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PubMed2026

Early hepatic protein responses to dietary restriction-refeeding in Japanese quail: A proteomic investigation.

Feed intake and refeeding after nutrient scarcity induce rapid metabolic adaptations in the poultry liver; however, hepatic proteomic recovery pathways in the early hours post-refeeding remain poorly defined. This study aimed to characterize early liver protein signatures in Japanese quail (Coturnix japonica) recovering from nutritional stress under two refeeding conditions. Eighteen 12-week-old male quails (245.20 ± 0.213 g) were assigned to three groups (n = 6): control fed ad libitum (12.13 MJ/kg), 24 h feed deprivation followed by 6 h refeeding, and 24 h low metabolizable energy (6.30 MJ/kg) diet followed by 6 h refeeding. In total, 854 proteins were identified, of which 515 met the filtering criteria. The low metabolizable energy refeeding showed higher abundance of proteins linked to ATP binding and carbohydrate/carboxylic acid metabolism, alongside detoxification-related proteins, while suppressing translation/RNA-binding machinery and antioxidant pathways. Feed-deprived refeeding enriched in oxidative phosphorylation and mitochondrial complex I assembly with reduced cytoplasmic translation, NMD-related components, and sulfur compound metabolism. A direct comparison indicated divergent recovery strategies: low metabolizable energy refeeding mainly reflected oxidoreductase activity and translation initiation, whereas feed-deprived refeeding potentially enriched mitochondrial ATP production and glutathione-based defenses. Our analysis indicate that 6 h of refeeding initiates an early, incomplete recovery toward hepatic homeostasis, with the severity of prior nutritional restriction dictating distinct liver metabolic priorities. Collectively, these findings might provide a preliminary understanding of the hepatic mechanisms involved in recovery from nutrient deprivation and may help in the development of feeding strategies for managing metabolic recovery in poultry. However, these findings should be considered hypothesis-generating pending further validation.

Animals↗

PubMed2026

The present and future of nonviral delivery-based genome editing for hereditary hearing loss.

PURPOSE OF REVIEW: This review summarizes nonviral genome-editing delivery platforms for hereditary hearing loss, focusing on lipid nanoparticles (LNPs) and engineered virus-like particles (eVLPs), and discusses their advantages over adeno-associated virus-based delivery, as well as the barriers to clinical translation. RECENT FINDINGS: Recent advances have established LNPs as a clinically advanced nonviral platform, although challenges related to inner ear biodistribution, cell type specificity, endosomal escape, and immunogenicity remain to be addressed. In parallel, eVLPs have undergone substantial technical evolution, progressing from early low efficiency systems to advanced base editor- and prime editor-eVLP architectures that enhance cargo loading and editing efficiency. Extracellular vesicle-based genome editing has also emerged as an additional platform, although issues related to reproducibility, loading efficiency, and scalability remain major hurdles. SUMMARY: Nonviral genome editing platforms expand the therapeutic toolkit for hereditary hearing loss by enabling transient delivery of genome editors with potential safety advantages. Future efforts should focus on characterizing biodistribution and immunogenicity, refining cell type-specific tropism, and establishing scalable manufacturing processes to enable successful clinical translation.

Humans↗

PubMed2026

Proteomic and phosphoproteomic profiles of time-dependent dynamic changes in LPS-induced macrophage polarization.

The temporal proteomic and phosphoproteomic reprogramming during early M1 macrophage polarization (0-6 h) remains poorly understood. We performed time-resolved proteomic and phosphoproteomic analyses of LPS-stimulated RAW264.7 macrophages at seven time points within 6 h. Time-clustering of differentially expressed molecules revealed two patterns: initial change with partial recovery, and sustained dysregulation. Upregulated proteins and phosphorylation sites were enriched in the Rho GTPase signaling pathway, T-cell receptor signaling pathway, NF-κB cascade, osteoclast differentiation pathway, and antiviral immune pathway. Downregulated pathways were associated with cell cycle regulation, chromatin remodeling, RNA metabolism, and mRNA processing, indicating resource reallocation to prioritize acute inflammatory responses. Kinase-substrate network analysis confirmed the mitogen-activated protein kinase (MAPK), cyclin-dependent kinase (CDK), protein kinase B (AKT), and ribosomal S6 kinase (RSK) families as core upstream phosphorylation regulators. Integrated analysis revealed synergistic and antagonistic relationships between proteomic and phosphoproteomic changes. This study provides a temporal molecular atlas of M1 polarization, delineating inflammatory signaling dynamics and offering a basis for therapeutic target discovery in inflammatory diseases. SIGNIFICANCE: Macrophage M1 polarization is a central event in innate immune defense against pathogenic invasion, yet its dysregulation is a pivotal driver of the onset and progression of a broad spectrum of inflammation-associated disorders, spanning autoimmune diseases, infectious conditions and inflammatory bone diseases, making the dissection of its molecular regulatory mechanisms an urgent research priority in immunology and translational medicine. Dynamic molecular events within 0-6 h after LPS stimulation are critical for initiating and shaping M1 inflammatory activation, yet systematic time-resolved proteomic and phosphoproteomic profiling remains insufficient.In this study, we comprehensively characterized temporal proteome and phosphoproteome changes at seven consecutive time points during macrophage polarization, clarified two distinct dynamic molecular patterns, identified core signaling pathways and key kinase regulators involved in inflammatory reprogramming, and uncovered the leading role of post-translational phosphorylation modifications in initiating polarization. This work delineates the time-series molecular atlas of early macrophage activation, provides novel insights into the temporal regulatory mechanism of inflammatory signaling networks, and lays a solid experimental foundation for exploring new intervention targets and regulatory nodes in clinical translational research.

Lipopolysaccharides↗

PubMed2026

Targeting Gasdermins for Therapeutic Interventions in Central Nervous System Injury.

Central nervous system (CNS) injuries are the leading cause of permanent disability and premature death in adults worldwide, with their incidence continuing to rise amid social development. These injuries not only severely impair the quality of life but also impose a heavy burden on the global public health system. Current clinical interventions, such as decompression and thrombolysis, can alleviate primary injury but fail to effectively reverse the secondary neuroinflammatory damage. Traditional anti-inflammatory therapies, which cannot block the upstream source of the inflammatory cascade, have led to repeated failures in global clinical translation research over the past decades. Gasdermins were first characterized in studies of systemic inflammatory diseases. These proteins form transmembrane pores to drive the release of proinflammatory factors and inflammatory cell death, serving as key mediators of host innate immunity. Recent studies have revealed that gasdermins play critical roles in regulating the initiation and amplification of neuroinflammation following CNS injury. To clarify the therapeutic potential of gasdermins as targets for injury repair, this review systematically summarizes the structure and function of gasdermins, as well as their cell-specific activation and regulatory mechanisms. We further elaborate on their pathological roles in these injuries and the corresponding therapeutic strategies, aiming to provide a theoretical reference for basic research and clinical translation in this field.

Humans↗

PubMed2026

Bioactive peptides for meat quality and preservation: Integrating peptidomics and computational screening.

Bioactive peptides generated from meat proteins, fermented meat products, and slaughter by-products have attracted increasing attention as functional molecules for improving meat quality and preservation. In meat systems, peptides can be produced through endogenous postmortem proteolysis, microbial fermentation, gastrointestinal digestion, or controlled enzymatic hydrolysis of underutilized animal by-products. These peptides are closely associated with key meat science endpoints, including postmortem tenderization, oxidative stability, color retention, flavor development, microbial inhibition, and the valorization of processing by-products. However, although high-resolution peptidomics has greatly expanded the identification of meat-derived peptide sequences, their translation into practical meat applications remains limited by matrix interactions, processing stability, sensory constraints, safety concerns, and insufficient validation in real meat systems. This review synthesizes recent advances in meat-related peptidomics and computational screening, including sequence-based prediction, machine learning, molecular docking, molecular dynamics, stability assessment, and safety-oriented filtering. Particular attention is given to how these approaches can prioritize peptides with antioxidant, antimicrobial, flavor-modulating, and preservation-related functions under meat-specific technological constraints. By integrating peptide generation pathways, mass spectrometry-based identification, in silico prioritization, and meat quality endpoints, this review proposes a stage-gated framework for translating meat-derived bioactive peptides from discovery to application. Future research should strengthen matrix-specific validation, standardized peptidomic reporting, and safety assessment to support the use of bioactive peptides in meat quality improvement, clean-label preservation, and circular utilization of meat industry by-products.

Animals↗

PubMed2026

Efficacy of pharmacological and microbiota-based therapies in preclinical models of autism spectrum disorder: a systematic review.

BACKGROUND: Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental condition in which pharmacological and microbiota-targeted interventions are emerging as promising therapeutic avenues. Animal models are the main tool to investigate etiology, molecular mechanisms and screening for pharmacological therapies. Methodological differences, outcome measure variability, incomplete reporting, biological confounders, and overgeneralization of the results made evaluating innovative pharmacological agents challenging. These limitations in the field highlight a need for systematic and standardized research to reliably assess and translate pharmacological interventions from ASD animal models to human clinical relevance. SUBJECTS: This systematic review synthesized efficacy evidence for pharmacological and microbiota-based therapies across established ASD animal models. RESULTS: We identified 52 recent (2010-2025) studies that reported key ASD behavioral outcomes after pharmacological or microbiota-focused treatments. Interventions were grouped into therapeutic classes - including oxytocinergic agents, E/I balance therapeutic targets, metabolic drugs, cannabinoids, purine-based interventions and emerging targets - alongside microbiota-directed strategies such as probiotics, prebiotics, and fecal microbiota transplantation. By integrating effect directions and robustness across models, we identified most potential drug candidates, evaluated the efficacy of novel strategies, and recognized critical translational gaps. The reviewed studies demonstrate that ASD-like behavioral deficits in preclinical models can be modulated through interventions targeting diverse biological systems, including neurotransmission, neuroinflammation, metabolism, and the gut-brain axis. CONCLUSIONS: These findings support the multifactorial nature of ASD pathophysiology which arises from a network of interacting systemic processes rather than a single molecular defect. It could explain the limited success of traditionally narrowly targeted interventions and suggest a paradigm shift into a more systemic approach.

Animals↗

PubMed2026

Effects of acute resistance exercise on prefrontal oxygenation and task-switching performance: Considerations of loading strategies and blood flow restriction.

Although acute resistance exercise (RE) has been proposed to influence cognitive flexibility and underlying neural mechanisms, it remains unclear whether these effects vary across loading strategies and whether exercise-induced prefrontal hemodynamic responses translate into cognitive outcomes. The present study examined (1) prefrontal cortex (PFC) oxygenated hemoglobin (O2Hb) responses across exercise sets and conditions, (2) the effects of low-load (LL), LL with blood flow restriction (BFR), and high-load (HL) RE on task-switching performance, and (3) whether exercise-related PFC O2Hb responses were associated with pre- to post-exercise changes in task-switching performance. Thirty physically active adults completed three randomized, counterbalanced RE conditions consisting of four sets of barbell squats. LL was performed at 30% one-repetition maximum (1RM) with and without BFR, whereas HL was performed at 70% 1RM. Cognitive flexibility was assessed pre- and post-exercise using a modified Stroop task, indexed by switch-cost reaction time (RT) and accuracy. PFC O2Hb was assessed using functional near-infrared spectroscopy during exercise and expressed as changes from the resting baseline for each set (Sets 1-4). PFC O2Hb increased across sets, rising from Set 1 to Set 3 before plateauing, with no differences observed across conditions. Switch cost RT and accuracy did not improve from pre- to post-exercise, and no differences across conditions were detected. PFC O2Hb during the final set was not associated with changes in switch cost. These findings suggest that although acute RE elicits robust increases in prefrontal hemodynamic activity, such responses may not translate into acute improvements in cognitive flexibility.

Humans↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2026

Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

Humans↗

PubMed2026

Meta-PseU: A meta-classifier for robust prediction of RNA pseudouridine modification sites from long sequences.

BACKGROUND AND OBJECTIVES: Pseudouridine (Ψ) represents one of the most abundant and conserved RNA modifications. Ψ provides an additional hydrogen-bond donor that enhances RNA structural stability and modulates translation. It participates in diverse biological processes, including RNA-protein interactions, splicing, translational control, and stress responses. Aberrant pseudouridylation is implicated in cancer, neurodegenerative disorders, and autoimmune diseases. Despite its biological importance, experimental identification of Ψ sites remains time-consuming and costly, limiting the feasibility of transcriptome-wide profiling. Computational approaches have therefore become essential complements to experimental techniques. However, state-of-the-art machine-learning and deep-learning predictors often suffer from limited generalizability due to small training datasets. To overcome these issues, we aim at constructing new long-sequence datasets and developing a novel Ψ site predictor. METHODS: New long-sequence datasets were constructed as benchmarks for RNA Ψ-site prediction. The Ψ modification sites in RMBase 3.0 were mapped to the reference genomes across three species of human, mouse, and yeast, and the RNA sequences with a length of 201 were generated by extending the upstream and downstream from the mapped, central sites. To eliminate sequence redundancy, the sequences were clustered using CD-HIT with a 70% sequence identity threshold. We developed Meta-PseU, a logistic regression-based meta-classifier that considered 118 machine learning and deep learning classifiers. The datasets and programs are freely accessible at https://github.com/kuratahiroyuki/MetaPseU. RESULTS: By optimizing model configuration, we proposed the Meta-PseU model stacking 32 machine learning and deep learning classifiers out of 118 classifiers. Meta-PseU substantially improved model generalizability, overcoming a key limitation of existing approaches. It greatly outperformed state-of-the-art predictors and achieved increasing accuracy with increasing sequence length. CONCLUSIONS: Long-sequence datasets were newly constructed as benchmarks for RNA Ψ-site prediction. Meta-PseU offers a new framework for robust Ψ-site identification by using long sequences.

Pseudouridine↗

PubMed2026

Copper-Containing Surface Engineering for Soft-Tissue Biomedical Devices: Structure-Function Relationships and Ion Release-Driven Biological Performance, A Systematic Review.

Copper and copper-based materials have gained increasing attention for the functional modification of implantable medical devices intended for prolonged soft-tissue contact, including vascular stents, catheters, and intrauterine devices. Owing to their broad-spectrum antimicrobial activity, redox reactivity, and involvement in angiogenesis and cellular signaling, copper-based systems offer significant potential for multifunctional surface engineering. However, achieving a balance between antibacterial efficacy, corrosion behavior, controlled ion release, and cytocompatibility remains a critical challenge. This PRISMA-compliant systematic review analyzes copper-containing materials and surface modification strategies for soft-tissue biomedical applications. A structured search of Scopus, Web of Science, and PubMed (2015-2025) identified 65 eligible studies. The review encompasses bulk copper-containing alloys, electrochemical and chemical surface modification techniques, physical vapor deposition approaches, and advanced hybrid systems integrating copper with polymers, hydrogels, or metal-phenolic networks. Across the reviewed literature, antibacterial performance was strongly dependent on copper concentration, microstructural distribution, and spatiotemporal ion release profiles. Moderate, well-controlled copper incorporation frequently improved antibacterial efficacy while maintaining acceptable hemocompatibility and cytocompatibility, particularly in vascular and blood-contacting devices. In contrast, excessive copper loading often accelerated corrosion and induced adverse cellular responses. Emerging multifunctional architectures demonstrated improved regulation of biological interactions, enabling simultaneous antibacterial, antithrombotic, and proendothelial effects. Overall, copper-based surface technologies represent a versatile platform for soft-tissue implant modification. Future translational progress will require precise control of copper release kinetics and comprehensive long-term in vivo validation to ensure safety and sustained therapeutic performance. From the authors' perspective, the most promising future direction involves multifunctional copper-based hybrid coatings capable of dynamically regulating ion release, host tissue integration, and antibacterial performance simultaneously. Strategies integrating hierarchical architectures, stimulus-responsive release systems, and clinically scalable fabrication methods are expected to play a key role in translating copper-containing surfaces from experimental concepts toward commercially viable soft-tissue biomedical devices.

Copper↗

PubMed2026

Stage shift, histological differentiation, and survival patterns of lung squamous cell carcinoma versus adenocarcinoma in low-dose CT screening.

BACKGROUND: Whether LDCT-associated stage shift translates into similar survival patterns across lung cancer histologies remains uncertain. We compared stage shift, histological differentiation, tumor characteristics, and survival between lung squamous cell carcinoma (LUSC) and adenocarcinoma (LUAD) in the National Lung Screening Trial. METHODS: Among participants diagnosed with LUSC or LUAD, stage distribution and histological differentiation were compared between LDCT and chest X-ray (CXR) arms. Survival among diagnosed cases was measured from randomization. Multivariable models tested screening arm-by-histology interactions. Screen-detected LDCT tumors were compared by histology. RESULTS: During 6.5 years of median follow-up, 498 LUAD and 249 LUSC cases were diagnosed in the LDCT arm, and 374 and 212, respectively, were diagnosed in the CXR arm. LDCT was associated with higher odds of stage I disease for LUAD (adjusted odds ratio [aOR], 2.48; 95% CI 1.88-3.28) and LUSC (aOR, 1.71; 95% CI 1.17-2.48), without significant interaction (P = 0.116). LDCT was associated with lower hazard of lung cancer-specific death among diagnosed LUAD cases (adjusted hazard ratio [aHR], 0.54; 95% CI 0.43-0.66), but not among diagnosed LUSC cases (aHR, 1.04; 95% CI 0.78-1.39; P for interaction<0.001). LUSC had lower screening sensitivity, more frequent detection in annual screening rounds, greater prediagnostic tumor size increase, and fewer well-differentiated stage I tumors than LUAD. CONCLUSION: LDCT was associated with stage shift for both subtypes, but favorable survival patterns among diagnosed cases were mainly observed for LUAD. Lower screening sensitivity, greater prediagnostic tumor size increase, and poorer histological differentiation may help explain why stage shift did not translate into similar survival patterns for LUSC. TRIAL REGISTRATION: ClinicalTrials.gov, NCT00047385.

Humans↗

PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗

PubMed2026

Quantitative N-glycoproteomic analysis reveals glycosylation signatures of plasma immunoglobulin G in sepsis.

INTRODUCTION: Sepsis is a life-threatening condition resulting from organ dysfunction due to a dysregulated immune response to infection. Immunoglobulin G (IgG) plays a role in modulating immune responses. However, the precise IgG subclass-specific N-glycosylation profiles in patients with sepsis remain poorly characterized. METHODS: This study aimed to define the site-specific N-glycosylation signatures of plasma IgG subclasses in sepsis patients with different prognoses using quantitative glycoproteomics. By employing our established GlycoQuant strategy, we quantified the intact N-glycopeptides (IGPs) of IgG subclasses in 40 healthy controls and 40 sepsis patients with a clear prognosis. RESULTS: We identified 12 IGPs with altered abundances between patients with sepsis and healthy controls. After Benjamini-Hochberg (BH) correction of the 31 outcome-stratified IGP comparisons, IGP24 and IGP25 remained significant and met the prespecified fold-change criterion. Global BH correction across 124 IGP-clinical parameter correlations retained positive associations of IGP19, IGP22, and IGP23 with procalcitonin (PCT). In exploratory outcome-stratified ROC analyses, candidates were selected using the original unadjusted P-value and fold-change screen; five IGPs were evaluated, with IGP25 and IGP24 yielding the highest individual AUCs. Collectively, our findings underscore the potential of IgG subclass-specific glycosylation profiling as a novel translational approach for clinical applications in sepsis management. SIGNIFICANCE: Sepsis remains a leading cause of global mortality, with patient outcomes heavily dependent on timely diagnosis and accurate prognosis. The dysregulated host immune response, particularly involving immunoglobulins, is central to its pathophysiology. This study provides a significant advance in the field of clinical glycoproteomics by applying a quantitative, site-specific strategy to delineate the plasma IgG subclass N-glycosylation landscape in sepsis. We report, for the first time, a panel of subclass-specific intact IgG N-glycopeptides (IGPs) that are significantly altered in sepsis patients compared to healthy controls. The identified IGPs not only demonstrate diagnostic and prognostic potential but also show a significant correlation with procalcitonin, a key clinical severity index. These findings bridge a critical knowledge gap by moving beyond bulk IgG glycosylation analysis to subclass-resolved profiling, offering novel molecular insights into sepsis immunopathology. The identified glycosylation signatures hold substantial translational promise as a foundation for developing innovative, glycan-based biomarker panels to improve the precision management of this heterogeneous and life-threatening syndrome.

Humans↗

PubMed2026

Transverse Tibial Transport for Limb Salvage in Ischemic Lower Extremity Disease: Technique, Mechanisms, and Clinical Outcomes-A Systematic Review.

Transverse tibial transport (TTT) is a surgical technique derived from Ilizarov's distraction osteogenesis principles that stimulates angiogenesis and microcirculatory regeneration in the ischemic lower limb without directly manipulating macrovascular anatomy. By creating a proximal tibial cortical bone window and distracting it transversely using an external fixator, TTT triggers converging cascades of growth factor release, endothelial progenitor cell mobilization, and immunomodulation that translate into improved distal limb perfusion and wound healing. Combined TTT plus endovascular therapy improves amputation-free survival versus endovascular therapy alone. Prospective randomized trials and standardized international protocols are needed to consolidate TTT's role in multidisciplinary limb salvage pathways.

Humans↗

PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗

PubMed2026

Human endogenous retroviruses leading to autoimmune diseases.

Human endogenous retroviruses (HERVs) comprise approximately 8% of the human genome and were long regarded as inert remnants of ancestral retroviral infections. Increasing evidence indicates that HERVs are active genomic elements capable of influencing transcriptional programs, modulating immune responses, and contributing to disease pathogenesis. Under physiological conditions, HERV expression is tightly controlled by epigenetic mechanisms; however, infections, chronic inflammation, aging, and diverse environmental stimuli can promote HERV reactivation. HERV-derived RNAs and proteins engage innate immune sensors and trigger antiviral-like responses through mechanisms of viral mimicry, leading to activation of type I interferon and other inflammatory pathways. HERV dysregulation has been associated with disease-relevant immune pathways. This review summarizes recent advances linking HERVs to autoimmune disease pathogenesis and discusses their potential translational relevance as biomarkers and therapeutic targets.

Humans↗

PubMed2026

Hierarchical modeling of tumor subtypes in cell lines using large-scale genomic datasets.

Cancer cell lines (CLs) are widely used to study tumor biology and drug response, yet their translational relevance is often limited by inaccurate subtype annotations. Existing CL-tumor matching approaches are frequently constrained by flat classification schemes, weak subtype definitions, and the exclusion of normal tissue references, leading to potential confounding of tumor-specific and tissue-of-origin signals. To address these limitations, a hierarchical classification (HC) framework is presented in which CLs are aligned with patient tumors across biological resolutions, from organ to molecular subtype. Gene expression profiles from 802 CLs, 5,612 tumors from The Cancer Genome Atlas (TCGA) , and 8,939 non-cancerous tissues were integrated to separate oncogenic signals from tissue-specific signals. Node-specific features were selected using maximum relevance minimum redundancy, and balanced accuracies of 89% in cross-validation and 75%, and 80% on external datasets were achieved. Through the framework, 43 CLs were reassigned, and clinically relevant underrepresented subtypes were identified.

cancer cell lines↗