Abstract: By integrating high throughput eQTL and pQTL data generated using different platforms, in this study, the relationship between transcriptome and proteome, as well as, the efficacy of platforms in measuring transcript and protein levels in blood were investigated. eQTL data were obtained from the eQTLGen study that used Microarray and INTERVAL study that relied on RNASeq platform to measure transcripts. pQTL data were obtained from UK Biobank study that used Olink and deCODE study that used SomaScan platform to measure proteins. A total of 1,162 genes that were shared between the four platforms were selected and investigated.The outcome of Mendelian randomization analysis identified 211 genes that their transcript levels significantly (P<5e-8) predicted their protein levels across the panels. Similarly, genetic correlation analysis identified 67 genes that share significant correlation. %12(N=25) of genes identified through Mendelian randomization and 7% of those identified through genetic correlation showed negative associations. Cross-platform analysis revealed in INTERVAL-UKBB panel the effect size of SNPs on eQTLs and pQTLs show the highest correlation; while in eQTLGen-deCODE panel this value was the lowest. Co-localization analysis further confirmed these findings and indicated genes with strong evidence of colocalization in their eQTLs and pQTLs encode intracellular proteins while those with trivial evidence of colocalization encode secretory proteins that undergo glycosylationIntegrating both transcriptome and proteome for biomarker discovery and locus annotation is important, as overall genetics of transcriptome and proteome are not the same. RNASeq and Olink platforms provide more accurate measures of RNA and protein levels.

Abstract: The "Binding Problem" in neuroscience remains unsolved due to the temporal lag of synaptic transmission, which operates at scales (> 1 ms) insufficient for sub-millisecond conscious integration. Here, we propose a framework for cellular cognition anchored in acoustic-optical field coupling within the structured medium of the cytoplasm. We demonstrate that the microtubule (MT) lattice functions as a tunable acoustic metamaterial, where the 5,281 phosphorylation states of the "Hameroff Byte" act as a stochastic configuration space for phononic filtering. Integrating behavioral data from non-neural organisms like Stentor coeruleus, we show that associative conditioning within these molecular networks maximizes Integrative Causal Emergence (ICE). This process reifies the cellular "Self" as a unified causal agent through a "Wavefront Lock" mechanism—a state of high-order aperiodic symmetry protected from thermal decoherence by hierarchical cytoplasmic heterogeneities.

Abstract: Rice chalkiness is a key constraint on high-quality rice breeding, and unbalanced sucrose transport and starch metabolism are its primary causes. To clarify the molecular mechanism by which OsSUT2 regulates rice grain chalkiness formation, the rice cultivar TP309 was used as material, and ossut2 homozygous mutants were generated via CRISPR/Cas9. Systematic studies were performed using gene overexpression complementation, phenotypic identification, cytological observation, transcriptome sequencing and haplotype analysis. Results showed that loss of OsSUT2 function significantly increased grain chalkiness, deteriorated agronomic traits, induced carbon assimilate accumulation in leaves, blocked sugar transport and starch synthesis in grains, and destroyed starch fine structure; normal phenotype was fully restored by OsSUT2 overexpression. OsSUT2 was expressed in both source and sink organs, with the most obvious inhibition detected in panicles. Mutation of OsSUT2 disordered sucrose and starch metabolic pathways. Three main haplotypes of OsSUT2 were identified in natural populations, with significant indica–japonica differentiation. OsSUT2 is confirmed as a key regulator of rice chalkiness, providing gene resources and theoretical support for rice quality improvement.

Abstract: Background and Aims: This study aimed to systematically search for molecular biomarkers that contribute to the risk of coronary artery disease (CAD). Methods and Results: A SNP-based multiomics data analysis plan was used to identify biomarkers contributing to the risk of CAD through a two-step discovery and validation design. By integrating CAD GWAS data with epigenome, transcriptome, and proteome quantitative trait loci (QTLs) from blood, 44 CpG sites, 37 transcripts, and 27 protein biomarkers were identified contributing to the risk of CAD. The identified biomarkers shared interactions and were enriched in lipid metabolism-related processes. The PCSK9 protein was under the regulatory impact of the APOC1, GZMA, and GRN proteins. The impact of SMARCA4 and PSRC1 transcripts on CAD were mediated through lipids, whereas the influence of the FES transcript on the risk of CAD was attributed to blood pressure. Finally, while 53% of the transcripts identified through the discovery stage were validated, this ratio was 20% for the protein biomarkers and 24% for the CpG sites. Conclusions: This study identified biomarkers contributing to the risk of CAD through a two-step discovery and validation analyses; furthermore, it provided insights into the paths by which several biomarkers influence the risk of CAD and underlined the efficiency of transcriptome platforms in identifying biomarkers.

Abstract: A cell-based screening system for viral protease inhibitors was developed using firefly luciferase fragment complementation and validated on the SARS-CoV-2 3CLpro model. The optimal luciferase variant incorporating the VLQSGF proteolytic site (Luc III) retained 88% of its native activity. A critical requirement for system performance was the use of an extended nsp4–nsp6 fragment of the viral polyprotein rather than the mature protease, underscoring the importance of the native context for 3CLpro activity. The bicistronic construct pCAG-Luc-III-IRES-nsp4-6 enables coordinated expression of the reporter and protease, thereby increasing assay reproducibility. IC50 values obtained in this system for nirmatrelvir and GC376 correlated with live-virus assay data but differed significantly from those of a cell-free FRET assay, reflecting the impact of cellular barriers. This approach combines simplicity, a standard substrate, and high reproducibility, making it promising for high-throughput screening in basic laboratory settings and adaptable to other viral proteases.

Abstract: Background: The growing demand for versatile laboratory automation is exemplified in the context of liquid biopsy, where multi-analyte approaches are increasingly recognised for their potential to enhance diagnostic sensitivity in oncology. However, current practice often necessitates the use of dedicated instruments and workflows for the extraction of each analyte, posing financial and logistical barriers for automated multi-analyte liquid biopsy. Methods: Here, we present Robotic Centrifugal Microfluidics (RoCM), an all-in-one platform that combines the versatility of centrifugal microfluidics and operational flexibility of robotic liquid handling. This combination enables the automation of complex micro- and macrofluidic protocols, realised through the use of (1.) exchangeable microfluidic cartridges and (2.) programmable robotic operations such as in-rotation liquid supply, magnetic bead manipulation, or microfluidic valving. In-rotation robotic liquid manipulation maintains fluid control under centrifugal forces and reduces the cartridge footprint associated with pre-loaded liquid reservoirs. Platform applicability was demonstrated using two exemplary liquid biopsy workflows: the extraction of cell-free DNA (cfDNA) from blood plasma using RoCM-cfDNA slices and the extraction of extracellular vesicles (EVs) from blood plasma using RoCM-EV slices. Results: In a pilot study with patient samples from different cancer entities, the RoCM-cfDNA slices yielded comparable variant allele frequencies to a commercial bead-based instrument, while the RoCM-EV slices achieved a recovery of a greater diversity of EV subpopulations and up to one magnitude higher recovery than semi-automated size exclusion chromatography. Conclusion: By simply exchanging cartridges, RoCM enables the extraction of diverse analytes within a single automated system. Its application can be extended to further analytes, such as circulating tumor cells (CTCs), or to applications beyond liquid biopsies, where versatile micro- and macrofluidic protocols benefit from implementation in a single automation instrument.

Abstract: Background: Deep learning has become an important tool for predicting mutation-induced changes in binding free energy (ΔΔG). However, most current state-of-the-art methods rely heavily on paired wild-type (WT) and mutant (MT) complex structures during both training and inference. This dependence on post-mutation structural information substantially limits their practical utility in real-world scenarios, such as clinical diagnosis and early-stage drug screening, where mutant structures are difficult to obtain experimentally in a timely manner. Methods: To evaluate model performance in more realistic and challenging translational settings, we conducted a systematic benchmark of graph-based deep learning models under a WT-only inductive setting. We constructed a full-protein heterogeneous graph framework that incorporates long-range spatial constraints to implicitly infer mutational effects from static wild-type structures. We compared it against a sequence-based vector baseline model. Results: Through a systematic evaluation on the MdrDB dataset, we revealed a critical generalization gap. Although random splitting yielded relatively high predictive correlation due to homologous data leakage (Pearson R ≈ 0.55), model performance dropped sharply under a strict UniProt-based cross-protein split designed to simulate prediction on truly unseen targets (Pearson R ≈ 0.15). Although the absolute performance remained limited, the graph-based model showed a weak but consistent improvement over the sequence baseline, which was close to random guessing (Pearson R ≈ 0.04). Conclusions: Further analyses suggest that the performance bottleneck may partly arise from intrinsic experimental noise in the dataset (i.e., label inconsistency) and from the absence of conformational entropy (dynamic) information in static WT structures. This study indicates that random splitting can lead to substantial overestimation of model generalizability. It highlights the need to integrate physical priors and dynamic features to overcome the current limitations of drug resistance prediction when explicit mutant structures are unavailable.

Abstract: There has been an immense concern in the healthcare industry about the globally raising rate of cardiovascular disease (CVD). As per recent WHO reports, CVD is the leading cause of disability, hospitalization and premature death. Studies indicate oxidative stress negatively impacts heart and vascular system which could potentially lead to myocardial infarction, hypertension, cardiomyopathies, atherosclerosis and diabetic heart failure, highlights its significance as prognostic indicator in cardiovascular conditions Currently, Oxidative stress and its negative effect can be accessed by many multiple experimental tools in both, in-vitro and in-vivo settings. Nowadays, many common experimental assays are used for in-vitro and in-vivo evaluation of oxidative stress and its negative effects on the cardiovascular system. This review aims to serve as a comprehensive guide for researchers seeking to evaluate impact of oxidative stress on DNA damage in CVD utilizing standardized methods published by leading institutions. To achieve this, we analyzed 208 relevant articles from prominent databases such as Scopus, PubMed, ScienceDirect, etc. summarizing experimental validation of oxidative stress measurements from 1955 to the present.

Abstract: Liquid biopsy has evolved beyond its original role as a minimally invasive approach for mutation detection and is now being developed as a broader analytical framework for cancer detection, stratification, and longitudinal monitoring. Improvements in next-generation sequencing, assay chemistry, and computational analysis have increased analytical sensitivity, including in settings with low tumor fraction and very low variant allele abundance. These advances have expanded the utility of cfDNA analysis in measurable residual disease assessment and in the detection of low-abundance tumor-derived signals across multiple clinical contexts. At the same time, the field has shifted toward interpreting cfDNA as a carrier of higher-order biological information rather than solely a substrate for mutation calling. Fragmentation profiles, nucleosome positioning, and chromatin accessibility patterns derived from plasma DNA have been used to infer transcriptional and regulatory states, raising the possibility that cfDNA may capture functional tumor states not readily accessible through genotype-focused assays alone. These developments have prompted growing interest in chromatin-informed cfDNA analysis as a means of identifying pathway activity, enhancer usage, transcription factor occupancy, and potentially actionable biological dependencies. However, the translational relevance of many such inferences remains incompletely established. In this review, we examine the analytical advances underlying these approaches, assess the current evidence supporting their biological and clinical utility, and consider the extent to which cfDNA-derived regulatory inference may contribute to adaptive oncology and therapeutic decision-making.

Abstract: Cancer-associated cachexia (CAC) is a multifactorial syndrome driven by a profound metabolic and inflammatory dysregulation. Due to the central role of the insulin growth factor 1 (IGF-1) pathway in regulating muscle mass, energy metabolism, and inflammation, this study evaluated the relevance of IGF-1 axis-related genetic variants to CAC onset and their impact on overall survival (OS) in a cohort of 140 cancer patients. Five single-nucleotide polymorphisms (SNPs) were evaluated, including IGF1 rs6220, insulin-like growth factor 1 receptor (IGF1R) rs2016347 and rs2684788, growth hormone receptor (GHR) rs6873545 and insulin receptor substrate 1 (IRS1) rs1801278. The IGF1 rs6220 GG and GHR rs6873545 CC genotypes were associated with increased CAC risk in male patients. Younger patients (< 63 years) with the rs6873545 CC genotype also had a higher prevalence of CAC. For pre-CAC and CAC patients, subgroup analyses on patients’ OS were conducted. Among older patients and those with high prognostic nutritional index (PNI; > 44.2), the IGF1 rs6220 G allele was associated with longer OS. Conversely, the IGF1R rs2016347 G allele and rs2684788 T allele were linked to poorer OS across multiple pre-CAC and CAC subgroups. The effects of GHR rs6873545 varied across subgroups, suggesting context-dependent activity. This study highlights the functional heterogeneity of IGF-1 axis-related genetic variants as predictors of CAC development and patient survival. Further validation in larger cohorts is warranted.

Abstract: Background: Metadichol® (nano policosanol) is a novel nanoemulsion of long-chain alcohols derived from natural food sources that has demonstrated broad immunomodulatory properties via inverse agonism of the vitamin D receptor (VDR). Its ability to modulate gene expression across diverse cancer cell lines presents a unique opportunity to explore its anticancer mechanisms through cytokine and transcription factor regulation.Objective: This study investigated the effect of Metadichol at five concentrations (0.1 pg/mL to 100 ng/mL) on the expression of interleukin-15 (IL-15), T-box transcription factor 21 (TBET/TBX21), and eomesodermin (EOMES) in six human cancer cell lines representing distinct tumor types: A549 (lung adenocarcinoma), FaDu (pharyngeal squamous cell carcinoma), HCT116 (colorectal carcinoma), HeLa (cervical adenocarcinoma), HepG2 (hepatocellular carcinoma), and U87MG (glioblastoma multiforme).Methods: Quantitative gene expression analysis was performed on all six cell lines treated with Metadichol at concentrations ranging from 0.1 pg/mL to 100 ng/mL. Fold-change values were normalized to untreated controls. IL-15, TBET, and EOMES expression were quantified to assess dose-response relationships.Results: IL-15 expression was consistently and significantly upregulated across all six cancer cell lines following Metadichol treatment. The most pronounced induction was observed in HCT116 cells at 1 pg/mL (4.30-fold), followed by HepG2 at 1 pg/mL (3.66-fold), HeLa at 1 ng/mL (3.04-fold), FaDu at 1 pg/mL (2.81-fold), U87MG at 1 ng/mL (2.69-fold), and A549 at 1 pg/mL (2.45-fold). TBET and EOMES exhibited variable, cell line-dependent expression patterns, with selective upregulation in certain contexts. The ultra-low effective concentrations (picogram range) are consistent with Metadichol's known mechanisms of action.Conclusions: Metadichol potently and consistently induces IL-15 expression across multiple cancer cell types. Given IL-15's central role in activating natural killer (NK) cells, cytotoxic CD8+ T cells, memory T cells, and B cells, this finding positions Metadichol as a promising immunomodulatory compound capable of reactivating innate and adaptive antitumor immunity. The concurrent modulation of TBET and EOMES, key transcription factors governing effector lymphocyte differentiation, further supports a broad immunostimulatory mechanism. These results warrant further preclinical and clinical investigation of Metadichol as an adjunct cancer immunotherapy.

Abstract: Post-translational modiffcations (PTMs) are pivotal in modulating protein function and cellular processes. However, experimental identiffcation of PTM sites remains costly and labor-intensive. Recent advances in artiffcial intelligence (AI) have empowered accurate and scalable in silico PTM site prediction from largescale proteomic data. In this review, we provide a comprehensive and up-to-date overview of AI-driven PTM site prediction across more than ten PTM classes, covering single-PTM site prediction, multiple-PTM site prediction, inter-site crosstalk prediction, and functional prediction of modiffcation sites. We systematically analyze and compare key AI frameworks, from conventional machine learning to deep learning, and summarize representative tools. We also identify key challenges and propose future directions for improvement. To foster application and ongoing progress, we provide practical guidelines for method selection and have established a dedicated website, which serves as a community benchmarking resource for the development of PTM site prediction tools. This website will be regularly updated with emerging prediction tools. By integrating comprehensive literature analysis with a dynamic online resource, we aim to provide a robust cornerstone for understanding current capabilities and guiding the future development of PTM site prediction tools, thereby promoting the integration of AI into practical biomedical research applications.

Abstract: Background: Inherited hypomorphic Artemis alleles have been identified in patients that cause combined immunodeficiency syndromes of varying severity. Characteristically, these are premature translation termination mutants (D451X, T432X, S385X; where X represents stop codon) resulting in either full or partial loss of C terminus. Functional evidence exists, suggesting a role of these hypomorphic mutants in impairing general double-strand break (DSB) repair bringing about genomic instability and protumorigenic chromosomal rearrangements; a discrete function outside of its canonical function in V(D)J recombination. Here, we characterize the effect of these mutants on episomal end-joining substrates in a model system for DSBs induced in a near physiological environment. Results: We employed replica plating assay to determine the effect on repair fidelity of endjoining upon overexpression of different Artemis variants. We found, markedly increase nuclease activity of the S385X (βCASP) mutant resulting in increase in number of episomes with truncations. Further, we sought to determine the effect of inhibition of DNA-PKcs phosphorylation (T2609 cluster) in regulation of episomal repair fidelity in cell lines expressing Artemis mutants. Upon inhibition of phosphorylation, we found out reduced number episomes with truncations resulting in increase in repair fidelity. Conclusions: Our work provides a novel venue to study the effects of Artemis mutants using an episomal system containing an inducible DSB. Our work indicates that the S385X deletion is more nucleolytically active probably because it is less regulated (lacks C terminal domain). We also provide evidence for an important role of DNA-PKcs in facilitating endonuclease activity of hypomorphic Artemis mutants.

Abstract: Extracted from Ganoderma lucidum mycelium, the developed β-1,3;1,6-glucan rich polysaccharides have the potential for industrial production of health food products due to their inhibition of metabolic syndrome, immunomodulatory and anti-tumor activities and other health benefits. Ganoderma polysaccharides have also been found to promote skin health, particularly due to their antioxidant and anti-ageing properties. The present study investigates the skin-protective properties of polysaccharides purified from Ganoderma mycelium cultivated using stress-tolerance technology and a fully plant-based medium. The effects of the polysaccharides are investigated in both in vitro and human studies. The research results indicate that the developed Ganoderma lucidum polysaccharides effectively inhibit tyrosinase activity and melanin production in B16F10 cells; they also promote cell migration and wound closure in scratch assays within NIH 3T3 cells. In human studies, Ganoderma lucidum polysaccharides demonstrated no potential for skin irritation while effectively reducing skin wrinkles, enhancing skin brightness, diminishing erythema, and increasing epidermal hydration. In hot-flux patch-induced erythema experiments, these polysaccharides were found to be capable of alleviating erythema severity by up to 48%. The research conducted to date has demonstrated that industrially produced Ganoderma lucidum polysaccharides, produced using innovative technology, have the potential for application in skin-related industries.

Abstract: Background: Pancreatic ductal adenocarcinoma (PDAC) is associated with high mortality rates therefore early diagnosis and treatment is essential in reducing mortality. Blood tests that can identify high-risk individuals is an unmet medical need. Methods: In this pilot observational study, remnant samples from routine clinical lab orders were tested on 265 patients presenting with abdominal pain and/or presence of pancreatitis, pancreatic cyst, gall stones, abdominal infections, hepatic cirrhosis/hepatitis, non-pancreatic cancer, or pre-, new onset and uncontrolled diabetes. Serum apolipoprotein A2 isoforms, a novel tumor marker, and carbohydrate antigen (CA) 19-9 were correlated to 3 demographic, 4 behavioral and 9 clinical risk factors associated with PDAC. Results: In a univariate analysis using tumor markers as a continuous variable, males, alcohol, smoking, opioid use, pancreatitis, pancreatic cyst and hepatic cirrhosis/hepatitis were associated with an abnormal apoA2 isoforms. In a multivariate analysis, all remained significant except for males and opioid use. CA19-9 was associated with smoking, obesity, pancreatic cyst, cirrhosis/hepatitis, non-pancreatic cancer, and pre- and uncontrolled diabetes. After multivariate analysis, cirrhosis/hepatitis, non-pancreatic cancer and prediabetes remained significant. When categorizing marker data using pre-established cutoffs, patients with abnormal apoA2 had 4.4 risk factors vs. 3.2 with normal apoA2 )< 0.0001). No difference between normal and abnormal results were observed for CA19-9. Conclusions: Correlating known PDAC risk factors, apoA2 isoforms and CA19-9 were associated with different factors, suggesting thereby providing independent information on risk. This may justify apoA2 isoforms as part of an algorithm with CA19-9 for cancer screening, particularly for high-risk individuals (e.g. genetic risk).

Abstract: Exercise adaptation depends on overload that is resolved by recovery, yet the same biology becomes maladaptive when immune, endocrine, metabolic, and muscle-centered stress signals fail to normalize. Exercise-induced maladaptation represents a systems-level failure of biological resolution, with direct relevance to disease-like dysregulation. Functional overreaching, non-functional overreaching, and overtraining syndrome remain difficult to diagnose because no single biomarker provides adequate specificity, temporal stability, or clinical portability. This narrative review synthesizes human and mechanistic evidence across proteomics, transcriptomics, metabolomics, endocrine profiling, extracellular vesicles, and mitochondrial quality-control biology to define the molecular architecture most relevant to athlete monitoring. Across these layers, the most coherent signatures cluster in immune-acute-phase activation, redox-buffering strain, endocrine drift, altered substrate availability, excitation-contraction dysfunction, integrated stress-response signaling, and defects in autophagy-mitophagy and lysosomal remodeling. Three translational elements emerge from this synthesis: a systems-convergence model of recovery failure, a staged biomarker deployment hierarchy, and a provisional Recovery Failure Index. The practical priority is therefore not a solitary marker, but serial phenotype-anchored multimarker panels that connect circulating signals with muscle-centered biology and support decision-making before prolonged recovery failure becomes entrenched.

Abstract:

Inflammation is commonly treated using non-steroidal anti-inflammatory drugs, analgesics, corticosteroids, and immunosuppressive agents. This study evaluated the anti-inflammatory activity of a novel serine protease inhibitor, RFIP1, isolated from Rhamnus frangula, using a xylene-induced ear inflammation model in male BALB/c albino mice. Fifty mice were randomly divided into five groups: Group A (control), Group B (xylene-induced inflammation treated with saline, 0.1 mL), Group C treated with dexamethasone (1.25 mg/kg body weight), Group D treated with RFIP1 (2.4 mg/kg body weight), and Group E treated with RFIP1 (4.8 mg/kg body weight). All treatments were administered intravenously via the tail vein for six consecutive days. Xylene induction caused a significant increase in neutrophil serine protease (NSP) expression and elevated serum levels of the pro-inflammatory mediators interleukin-1β, interleukin-6, tumor necrosis factor-α, and nitric oxide. Treatment with Dexamethasone and low-dose RFIP1 significantly reduced NSP expression and cytokine levels. Notably, administration of RFIP1 at 4.8 mg/kg produced a significant dose-dependent reductionr in NSP expression and inflammatory mediators compared with both Dexamethasone and the lower RFIP1 dose. These findings suggest that RFIP1 possesses potent anti-inflammatory activity and may represent a promising therapeutic candidate for inflammatory conditions.

Abstract: Discriminating closely related DNA sequences remains a major challenge in molecular diagnostics, genotyping, and hybridization-based assays. A recurring paradox is that exact Watson–Crick complements can cross-hybridize, whereas deliberately imperfect probes sometimes discriminate better. This review links classical probe-selection studies with later work on thermodynamics, mismatch topology, and nucleation kinetics to propose a practical framework for differential specificity. Across these studies, specificity is not explained by total complementarity alone. It depends on whether short stack-supported complementary islands survive mismatching strongly enough to support nucleation and, for primers, polymerase extension. Mismatch count still matters, but mismatch position, spacing, clustering, and chemical class determine whether those local substructures remain productive in the intended duplex and collapse in the strongest competing alignment. We therefore present the island concept as an intermediate explanatory layer that complements nearest-neighbor thermodynamics rather than replacing it. The scoring language is offered as a transparent ranking heuristic, not as a validated universal physical function. The practical design rule is to maximize the functional gap between the intended landscape and the strongest off-target landscape, rather than to optimize perfect matching or melting temperature alone.

Abstract: Human neutrophil elastase (HNE) is a key driver of inflammatory lung disorders, pro-moting extracellular matrix degradation and tissue damage. While inhibitors such as Sivelestat and Alvelestat are clinically relevant, their performance within the aggressive oxidative microenvironment of diseased lungs remains poorly understood. Here, we employed an integrated in vitro and in silico approach to investigate their behavior under physiological and oxidative conditions and to provide a molecular-level interpretation. Under physiological conditions, enzymatic assays and steady-state kinetics confirmed that both compounds act as competitive inhibitors, with Sivelestat displaying higher baseline potency. Under oxidative stress, however, Sivelestat exhibited a near-complete loss of activity, whereas Alvelestat retained its inhibitory efficacy. Molecular modeling and molecular dynamics simulations of native and oxidized HNE variants provided a structural rationale for this divergence. Alvelestat, as a non-covalent inhibitor, maintains stable binding despite increased flexibility of the active site induced by oxidative modifications. In contrast, Sivelestat, which acts through a reversible covalent mechanism, requires a precise pre-acylation geometry. Oxidation-induced remodeling of the S1 pocket disrupts the near-attack configuration required for covalent bond formation, thereby im-pairing inhibition. Overall, these findings indicate that oxidative stress selectively compromises covalent inhibition while preserving enzymatic activity, highlighting redox re-silience as a key parameter in the design of next-generation HNE inhibitors.

Abstract: The Hodgkin--Huxley model has provided an extraordinarily successful phenomenological description of the action potential for over seven decades. Its predictive power and mathematical elegance have made it a cornerstone of modern neuroscience. However, the model incorporates mechanistic assumptions about the nanoscale ionic environment of the axonal membrane that were necessarily simplified in 1952 and that modern biophysics allows us to examine critically. In this article, we identify eight independent physical inconsistencies in the mechanistic interpretation of the Hodgkin--Huxley model. These concern: the gel-phase nature of the axoplasm and its consequences for ionic activity; the insufficient ionic reservoir of the peri-membrane volume; the physical implausibility of ionic replenishment at physiological firing rates; ionic congestion and inter-species competition in confined spaces; the reductive representation of ion channels as single conductance parameters; the uncertain relationship between crystallographic channel structures and physiological reality; the osmotic paradox created by intra-pore ionic concentrations; and the systematic physical limitations of patch-clamp recordings. None of these arguments contests the experimental measurements on which the model is based. All of them contest the physical plausibility of the mechanistic interpretation placed on these measurements. The cumulative and mutually reinforcing nature of these inconsistencies suggests that the mechanistic foundations of the Hodgkin--Huxley model deserve serious and systematic re-examination.