Mechanistic Categories Research Articles

Temporal Association Rule Mining: Race-Based Patterns of Treatment-Adverse Events in Breast Cancer Patients Using SEER-Medicare Dataset.

Disparities in the screening, treatment, and survival of African American (AA) patients with breast cancer extend to adverse events experienced with systemic therapy. However, data are limited and difficult to obtain. We addressed this challenge by applying temporal association rule (TAR) mining using the SEER-Medicare dataset for differences in the association of specific adverse events (AEs) and treatments (TRs) for breast cancer between AA and White women. We considered two categories of cancer care providers and settings: practitioners providing care in the outpatient units of hospitals and institutions and private practitioners providing care in their offices. We considered women enrolled in the Medicare fee-for-service option at age 65 who qualified by age and not disability, who were diagnosed with breast cancer with attributed patient factors of age and race, marital status, comorbidities, prior malignancies, prior therapy, disease factors of stage, grade, and ER/PR and Her2 status and laterality. We included 141 HCPCS drug J codes for chemotherapy, biotherapy, and hormone therapy drugs, which we consolidated into 46 mechanistic categories and generated AE data. We consolidated AEs from ICD9 codes into 18 categories associated with breast cancer therapy. We applied TAR mining to determine associations between the 46 TR and 18 AE categories in the context of the patient categories outlined. We applied the spark.mllib implementation of the FPGrowth algorithm, a parallel version called PFP. We considered differences of at least one unit of lift as significant between groups. The model's results demonstrated a high overlap between the model's identified TR-AEs associated set and the actual set. Our results demonstrate that specific TR/AE associations are highly dependent on race, stage, and venue of care administration. Our data demonstrate the usefulness of this approach in identifying differences in the associations between TRs and AEs in different populations and serve as a reference for predicting the likelihood of AEs in different patient populations treated for breast cancer. Our novel approach using unsupervised learning enables the discovery of association rules while paying special attention to temporal information, resulting in greater predictive and descriptive power as a patient's health and life status change over time.

Physiology Core Concepts Guide Successful Mechanistic Reasoning

Mechanistic reasoning is a powerful strategy used to organize one’s thinking by leveraging the properties and behaviors of underlying entities to explain how and why phenomena occur. Modell identified seven “general models” (i.e., principles) in physiology that could serve as a cognitive framework for students to use to reason mechanistically about a variety of seemingly-distinct processes. Many of these principles are also Physiological Core Concepts. Though there are frameworks to analyze students’ use of mechanistic reasoning, there is currently no framework to analyze how physiology students use principles or Core Concepts to support their use of mechanistic reasoning to solve complex problems. In this study we ask two research questions about students use of the principle of Flux (i.e., the Core Concept of Flow Down Gradients) 1) How do students use the Flux Relationship to guide their mechanistic reasoning? 2) Are students who use the Flux Relationship to guide their mechanistic reasoning more successful when solving complex organismal biological problems? We recorded and transcribed in-class discussions of 14 introductory biology students in 4 small groups as they worked across a term to solve complex problems related to the principle of Flux (rate of movement ∝ gradient/resistance). To address RQ1, we combined emergent codes with established frameworks for mechanistic reasoning and Flux reasoning. We iteratively applied our codes to a subset of transcripts, compared interpretations and coding, revised the framework and re-coded. In this way we developed a framework to characterize how students use the Flux Relationship to guide their mechanistic reasoning. To address RQ2, we scored each discussion for accuracy and whether or not the students used the Flux relationship to guide their mechanistic reasoning. We then used a chi-square test to determine if more students who used Flux to guide their reasoning were accurate. RQ1 results: Our principle-based mechanistic reasoning framework for Flux includes two established mechanistic reasoning categories: identification (introduction of components of a system) and chaining (using the relationships between components to reason mechanistically). The framework then identifies how students use flux, or don’t, while discussing a complex physiological problem. For example, students may 1) identify or chain without using Flux, 2) identify an aspect of the Flux Relationship without using it to chain, 3) use the Flux relationship to chain. RQ2 results: We found that students who used the Flux Relationship to chain were significantly more likely to be accurate in their discussion. Our study adds to the current literature by providing researchers and instructors with a more meaningful analysis of how students use physiological principles/Core Concepts when mechanistically reasoning to solve complex problems. Additionally, our results support the claim that supporting students' use of principles/Core concepts can lead to higher accuracy in their reasoning. Finally, our framework can be used to measure the impact of instructional interventions designed to increase the use of students' principle-based mechanistic reasoning. Work funded by NSF. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Abstract SY11-02: Small molecule inhibitors of RNA modifying enzymes as precision cancer therapeutics

Abstract Over 100 modifications to RNA are known to occur in human cells, where they influence many aspects of RNA biology, including protein and nucleic acid interactions. Our survey of human RNA-modifying enzymes with selectively essential phenotypes in specific cancers, suggests that these cancer-essential enzymes fall into four mechanistic categories: group transfer enzymes; base editors; nucleases; and helicases. Our team has discovered nanomolar or picomolar inhibitors of cancer-essential enzymes within each of these four categories. DHX9, a novel cancer target, is a multifunctional DEAH-box RNA helicase which can unwind regions of double-stranded DNA and RNA helices but has a greater propensity for secondary structures such as DNA/RNA hybrids (R-loops), circular RNA and DNA/RNA G-quadruplexes. DHX9 interacts with and regulate a large variety of proteins, including key proteins in DNA damage repair pathways such as BRCA1, ATR, Ku86, and WRN. We previously demonstrated that DHX9 inhibition is selectively efficacious in microsatellite high (MSI-H)/defective mismatch repair (dMMR) tumor models, in vitro and in vivo. Further profiling of DHX9 inhibitors across a broad panel of cancer cell lines reveals that tumor cells with Loss-of-Function (LOF) mutations in the DNA damage repair genes BRCA1 and/or BRCA2 (as defined by somatic mutations including single-nucleotide variants and/or copy number loss), are also selectively responsive to DHX9 inhibitor treatment. Selective dependence on DHX9 was observed in both ovarian and breast cancer cell lines that exhibit BRCA1 and/or BRCA2 LOF. DHX9 inhibition leads to increased RNA/DNA secondary structures such as R-loops and G-quadruplexes, resulting in subsequent DNA damage and increased replication stress. Cell lines that exhibit BRCA1 and/or BRCA2 LOF appear unable to resolve this replication stress and showed S-G2 phase cell cycle arrest prior to onset of apoptosis. An orally bioavailable DHX9 inhibitor was dosed in vivo to assess DHX9 dependency within multiple human xenografts representing triple negative breast cancer and high-grade serous ovarian cancer with BRCA1 and/or BRCA2 LOF. In all models, DHX9 inhibition was well tolerated for a period of up to 28 days, with robust and significant tumor growth inhibition - including tumor regression - observed in multiple BRCA1 and/or BRCA2 LOF models; in contrast, minimal tumor growth inhibition was observed in BRCA1 and BRCA2 wild type models. These results portend DHX9 inhibition as a novel treatment modality for patients with BRCA1 and/or BRCA2 LOF across multiple tumor types, including breast and ovarian cancer. Citation Format: Robert A. Copeland, Jennifer Castro, Matthew H. Daniels, Deepali Gotur, Young-Tae Lee, Shihua Yao, David Brennan, Brian T. Johnston, Monique Laidlaw, Sunaina Pai, Jie Wu, Rishabh Bansal, Anugraha Raman, Shane M. Buker, Julie Liu, E. Allen Sickmier, Kevin Knockenhauer, Chuang Lu, Stephen J. Blakemore, Serena J. Silver, P. Ann Boriack-Sjodin, Kenneth W. Duncan, Jason A. Sager. Small molecule inhibitors of RNA modifying enzymes as precision cancer therapeutics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr SY11-02.

Autoinflammatory Contributors to Cytokine Storm.

Cytokine Storm is a complex and heterogeneous state of life-threatening systemic inflammation and immunopathology. Autoinflammation is a mechanistic category of immune dysregulation wherein immunopathology originates due to poor regulation of innate immunity. The growing family of monogenic Systemic Autoinflammatory Diseases (SAIDs) has been a wellspring for pathogenic insights and proof-of-principle targeted therapeutic interventions. There is surprisingly little overlap between SAID and Cytokine Storm Syndromes, and there is a great deal to be inferred from thoseSAID that do, and do not, consistently lead to Cytokine Storm. This chapter will summarize how illustrations of the autoinflammatory paradigm have advanced the understanding of human inflammation, including the role of autoinflammation in familial HLH. Next, it will draw from monogenic SAID, both those with strong associations with cytokine storm and those without, to illustrate how the cytokine IL-18 linksinnate immune dysregulation and cytokine storm.

Molecular Targeted Therapies in Glioblastoma Multiforme: A Systematic Overview of Global Trends and Findings.

This systematic review assesses current molecular targeted therapies for glioblastoma multiforme (GBM), a challenging condition with limited treatment options. Using PRISMA methodology, 166 eligible studies, involving 2526 patients (61.49% male, 38.51% female, with a male-to-female ratio of 1.59/1), were analyzed. In laboratory studies, 52.52% primarily used human glioblastoma cell cultures (HCC), and 43.17% employed animal samples (mainly mice). Clinical participants ranged from 18 to 100 years, with 60.2% using combined therapies and 39.8% monotherapies. Mechanistic categories included Protein Kinase Phosphorylation (41.6%), Cell Cycle-Related Mechanisms (18.1%), Microenvironmental Targets (19.9%), Immunological Targets (4.2%), and Other Mechanisms (16.3%). Key molecular targets included Epidermal Growth Factor Receptor (EGFR) (10.8%), Mammalian Target of Rapamycin (mTOR) (7.2%), Vascular Endothelial Growth Factor (VEGF) (6.6%), and Mitogen-Activated Protein Kinase (MEK) (5.4%). This review provides a comprehensive assessment of molecular therapies for GBM, highlighting their varied efficacy in clinical and laboratory settings, ultimately impacting overall and progression-free survival in GBM management.

Nociplastic pain in research and practice : Overview of biopsychosocial principles, possibilities and difficulties

Traditionally, two mechanistic pain categories were distinguished: nociceptive and neuropathic pain. After the definitions of these two mechanistic descriptors were refined more precisely in the International Association for the Study of Pain (IASP) taxonomy in 2011, alarge group of patients remained whose pain could not be assigned to either of the two categories. Nociplastic pain was therefore proposed as athird mechanistic descriptor in 2016. This review article presents the current state of the integration of nociplastic pain into research and clinical practice. In particular, the possibilities and difficulties of applying this concept are addressed from ahuman and animal experimental research perspective.

Modes-of-action of antifungal compounds: Stressors and (target-site-specific) toxins, toxicants, or toxin-stressors.

Fungi and antifungal compounds are relevant to the United Nation's Sustainable Development Goals. However, the modes-of-action of antifungals-whether they are naturally occurring substances or anthropogenic fungicides-are often unknown or are misallocated in terms of their mechanistic category. Here, we consider the most effective approaches to identifying whether antifungal substances are cellular stressors, toxins/toxicants (that are target-site-specific), or have a hybrid mode-of-action as toxin-stressors (that induce cellular stress yet are target-site-specific). This newly described 'toxin-stressor' category includes some photosensitisers that target the cell membrane and, once activated by light or ultraviolet radiation, cause oxidative damage. We provide a glossary of terms and a diagrammatic representation of diverse types of stressors, toxic substances, and toxin-stressors, a classification that is pertinent to inhibitory substances not only for fungi but for all types of cellular life. A decision-tree approach can also be used to help differentiate toxic substances from cellular stressors (Curr Opin Biotechnol 2015 33: 228-259). For compounds that target specific sites in the cell, we evaluate the relative merits of using metabolite analyses, chemical genetics, chemoproteomics, transcriptomics, and the target-based drug-discovery approach (based on that used in pharmaceutical research), focusing on both ascomycete models and the less-studied basidiomycete fungi. Chemical genetic methods to elucidate modes-of-action currently have limited application for fungi where molecular tools are not yet available; we discuss ways to circumvent this bottleneck. We also discuss ecologically commonplace scenarios in which multiple substances act to limit the functionality of the fungal cell and a number of as-yet-unresolved questions about the modes-of-action of antifungal compounds pertaining to the Sustainable Development Goals.

Abstract 11953: Sudden Death Associated With Therapeutic Levels of Methadone: Significance of Drug Interactions

Introduction: Methadone for treatment of addiction or chronic pain can be complicated by QT interval prolongation and increased risk of sudden cardiac death (SCD). The concurrent use of additional QT-prolonging medications, such as selective serotonin reuptake inhibitors (SSRIs), may contribute to additional increased risk. We conducted a community-based study to assess the potential role of drug combinations in SCD. Methods: We ascertained residents of Multnomah Co., OR (2002-18) and Ventura Co., CA (2015-21) who suffered SCD and had toxicology reports available. We excluded those under age 18 and those with evidence of recreational drug use or drug overdose. Methadone levels and medications were captured on toxicology reports from the medical examiner. We used logistic regression to compare characteristics and the presence of select medications in those with and without therapeutic levels of methadone at the time of SCD. Results: Of the 789 subjects who met our criteria, the mean (25th-75th percentile) age was 49 (39-59) years, 34% were females, and 85 (11%) had therapeutic levels of methadone. Subjects on methadone were younger, more likely to be female, and had a higher prevalence of SSRIs and benzodiazepines compared to those without methadone (Table). Subjects with methadone had a 3.37 higher odds of SSRI use compared to those without methadone (95% CI: 1.87-6.09). When stratified by mechanistic category, those with methadone had higher odds of QT-prolonging SSRIs [OR (95% CI) = 2.87 (1.35-6.13)] and higher odds of other SSRIs [OR (95% CI) = 3.20 (1.40-7.32)] compared to those without methadone. Combinations of methadone with other selected drug classes did not reach statistical significance. Conclusion: These postmortem findings suggest that the combination of methadone with SSRIs may be associated with additional risk of SCD and warrant further investigation. Drug interactions are a potentially avoidable risk factor for SCD in patients receiving methadone.

Are Existing Modeling Tools Useful to Evaluate Outcomes in Mangrove Restoration and Rehabilitation Projects? A Minireview

Ecosystem modeling is a critical process for understanding complex systems at spatiotemporal scales needed to conserve, manage, and restore ecosystem services (ESs). Although mangrove wetlands are sources of ESs worth billions of dollars, there is a lack of modeling tools. This is reflected in our lack of understanding of mangroves’ functional and structural attributes. Here, we discuss the “state of the art” of mangrove models used in the planning and monitoring of R/R projects during the last 30 years. The main objectives were to characterize the most frequent modeling approach, their spatiotemporal resolution, and their current utility/application in management decisions. We identified 281 studies in six broad model categories: conceptual, agent-based (ABM), process-based (PBM), spatial, statistical, and socioeconomic/management (ScoEco). The most widely used models are spatial and statistical, followed by PBM, ScoEco, and conceptual categories, while the ABMs were the least frequently used. Yet, the application of mangrove models in R/R projects since the early 1990s has been extremely limited, especially in the mechanistic model category. We discuss several approaches to help advance model development and applications, including the targeted allocation of potential revenue from global carbon markets to R/R projects using a multi-model and integrated approach.

Risk assessment in gene therapy and somatic genome-editing: An expert interview study

BackgroundInnovation in gene therapy and genome editing raises high expectations for therapeutic breakthroughs. With the increasing maturity of the field, gene products using “gene transfer” technology, such as viral vectors, designer nucleases, incl. CRISPR/Cas, as the most recent, are frequently tested in clinical trials. Before such trials are launched, the anticipated risks and benefits of using gene transfer technologies must be evaluated to ascertain an ethical balance of risks and benefits. MethodsWe conducted semi-structured interviews with experts (n=15) in gene therapy/genome editing. We applied thematic text analysis to identify the qualitative spectrum of strengths, weaknesses, opportunities, and threats (SWOT) of a risk assessment approach to gene therapy/genome editing research based on a comprehensive set of nine mechanistic categories of adverse reactions combined with estimates of risk probability according to World Health Organization (WHO) adverse reaction terminology. ResultsOur study revealed a clear demand for a structured approach to risk assessment gene therapy/genome editing. The interviews indicate that the nine presented mechanistic categories may be helpful to structure this risk assessment prior to initiating a new study. The interviews revealed a broad spectrum of practice-oriented SWOT, described in detail in this manuscript. DiscussionThe here presented SWOT for a structured approach to risk assessment prior to clinical trials with gene therapy/genome editing inform the refinement and implementation of such standardized approaches and the discussion among researchers, regulators, and funders. To overcome potential weaknesses and threats in the application of such a risk-based approach, the mechanistic categories need to be case-sensitive and complemented by information on the validity of relevant animal models, long-term risks, and information about patient characteristics.

Recycled Translation: Repurposing Drugs for Stroke.

Stroke, which continues to be a leading cause of death and long-term disability worldwide, has often been described as a clinical graveyard. While multiple small molecule therapeutics have undergone clinical trials in stroke, currently only one Food and Drug Administration (FDA)-approved medication exists for the treatment of stroke, the biological, recombinant tissue plasminogen activator (rt-PA). Repurposing of therapeutics which have previously gained FDA approval for alternative indications serves as a prospective option for stroke therapeutic translation. In contrast to de novo drug development, repurposing strategies have patient-centered and economic advantages. These include increased safety, increased chance of approval, decreased time to approval, and decreased capital investment. Presently, 37 active stroke clinical trials utilize repurposed therapeutics with various initial indications and dosing paradigms. The currently studied repurposed therapeutics fall into six mechanistic categories: (1) anticoagulation; (2) vasculature integrity, response, or red blood cell (RBC) alterations; (3) immune system regulation; (4) neurotransmission; and (5) neuroprotection. Directed hypothesis-driven computational investigation utilizing drug databases, in silico drug-protein interaction modeling, genomic data, and consensus methodology can determine if the current mechanistic repurposing categories have the highest chance of translational success or if other mechanistic avenues should be explored. With this increased focus on repurposed therapeutic strategies over de novo strategies, evolution and optimization of regulatory protections are needed to incentivize innovators and investigators.

T Cell Recognition of Tumor Neoantigens and Insights Into T Cell Immunotherapy.

In cancer, non-synonymous DNA base changes alter protein sequence and produce neoantigens that are detected by the immune system. For immune detection, neoantigens must first be presented on class I or II human leukocyte antigens (HLA) followed by recognition by peptide-specific receptors, exemplified by the T-cell receptor (TCR). Detection of neoantigens represents a unique challenge to the immune system due to their high similarity with endogenous ‘self’ proteins. Here, we review insights into how TCRs detect neoantigens from structural studies and delineate two broad mechanistic categories: 1) recognition of mutated ‘self’ peptides and 2) recognition of novel ‘non-self’ peptides generated through anchor residue modifications. While mutated ‘self’ peptides differ only by a single amino acid from an existing ‘self’ epitope, mutations that form anchor residues generate an entirely new epitope, hitherto unknown to the immune system. We review recent structural studies that highlight these structurally distinct mechanisms and discuss how they may lead to differential anti-tumor immune responses. We discuss how T cells specific for neoantigens derived from anchor mutations can be of high affinity and provide insights to their use in adoptive T cell transfer-based immunotherapy.

Investigating the role of multiple categorization tasks in a curriculum designed around mechanistic patterns and principles

The goal of the present work is to extend an online reaction categorization task as a research instrument to a formative assessment tool of students’ knowledge organization for organic chemistry reactions. Herein, we report our findings from administering the task with undergraduate students in Organic Chemistry II, at a large, research intensive Canadian university, including the relationship between instrument and exam scores. The online categorization task uses 25 reaction cards that participants were asked to sort first into categories of their choosing (i.e., an open sort) then into the mechanistic categories defined in a Patterns of Mechanisms curriculum (i.e., a closed sort). We observed a small, significant correlation between how learners chose to organize their knowledge (i.e., open sort) and their cued ability (i.e., match with expert sort) at the beginning of the Organic Chemistry II course (N = 65, r = 0.28, p = 0.026). We conducted a correlation analysis between students’ scores on the open and closed sort tasks and academic achievement. We found a strong relationship between the scores in the online categorization tasks and Organic Chemistry II exams, especially from the closed sort tasks (N = 43, r = 0.70, p = 0.000). To date, no other discipline specific card-sort tasks have shown such a strong correlation with final assessment grades. We also found an increasing relationship between students’ choice and ability over time as students developed their expertise in the domain. This work also added evidence to the validity and reliability of the organic card-sort instrument, through multiple measures. Educators and students could use the card sort task as a self-assessment measure and as part of classroom activities related to mechanistic analysis. Future work is needed to investigate how card sort tasks of this type are connected with expertise in other settings.

Historical and legal discourse in the context of classical and postclassical legal understanding

Іntroduction. Historical and legal science, as well as the science of law in general, is acutely faced with challenges related to the new stage of development of humanities knowledge and the corresponding change of research paradigm that occurs during the struggle between classical and nonclassical (postclassical) types of legal understanding Тhe aim of the article. Тhese processes need to be understood and "adapted" in particular in the historical and legal discourse. In particular, it is proposed to analyze the phenomenon of deformation of the phenomenon of law, and the resulting differentiation of the subject, in particular in historical and legal research, and the coherence of research tools offered within the classical and nonclassical types of legal understanding Results. Modern methodological research is a natural reaction of the domestic legal process to the dominance of the monistic materialist approach to the study of legal phenomena, which actualizes anthropological and axiological approaches. Both anthropologization and axiologization of law cause the deformalization of the phenomenon of law, creating a conceptual In the light of the above, it seems important to consider in relation to the relationship such concepts as legal reality (historical and legal reality), legal life, legal system as central, and legal space, legal field, legal environment as peripheral. At the same time, attention is drawn to the normative nature of the legal system, the ontological nature of legal reality, the inconsistency of legal life as a starting point in the choice of methodological tools. Introduction to the historical and legal discourse of «ontological metaphors»: legal communication, legal event, legal life, legal space, legal field, legal environment, etc., will activate the intersubjective model of knowledge of law as a sociocultural phenomenon, draw attention to the dynamics of law, using an arsenal of non-classical methodology. Conclusions. An overview of some trends that lead to a change in the object and subject of jurisprudence shows a radical change in its methodology, which should form research tools to answer new research questions. This process is part of the process of modern «cultivation» of integrated thinking as opposed to or in addition to analytical and systemic, which is characterized by consideration of reality in mechanistic categories, and, being irreversible, requires appropriate historical and legal reflections

Criteria for assessing the reliability of toxicity predictions: I. TIMES Ames mutagenicity model

The strain-dependent bacterial reverse mutation assay (Ames test) is typically part of regulatory batteries of methods used for in vitro evaluation of substances eliciting mutagenicity via short-chain-length DNA damage. This study was undertaken in an effort to make the Ames endpoint more amenable to category formation to support assessments of mutagenesis via non-test methods. The plethora of Ames data has allowed for the establishment of well-document mechanistic-based structural alerts and thereby, DNA-binding profiles. These alerts/profiles are linked to positive and in some cases negative response categories for chemicals. These mechanistic categories form a strong basis for addressing the reliability of predictions as the results obtained from such models are likely to be transparent and mechanistically justifiable. Such mechanistic transparency establishes and rationalizes the structure–property causality principle and thus, adds important weight-of-evidence to the prediction. In the present study a set of criteria for evaluating reliability of the TIMES Ames with S9 (ver. 17.17) predictions are presented and demonstrated. These criteria are consistent with previous considerations; however, they stress mechanistic considerations based on metabolism, especially metabolic activation of non-reactive parent chemicals.

Solving the problems of gas leakage at laparoscopy.

Gas leakage during minimally invasive surgery is an aerosolization hazard. Sensitive optical and thermographic imaging can demonstrate and differentiate between mechanistic categories, enabling engineering solutions to fortify surgical care against pollutants and pathogens affecting operating room teams. Areas for improvement.

Meiotic drive

In this Quick Guide, Srinivasa and Zanders provide an overview of meiotic drivers and the diverse mechanisms these genetic elements use to bias their transmission to the next generation.

Fusogens: Chemical Agents That Can Rapidly Restore Function After Nerve Injury

BackgroundRestoring function after nerve injury remains one of medicine's greatest challenges. The current approach of epineurial coaptation does not address the fundamental insult at the molecular level: a discontinuity in the axonal membranes. Membrane fusion is possible through agents collectively called chemical fusogens, which are heterogeneous in structure and mechanism of action. We sought a unifying system for classifying fusogens to better understand their role in cell fusion. Materials and methodsWe conducted a comprehensive literature review to identify the most commonly cited chemical fusogens, their structures, mechanisms of actions, and clinical applications to date. We identified seven chemical fusogens (polyethylene glycol, chitosan, dextran sulfate, n-nonyl bromide, calcium, sodium nitrate, and H-α-7), which have each been studied to different extents in protoplasts, animals, and humans. ResultsChemical fusogens achieve cell fusion by one of two ways: bringing cells in close enough proximity to each other so the inherent fluidity of the phospholipid membrane allows for their rearrangement or modifying the surface charges of the membranes to diminish repellent charges. Sowers initially put forth a classification system that identified these agents as cell aggregators and membrane modifiers, respectively. We adapted this classification system in the setting of axonal membrane fusion and hypothesized that the most effective approach to axonal membrane repair is likely combination of both. ConclusionsChemical fusogens could be grouped into two mechanistic categories—cell aggregators and membrane modifiers. For axonal membrane fusion, a combination of both mechanisms can significantly contribute to advancing outcomes in peripheral nerve repair via a chemical-surgical intervention.

Systems Medicine Disease: Disease Classification and Scalability Beyond Networks and Boundary Conditions.

In order to accommodate the forthcoming wealth of health and disease related information, from genome to body sensors to population and the environment, the approach to disease description and definition demands re-examination. Traditional classification methods remain trapped by history; to provide the descriptive features that are required for a comprehensive description of disease, systems science, which realizes dynamic processes, adaptive response, and asynchronous communication channels, must be applied (Wolkenhauer et al., 2013). When Disease is viewed beyond the thresholds of lines and threshold boundaries, disease definition is not only the result of reductionist, mechanistic categories which reluctantly face re-composition. Disease is process and synergy as the characteristics of Systems Biology and Systems Medicine are included. To capture the wealth of information and contribute meaningfully to medical practice and biology research, Disease classification goes beyond a single spatial biologic level or static time assignment to include the interface of Disease process and organism response (Bechtel, 2017a; Green et al., 2017).

The Genetics of Primary Microcephaly.

Primary microcephaly (MCPH, for "microcephaly primary hereditary") is a disorder of brain development that results in a head circumference more than 3 standard deviations below the mean for age and gender. It has a wide variety of causes, including toxic exposures, in utero infections, and metabolic conditions. While the genetic microcephaly syndromes are relatively rare, studying these syndromes can reveal molecular mechanisms that are critical in the regulation of neural progenitor cells, brain size, and human brain evolution. Many of the causative genes for MCPH encode centrosomal proteins involved in centriole biogenesis. However, other MCPH genes fall under different mechanistic categories, notably DNA replication and repair. Recent gene discoveries and functional studies have implicated novel cellular processes, such as cytokinesis, centromere and kinetochore function, transmembrane or intracellular transport, Wnt signaling, and autophagy, as well as the apical polarity complex. Thus, MCPH genes implicate a wide variety of molecular and cellular mechanisms in the regulation of cerebral cortical size during development.