized in Intensive Care Department with the diagnosis of pancreatitis.

On admission, the work-ups looking for the usual causes of pancreatitis (alcohol, gallstones, medications induced, hypercalcemia, traumatic, chemical exposures, hereditary diseases, infections) were negative. Regarding the skin patient’s lesion and anamnesis, the diagnosis of Lyme disease was evoked. His Lyme serology was strongly IgM positive and confirmed by Western Blot. He was treated with ceftriaxone associated with effective analgesic therapy. The clinical and biological course was uneventful and the patient was discharged from the hospital after 3 weeks. The relationship between Lyme disease and acute pancreatitis was strongly suspected.

Discussion

Lyme disease is an endemic zoonosis, transmitted to humans by a tick bite causing a multisystemic impairment due to a Gram-negative bacillus, Borrellia burgdorferi [1]. The disease schematically includes two phases and a polymorphism in clinical manifestations: a primary phase with chronical migrans erythema and articular signs (80% of cases), a secondary phase of heterogeneous and lymphatic dissemination, inaugurated by flu-like symptoms and associating neurological, cardiac or articular signs that could become chronic [2]. Each of these attacks could be inaugural or/and isolated [3]. Concerning the anamnesis, only 30% of patients remember a tick bite [4].

The heterogeneity of presentation in Lyme disease includes the serodiagnosis as a central investigation for confirmation [5]. Hepatic impairment due to Lyme disease, including hepatitis and hepatomegaly, is inconsistent, commonly found in early stage but often asymptomatic and with plasmatic manifestations [6]. A moderate hypertransaminasemia (2 to 3 N) could be noted, predominating on the ALAT. This hepatic biologic involvement is present in 27 to 66% of cases [7]. This can be explained by a systemic, lymphatic migration of the incriminated bacteria and a secondary hepatic sequestration [8]. To our knowledge, this physiopathological evolution to explain liver disorders has never been described for pancreas but is probably similar.

IMROJ 19 - 139_N Pichon_F1

Figure a Skin lesion, 2 weeks after supposed tick bite. b Skin lesion, 4 weeks after supposed tick bite. c Abdominal CT scan showing pancreas (arrow) and the pseudocyst at the tail (head arrows).

Regarding the treatment of Lyme disease, the cycline are recommended for the uncomplicated forms. An antibiotic treatment with cephalosporins could be considered for cardiac, neurological or complicated cases [2]. The evolution is favorable in 85% of patients, including hepatic acute injuries [9].

In our case, the skin lesion associated with a supposed tick bite, the anamnesis, the absence of other cause of pancreatitis, the favorable evolution under antibiotic treatment and especially the strong positivity of the serology are in favor of a Borrelia burgdoferi infection.

Conclusion

The authors report the first case of pancreatitis revealing a Lyme disease. Clinical, biological and evolutionary findings support the responsibility of Lyme disease in the pathogenesis of our pancreatitis case.

Acknowledgment

The authors thank Dr Sommabere from the department of bacteriology for his contribution on the project.

References

  1. Steere AC, Grodzicki RL, Kornblatt AN, Barbour AG, Burgdorfer W, et al. (1983) The spirochetal etiology of Lyme disease. N Engl J Med 308: 733–740.
  2. Sanchez JL (2015) Clinical Manifestations and Treatment of Lyme Disease. Clin Lab Med 35: 765–778.
  3. Sanchez E, Vannier E, Wormser GP, Hu LT (2016) Diagnosis, Treatment, and Prevention of Lyme Disease, Human Granulocytic Anaplasmosis, and Babesiosis: A Review. JAMA 315: 1767–1777.
  4. Cameron DJ, Johnson LB, Maloney EL (2014) Evidence assessments and guideline recommendations in Lyme disease: the clinical management of known tick bites, erythema migrans rashes and persistent disease. Expert Rev Anti Infect Ther 12: 1103–1135.
  5. Kim B (2017) Western Blot Techniques. Methods Mol Biol Clifton NJ 1606: 133–139.
  6. Goellner MH, Agger WA, Burgess JH, Duray PH (1988) Hepatitis due to recurrent Lyme disease. Ann Intern Med 108: 707–708.
  7. Steere AC, Bartenhagen NH, Craft JE, Hutchinson GJ, Newman JH, et al. (1983) The early clinical manifestations of Lyme disease. Ann Intern Med 99: 76–82.
  8. Imai DM, Samuels DS, Feng S, Hodzic E, Olsen K, et al. (2013) The early dissemination defect attributed to disruption of decorin-binding proteins is abolished in chronic murine Lyme borreliosis. Infect Immun 81: 1663–1673.
  9. Horowitz HW, Dworkin B, Forseter G, Nadelman RB, Connolly C, et al. (1996) Liver function in early Lyme disease. Hepatol Baltim Md 23: 1412–1417.

Ultrasonographic aspect of Pulmonary Emboli

DOI: 10.31038/IMCI.2019221

 

The availability of thoracic ultrasonography (TUS) for the diagnosis of pulmonary infarct was demonstrated more than 15 years ago [1]. With so, the typical TUS aspect of the embolic lesion remain largely unknown by physicians that otherwise use this tool in an every-day basis. More than two triangular or rounded hypo-echogenic areas bigger than 5 mm, occasionally accompanied by small pleural effusion are suggested TUS criteria for pulmonary infarct diagnosis [2]. Computed tomography angiography (CTA) is the gold standard for the diagnosis [3].

The ultrasonographic aspect of pulmonary embolism is shown here correlating to the accompanied computed tomography of the same lesion.

A 28-year-old man, affected by Smith-Lemli-Opitz syndrome, was admitted to the intensive care unit because of a severe respiratory failure and hemodynamic shock requiring mechanical ventilation and vasopressors. Two weeks before admission, ambulatory antibiotic treatment was started for suspected pneumonia. The CTA study (Panel A) shows multiple pulmonary infarcts on both lungs (arrows). TUS examination (Panel B) of the right thorax shows the corresponding sonogram image for the tomography finding (arrowhead).

Multiple sub-pleural lesion occupied the entire space between two ribs. The lung parenchyma behind the lesion is hyperechoic and shows typical B lines of interstitial lung (small arrows). The central hyperechogenic rounded image at the ultrasonography matches the hypodense rounded bronchi (black arrows) at the CTA.  The limit between the hypo-echogenic area and the surrounding lungs is shattered. A deeper bronchial reflex with air-flow Doppler signal can be seen at times when the lung tissue becomes consolidated [4].

Thrombotic occlusion of the right peroneal vein and posterior tibial vein found at duplex ultrasonography was the source of the embolism.

The patient condition responded to heparin treatment.

IMCI 19 - 113_Daniel J. Jakobson_F1

Panel A. Computed Tomography image.

IMCI 19 - 113_Daniel J. Jakobson_F2

Panel B. Ultrasound image.

References

  1. Mathis G, Wolfgang B, Reissig A, et al. (2005) Thoracic ultrasound for diagnosing pulmonary embolism: a prospective multicenter study of 352 patients. Chest  128: 1531–1538.
  2. Reissing A, Heyne JP, Kroegel C (2001) Sonography of lung and pleura in pulmonary embolism: sonomorphologic characterization and comparison with spiral CT scanning. Chest 120: 1977–1983.
  3. RathbunSW, Raskob GE, Whitsett L. (2000) Sensitivity and specificity of helical computed tomography in the diagnosis of pulmonary embolism: a systematic review. Ann Intern Med 132: 227–232.
  4. Blancas R, Ballesteros-Ortega D, Martinez-Gonzales O (2018) Central bronchial reflex finding in pulmonary infarct. Med Intensiva 42: 69.

Novel Pathology-Related Hub Genes in Focal Segmental Glomerulosclerosis

DOI: 10.31038/JCRM.2019252

Abstract

Objectives: Focal Segmental Glomerulosclerosis (FSGS) is a progressive glomerular disease. The pathogenesis of this disease, however, remains unclear. Here, we attempted to identify key candidate genes in FSGS through stringent bioinformatic analysis.

Methods: We systematically searched the Gene Expression Omnibus database for gene expression microarrays derived from human glomeruli tissues with FSGS. First, we identified differentially expressed genes (DEGs) by using the Limma package in R. Then, we subjected these DEGs to Gene Ontology (GO) analysis for further analysis. Finally, we constructed Protein–Protein Interaction networks (PPI) through four different methods and performed intersection analysis to further refine our results.

Results: A total of 627 DEGs were identified between the FSGS and control groups, among which 534 were up-regulated and 93 were down-regulated. GO analysis revealed that the DEGs were enriched in mRNA processing, cell adhesion molecule binding, and cadherin binding. Furthermore, via PPI, 7 DEGs overlapped in the four groups constructed through different analytical approaches. We also validated the overlapped 7 hub genes in in vitro experiments, including RBM5 and HNRNPF, with potentially important roles in the development of FSGS.

Conclusion: Our study provides a valuable resource for novel biomarkers and therapeutic targets for FSGS.

Keywords

Focal segmental glomerulosclerosis; Bioinformatic analysis; RBM5; HNRNPF

Introduction

Focal Segmental Glomerulosclerosis (FSGS) is a primary glomerular disease that manifests with heavy proteinuria [1]. It is the leading cause of the development of end-stage renal disease. Typically, FSGS lesions present a segmental manifestation that includes parietal cell migration, hyaline deposition, capillary collapse, and intracapillary thrombi.  Recent studies suggest that podocyte injury may play a key role in FSGS lesions [2]. Injury and loss of podocytes result in foot process effacement and protein loss [3]. However, the pathogenesis of FSGS remains unclear, and the present diagnostic and therapeutic methods for this disease remain inadequate. Oxidative stress has been implicated in the development and progression of this FSGS [4,5]. Nuclear factor E2-related factor 2 (Nrf2) is a transcription factor that can potently induce the production of numerous antioxidants and prevent the generation of oxidative stress in renal fibrosis and inflammation [6–8]. Furthermore, apoptosis and the renin–angiotensin system are strongly involved in FSGS-related injury
[9–12]. Nevertheless, a considerable amount of important FSGS genes remain unidentified given the lack of global analysis.

With the development of bioinformatic analysis technology, gene expression profiling analysis has been increasingly used to explore molecular mechanisms and identify novel biomarkers in various diseases [13–16]. Bioinformatic analysis is mainly used to predict novel diagnostic biomarkers and therapeutic targets associated with tumors, such as bladder cancer [17], meningioma [18], and hepatocellular carcinoma [19]. The application of bioinformatic analysis in renal diseases, such as renal cell carcinoma [20], lupus nephritis [21], IgA nephropathy [22], and chronic kidney disease [23], has begun to develop gradually. However, up to now, no study has subjected FSGS to bioinformatic analysis. Thus, exploring the underlying crucial genes and effective therapeutic targets for FSGS through bioinformatic analysis is necessary.

In this study, we downloaded the gene expression profile datasets of FSGS from the Gene Expression Omnibus (GEO) database and investigated Differentially Expressed Genes (DEGs) between FSGS and control samples by using the Limma package in R. We performed Gene Ontology (GO) enrichment analysis for all DEGs. In addition, we constructed protein–protein interaction (PPI) networks, identified novel hub genes, and validated them in in vitro experiments. Our study aimed to predict novel diagnostic biomarkers and potential therapeutic targets for FSGS.

Materials and Methods

Data collection

Gene expression profiles were retrieved from NCBI’s GEO database (http://www.ncbi.nlm.nih.gov/geo/) by using the key words “focal segmental glomerulosclerosis” with the following criteria: 1) the study type is expression profiling by array, 2) the attribute name is tissue, 3) the organism of interest is Homo sapiens, and 4) the platform used is the Affymetrix Human Genome U133A Array. Ultimately, on the basis of the above criteria, we selected dataset GSE47185 of FSGS. Original CEL files were used for further bioinformatic analysis.

Data preprocessing

CEL files were normalized and converted to expression profiles by using the Affy package of R [24]. In brief, the original data were read using the Affy Bioconductor package and preprocessed for normalization through the robust multiarray analysis method, which includes background correction, normalization, expression calculation and batch effects removal. After obtaining the gene expression value, genes were annotated with the hgu133A.db and annotate software packages.

DEG analysis

The Limma package of R [25] was used to analyze DEGs after preprocessing. The linear fit method, Bayesian analysis, and t-test algorithm were used to calculate the P and FC values. DEGs were screened by setting a cut-off value of |log 2 Fold Change (FC)| > 1 and P < 0.05. The ggplot2 software package was used to visualize results. Moreover, to identify and visualize the DEGs between FSGS and normal samples, we generated a heat map of the top 10% DEGs by using the heatmap package (Version 1.0.8).

GO enrichment analysis for DEGs

The GO consortium includes three independent branches: Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). In this study, we subjected the identified DEGs to GO enrichment analysis by using R and the clusterProfiler package [26]. P < 0.05 was used as the threshold for the identification of significant GO terms.

PPI Network Construction

To explore the relationships among the top 30% DEGs, we used the online tool STRING (Search Tool for the Retrieval of Interacting Genes/Proteins; http://string.embl.de/) database for the construction of PPI networks. The minimum required interaction score of 0.4 was used as the significant cut-off threshold. Then, the obtained PPI interaction networks were visualized by using Cytoscape software (version 6.3).

Hub-gene screening

On the basis of the STRING results, we introduced the four methods Degree, EPC, Maximal Clique Centrality (MCC), and DMNC [27] to rank the importance of nodes in the PPI networks and to further identify hub genes from the top 30% DEGs. Nodes with high centrality scores were considered hub genes. We applied the R package Venn diagram (version1.6.17, https://cran.r-project.org/web/packages/VennDiagram/) [28] to identify overlapping DEGs among these hub genes.

Degree (Deg(v) = |N(v)|) is a computing tool in Cytoscape software [29]. The default filter “in and out” was between 7 and 42 in the present study.

MCC is a topological analysis method in CytoHubba [27]. Given a node v, the MCC of v is defined as MCC(v) = ∑CÎS(v)(|C|−1)!.

Maximum Neighborhood Component (MNC) is another computing tool in cytoHubba. MNC(v) = |V(MC(v))|, where MC(v) is a maximum connected component of G[N(v)], and G[N(v)] is the induced subgraph of G by N(v). On the basis of MNC, Lin et al. proposed that DMNC(v) = |E(MC(v))|/ |V(MC(v))| ε , where ε = 1.7.

Cell culture

Conditionally immortalized human podocytes (LY893) were kindly provided by Dr. Lan Ni and Moin Saleem(Bristol, U.K.). Podocytes were cultured in RPMI 1640 medium(Gibco)supplemented with 10% Fetal Bovine Serum (Gibco) and 1% Insulin-Transferrin-Selenium(Invitrogen) at 33°C under 5% CO2 for propagation, then were thermo switched to 37°C under 5% CO2 when at 60% confluency for differentiation. The differentiated podocytes were incubated with adriamycin to construct an in vitro model for FSGS [30].

Quantitative Real-Time PCR

Total RNA was extracted using the RNeasy Plus Mini Kit (BioTeke RP1202) in accordance with the manufacturer’s instructions. The cDNA was obtained by reverse transcription, amplified and detected using a SYBR Green Supermix kit (Takara). Then, a BIO-RAD CFX-96 Real-Time PCR system (Bio-Rad) was used for PCR analysis under the following conditions: 95 °C for 3 min, followed by 40 cycles of 95 °C for 10 s and 51 °C for 30 s. The primer sequences used for PCR are listed in Table 1. Statistical differences were determined by Student’s t-test using R “ggpubr” package(version 0.1.8, https://CRAN.R-project.org/package=ggpubr).

Table 1. Primer sequences for RT-PCR.

Gene

Forward primer

Reverse primer

FUS

5’ GCAGGAGTTTGTGGAGTG 3’

5’ TGAGTACAGGCAGGATGTG 3’

DHX15

5’ CTTTACAAGCAGGGACTA 3’

5’ TCAAGAACAGTAGAGGGAT 3’

PRPF31

5’ TGTCGGGCTTCTCGTCTA 3’

5’ CACCTTCCCTTCTGTGCTCT 3’

PQBP1

5’ CAAGAAGGCAGTAAGCCGAAAG 3’

5’ TGTGGTGTCAGCGCCAGTC 3’

RBM5

5’ GGTGCGAAATGGAGATGA 3’

5’ AGAGTTGCTGGTGCCTGA 3’

HNRNPR

5’ AAGTCCCACAGAACCGAGAT 3’

5’ AACCCTGAGAAGAACTGAACAA 3’

TRA2B

5’ CACATACGCCAACACCAG 3’

5’ TCCTCCACCTCCTCCTCT 3’

GAPDH

5’ CTTTGGTATCGTGGAAGGACTC 3’

5’ GTAGAGGCAGGGATGATGTTCT 3’

Results

DEGs identification

This dataset GSE47185 contains the mRNA expression profiles of 13 FSGS samples and 14 control samples (normal tissue of renal tumor excision). Under the threshold of |log 2 fold change (FC)| > 1 and adj.P value < 0.05, 627 DEGs were identified between the FSGS and control groups. These DEGs included 534 up-regulated and 93 down-regulated DEGs. The results of expression-level analysis are presented as a volcano plot in Fig. 1A. As shown in Table 2, RPS4Y1, PLPP3, DDX3Y, SART3, TCF4, TROVE2, IQGAP1, MBP, CALD1, and RBFOX2 are the 10 most significantly up-regulated genes, whereas CYP4A11, FOSB, EGR1, G6PC, ALB, CTSZ, PPP3R1, XIST, PCK1, and HPGD are the 10 most significantly down-regulated genes. The more information of all DEGs is listed in supplementary Table 1.

Fig1

Figure 1.
Visualization of DEGs
A, The volcano plot of differentially expressed genes between FSGS and healthy tissues. The red plots represent up-regulated genes, green plots represent down-regulated genes, while grey plots represent non-significant genes. The volcano plot was constructed using the ggplot2 package of the R language; B, A heatmap of the top 10% DEGs. The horizontal axis denotes the different samples, and the vertical axis denotes different DEGs.blue, normal samples; red, FSGS samples; purple clusters represent up-regulated DEGs and green clusters represent downregulated DEGs. Color key represents the Z-score based on the Gene expression value.

Table 2. The Most Significant 10 Up-Regulated Genes And Down-Regulated Genes.

Gene Symbols

Log FC

Average Expression level

Adj.P. Value

RPS4Y1

2.569009

9.570289

1.11E-02

PLPP3

2.485089

9.251834

7.13E-07

DDX3Y

2.478844

7.003028

1.62E-02

SART3

2.470577

7.660357

5.82E-10

TCF4

2.41899

7.335133

1.27E-09

TROVE2

2.355365

9.457713

7.39E-07

IQGAP1

2.290646

8.145384

2.19E-08

MBP

2.255968

7.587945

3.91E-10

CALD1

2.192563

8.074506

2.21E-09

RBFOX2

2.164463

8.698168

8.22E-09

CYP4A11

-2.41227

9.128665

1.42E-05

FOSB

-2.35626

8.074382

4.53E-07

EGR1

-2.18278

10.28014

1.01E-07

G6PC

-2.07454

6.566343

5.57E-04

ALB

-2.0735

9.571928

1.21E-03

CTSZ

-1.91281

8.256097

3.49E-08

PPP3R1

-1.88153

7.627366

1.74E-06

XIST

-1.86386

7.615666

1.50E-02

PCK1

-1.7723

10.99439

2.73E-04

HPGD

-1.7174

10.07267

3.87E-06

The heatmaps of the top 10% DEGs are shown in Fig. 1B. The data are presented in a matrix format, in which rows represent individual genes, and columns represent individual samples. The purple and green colors indicate up-regulated and down-regulated genes, respectively. The hierarchy cluster analysis indicated that FSGS and control groups could be distinguished from each other on the basis of their different expression patterns.

Functional enrichment analysis

To reveal the biological functions of DEGs, we used the clusterProfiler package for GO analysis. We set adj.P value < 0.01 to identify significantly enriched GO terms. The top eight GO terms for the DEGs enriched in the BP, CC, and MF are shown in Figure 2. The DEGs were mainly involved in GO terms that included mRNA processing, regulation of mRNA metabolic process, antigen processing and presentation of exogenous antigen, focal adhesion, cell adhesion molecule binding, cadherin binding, and actin binding. Among these terms, the MFs related to focal adhesion (GO:0005925) [31,32], cell adhesion molecule (GO:0050839) [33,34], cadherin (GO:0045296) [35], and actin binding (GO:0003779) [36] were all confirmed to be involved in glomerulosclerosis. Detailed information on the top eight GO terms is shown in Table 3.

JCRM_Zhongying Huang_F2

Figure 2. The 8 most significant enriched GO terms of DEGs
The adj.P value< 0.01 was used as the threshold for the identification of significant GO terms. The Gene ontology covers the biological process, cellular component, and molecular function.The horizontal axis represents the gene counts, the vertical axis represents GO terms.Green column graphs represent biological process(BP) GO term; orange column graphs represent cellular component(CC) GO term; and blue column graphs represent molecular function (MF) GO term.

Table 3. The Most Significantly Enriched GO Terms In BP ,CC and MF.

JCRM_Zhongying-Huang_F5

PPI Network Construction and Hub-Gene Screening

The major part of the constructed PPI network is presented in Figure 3A. To further reduce the scope for analysis, we analyzed the PPI network by using the four analysis methods in CytoHubba based on the R package Venn diagram. We used the top ranked 20 DEGs to identify seven overlapping hub genes screened through the four CytoHubba methods (Degree, EPC, MCC, and DMNC) in cytoscape software (Figure 3B–F). The seven overlapping hub genes included FUS, DHX15, PRPF31, PQBP1, RBM5, HNRNPR, and TRA2B. Strikingly, the identified hub DEGs in our study have never been reported in literature related to FSGS. In addition, these genes simultaneously ranked to the high position by the four different CytoHubba methods suggests they may play important roles in the development of FSGS.

Fig3

Figure 3: Protein-protein interaction (PPI) networks of DEGs and screening of hub genes
A: The major part of PPI network; B: Venn diagram of differentially expressed genes based on four screening methods including “Degree”, “EPC”, “MCC”, and “DMNC”; C: PPI of DEGs screened by the method “Degree” in Cytohubba; D: PPI of DEGs screened by the method “EPC” in Cytohubba; E: PPI of DEGs screened by the method “MCC” in Cytohubba; F: PPI of DEGs screened by the method “DMNC” in Cytohubba. The depth of red represents the rank of the hub genes.

Gene Expression Validation in In -Vitro Experiments

We validated the 7 top ranked hub genes expression in the FSGS model in vitro (Figure 4). Quantitative real-time PCR indicated that the mRNA levels of FUS, DHX15, PQBP1, RBM5, and HNRNPR were up-regulated after they were stimulated by adriamycin (ADR). The changes in PRPF31 and TRA2B mRNA were statistically insignificant. Except for PRPF31 and TRA2B, the changes in all of the other gene expression levels were consistent with the bioinformatic analysis results (Supplementary Table 1), with the accordance rate reaching 70% approximately.

R Graphics Output

Figure 4. In vitro validation for the novel hub genes
Adriamycin (ADR) (0.125 ug/ml) was used to stimulate confluent conditionally immortalized human podocytes (LY893) for 0 (control) and 48 h. The mRNA expression levels of 7 novel top hub genes were measured by quantitative real-time PCR. The mRNA expression levels of the target genes were normalized to that of GAPDH. The data in three separate experiments were presented as mean ± SD (n=3). *Significantly changed expression levels in ADR-stimulated cells compared with the controls (P<0.05). N.S., no significant difference (P>0.05)

Extended information on Potential Hub Genes

On the basis of the above results, we used the abbreviations of the seven hub genes and FSGS as keywords to search the NCBI database for identifying the potential relationship between these hub genes and FSGS. Search results revealed that the seven hub genes have never been reported in literature related to FSGS. Then, we carefully collated information relevant to the biological functions and signaling pathways that involve these hub genes on Gene Cards website (https://www.genecards.org). The results indicate that heterogeneous nuclear ribonucleoprotein F (HNRNPF) is closely related to Nrf2 gene expression, renal angiotensinogen gene expression, the TGF-β1 signaling pathway, and oxidative stress. Moreover, RNA-binding motif protein 5 (RBM5) is involved in apoptosis induction in many tumors. Ultimately, in accordance with the accepted pathogenesis of FSGS, we selected HNRNPF and RBM5 as representative targets for further discussion. Extended information on HNRNPF and RBM5 are shown in Table 4.

Table 4. Extended Information of The Potential MN-Related Hub Genes.

Gene

Function

Disease/Cells

DOI

Authors

HNRNPF

Stimulates renal Ace-2 gene expression and prevents TGF-β1-induced kidney injury

Diabetes

10.1007/s00125-015-3700-y

Lo CS, Shi Y, Chang SY

Mediate renal angiotensinogen gene expression and prevention of hypertension and kidney injury

Diabetes

10.1007/s00125-013-2910-4

Abdo S, Lo CS, Chenier I

Inhibits Nrf2 Gene Expression

Diabetic mice

10.1210/en.2016-1576

Ghosh A, Abdo S, Zhao S

Against oxidative stress

Diabetic mice

10.2337/db16-1588

Lo CS, Shi Y, Chenier

Suppresses angiotensinogen gene expression

Diabetic mice

10.2337/db11-1349

Lo CS, Chang SY, Chenier I

Modulate the alternative splicing of the apoptotic mediator Bcl-x

Human HeLa cells

10.1074/jbc.M501070200

GarneauD,Revil T, Fisette JF

Modulates angiotensinogen gene expression

Diabetes

10.1681/ASN.2004080715

Wei CC, Guo DF, Zhang SL

RBM5

Inhibition of Wnt/β-catenin signaling and induction of apoptosis

Gliomas

10.1186/s12957-016-1084-1

Jiang Y, Sheng H, Meng L

Impacts cell proliferation and apoptosis

Lung cancer

10.1615/JEnvironPatholToxicolOncol.2017019366

Prabhu VV, Devaraj N

Regulates the activity of Wnt/β-catenin signaling

Alveolar epithelial injury

10.3892/or.2015.3828

Hao YQ, Su ZZ, Lv XJ

Promotes caspase activation

Human neuronal cells

10.1038/jcbfm.2014.242

Jackson TC, Du L,Janesko-Feldman K

Promotes neuronal apoptosis

Spinal cord injury

10.1016/j.biocel.2014.12.020

Zhang J, Cui Z, Feng G

Inhibits cell growth and induces apoptosis

Lung adenocarcinoma

10.1186/1477-7819-10-160

Shao C, Zhao L, Wang K

Discussion

Bioinformatics is a newly developed interdisciplinary subject that combines biological science and computer science. Over the past few years, a growing body of research has used gene expression profiles to explore key genes in the pathogenesis of numerous diseases [15,16,40,41]. To our knowledge, our study is the first work that subjected FSGS to bioinformatic analysis. We identified 627 DEGs between the FSGS and control groups. These DEGs included numerous DEGs that have not been previously reported to be involved in FSGS. Then, we predicted DEG functions on the basis of GO annotations. The GO terms we identified included focal adhesion [31,32], cell adhesion molecule [33,34], cadherin [35], and actin binding [36]. These processes are associated with glomerulosclerosis. For example, the genetic deletion of Epb41l5, a podocyte-specific focal adhesome component, results in podocyte detachment, severe proteinuria, and focal segmental glomerulosclerosis development [31]. In immortalized human podocytes, the overexpression of R431C mutant ANLN, an F-actin binding cell cycle gene, enhances podocyte motility [36]. Next, we identified seven overlapping hub genes by constructing PINs through four different analytical methods. Through an accurate search of the NCBI database, we identified HNRNPF and RBM5 as the representative targets for further elaboration.

HNRNPF is a protein-coding gene associated with gene expression. However, no research has been conducted on the role and mechanisms of HNRNPF in FSGS. In this work, we found that HNRNPF is an important DEG among the seven overlapping hub genes in the PPI networks. In addition, HNRNPF is deeply involved with Nrf2 [42], a renal angiotensinogen gene that is expressed in the kidney [43]. Furthermore, in diabetic mice, HNRNPF participates in the TGF-β1 signaling pathway [44] and oxidative stress [45]. These genes and pathways have been confirmed to play vital roles in the pathogenesis of FSGS [4,11,46]. One research suggested that osthole could improve FSGS by activating the Nrf2 antioxidant pathway [47]. TGF-β1 reduces WT1 expression in mouse podocytes and cultured human podocytes before overt glomerulosclerosis begins [46]. In addition, damage to podocytes stimulates TGF-β1 and TGF-βIIR expression in glomerular epithelial cells; this effect eventually leads to extracellular matrix overproduction [48]. On the basis of our analytical results, we conclude that HNRNPF likely participates in FSGS through oxidative stress-associated genes and pathways and is a potential biomarker for this disease.

RBM5 is a nuclear RNA-binding protein that is often genetically deleted in renal cancer49. Unfortunately, the role of RBM5 in FSGS remains unreported. In our study, we identified RBM5 as an up-regulated hub gene in FSGS. Moreover, RBM5 actively participates in apoptosis induction in tumors [50,51]. Apoptosis-induced podocyte damage is a key factor in the pathogenesis of FSGS [12,52]. On the basis of previous findings combined with our present bioinformatic analysis results, we speculate that RBM5 may participate in apoptosis promotion during FSGS progression.

Conclusion

Our study provides a fast, powerful, and effective strategy for the discovery of novel diagnostic biomarkers and therapeutic targets for FSGS. Our results suggest that HNRNPF and RBM5 are molecular candidates for the diagnosis and treatment of FSGS. However, our results are preliminary, and further work is needed to decipher these candidate genes.

Author contributions

Q.M and Z.H designed the research; Z.H analyzed the data and performed the research; D.Z wrote the manuscript. All authors read and approved the final manuscript.

Qianhong Ma and Dongmei Zhang contributed equally to this work and should be considered co-first authors.

Funding

This work was supported by grants from the National Natural Science Foundation of China (Grant No. 81200453).

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Supplementary Table 1

gene.symbols

logFC

AveExpr

ts

P.Value

adj.P.Val

B

RPS4Y1

2.569009422

9.570289361

3.101358611

0.004371381

0.011115768

-2.711721859

PLPP3

2.485089122

9.251833831

7.62668606

2.66E-08

7.13E-07

9.068379301

DDX3Y

2.478844353

7.003027727

2.920309189

0.006843408

0.016208722

-3.132862184

SART3

2.470577198

7.660356798

12.25209245

9.40E-13

5.82E-10

19.16340462

TCF4

2.418989646

7.335132517

11.59356528

3.44E-12

1.27E-09

17.89787797

CYP4A11

-2.412274749

9.12866545

-6.123580669

1.33E-06

1.42E-05

5.192258061

FOSB

-2.356258176

8.074381933

-7.877904555

1.42E-08

4.53E-07

9.690625188

TROVE2

2.355365338

9.457713493

7.605950945

2.80E-08

7.39E-07

9.016659035

IQGAP1

2.290645788

8.14538406

9.69432624

1.94E-10

2.19E-08

13.93671972

MBP

2.255967535

7.587944516

12.62989302

4.56E-13

3.91E-10

19.86587003

CALD1

2.192562666

8.074505734

11.18804096

7.85E-12

2.21E-09

17.09171526

EGR1

-2.182777112

10.2801361

-8.747798502

1.72E-09

1.01E-07

11.78050827

RBFOX2

2.164462681

8.69816763

10.33705244

4.72E-11

8.22E-09

15.33081151

IGFBP5

2.148130254

9.995294632

5.537086859

6.45E-06

5.09E-05

3.626766637

CDC42BPA

2.079395261

9.770721553

7.925115034

1.26E-08

4.20E-07

9.806647308

SMAD1

2.076607382

7.949965987

8.465377555

3.38E-09

1.61E-07

11.11324049

G6PC

-2.074536589

6.566342687

-4.459033616

0.000121983

0.000557225

0.736400459

ALB

-2.073497949

9.571928342

-4.114808392

0.000309678

0.001209773

-0.171567409

ZEB1

2.066318697

8.614493251

10.45925661

3.63E-11

7.03E-09

15.5895635

TPM1

2.060897076

9.232448331

10.04753441

8.88E-11

1.30E-08

14.70977983

FERMT2

2.05930113

7.350506265

7.771402732

1.85E-08

5.49E-07

9.427823748

LUC7L3

2.047718235

8.759118544

8.718232863

1.85E-09

1.06E-07

11.71116708

RAN

2.025548667

10.29196832

11.46235083

4.49E-12

1.47E-09

17.63930778

SLC25A36

2.003665861

8.269066322

8.586494779

2.53E-09

1.31E-07

11.40073865

HLA-DRA

1.992802428

11.12961293

7.574703865

3.03E-08

7.84E-07

8.938615772

HTR2B

1.980142562

6.49870054

5.606952454

5.34E-06

4.35E-05

3.814138409

PCYOX1

1.966411418

7.433879894

7.068593108

1.10E-07

2.03E-06

7.65787493

ATRX

1.961414528

6.588331245

11.16381872

8.25E-12

2.27E-09

17.04289981

DYNC1LI2

1.955508721

9.605304116

10.85559765

1.57E-11

3.64E-09

16.41511633

CD99

1.9554319

9.927197771

8.57318363

2.61E-09

1.33E-07

11.36923982

CHD4

1.94490416

8.814477542

16.59170115

5.35E-16

9.51E-12

26.31762235

MYLIP

1.938154662

9.419703765

7.548038454

3.24E-08

8.23E-07

8.871918527

HNRNPF

1.934376435

8.42993207

13.0216741

2.19E-13

2.32E-10

20.57678872

HLA-DQB1

1.933559554

9.283173095

6.877515473

1.81E-07

2.96E-06

7.16657257

FHL1

1.916965148

7.704294928

10.06888924

8.47E-11

1.27E-08

14.75597408

CTSZ

-1.912814074

8.256096924

-9.422794947

3.59E-10

3.49E-08

13.3308037

DDX17

1.900816911

7.156016292

6.849768897

1.94E-07

3.11E-06

7.094896981

THUMPD1

1.891011353

7.141971356

13.73428793

6.01E-14

1.12E-10

21.82576576

PPP3R1

-1.881530742

7.627366273

-7.148865697

8.96E-08

1.74E-06

7.863044486

TNFRSF11B

1.87836792

7.853323079

7.02736233

1.23E-07

2.20E-06

7.552206366

XIST

-1.863860797

7.615665673

-2.956875261

0.006256155

0.015025427

-3.048847061

SON

1.851258175

7.997946847

8.840334385

1.39E-09

8.53E-08

11.99675806

GLUL

1.846739708

7.937765075

9.235752198

5.51E-10

4.67E-08

12.90751377

DKK3

1.838529383

7.235094143

6.88002716

1.80E-07

2.95E-06

7.173056725

BTG1

1.820054032

7.907833291

11.00485807

1.15E-11

2.94E-09

16.72066783

TRIB2

1.81921404

8.182950746

8.768912824

1.64E-09

9.74E-08

11.82995473

RBM5

1.808667325

8.306181873

13.85226391

4.87E-14

1.09E-10

22.02721576

HLA-B

1.803285216

12.34984324

6.340923899

7.42E-07

8.94E-06

5.766672368

MYH10

1.802983311

6.632652763

13.31027341

1.29E-13

1.80E-10

21.08934934

STAG2

1.793115173

5.860191501

13.76494033

5.69E-14

1.12E-10

21.87824888

GBP1

1.783205635

6.97780633

8.494851598

3.15E-09

1.53E-07

11.18338801

RPA1

1.779726763

9.056001662

9.084532378

7.82E-10

5.91E-08

12.56176241

BPTF

1.776426474

7.907083176

7.356751223

5.27E-08

1.17E-06

8.390867161

PCK1

-1.772301574

10.99438749

-4.775458625

5.15E-05

0.000272763

1.580947098

BTN3A3

1.772184439

6.173764331

8.425121851

3.73E-09

1.74E-07

11.01724161

TAGLN

1.764332395

9.007016683

5.779780851

3.34E-06

3.00E-05

4.276795382

PSMA7

1.763068956

6.889044422

11.69687487

2.80E-12

1.22E-09

18.09994223

HLA-DPA1

1.762375082

10.82315093

6.098738113

1.42E-06

1.50E-05

5.126369472

MYLK

1.75861114

10.39524956

6.152944852

1.23E-06

1.34E-05

5.270080435

YIPF6

1.747988074

9.294461389

8.337256865

4.61E-09

2.03E-07

10.80694239

COL3A1

1.741079028

10.03488916

6.880350494

1.80E-07

2.95E-06

7.173891393

ACTB

1.739818254

10.62946301

4.630671948

7.64E-05

0.000379292

1.193646749

DHX15

1.737775319

10.02003843

9.85519139

1.36E-10

1.72E-08

14.29091385

PPIG

1.731145889

7.407036172

7.251707604

6.89E-08

1.43E-06

8.124803038

SCAF11

1.721893208

9.371324456

10.2275776

5.99E-11

9.60E-09

15.09731143

HPGD

-1.717401682

10.07266901

-6.740627536

2.59E-07

3.87E-06

6.812168581

DPP8

1.715249149

9.638588643

8.318534824

4.82E-09

2.09E-07

10.76199707

WSB1

1.714938423

8.596432117

6.91877743

1.62E-07

2.75E-06

7.273007517

ENC1

1.714016384

6.351234133

9.286980973

4.90E-10

4.30E-08

13.02392792

FN1

1.713084538

9.852710944

6.048869862

1.62E-06

1.68E-05

4.993973035

UPF3A

1.706676789

8.950115438

11.59802708

3.41E-12

1.27E-09

17.90663237

KRT19

1.703164941

8.133804492

4.840602977

4.31E-05

0.000235133

1.755562232

BCLAF1

1.697469525

8.139217001

9.830297038

1.44E-10

1.80E-08

14.23633277

FBXO21

1.697287783

9.28818289

5.462538041

7.90E-06

5.94E-05

3.426663378

IFNGR1

1.695558168

9.158556204

8.094628782

8.32E-09

3.11E-07

10.22081389

HCK

1.695111511

7.690905947

7.857688804

1.49E-08

4.67E-07

9.640854784

CXCL2

-1.686811585

6.477878156

-4.50798237

0.000106777

0.000500487

0.866554554

PHACTR2

1.679845963

7.782456361

7.807529456

1.69E-08

5.14E-07

9.517134367

HNRNPM

1.673764753

11.0821542

11.69753948

2.80E-12

1.22E-09

18.10123784

CAMK2N1

-1.651602298

10.19208306

-6.765897326

2.42E-07

3.70E-06

6.877740372

FUS

1.650968802

9.619854977

13.27029455

1.39E-13

1.82E-10

21.01889984

PLPBP

1.650135185

5.59234436

12.81694912

3.21E-13

2.98E-10

20.20749556

PSMB8

1.646543823

10.456135

7.886039857

1.39E-08

4.48E-07

9.71063892

SEC63

1.640469887

7.954060099

6.382735607

6.64E-07

8.19E-06

5.876729688

PALLD

1.635448714

8.943061698

7.556440719

3.17E-08

8.09E-07

8.892944454

ZBTB16

1.635125895

7.66230392

3.653696968

0.001057166

0.003348994

-1.358707431

RBM25

1.63291277

6.324655258

8.650201845

2.17E-09

1.18E-07

11.5511557

TYROBP

1.630051539

7.893243583

5.141218255

1.90E-05

0.00012023

2.563038692

DACH1

1.623091259

7.101855154

6.801703043

2.20E-07

3.44E-06

6.970537686

PSMC3

1.612543512

9.366600505

5.93353245

2.21E-06

2.14E-05

4.68711502

RBMS1

1.6103609

8.650805194

4.65105936

7.23E-05

0.000362333

1.248105923

PGK1

1.607870056

10.36413941

5.403307466

9.28E-06

6.73E-05

3.267574125

NPEPPS

1.606521068

8.421329051

7.835158094

1.58E-08

4.88E-07

9.585322216

PTPRB

1.604906513

9.444331687

7.330988837

5.62E-08

1.23E-06

8.325736785

TNPO1

1.599343907

9.775968964

8.87594367

1.27E-09

8.10E-08

12.07966014

EZR

1.591497713

8.757810465

5.97334531

1.98E-06

1.97E-05

4.793136085

MAGED2

1.586605561

10.30790428

12.89378949

2.78E-13

2.81E-10

20.3466641

ANKRD12

1.580638115

7.392354398

6.693843176

2.92E-07

4.26E-06

6.690596121

FNBP1

1.575997647

8.157390697

8.31499427

4.86E-09

2.10E-07

10.75349206

NR4A2

-1.57481871

6.24079609

-8.527861329

2.91E-09

1.45E-07

11.26180965

SERBP1

1.573844316

8.951883468

11.22792569

7.23E-12

2.19E-09

17.17193154

SPAG9

1.573771613

5.928584131

12.82512531

3.16E-13

2.98E-10

20.22233593

SCAMP1

1.572326984

7.135740239

7.680340099

2.32E-08

6.50E-07

9.201956772

RAB1A

1.571530153

8.448144883

6.477980274

5.16E-07

6.71E-06

6.126856918

TAOK3

1.570396428

7.085559648

9.345798665

4.28E-10

3.85E-08

13.15714066

SRGN

1.568368396

9.423138514

5.18175814

1.70E-05

0.000109899

2.672037194

ZFYVE21

1.5676445

6.846082627

7.262978403

6.69E-08

1.40E-06

8.153414092

HRG

-1.562922635

8.320562127

-4.315974661

0.000179859

0.000770138

0.357348521

PWP1

1.561834997

8.446947472

8.857117309

1.33E-09

8.24E-08

12.03585221

SKAP2

1.561530224

9.277742191

7.33501641

5.57E-08

1.22E-06

8.335924267

UBXN4

1.551349919

9.105993907

6.390043816

6.51E-07

8.06E-06

5.895950838

PECAM1

1.550870521

9.054536986

6.663178499

3.17E-07

4.53E-06

6.610791348

MBNL2

1.550572397

7.066502239

10.39994319

4.12E-11

7.62E-09

15.46422432

SOX9

-1.548564432

8.004071342

-6.562764085

4.13E-07

5.60E-06

6.348811078

TRIM2

1.546572067

8.578770031

8.831695492

1.41E-09

8.68E-08

11.97661948

HNRNPR

1.545721471

9.080452193

10.58685737

2.76E-11

5.70E-09

15.85761598

MYOF

1.54258444

8.617870178

7.043673577

1.18E-07

2.13E-06

7.594032778

NR3C1

1.537314952

8.658438842

7.183782173

8.20E-08

1.62E-06

7.95205499

TFPI2

1.530489929

8.411366859

3.999919431

0.000421606

0.001565513

-0.471022596

TMEM47

1.524753951

8.263583054

6.004266035

1.83E-06

1.84E-05

4.875407299

PURA

1.522929493

7.624254878

6.128405127

1.31E-06

1.41E-05

5.20504847

RAB31

1.519313807

8.597342752

6.411935462

6.15E-07

7.72E-06

5.95349937

CYB5B

1.515075529

7.815221603

11.7624269

2.46E-12

1.16E-09

18.22746762

RIT1

1.514871553

7.235874533

7.407198303

4.63E-08

1.07E-06

8.518169631

PRPF31

1.51015233

6.970023854

7.948526057

1.19E-08

4.05E-07

9.864072564

ALDOB

-1.504568555

8.472159452

-3.061872507

0.004824275

0.012090824

-2.80464753

MAP4

1.503555867

8.031894594

5.975611845

1.97E-06

1.96E-05

4.799168802

RAB2A

-1.502751043

8.315889782

-16.2138848

9.61E-16

9.51E-12

25.7672033

BTN3A2

1.502705438

7.877942556

6.982291597

1.38E-07

2.40E-06

7.436477371

GMDS

1.497770885

9.599429406

7.605970724

2.80E-08

7.39E-07

9.016708396

CD163

1.496518651

7.363641477

3.780358703

0.000756817

0.002538255

-1.036824644

PKN2

1.492252244

7.396561746

9.26254208

5.18E-10

4.46E-08

12.96843728

PICALM

1.48883338

7.452309182

7.882640103

1.40E-08

4.50E-07

9.702276208

LYN

1.48458898

7.748906756

6.995137981

1.33E-07

2.35E-06

7.469486452

ARL6IP5

1.481819394

9.42435093

6.181264534

1.14E-06

1.26E-05

5.345073096

SH3BP5

1.480757339

9.948617496

7.175018224

8.38E-08

1.65E-06

7.929726958

CYTH2

1.478569815

8.695293012

14.0977443

3.17E-14

7.84E-11

22.44167418

HSP90AA1

1.475101446

10.7822491

5.488461362

7.36E-06

5.62E-05

3.49626449

PDPK1

1.474624621

8.188917333

5.243240876

1.44E-05

9.60E-05

2.837346192

SMC3

1.470872089

7.133595994

11.12401077

8.96E-12

2.41E-09

16.96251038

MBTPS1

1.465393655

8.496997806

7.766899015

1.87E-08

5.53E-07

9.416678054

TIMP3

1.463150322

9.290336056

7.335248955

5.56E-08

1.22E-06

8.336512413

SSRP1

1.462015221

7.615804716

9.043612741

8.60E-10

6.37E-08

12.46766016

NAA35

1.458486577

6.726259229

8.092465915

8.37E-09

3.11E-07

10.21555345

KIF5B

1.454182588

8.394875765

5.616323045

5.20E-06

4.27E-05

3.839255868

DHRS7

1.454116053

7.744616405

9.572938924

2.56E-10

2.65E-08

13.66709981

CNPY2

1.453764841

8.534477403

8.634059418

2.26E-09

1.20E-07

11.51309488

FKBP5

1.452723718

7.291698701

4.149986734

0.000281682

0.001116854

-0.079470163

COL6A3

1.447501816

7.827000817

4.905985588

3.61E-05

0.000203504

1.930987853

COL4A2

1.446665268

7.456526747

6.876736022

1.81E-07

2.96E-06

7.164560203

CALB1

-1.44533189

9.757638848

-2.605850418

0.01452899

0.030801016

-3.831208449

IGF1

-1.437115619

9.406113438

-3.336824653

0.002407335

0.006723822

-2.146238464

NELFCD

1.434489749

9.91051469

11.67139035

2.95E-12

1.22E-09

18.0502204

YWHAB

1.431615777

8.640142735

5.544355542

6.32E-06

5.00E-05

3.646268215

RGCC

1.429769752

7.679603868

4.732902335

5.78E-05

0.000299666

1.466986783

SLC25A6

1.428984166

11.61631244

9.101576376

7.52E-10

5.75E-08

12.60089001

ID3

1.428233521

10.14589451

8.266105018

5.47E-09

2.29E-07

10.63587854

TRA2B

1.424948669

9.497698712

8.624014607

2.31E-09

1.22E-07

11.48939306

GDI2

1.424531564

9.161869349

6.615965347

3.59E-07

4.97E-06

6.48773575

CRHBP

1.423223574

10.85750131

3.501852173

0.001572242

0.004679346

-1.739506163

EID1

1.422600712

9.21370437

7.376173329

5.01E-08

1.13E-06

8.439915301

SET

1.420718447

10.19887727

8.55531067

2.73E-09

1.37E-07

11.32690802

FOS

-1.420440832

9.273302043

-3.000916158

0.005612259

0.013756238

-2.946942453

VDAC1

1.415009019

8.172894871

7.118018567

9.71E-08

1.85E-06

7.784289799

WASHC4

1.412990786

7.331446343

8.01490027

1.01E-08

3.56E-07

10.02649009

UMOD

-1.412526572

11.37016849

-2.38566243

0.024069305

0.047104894

-4.291378203

SERTAD2

1.411604795

8.175798374

8.513812449

3.01E-09

1.48E-07

11.22845173

PQBP1

1.411256434

7.748400335

6.769612323

2.40E-07

3.68E-06

6.887374717

CEP350

1.409862497

6.337432566

7.135793044

9.27E-08

1.79E-06

7.829682626

GLYAT

-1.407015938

9.579743485

-4.089844356

0.000331191

0.001276585

-0.236812106

ICAM2

1.406910331

9.771163793

5.247150209

1.42E-05

9.52E-05

2.84785686

GNB1

1.402945544

9.130430125

10.649444

2.42E-11

5.13E-09

15.98830239

HLA-DMB

1.401194865

10.17298113

8.848947598

1.36E-09

8.38E-08

12.01682652

EDNRB

1.398466757

8.373823696

4.94196731

3.27E-05

0.000188026

2.02759004

ESF1

1.398047621

7.471743775

13.05457765

2.06E-13

2.30E-10

20.63569798

AIDA

1.397041007

7.692499594

8.873300307

1.28E-09

8.11E-08

12.07351212

TOP1

1.396150489

8.310821776

7.122657614

9.59E-08

1.83E-06

7.796140615

SNX1

1.393315129

7.77005298

9.617845685

2.31E-10

2.46E-08

13.76708049

CLDND1

1.389278478

8.356235731

7.353059136

5.32E-08

1.18E-06

8.381538064

TSPAN3

1.38922393

9.719295375

4.512799487

0.000105387

0.000495607

0.879374553

SLC7A8

1.388681022

9.335582412

4.354999431

0.000161804

0.000706402

0.460535324

ARHGAP5

-1.388500976

8.373041869

-8.710601093

1.88E-09

1.07E-07

11.69324854

ILF3

1.386329503

6.940492403

8.230768777

5.97E-09

2.45E-07

10.55066888

ATG12

1.384776561

7.242819619

7.607340868

2.79E-08

7.39E-07

9.020127669

ZNF148

1.382618168

8.099582442

12.45022932

6.42E-13

4.62E-10

19.53391159

DLC1

1.379754481

9.408529743

8.406044121

3.90E-09

1.80E-07

10.97166939

MYDGF

1.379051924

8.854924998

9.767015768

1.65E-10

1.97E-08

14.09720669

PSD3

1.379024578

6.766600545

5.210698818

1.57E-05

0.000103008

2.749850991

CDC37

1.377078992

9.468182931

10.45472383

3.67E-11

7.04E-09

15.58000157

KPNB1

1.376929789

9.458847188

9.024827414

8.99E-10

6.56E-08

12.4243824

FOXN3

1.375967793

6.83901797

8.165674647

6.99E-09

2.76E-07

10.39326148

LIMCH1

1.375409137

9.097684708

5.56555133

5.97E-06

4.77E-05

3.703125677

MLEC

1.374524107

9.243909884

6.009168559

1.80E-06

1.82E-05

4.888445732

FABP5

1.370563815

7.473351905

3.638781888

0.001099405

0.003461152

-1.396367326

SCNN1A

-1.367945371

9.167955994

-4.114464255

0.000309965

0.001210427

-0.172467464

TPR

1.367879082

8.216314258

4.77899159

5.10E-05

0.000270724

1.590412014

HSP90AB1

1.36640902

9.19132412

5.419142441

8.89E-06

6.51E-05

3.310113519

ABCF2

1.364898619

7.12749747

8.687031396

1.99E-09

1.11E-07

11.63785903

HNRNPD

1.36405605

9.094619756

8.738208165

1.76E-09

1.02E-07

11.75802893

TGFBR2

1.362070619

7.153715484

5.381838143

9.84E-06

7.03E-05

3.209890462

CD14

1.361767811

8.618764289

5.636868061

4.92E-06

4.09E-05

3.894313936

FRZB

1.361170602

9.126704666

5.082735903

2.22E-05

0.000137296

2.405816869

BBS4

1.360150632

7.202996413

8.971308938

1.02E-09

7.08E-08

12.30081903

HLA-G

1.359808391

10.72098347

5.244182836

1.43E-05

9.58E-05

2.839878762

SWAP70

1.359427598

9.113593244

5.306026158

1.21E-05

8.33E-05

3.006138059

TNPO3

1.358231729

7.693616767

10.42872351

3.88E-11

7.26E-09

15.52510066

LACTB2

-1.355852383

7.530616

-6.671617676

3.10E-07

4.45E-06

6.632763722

CEP57

1.352993373

5.052119046

12.37324173

7.44E-13

4.88E-10

19.39050197

MYO6

1.352421261

9.176047271

5.255177235

1.39E-05

9.36E-05

2.869438127

EPRS

1.351852256

8.607022753

8.649814499

2.17E-09

1.18E-07

11.55024283

LRBA

-1.349799955

8.811192382

-8.795602403

1.54E-09

9.30E-08

11.8923698

CXCR4

1.347649061

8.222517485

3.684201964

0.000975644

0.003135087

-1.281519314

SRRM2

1.347568684

8.460042382

5.001654871

2.78E-05

0.000165066

2.187913815

RPLP0

1.34749233

11.59881981

12.16742107

1.11E-12

6.17E-10

19.00364445

ITGA8

1.343209501

8.187995584

5.506346749

7.01E-06

5.43E-05

3.544273858

DDX39A

1.342371076

8.373749992

14.91487814

7.84E-15

2.61E-11

23.77683811

SSBP2

1.341694376

8.032258834

10.36379012

4.46E-11

8.01E-09

15.38759602

HMGCR

1.340708611

7.991412194

7.949010066

1.19E-08

4.05E-07

9.865259037

STX11

1.338253755

6.131744045

8.963225837

1.04E-09

7.12E-08

12.28212241

TTF1

1.335503511

7.454129952

7.565990872

3.10E-08

7.95E-07

8.916832104

AZIN1

1.331515063

6.892036517

13.60307467

7.59E-14

1.17E-10

21.59996308

OAS1

1.328744906

7.180037476

4.581369291

8.74E-05

0.000424389

1.062067683

PLEKHB1

-1.325016546

7.570574431

-8.601436826

2.44E-09

1.27E-07

11.43606785

TMEM230

1.322637972

8.978392691

6.664841119

3.15E-07

4.52E-06

6.615120736

BMPR2

1.321446254

6.459157792

7.530440342

3.39E-08

8.48E-07

8.827852131

ZNF207

1.320509797

9.748881671

7.924554461

1.27E-08

4.20E-07

9.805271384

RAD21

1.320165778

7.728846175

7.217996731

7.51E-08

1.53E-06

8.039137581

HCLS1

1.318686529

9.088478783

7.557331729

3.17E-08

8.09E-07

8.895173607

COG7

1.318315987

7.957007308

6.947663903

1.51E-07

2.60E-06

7.34740937

TSPAN5

1.317168741

7.574432985

7.172334408

8.44E-08

1.66E-06

7.922887565

CALR

-1.316887257

9.46952319

-6.187797543

1.12E-06

1.24E-05

5.362364282

CASP1

1.315406814

7.280061706

6.917554285

1.63E-07

2.75E-06

7.269855086

CD53

1.314626339

9.310209635

4.82374315

4.51E-05

0.000244358

1.710352793

STAT1

1.311254404

7.989702392

8.164628307

7.01E-09

2.76E-07

10.39072663

NASP

1.311023485

8.658422087

8.195333579

6.50E-09

2.59E-07

10.46505182

ATF3

-1.309777911

9.32422844

-4.436888056

0.000129551

0.000586505

0.677587657

LRRFIP1

1.308595239

9.842768068

6.02460185

1.73E-06

1.77E-05

4.929480606

BLVRA

1.307890164

7.463070636

7.883905699

1.40E-08

4.49E-07

9.705389497

TRAM2

1.307573251

8.603172154

6.048923045

1.62E-06

1.68E-05

4.994114324

SPTLC1

1.306960571

7.365444445

8.208877143

6.29E-09

2.54E-07

10.49779515

COL4A3

1.303589773

10.63769098

5.004570724

2.75E-05

0.000164118

2.195748058

DDX41

1.303536766

9.473571199

11.00669283

1.14E-11

2.94E-09

16.72440575

ACTR3

1.302268329

10.75060111

7.097878642

1.02E-07

1.93E-06

7.732811998

ACLY

1.302204054

8.910503391

7.337433307

5.53E-08

1.22E-06

8.342036709

FNTA

1.301806985

9.235960273

8.985880851

9.85E-10

6.90E-08

12.33450181

FCN1

1.301737217

7.825729707

4.507676388

0.000106866

0.000500799

0.865740298

GPM6B

1.30102056

7.554873441

5.23789948

1.46E-05

9.70E-05

2.822985149

NUDC

1.299825767

10.1129032

9.124297995

7.13E-10

5.57E-08

12.65298921

PHB

1.299280352

9.036698753

8.34467426

4.52E-09

2.00E-07

10.82473593

USP1

1.299122208

5.712328518

6.903628133

1.69E-07

2.81E-06

7.233951504

UGCG

1.299100428

7.637255196

6.564098659

4.11E-07

5.59E-06

6.352299401

NUP88

1.298049189

7.778731314

11.63242519

3.19E-12

1.26E-09

17.97404068

LEPROT

1.297113978

11.60207857

4.961828441

3.10E-05

0.000180137

2.080928206

SORD

-1.296373291

9.712182519

-3.408042698

0.002004522

0.00576643

-1.971684138

RNASEH1

1.296211957

8.062974187

7.743815272

1.98E-08

5.73E-07

9.359509858

BGN

-1.293749031

10.83426956

-5.497336355

7.19E-06

5.53E-05

3.520088593

EPB41L1

-1.293565904

8.131970301

-8.910275853

1.18E-09

7.65E-08

12.15942346

NT5DC2

1.290854598

7.006657669

5.49509333

7.23E-06

5.55E-05

3.514067608

EIF5

1.290509517

9.992535565

5.777169499

3.37E-06

3.01E-05

4.26981525

JUND

-1.288819693

11.17357526

-13.61921949

7.38E-14

1.17E-10

21.62784642

PLK2

1.287415552

7.339410911

5.759288171

3.53E-06

3.14E-05

4.222009283

M6PR

1.286522924

7.51082701

7.771500991

1.85E-08

5.49E-07

9.428066888

DHRS7B

1.286003609

9.123234779

8.514469828

3.01E-09

1.48E-07

11.23001323

TCF7L1

-1.284417857

9.471134171

-6.21840816

1.03E-06

1.16E-05

5.443338468

TMEM140

1.284344829

9.254354193

10.94233816

1.31E-11

3.20E-09

16.59303621

CYP27B1

-1.28385958

8.032205684

-5.997251595

1.86E-06

1.87E-05

4.856749376

KRAS

1.281495706

8.377790663

6.362344392

7.01E-07

8.55E-06

5.823074564

GMFG

1.279945687

9.288317601

7.459463711

4.06E-08

9.68E-07

8.64973168

PSMA3

1.279565875

10.16490647

5.83933803

2.85E-06

2.64E-05

4.435894253

CNOT2

1.279300816

8.782442522

6.434254514

5.79E-07

7.35E-06

6.01212757

KLC1

1.277054189

9.104980272

7.524681574

3.44E-08

8.56E-07

8.813423521

PLPPR1

-1.276165848

8.240622771

-3.816088742

0.000688405

0.002344447

-0.945391009

ABCG1

1.275777921

7.186008684

6.811441439

2.15E-07

3.37E-06

6.995753313

DAZAP2

1.274142925

9.739018005

7.637559175

2.59E-08

7.01E-07

9.095478672

ERLIN2

-1.273846883

9.305200067

-7.370850264

5.08E-08

1.14E-06

8.426477123

GC

-1.272445074

6.332951466

-2.524545429

0.017546408

0.036095513

-4.004098064

RNF13

1.268534955

10.26760703

5.355043954

1.06E-05

7.48E-05

3.137888261

COL1A2

1.266974585

7.179573515

3.501641194

0.001573104

0.004681133

-1.740031084

ZC3H15

1.266150743

9.172149178

5.200299793

1.61E-05

0.000105414

2.721890801

KMT5B

1.265913904

8.649285188

7.46494521

4.00E-08

9.61E-07

8.663510146

PUM1

1.263073454

8.134411951

6.955641137

1.48E-07

2.55E-06

7.367939869

ADD3

1.258422615

8.997988364

5.947988367

2.12E-06

2.08E-05

4.725622462

KPNA2

1.255126625

8.907075287

6.720095976

2.73E-07

4.03E-06

6.758843409

HLA-DMA

1.254424027

10.37397051

5.785073702

3.29E-06

2.96E-05

4.290942076

THEMIS2

1.253798071

7.019422688

6.789113515

2.28E-07

3.53E-06

6.937924834

GNL3L

1.252325393

7.124510447

10.81838227

1.69E-11

3.89E-09

16.33847971

PSMB4

1.249957625

10.72159802

9.265970538

5.14E-10

4.44E-08

12.97622687

AGMAT

-1.248270761

10.8536785

-3.824270959

0.000673611

0.002304987

-0.924415444

GIPC2

-1.247317538

7.723359098

-4.309932572

0.000182827

0.000781047

0.341387838

DHX9

1.246849962

6.351974134

7.128339548

9.45E-08

1.81E-06

7.810652181

EWSR1

1.246648172

8.535866582

10.08327007

8.21E-11

1.24E-08

14.78704752

SETD5

1.245898909

7.482287092

7.89652774

1.36E-08

4.40E-07

9.736427478

EIF4G1

1.244109972

7.486954023

8.936556557

1.10E-09

7.39E-08

12.22037107

SRSF3

1.243986139

9.803111657

7.286475154

6.30E-08

1.34E-06

8.21301214

ILF2

1.243442066

9.552958146

6.68055437

3.03E-07

4.37E-06

6.656023645

CYP26B1

-1.242450416

8.634347541

-2.397548309

0.02343442

0.046015965

-4.26720407

SERPINE1

1.240736407

6.725458286

4.521128924

0.000103025

0.000486376

0.901546668

PSMD4

1.240423225

10.38557871

10.02544818

9.32E-11

1.33E-08

14.66193831

PDLIM3

1.239425245

7.796445864

8.153301921

7.21E-09

2.81E-07

10.36327807

TMEM87A

1.238174918

8.636838914

8.683893986

2.01E-09

1.12E-07

11.63048026

PRRC2C

1.238003417

7.756011399

7.200125227

7.86E-08

1.58E-06

7.993668362

SNX4

1.237495753

7.321680798

7.530106662

3.39E-08

8.48E-07

8.827016208

NRP1

1.236313178

9.129569229

5.883209216

2.53E-06

2.39E-05

4.552965336

WAC

1.235001654

7.03036538

7.197894999

7.90E-08

1.58E-06

7.98799151

SASH1

1.234739955

8.294239034

7.261823684

6.71E-08

1.40E-06

8.15048352

TAP1

1.234734308

9.251835212

8.342764658

4.55E-09

2.01E-07

10.82015571

CHMP2A

1.234514701

10.38085677

5.726260811

3.86E-06

3.38E-05

4.133668805

ARPC1B

1.23312388

9.847594921

6.629826451

3.46E-07

4.86E-06

6.523885973

C1QA

1.232874166

7.267276992

3.49242546

0.001611225

0.004776231

-1.762948256

ZFPM2

-1.231930712

8.037949772

-5.308203625

1.20E-05

8.29E-05

3.011991311

APLP2

1.231910612

10.32603877

7.606109272

2.80E-08

7.39E-07

9.017054163

UTP14A

1.231347458

7.661719061

7.121605911

9.62E-08

1.84E-06

7.793454173

CAT

1.229093195

8.87973752

6.872999304

1.83E-07

2.99E-06

7.15491194

TIA1

1.227747542

7.247584903

9.010951208

9.29E-10

6.61E-08

12.39238301

NAGK

1.22754899

9.926020105

10.36793433

4.42E-11

8.00E-09

15.39638877

TM9SF1

1.227465595

8.623773028

8.253859905

5.64E-09

2.34E-07

10.60636978

GLG1

1.226621678

9.826971817

5.632513248

4.98E-06

4.12E-05

3.882644978

XPNPEP1

1.225389014

8.466825777

7.86771117

1.46E-08

4.60E-07

9.665536137

HYAL2

1.22355501

9.281267332

6.500981316

4.86E-07

6.40E-06

6.18713754

ASF1A

1.223294692

7.739507727

7.51950823

3.48E-08

8.66E-07

8.80045816

WARS

1.221822477

9.608411649

7.31487298

5.86E-08

1.27E-06

8.284953181

TMCO3

1.22011721

9.102616418

4.979257633

2.95E-05

0.000173536

2.127743623

KTN1

1.218156632

6.898784102

5.40406927

9.26E-06

6.72E-05

3.269620769

NNT

1.215568091

6.952880722

5.386246015

9.72E-06

6.96E-05

3.221734225

XPNPEP2

-1.215559549

8.851730714

-3.325159474

0.002480356

0.006893212

-2.174682385

FLRT3

1.210792751

10.14839181

4.168211768

0.000268176

0.001070866

-0.031686858

FADS3

1.209980177

7.375563699

8.103163063

8.15E-09

3.06E-07

10.24156454

ENO1

1.209953356

7.883385651

6.223548255

1.01E-06

1.15E-05

5.45692833

HLA-F

1.209909743

11.83477086

8.124091232

7.74E-09

2.96E-07

10.29240912

PTPRC

1.208351307

5.832030673

8.047207456

9.35E-09

3.38E-07

10.10533478

SYT1

-1.208255658

7.30889895

-4.705882758

6.23E-05

0.000319624

1.394681992

COMT

1.206977299

9.312304131

7.752820425

1.94E-08

5.66E-07

9.381819803

PLS1

-1.206303455

8.261543055

-5.496977729

7.19E-06

5.53E-05

3.519125937

ITGB2

1.205909695

7.91890843

5.254326121

1.39E-05

9.37E-05

2.867149862

SMARCE1

1.205666484

8.972747998

7.373668132

5.04E-08

1.13E-06

8.433591315

SEC14L1

1.205033056

7.939442936

6.08339441

1.48E-06

1.55E-05

5.085651874

POSTN

1.201856865

10.82658185

3.379886179

0.00215532

0.006130585

-2.040877409

DEFB1

-1.200644494

10.99447884

-3.02989907

0.005223435

0.012932645

-2.879463619

TYRP1

-1.200351272

6.207994431

-3.539545898

0.001425307

0.004302374

-1.645531253

IMP3

1.199878035

9.403506224

8.37029393

4.25E-09

1.92E-07

10.88613742

SMARCA2

1.199515299

6.947329006

7.267526219

6.61E-08

1.38E-06

8.164954491

AKAP8L

1.199316813

6.916908369

7.807966077

1.69E-08

5.14E-07

9.518212724

PSMD13

1.198610121

8.052040062

10.25115059

5.69E-11

9.46E-09

15.14772671

COPS8

1.1970076

9.204275007

7.044000749

1.17E-07

2.13E-06

7.594871427

AKAP13

1.19666266

7.792140231

6.987365814

1.36E-07

2.38E-06

7.449517889

RRAD

1.196066763

6.953293655

7.077275531

1.08E-07

2.01E-06

7.680102108

PLEKHO1

1.195459638

7.723639306

7.159751859

8.72E-08

1.70E-06

7.89081119

FOLR1

-1.193436488

6.956916716

-3.734922458

0.000853477

0.002814152

-1.152704059

PPIC

1.192607632

8.477886659

6.960597292

1.46E-07

2.53E-06

7.380691668

SNX2

1.192127734

8.610500524

6.157301546

1.21E-06

1.33E-05

5.281621255

MBD4

1.185770843

8.009202546

7.069175367

1.10E-07

2.03E-06

7.659365794

AP3D1

1.18500723

8.6135603

7.725903422

2.07E-08

5.94E-07

9.315102851

HBB

1.184947119

11.93377932

3.343143234

0.002368653

0.006631573

-2.130813886

RNF114

1.183206283

9.30418479

9.465288756

3.26E-10

3.26E-08

13.42629734

ABHD17A

1.181106471

9.170196843

8.329715797

4.69E-09

2.06E-07

10.78884453

LRRC42

1.180209467

7.573503367

8.941836312

1.09E-09

7.34E-08

12.23260386

DDAH1

-1.180008114

11.46433716

-5.151767722

1.84E-05

0.000117466

2.591402158

TSC22D3

1.179816234

8.262843013

4.224652868

0.000230275

0.000942027

0.116582968

GYG1

1.177529499

9.834468492

6.016373152

1.77E-06

1.80E-05

4.907603688

UQCRC2

1.176924599

9.899057439

7.23706376

7.15E-08

1.47E-06

8.087606913

FKBP11

1.176919235

8.727123226

6.266120256

9.06E-07

1.04E-05

5.569402926

TM4SF1

1.176815697

8.331708401

5.239153764

1.45E-05

9.68E-05

2.826357468

AHCYL1

1.176691914

8.745386211

5.330391797

1.13E-05

7.91E-05

3.071632149

SLC28A1

-1.176464801

6.772070338

-8.212773139

6.23E-09

2.52E-07

10.50720966

LGALS3

1.176104581

9.941755013

7.549802161

3.23E-08

8.21E-07

8.876332784

CAVIN2

1.174754505

6.982038785

5.495846891

7.22E-06

5.54E-05

3.516090419

SERPINH1

1.174275681

9.648430496

6.407521754

6.22E-07

7.79E-06

5.941900078

ACTA2

1.173648029

10.38204081

5.932733912

2.21E-06

2.15E-05

4.684987509

HNRNPA2B1

1.173592582

11.06475641

9.665793594

2.07E-10

2.28E-08

13.87352619

AP1S2

1.172535127

8.523906337

5.252624432

1.40E-05

9.41E-05

2.862574763

PNN

1.17087381

7.970795555

7.747188801

1.97E-08

5.70E-07

9.367868879

HLA-C

1.169664398

10.16761106

7.595608208

2.88E-08

7.54E-07

8.990840477

PRPF4B

1.169551009

6.935475927

5.67669604

4.42E-06

3.75E-05

4.000998961

USP48

-1.168630772

9.038096545

-7.747244509

1.97E-08

5.70E-07

9.368006903

MCM7

1.168505141

8.351995021

8.050385686

9.28E-09

3.36E-07

10.11308366

TUG1

1.168335892

9.165666062

6.054292701

1.60E-06

1.66E-05

5.008378733

CASP4

1.167446728

7.679803702

10.35532363

4.54E-11

8.03E-09

15.36962558

FOXO3

1.167411973

8.567526997

5.725633219

3.87E-06

3.38E-05

4.131989636

VAMP2

-1.166211897

7.442364851

-7.873871112

1.43E-08

4.56E-07

9.680699271

PDIA6

1.165260403

10.67248004

5.899377478

2.42E-06

2.31E-05

4.596082594

DDOST

1.164865434

10.9394614

6.470185623

5.27E-07

6.83E-06

6.10641779

PYCARD

1.163566225

7.46929959

5.864762597

2.66E-06

2.50E-05

4.503753551

ZNF24

1.162861739

7.210438195

7.357297992

5.26E-08

1.17E-06

8.392248586

TYRO3

1.161757961

8.189575049

5.5953718

5.51E-06

4.46E-05

3.783092394

ADH6

-1.16166635

8.207644366

-3.436207702

0.001863907

0.005412229

-1.902237878

ACSL3

1.16124775

8.408959315

3.546281515

0.001400494

0.004237233

-1.628698861

PARP2

1.161143625

8.581427075

11.19875359

7.68E-12

2.21E-09

17.11328056

MCCC2

-1.160816294

10.45719644

-5.897428416

2.43E-06

2.32E-05

4.590885687

ZNF22

1.159131186

7.261181636

12.5812987

5.00E-13

4.05E-10

19.77645675

RALBP1

1.158724753

8.274230454

7.859219723

1.49E-08

4.66E-07

9.644625716

NUP85

1.156176785

8.422135666

9.538080515

2.77E-10

2.81E-08

13.5892997

CPD

1.156112532

6.819716927

8.154881627

7.18E-09

2.81E-07

10.36710738

HCP5

1.155541743

8.967916149

5.892526241

2.47E-06

2.34E-05

4.577813709

AGTR1

1.155149645

6.968562585

5.121576314

2.00E-05

0.000125603

2.510230811

LIN37

1.155105526

6.577595527

9.667828073

2.06E-10

2.28E-08

13.87803581

CCT6A

1.154913313

9.14834035

7.235166054

7.18E-08

1.48E-06

8.08278478

HNRNPC

1.154717025

8.873047157

6.813932366

2.13E-07

3.35E-06

7.002201462

SLC13A1

-1.15376563

7.5476979

-3.18681201

0.003526296

0.009294589

-2.508681404

VAMP3

1.152730555

7.405947549

4.957816762

3.13E-05

0.000181512

2.070153776

MAP4K3

-1.150486671

7.339441604

-6.152728191

1.23E-06

1.34E-05

5.269506464

JMJD6

1.150291091

7.191119689

12.55420458

5.27E-13

4.05E-10

19.726484

HLA-DPB1

1.150218342

11.36426215

5.525706581

6.65E-06

5.21E-05

3.596230432

SRRM1

-1.149363305

9.137180405

-5.391189648

9.59E-06

6.88E-05

3.235017125

RRAGC

1.149352733

8.837854366

6.475336686

5.20E-07

6.75E-06

6.119925528

ZNF804A

-1.149172983

8.504688414

-5.863390141

2.67E-06

2.50E-05

4.500091338

PSME3

-1.148471143

9.262437167

-5.79437545

3.21E-06

2.90E-05

4.315800219

RBP4

-1.148117964

8.285701455

-2.430483745

0.021754392

0.043227525

-4.199808903

NMD3

1.147678273

7.096738478

5.993365837

1.88E-06

1.89E-05

4.846412134

PSMB9

1.144615141

10.39353049

6.638465727

3.38E-07

4.76E-06

6.546407914

SYNCRIP

1.144133474

7.533673599

5.254346869

1.39E-05

9.37E-05

2.867205645

YWHAH

1.142268582

8.609758108

3.448365406

0.001806203

0.005267323

-1.872190252

DNAJC8

1.141702145

9.890908337

12.41268851

6.90E-13

4.67E-10

19.46406954

ARF4

1.141135381

9.861513946

4.629461092

7.67E-05

0.000380207

1.190413168

CYP1B1

1.139251767

8.983971841

6.038927934

1.66E-06

1.71E-05

4.967557162

FGL2

1.139176467

10.00389177

5.396769049

9.45E-06

6.81E-05

3.250007688

TAX1BP1

1.137965

10.06609394

7.996955328

1.06E-08

3.69E-07

9.982636271

DNAJC7

1.136922569

8.975904344

5.350564296

1.07E-05

7.55E-05

3.125849226

SNRPB

1.13575687

10.70918168

8.092302578

8.37E-09

3.11E-07

10.21515617

WT1

1.132916354

9.05457011

4.948629513

3.21E-05

0.000185305

2.045480572

SLC13A3

-1.132662093

10.63519462

-3.356064493

0.002291407

0.006445268

-2.099233019

EFNB2

1.132430918

8.078269009

5.104816376

2.09E-05

0.000130601

2.465173592

GATM

-1.132347483

9.378659276

-4.820073926

4.56E-05

0.000246052

1.700515384

CNIH4

1.131683601

8.479595575

5.273080806

1.32E-05

8.97E-05

2.917571743

EPHX1

-1.131399427

8.499935775

-2.800395449

0.00915647

0.020779471

-3.404434478

SLC12A1

-1.131269099

9.982695474

-3.262062692

0.002913818

0.007915227

-2.327789665

DDX18

1.12754023

7.44385403

6.968940574

1.43E-07

2.48E-06

7.402152136

ALAS1

1.125667398

9.259205057

10.4312198

3.86E-11

7.26E-09

15.5303756

PIPOX

-1.123197227

9.810383723

-3.323828884

0.002488819

0.006915008

-2.177924155

TMEM204

1.123075986

10.94367823

5.734623996

3.78E-06

3.30E-05

4.156043341

IFI16

1.121716104

6.377629496

6.221519431

1.02E-06

1.15E-05

5.451564585

FDPS

1.121519611

9.476641456

5.462769647

7.89E-06

5.94E-05

3.427285292

ADSL

1.120968528

9.354870139

11.7975847

2.29E-12

1.13E-09

18.29564463

C21orf59

1.120241495

9.349721825

8.079662341

8.63E-09

3.19E-07

10.18440035

PLXDC2

1.119961822

6.674408432

6.694948461

2.91E-07

4.25E-06

6.693470854

IK

1.119516839

11.08344752

11.17279646

8.10E-12

2.26E-09

17.06100157

EPB41L5

1.119432184

8.719882514

5.112636291

2.05E-05

0.000128284

2.486196259

PAPOLA

1.11940591

9.203346945

6.556215488

4.20E-07

5.67E-06

6.331691821

BABAM1

1.118621088

9.799168898

13.13465146

1.78E-13

2.09E-10

20.77854969

EIF3A

1.118546997

8.851059563

7.373548512

5.05E-08

1.13E-06

8.433289332

10-Sep

1.11477609

7.162778757

5.1308438

1.95E-05

0.000123054

2.535146373

METTL3

1.114576495

9.512185519

10.75712554

1.93E-11

4.32E-09

16.21193997

RYBP

1.114362488

8.074681305

5.558649075

6.08E-06

4.84E-05

3.684612117

SRSF7

1.114068517

7.812810523

6.78434274

2.31E-07

3.57E-06

6.925561917

SPCS2

1.113642182

10.67916436

10.02665475

9.29E-11

1.33E-08

14.66455361

IL6ST

1.112439922

7.249378474

5.2191725

1.53E-05

0.000101131

2.772634323

CASP3

1.112233366

7.060212477

6.02808801

1.71E-06

1.75E-05

4.938747575

SLC22A8

-1.111645351

8.172587127

-2.906495713

0.007078547

0.016675257

-3.164457325

SLC34A1

-1.111436435

8.827172212

-3.462040019

0.001743365

0.005105454

-1.838343558

SLCO3A1

1.110078679

7.643092094

5.674934307

4.44E-06

3.76E-05

3.996281323

H2AFZ

1.109787786

10.36972324

7.309395678

5.94E-08

1.29E-06

8.271084918

TRAM1

1.109674164

9.330891829

5.475848882

7.62E-06

5.78E-05

3.462403688

CPM

1.108350817

6.344952054

4.511146811

0.000105862

0.000497192

0.874975993

SF3B4

-1.106855958

8.447412686

-11.66922686

2.96E-12

1.22E-09

18.04599558

GPKOW

1.105821203

9.402386711

15.05894645

6.17E-15

2.61E-11

24.00537577

COMMD4

1.105582864

8.633374558

9.573721267

2.55E-10

2.65E-08

13.66884399

NKTR

1.105103578

7.436472493

6.31627032

7.93E-07

9.41E-06

5.701708637

GZMB

1.104615964

6.213304274

4.943201366

3.26E-05

0.000187539

2.030903852

PRKAR1A

1.103349619

9.085496498

6.930113803

1.58E-07

2.69E-06

7.302217123

GAR1

1.103322681

8.867905942

11.69566488

2.81E-12

1.22E-09

18.09758328

PCMT1

1.10293129

9.090122013

8.57858399

2.58E-09

1.32E-07

11.38202189

OSBPL1A

1.101591227

7.294578344

6.029552935

1.71E-06

1.75E-05

4.942641418

CSNK1A1

1.101429726

9.136865726

8.271107725

5.41E-09

2.27E-07

10.64792844

FRY

1.101325533

7.416761028

4.840073415

4.32E-05

0.000235359

1.75414204

ETS2

1.10079836

9.955899787

6.80950633

2.16E-07

3.38E-06

6.990743531

C1QBP

1.099436575

9.443174021

7.90432057

1.33E-08

4.35E-07

9.755579866

CAMK1

1.098714967

7.277320139

8.659802769

2.12E-09

1.16E-07

11.57377593

TIGAR

1.098198376

7.2916035

5.49588839

7.21E-06

5.54E-05

3.516201816

ELF1

1.097652061

9.011134077

8.423523203

3.74E-09

1.74E-07

11.01342471

PPFIBP1

1.096172641

6.181551302

6.404621461

6.27E-07

7.83E-06

5.934277118

TCF25

1.096054149

8.091775108

6.227690486

1.00E-06

1.13E-05

5.467878422

CDC27

1.095817747

8.505153326

4.91940844

3.48E-05

0.000197882

1.967020368

SLA

1.094863831

8.203268999

6.487175988

5.04E-07

6.58E-06

6.150962717

NEU1

1.093178847

8.947170302

7.96855995

1.13E-08

3.91E-07

9.913156724

MTDH

1.092673248

9.333952516

4.191473779

0.000251861

0.001016156

0.029369908

RALYL

-1.091884713

8.179522241

-3.770225925

0.0007774

0.002597903

-1.062705647

EGR2

-1.090102058

6.469861827

-4.641895549

7.41E-05

0.000369589

1.223623989

SELENOW

1.089407334

10.83413383

8.564446583

2.67E-09

1.35E-07

11.34855174

DHX35

1.088913326

7.875257997

8.366012273

4.30E-09

1.94E-07

10.87588196

H2AFY

1.088300954

8.945648275

7.534738013

3.35E-08

8.41E-07

8.838617254

FEZ2

1.088132355

9.829802339

6.601202262

3.73E-07

5.13E-06

6.449212407

CXCL1

-1.08797836

7.890182363

-3.583978395

0.001269183

0.003906784

-1.534276712

TUBG1

1.086695059

8.745692451

9.151344631

6.70E-10

5.33E-08

12.71491244

EGFR

1.086214902

6.754578363

6.745928219

2.55E-07

3.84E-06

6.82592863

DERL1

1.086001096

6.89012709

6.279613636

8.74E-07

1.01E-05

5.605021567

CAV1

1.084584181

7.441861966

7.66312002

2.43E-08

6.70E-07

9.159125496

IDH3G

1.082946039

9.572242915

4.996077021

2.82E-05

0.000166977

2.172927883

DDIT4

1.082694616

9.431563963

4.287981287

0.000194025

0.000818704

0.283437599

MT1G

-1.082597887

12.96743712

-5.150025681

1.85E-05

0.000117877

2.586718443

TUBB3

1.081470203

10.81289795

9.271887653

5.07E-10

4.40E-08

12.98966696

EFHC1

1.08144924

8.498565098

9.503313964

2.99E-10

3.03E-08

13.51153807

MPHOSPH8

1.081120204

8.222430403

6.647821917

3.30E-07

4.67E-06

6.570790457

SLC39A6

1.080520811

8.612275262

5.321066886

1.16E-05

8.09E-05

3.046567941

GMPR2

1.080419808

10.1835269

9.982494926

1.02E-10

1.40E-08

14.56870652

PLPP1

1.079549675

12.22298887

4.588629924

8.57E-05

0.000417251

1.081433867

C3AR1

1.079533021

7.464575983

4.520362847

0.00010324

0.000487288

0.899507206

PKP4

1.078988705

7.219341697

4.508110069

0.00010674

0.000500419

0.86689438

SCG5

1.078692914

6.420091104

3.471874341

0.001699488

0.00500128

-1.81396971

CNOT8

1.078685586

8.779292263

6.264621562

9.09E-07

1.05E-05

5.565445885

MKNK2

1.07867287

9.762189026

11.32712576

5.91E-12

1.88E-09

17.37056083

ZNF721

1.077745797

8.69368259

4.543383862

9.70E-05

0.000462503

0.960815239

STK32B

-1.076764142

8.278102207

-6.487474074

5.03E-07

6.58E-06

6.151743993

DAB2

1.076679738

9.010118077

5.160270103

1.80E-05

0.000115318

2.614262168

WDR1

1.075060578

10.70569774

12.48277575

6.04E-13

4.48E-10

19.59432742

DDX50

1.072849739

10.42516752

5.249666541

1.41E-05

9.46E-05

2.854622248

CLDN3

1.07236717

6.08859028

4.843722356

4.27E-05

0.000233435

1.763928086

ZBTB20

1.071588517

9.17400408

4.031253406

0.000387637

0.001460553

-0.389561913

ASNA1

1.071351132

9.062987359

4.634014168

7.58E-05

0.000376438

1.202572636

COL4A3BP

1.069228532

7.496292481

6.771068462

2.39E-07

3.67E-06

6.891150626

DBT

1.068894976

10.81364504

6.479610391

5.14E-07

6.70E-06

6.131130702

KLF6

1.068595042

8.694058607

3.923383883

0.000517366

0.001851367

-0.669284058

KLHL20

1.068504768

7.038412418

10.21355215

6.17E-11

9.83E-09

15.06727982

EED

1.06808641

8.464217814

7.919227461

1.28E-08

4.24E-07

9.792194251

MTF2

1.067468895

7.289425208

9.731404613

1.79E-10

2.08E-08

14.01867335

USE1

1.067373964

8.080264229

8.78600915

1.57E-09

9.41E-08

11.86994673

DIP2C

-1.064411186

8.287020133

-4.950915171

3.19E-05

0.000184439

2.051618708

GUCY1A3

1.064369955

8.391717113

7.722954073

2.09E-08

5.98E-07

9.307786878

FABP1

-1.063475899

8.139323754

-2.732116105

0.010784021

0.023903519

-3.556239515

NMI

1.063418627

8.913174785

6.061674729

1.56E-06

1.63E-05

5.027985704

FNDC3B

1.062680278

7.298362048

6.320892845

7.83E-07

9.33E-06

5.713893204

C21orf33

1.062481266

7.988234956

3.425838621

0.001914528

0.005546208

-1.92783153

LAP3

1.062459662

10.15323608

5.869684864

2.62E-06

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HSP90B1

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ING3

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C1QB

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CMTM6

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ANKRD11

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PSMB10

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COL4A1

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NRN1

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RSRC2

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RAC1

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HADHA

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AUP1

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TXNIP

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TUBB

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LPL

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NPR3

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GNA11

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ARPC1A

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TSPAN4

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JUN

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COBL

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EIF3B

1.015917612

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COPS6

1.01571967

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PTPRO

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ZFP36

-1.015055803

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BUD23

1.014932236

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PBLD

-1.014740962

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SNTB2

1.014417654

7.961457348

6.282527181

8.67E-07

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5.612710524

CTDSPL

1.01439347

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RNF7

1.01409318

9.590065207

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3.18E-10

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13.45176623

PTPRN2

1.013187332

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ZBTB18

-1.012739735

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6.352323976

SERINC3

1.012252699

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EPHA4

1.012059148

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EXPH5

-1.011374294

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SLC14A1

1.010232523

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AMPH

-1.008900528

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HLA-J

1.00871551

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2.46E-09

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11.42665058

PRMT5

1.007933207

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7.939886786

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4.11E-07

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PON2

1.007800835

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FBXW2

1.007654936

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STX18

1.006898222

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MRPL9

1.00575951

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NCOA1

1.005421619

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7.267865336

EIF3D

1.004960463

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13.70089836

ZNF45

1.004939996

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3.67E-06

6.893262879

RBL2

1.004173461

7.21346767

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2.27E-09

1.21E-07

11.50942809

PTP4A2

1.003941839

7.140081341

8.279182405

5.30E-09

2.24E-07

10.66737061

CXADR

1.003893604

9.64625798

3.484927272

0.001642901

0.004854007

-1.781577202

TTC37

1.001622392

7.444769781

5.465907256

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ATP6V0E2

-1.001198901

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4.14E-08

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UROD

1.001043527

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25.49653811

THSD7A

1.001019085

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0.00056643

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NFIL3

-1.000765264

8.473403841

-5.219220674

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0.000101131

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POLR2A

-1.000463151

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Hedyotis diffusa and Indigo naturalis lack of clinical evidences for the anti-leukemia efficacy but possess gastrointestinal, hepatic, and kidney toxicity (白花蛇舌草和青黛缺乏抗白血病的临床证据且有胃肠道和肝肾毒性)

DOI: 10.31038/CST.2019451

Abstract

Although modern evidence-based medicine provides relatively effective medicines or therapies for cancer treatment, there are still many cancer patients in China who are treated with Chinese herbal medicine. Many formulas of the traditional Chinese medicines used to treat leukemia contain Hedyotis diffusa and Indigo naturalis, but their efficacy and side effects are quite vague. The authors have systematically searched and reviewed the relevant Chinese and English literatures of the past 40 years. The relevant research information has demonstrated that, although Hedyotis diffusa and Indigo naturalis can inhibit cancer or leukemia in vitro and in limited animal experiments, there were no clinical evidences to support the therapeutic efficacy of Hedyotis diffusa and Indigo naturalis in treating leukemia. Moreover, Indigo naturalis and Hedyotis diffusa showed gastrointestinal, hepatic, and kidney toxicity in clinical applications and are of allergic risk. Therefore, the net benefit of using Hedyotis diffusa and Indigo naturalis in the treatment of leukemia cannot be ascertained. Clinicians cannot use Hedyotis diffusa and Indigo naturalis as placebo, patients should not take risk to try them out.

Keywords

Indigo naturalis, Hedyotis diffusa, Indirubin, Leukemia, Cancer, Side effects

摘要

尽管现代循证医学为癌症的治疗提供了相对有效的治疗药物或方法, 目前在中国仍有许多癌症病人用中草药治疗。用于治疗白血病的中药方剂中很多都含有白花蛇舌草和青黛, 但其疗效和毒副作用都很模糊。笔者比较系统地查阅了相关的近40年的中外文献资料, 所得研究信息表明, 尽管白花蛇舌草和青黛在体外实验和有限的动物实验中有抑制癌症或白血病的作用, 但没有临床证据支持白花蛇舌草和青黛有治疗白血病的功效。而且, 青黛和白花蛇舌草在临床应用中显示有胃肠道和肝肾毒性, 有过敏的风险。因此, 白花蛇舌草和青黛在白血病治疗上的净效益无法确定, 医生不能把它们当作安慰剂, 患者不要冒险试用。

关键词

青黛, 白花蛇舌草, 靛玉红, 白血病, 癌症, 毒副作用

引言

自从靶向治疗[1]和免疫治疗[2]成功用于临床, 癌症治疗有了巨大的进步, 有些癌症变成了可治疗的慢性病 [3, 4]。尽管现代循证医学为癌症的治疗提供了相对有效的治疗药物或方法,目前在中国仍有许多癌症病人使用疗效和毒副作用都很模糊中草药治疗 [5–8]。其原因是多方面的, 主要包括经济承受能力, 病情轻重(如病急乱投医), 医生的习惯, 大众对中医中药的盲目信赖等等 [9]。这些信赖和习惯的背后, 是大众或社会对中医中药在癌症治疗中的作用缺乏全面、正确的认识。普通病人及病人家属认为中医中药是中国几千年流传下来的,应该有效且几乎没有什么副作用 [9]。中医药界基本上没有系统地研究和更新中药的疗效和毒副作用信息,盲从中药治病之本的宣称,套用祖传的中药配伍禁忌,忽略了或者无视了中草药的毒副作用。长期以来,中药的临床应用缺乏疗效和毒副作用评估。医患双方对中药临床应用的态度经常趋向于懈怠,病情轻时不在乎结果, 试试中医中药无所谓; 病重没有别的办法时或病重负担不起费用时, 试试中医中药也是一线希望。但寻医问药的最终目的是治愈疾病或减轻病痛,一个药物或一种办法能否治病, 不能靠信心或传说,必须有事实依据。中草药在癌症治疗中的疗效和毒副作用必须认真梳理、总结、和更新。

很多宣称能治疗白血病的中药处方都包含有青黛和白花蛇舌草 [5–8],它们常作为其药方中治疗白血病的“君药”或“臣药” [8]。治疗“热毒炽盛型” 白血病的常用中药包括青黛、大青叶、和白花蛇舌等 [5–7], 青黛被报道为改善白血病的解毒良药[7]。一个题为“一种治疗白血病的中药”的专利 (CN103272054B)[8]包含青黛和白花蛇舌草等十几味中药。白花蛇舌草和青黛是否确实有治疗白血病的疗效、 有何毒性和副作用?笔者比较系统地查阅了相关信息, 并将相关信息整理归纳如本文, 以其为医生和患者提供参考, 并引起医药界的警示。

白花蛇舌草的功效缺乏抗白血病的临床证据

白花蛇舌草(Hedyotis diffusa, or Oldenlandia diffusia) 为茜草科一年生草本植物。白花蛇舌草的成分复杂,有效成分不确切,不稳定,含量少 [10–13]。其记录在《中国药典》,《中药大辞典》, 及《中华本草》上的功效包括清热解毒、消痛散结、利尿除湿。主治肺热喘咳、咽喉肿痛、肠痈、疖肿疮疡、毒蛇咬伤、热淋涩痛、水肿、痢疾、肠炎、湿热黄疸 [11, 14, 15]。

体外和动物实验显示白花蛇舌草有细胞毒性和抗炎抗肿瘤作用 [11–17], 在中国使用的抗癌草药配方中, 约15% 含有白花蛇舌草 [16]。文献宣称白花蛇舌草能治疗多种癌症(包括白血病), 能增强常规化疗的功效并减少化疗的不良反应 [5, 6, 16, 18] 。这些文献中,白花蛇舌草作为方剂中的一味,加入常规化疗中,一起用于白血病病人,一般无对照组 [5, 16, 18] 。这些文献报道的白花蛇舌草在临床上的应用和结果都是模糊的 [5, 6, 11, 16, 18], 没有对照组,因此缺乏有效的临床数据。

山东中医药大学学报1998年报道了中西医结合治疗急性非淋巴细胞白血病 152 例的情况 [18],病人按中医分成四型,治疗方案是联合化疗加中药。不同中医型的急性非淋巴细胞白血病用了不同的中药方剂,其中的热毒炽盛型的方剂不含白花蛇舌草,其余三型的中药方剂包含白花蛇舌草。没有对照组。结果是完全缓解109 例(72%) , 部分缓解28例(18%), 总缓解率90% 。完全缓解的患者生存期122- 5475天, 平均634天 [18]. 因为常规化疗是已经被证实的治疗白血病的有效方法,这个回顾性报道中的化疗加中药且无对照组的结果是不能说明中药或白花蛇舌草有效的。

中医杂志1998年报道了中药配合化疗治疗急性白血病的疗效的回顾性观察 [6]。试验组采用中药辨证配合化疗,对照组接受常规化疗,试验组与对照组均为38 例。试验组按中医分为三型,不同的中药方剂用于不同的中医型白血病,只有用于热毒炽盛型的方剂含有白花蛇舌草。治疗结果是 试验组的总缓解率及1, 3, 5, 和5 年以上生存率均明显高于对照组,而且 试验组的恶心呕吐, 胸闷心慌, 肝功能异常等毒副反应发生率及感染, 出血,弥散性血管内凝血发生率明显明显低于对照组 [6]。除了样本小以外,这是一个成功的中药有利于白血病治疗的报告。但白花蛇舌草只出现在三个方剂中的一个,这个含白花蛇舌草的方剂用于热毒炽盛型白血病,白花蛇舌草在此报告中的作用不确定。而且前面提到的152 例急性非淋巴细胞白血病的报道中, 用于热毒炽盛型白血病的方剂不含白花蛇舌草 [18],白花蛇舌草是否用于中药辨证型的同一型白血病或是否进入一个方剂似乎是随机的,这就否定了用白花蛇舌草的必要性。这样的文献[6, 18]也就不能提供白花蛇舌草有利于白血病治疗的证据。

2015年获批的 题为“一种治疗白血病的中药” 的专利(CN103272054B)[8]说明书上陈叙,白血病病人接受常规化疗 或接受常规化疗加上此“发明”配方制成的水丸剂, 口服, 一次15g, 一日3次。每组60名病人, 一个疗程为3个月。从临床疗效,西医医疗效,中医医疗效,及安全性评价/不良事件的角度对两组进行了比较,认为常规化疗加此发明中药组的临床疗效优于常规化疗组。但是这个专利说明书[8]中列出的结果表明, 常规化疗及常规化疗加此发明中药配方在白血病治疗的疗效和不良反应方面没有区别。所列数字之间的差异没有统计学和生物学意义。换句话说,此发明中药对白血病治疗无效。此发明中药配方中包括青黛和白花蛇舌草等十几味中药。这是一个很好的否定青黛和白花蛇舌草治疗白血病的案例。出现这样一个无疗效的中药配方专利说明专利申请人和专利审批人都缺乏临床试验数据分析和统计学常识。

因为这些白花蛇舌草治疗白血病的宣称缺乏有效的临床数据支持, 故未得到一般医院的认同。Memorial Sloan Kettering癌症中心的网站上明确指出,白花蛇舌草的抗癌作用缺乏人的数据[19]。

从中文医学文献,中国的专利,百度,和美国Memorial Sloan Kettering 的网站上的信息来分析,白花蛇舌草的抗癌/白血病作用仍处于试验阶段,它在人体内很可能是无效的。

青黛的功效缺乏抗白血病的临床证据

青黛为爵床科植物马蓝、蓼科植物蓼蓝、十字花科植物菘蓝的叶或茎叶经加工制得的干燥粉末、团块或颗粒 [20, 21]。 中医认为青黛具清热解毒, 凉血消斑, 泻火定惊等功效 [20]。青黛的主要成分是靛蓝(5–8%), 靛玉红(0.05–0.4%), 异靛蓝, 色胺酮, 青黛酮, 青黛素及大量无机盐等。现代研究宣称青黛有抗肿瘤作用 [22–31]。靛玉红临床治疗协作组[23]于1980年在中华血液学杂志上发表了靛玉红用于治疗慢性粒细胞白血病的信息, 第一次有效地报告了与青黛有关的临床试验结果: 314例慢性粒细胞白血病患者, 口服青黛有效成分靛玉红片剂 150–200 毫克 (少数达300–400毫克), 每天两次, 持续1–6个月。82例(26%)完全缓解, 106例 (33%) 部分缓解, 87例 (28%) 改善, 40例 (13%) 无效。除了体重增加外, 大多数患者在服药开始一周后主观症状改善。其中靛玉红最低口服剂量 “150 毫克, 每天两次” 相当于青黛粉至少每天75克,这是临床上不可能达到的青黛粉剂量。此报道不能证明临床常用剂量(1.5–6 g) [32]的青黛或青黛粉有治疗白血病的疗效。

随后, 多篇文章报道了复方青黛片 [24, 25] 和黄黛片[26–28] 应用于急性早幼粒细胞白血病的治疗。黄世林等 [24] 1995年报道了复方青黛片对60例急性早幼粒细胞白血病患者的治疗情况。60例患者分为三组: 复方青黛片组10人, 复方青黛片+泼尼松组34人, 及复方青黛片+小剂量化疗组16人。疗程30–60天。此三组完全缓解率分别为100, 100, 和93.8%; 达完全缓解所需时间分别为28–55, 30–57, 和36–60天。三个组的疗效无显巨差异,复方青黛片+泼尼松组的肝损伤和肠道副作用较轻[24]。十多年后,潘登等[25]报道了复方青黛片联合全反式维甲酸治疗急性早幼粒细胞白血病的临床观察结果。全反式维甲酸组(22例), 复方青黛片(21例), 全反式维甲酸加复方青黛片组(18例)的缓解率及达缓解所需时间分别是86, 90, 94%及 29–42, 35–50, 24–35天。三个组的缓解率相近, 全反式维甲酸加复方青黛片组达缓解的时间似乎较短, 但肝肾功能损害及胃肠道反应发生率显著增高[25]. 复方青黛片含雄黄, 青黛, 太子参, 和丹参等,其中的含氧化砷的雄黄是治疗白血病的必须成分“君药“, 氧化砷联合全反式维甲酸可能缩短治疗白血病的起效时间, 但增加肝肾功能损害及胃肠道反应发生率却会影响病人的耐受力而降低治疗依从性。青黛等可能是此复方青黛片的“佐或使药”, 但其减轻氧化砷毒性的作用不如泼尼松 [24], 反式维甲酸加复方青黛片的肝肾及胃肠道毒性也没有因为有青黛的存在而减轻 [25]。

向阳等[26]2003年报道了复方黄黛片与化疗交替应用于62例急性早幼粒细胞白血病患者的长期生存情况, 尽管没有对照组, 此文认为复方黄黛片与化疗交替应用是有效可行的缓解后的治疗方案。 2006年, 中华血液学杂志发表了复方黄黛片II期临床试验协作组的试验结果 [27].120 名急性早幼粒细胞白血病患者随机分组入试验组口服复方黄黛片或对照组口服全反式维甲酸。用药达到完全缓解后停药, 用药时间最长到60天。 黄黛片(最高剂量每天7.5克)组的疗效为49±9天达到81%缓解 (59/73), 副作用反应率40%包括胃肠道反应,皮 疹, 和肝功能异常等; 全反式维甲酸 (30毫克, 每天三次)组的疗效为42±9天达到76%缓解 (56/74), 副作用反应率33%包括胃肠道反应, 肌肉关节疼痛, 骨骼疼痛, 皮疹, 和发热等 [27]。因此, 此II期临床试验的结论是黄黛片和全反式维甲酸在急性早幼粒细胞白血病的治疗中的疗效和副作用相当。

上述报道中, 尽管黄黛片和复方青片黛有治疗白血病的疗效[24–27], 但这些报道并不能证明青黛或青黛粉有治疗白血病的疗效。黄黛片和复方青黛片的主要成分是雄黄和青黛。雄黄和青黛的有效成分分别是二硫化二砷[28]和靛玉红[29]。砷剂有抗白血病作用, 这是中国70年代开始研究用砒霜精制成三氧化二砷制剂治疗白血病的基础 [28]。在复方青黛片或黄黛片中, 雄黄是治疗白血病的“主药”。青黛的有效成分靛玉红对慢性粒细胞白血病有效[23], 但靛玉红在复方青黛片或黄黛片中无法达到治疗白血病的有效剂量, 青黛在复方青黛片或黄黛片中的作用并不清楚。青黛被认为是黄黛片中的”佐药“, 即协助主药 (砷剂) 治疗兼症或消减主药 (砷剂) 的烈性、毒性 [30, 31], 但其减轻砷剂毒性的作用不如泼尼松[24]。如果不是复方青黛片或黄黛片, 或方剂里不含烈性毒药砷剂, 常规剂量青黛就失去了应用的基础, 也就很难起效。

前面已经提到的 “一种治疗白血病的中药“(CN103272054B)的专利[8]证实了那个包括青黛(“君药”)的中药配方对白血病无效 。Memorial Sloan Kettering 癌症中心网站上明确指出, 尽管大青叶和靛玉红多年来在中国被用于治疗白血病, 但没有临床证据显示它们能防癌或治疗癌症[33, 34]。

由此, 中外文献资料所涉及的近40年的研究表明, 中药方剂中的白花蛇舌草和青黛没有被证明有治疗白血病的功效。

白花蛇舌草和青黛的毒副作用

中草药在中国的应用历史悠久,人们多数想当然地认可中草药的有效性和安全性。但近些年中草药引起毒副反应的报道越来越多,众多文献表明中草药是中国导致药物性肝肾损害的重要因素甚至是首要因素[9,35–37]。白花蛇舌草和青黛在中国医药界和民间应用较广泛,对其临床应用的安全性应予以重视。关注常用中草药的安全使用,是医药界不容忽视的课题。

近年的文献中有关于白花蛇舌草和青黛毒副作用的报道。医药导报前不久报道了1例白花蛇舌草致急性肾损伤 [38]。社交网站上也有抱怨或指控白花蛇舌草导致肝肾毒副作用的贴子[39, 40]。陶志广2015年在浸大中医药上强调, “癌症病人不宜轻易自用半枝莲白花蛇舌草”[41]。 认为“寒底人”经常饮用半枝莲白花蛇舌草可能受其寒涼之害[41]。中国以外的网站上列出的白花蛇舌草毒副作用包括呼吸困难, 皮疹, 剧烈瘙痒[42],这些可能与过敏有关。此文还指出了白花蛇舌草的可能的抑制精子生成影响生育作用, 药物相互作用, 及潜在肝损伤风险[42]。

闵志强的小组对单味青黛的急性胃肠道作用和亚慢性毒副作用做了可靠的临床前研究[43, 44]。 急性胃肠道实验中[43],小鼠单次灌胃给予青黛饮片加水配制成的混悬液,青黛1 g /kg促进小鼠的肠推进及排便,对小鼠胃排空有促进趋 势。因此青黛可能存在一定的胃肠毒性[43]。小鼠青黛灌胃剂量1 g/kg等于按体表面积计算的人类的等效剂量 [45, 46] 4.9 g (按人的体重60 kg计算),这个等效剂量4.9 g 青黛是在临床常用剂量范围内 (1.5–6 g) [32]。

在90天的亚慢性毒副作用实验中[44],大鼠每日灌胃给予青黛饮片加水配制成的混悬液。所用青黛剂量 0.6, 1.2, 2.4 g生药/kg/天 分别等于人类等效剂量5.8、11.6、23.2 g/天。实验中的剂量相关性毒副反应包括高、中、低剂量组大鼠大便变软, 排便量均较对照组多,高剂量组个别动物出现稀便;各给药组体重增加明显低于对照组;各给药组动物摄食量均小于对照组 [44]。因此,正常大鼠亚长期灌胃给予青黛≥0.6g/kg/day的主要毒性反应为胃肠道反应,导致摄食量下降,体重增长缓慢。大鼠灌胃剂量0.6g/kg/day的人类等效剂量是5.8 g/天。这个等效剂量5.8 g 青黛接近临床常用剂量范围 (1.5–6 g) [32]。

青黛在临床应用中常是复方制剂中的一味,因此青黛在临床应用中的毒副作用常列在复方制剂下 [9, 27, 47–54]。张莉等[47]报道, 6例口服含青黛成分的中药1个月内发生消化道出血。首发症状为下腹痛 , 随后出现血便, 早期有血白细胞升高。内镜下直肠黏膜充血、水肿, 点片状糜烂 , 纵形或不规则形溃疡。病理显示有黏膜萎缩、退行性变,和小血管内纤维素性血栓形成 [47]。索宝军等[48]也报道了13例患者口服含青黛成分中成药后出现缺血性结肠黏膜损伤。临床表现为腹痛及血便, 肠镜下病变形态及病理活检符合缺血性损伤的表现, 病变较重, 呈慢性炎表现 [48]。

2013年4月12日,中国国家食品药品监督管理局发布第54期《药品不良反应信息通报》,通报了复方青黛丸(胶丸、胶囊、片)引起的消化系统不良反应,严重者表现为药物性肝损害和消化道出血 [54]。该通报中的复方青黛丸由青黛、乌梅、蒲公英等14味中药组成,不含雄黄。此通报例举了典型药物性肝炎病例和典型胃肠出血病例,并报告,2004年至2012年6月,中国国家药品不良反应监测中心病例报告数据库中有关复方青黛丸(胶丸、胶囊、片)病例报告344例,不良反应/事件主要累及消化系统、皮肤及其附件、精神系统等,临床主要表现如腹泻、腹痛、肝炎、肝功能异常、头晕等;严重病例报告23例,临床主要表现为药物性肝损害和胃肠出血 [54]。

尽管青黛作为复方制剂中的一味,复方青黛制剂的毒副反应不能全部归咎于青黛,但大鼠小鼠实验显示灌胃给人类等效剂量的青黛引起胃肠道不良反应,导致摄食量下降,体重增长缓慢 [43, 44]。因此口服青黛的胃肠道毒性是不能否认的。

也有报道,口服青黛能有效治疗中度溃疡性结肠炎[55, 56]。青黛在胃肠道无明显消化代谢,出现在大便中的青黛可能为已损伤的粘膜提供保护涂层,可能促进结肠黏膜愈合 [56]。但不同疾病情况下青黛的疗效[55, 56]不能否定青黛对胃肠道的刺激作用以及由此导致的毒副作用[43, 44]。

综上所叙,白花蛇舌草和青黛具有胃肠道及肝肾毒性,口服常规剂量的白花蛇舌草和/或青黛是可能导致胃肠道,肝,和/或肾毒副反应的。

白花蛇舌草和青黛在白血病治疗中的效益/风险比

考虑药物在疾病治疗中的效益/风险比是药物研发和批准的原则, 也是临床用药的原则。去除药品中可疑、无效、甚至也害的成分是减少风险、增加治疗效果的有效手段。近代中药的研究中,抗疟疾的青蒿素和治疗白血病的三氧化二砷 (砒霜)的发现和应用是两个剔除无关成分、提高效益/风险比的典范[28, 57]。从中药中发现的青蒿素的抗疟疾作用和三氧化二砷 (砒霜)治疗白血病的效应并不需要“臣佐使”。用于治疗白血病的中药也应该尽量去除方剂中可疑、无效、甚至也害的成分。

黄黛片和复方青中国黛有治疗白血病的疗效[24–27], 但药片中的靛玉红无法达到有效剂量[26],二硫化二砷是其治疗白血病的有效成分。复方青黛片或黄黛片中的青黛在的治疗白血病中的作用并不清楚, 但其毒副作用不能排除[34, 47, 48]。在2018年版“中国急性早幼粒细胞白血病诊疗指南”中, 静脉滴注三氧化二砷 0.16 mg/kg/day与口服复方黄黛片(主要含四硫化四砷的复方制剂) 60 mg/kg/day是可以互相替代的[58], 显然没有考虑青黛的胃肠道毒副作用[43, 44, 54], 没有考虑最大化效益/风险比。尽管青黛对胃肠道的影响可能依病情而不同, 对于患白血病的病人, 由于他们抵抗力极低, 在没有疗效的情况下, 青黛导致腹痛腹泻, 恶心呕吐是有害的。如急性胃肠道炎症增加菌血症/败血症的风险; 呕吐可能增加体弱病人吸入性肺炎的风险。白花蛇舌草也可能导致过敏和肝肾损伤 [19, 38]。从效益/风险比来看, 用含白花蛇舌草和青黛的中药治疗白血病是有害无益的#

众所周知,用于白血病的化疗、靶向治疗、和免疫治疗都有毒副作用,有些毒副作用还很严重。但是这些用于白血病的化疗、靶向治疗、和免疫治疗已经被证明有明确的临床疗效, 能延长病人的生命。在考虑效益/风险比之后,相关的化疗、靶向治疗、和免疫治疗成为循证医学对白血病的治疗手段 [1–4, 58]。目前, 白花蛇舌草和青黛治疗白血病的疗效不肯定。对急、重病人用不明疗效的治疗方法很可能延后有效的治疗手段,危及病人生命。最近发表的一项研究使用了美国国家癌症数据库的数据,一共研究了近200万名癌症患者,结果是使用补充治疗 [包括中医 (中药、针灸、指压、气功)、印度医学、食疗、芳香治疗、维生素治疗、精神或心理治疗、温泉治疗、氧气治疗等等]的癌症患者死亡率是常规癌症治疗的2倍 [59, 60]。其背后的原因,是接受补充治疗的癌症病人更倾向于拒绝进一步的常规癌症治疗 [59, 60]。这是用白花蛇舌草和青黛治疗白血病另一个风险,医生、患者、及患者家属都应该考虑。

结语

根据搜索查阅到的资料, 经思考、分析、整理,总结如下: 1) 没有临床证据显示白花蛇舌草和青黛在白血病治疗上有效; 2) 白花蛇舌草和青黛在临床应用中显示有胃肠道和肝肾毒性, 有过敏的风险; 3)白花蛇舌草和青黛在白血病的治疗上的净效益无法确定, 医生不能把它们当作安慰剂, 白血病患者不要冒险试用。

致谢

作者感谢Xuan Chi博士和Norman L Stockbridge博士的评论和建议。#谨以本文纪念因患白血病于2018年11月8日离世的钟政强先生。

免责声明

Disclaimer

本文反映了作者的观点, 不应该被解释为代表食品药品监督管理局(FDA) 的观点或政策。

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