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A phase I clinical study of the safety, tolerability, pharmacokinetics and pharmacodynamics of SHR-2106, an anti-CD40 antibody, following single intravenous or subcutaneous administration in healthy participants.

BACKGROUND: SHR-2106 is a humanized IgG1 monoclonal antibody that blocks CD40-CD40L interactions and has demonstrated immunosuppressive activity and graft-prolonging effects in preclinical studies. This first-in-human Phase I study evaluated the safety, pharmacokinetics, pharmacodynamics, and immunogenicity of single intravenous or subcutaneous doses of SHR-2106 in healthy adults. METHODS: This randomized, double-blind, placebo-controlled Phase I study enrolled healthy participants. Fifty-one participants were enrolled in seven cohorts and received five intravenous doses (50-1200 mg) or two subcutaneous doses (300 and 600 mg). Safety, serum pharmacokinetics, CD40 occupancy on B cells, and anti-drug antibodies were assessed using standard clinical and bioanalytical methods. RESULTS: SHR-2106 demonstrated a favorable safety and tolerability profile, and most treatment-emergent adverse events were mild to moderate laboratory abnormalities with incidence rates comparable to placebo. SHR-2106 exhibited nonlinear pharmacokinetics consistent with target-mediated drug disposition, with a dose-dependent increase in geometric mean terminal half-life following intravenous administration (1.83-10.7 days). Absolute bioavailability after subcutaneous administration was approximately 60%. CD40 occupancy exceeded 80% within 24 h at all doses, with saturation duration increasing from 7 to 70 days across the intravenous dose range and remaining comparable between routes at matched doses. Anti-drug antibody incidence decreased with increasing intravenous dose and did not significantly affect pharmacokinetics or pharmacodynamics. CONCLUSION: SHR-2106 was well tolerated and achieved rapid and sustained CD40 engagement, supporting dose and route selection for Phase II studies.

Humans

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

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

Implementation factors shaping British Columbia's drug decriminalization pilot: A systematic review with narrative synthesis.

BACKGROUND: In January 2023, British Columbia (BC) became the first Canadian province to implement a legally sanctioned drug decriminalization policy, removing criminal penalties for adults possessing 2.5 g or less of opioids, cocaine, methamphetamine, and MDMA. Introduced as a three-year pilot, it aimed to reframe substance use as a public health issue, reduce stigma, and improve health and social service engagement. Criminal penalties were reintroduced for drug possession in most public spaces in May 2024, and the pilot ended in January 2026. Its termination has been interpreted as policy failure; this review aimed to examine how the pilot was implemented in practice and to identify factors that shaped its operationalization and early implementation-relevant outcomes. METHODS: We conducted a systematic review with narrative synthesis of peer-reviewed literature examining implementation-relevant aspects of BC's decriminalization pilot. Six databases were searched (January-February 2026) for studies published May 31, 2022-February 1, 2026. The protocol was registered in PROSPERO (CRD420251271694). RESULTS: Twenty-seven studies were included. Four cross-cutting implementation barriers were identified: pilot design features, public and cross-sector communication gaps, limited frontline training, and insufficient funding and infrastructure. Design features included the 2.5 g possession threshold, misalignment with real-world drug use patterns; the three-year timeframe, which constrained system-level effects; and the May 2024 amendment, which introduced additional instability. The pilot was implemented without commensurate investment in harm reduction, treatment, or housing infrastructure, within already constrained systems. CONCLUSION: BC's decriminalization pilot suggests the effects of legal reform are shaped by implementation context. Early outcomes may reflect design features, institutional readiness, and system capacity rather than legal change alone; longer-term impacts remain uncertain. Future reforms should align legal change with coordinated implementation, operational guidance, public communication, and adequate service infrastructure.

British Columbia

Impact of estimated total blood volume on NT-proBNP response to angiotensin receptor-neprilysin inhibition in acute heart failure: Insights from the PREMIER study.

BACKGROUND: Sacubitril/valsartan (Sac/Val) reduces N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels in acute heart failure (AHF), particularly in patients with reduced ejection fraction. However, whether estimated total blood volume (TBV), calculated using anthropometric equations, is associated with heterogeneity in biomarker response remains uncertain. METHODS: This post hoc exploratory sub-analysis of the PREMIER randomized trial evaluated whether baseline estimated TBV was associated with heterogeneity in NT-proBNP reduction after Sac/Val compared with angiotensin-converting enzyme inhibitor/angiotensin receptor blocker (ACEI/ARB) therapy. Estimated TBV was calculated using validated anthropometric equations and dichotomized at the median (4.05 L). Patients were further stratified by left ventricular ejection fraction (LVEF <40% vs &#x2265;40%). The primary endpoint was the proportional change in NT-proBNP from baseline to Week 8. RESULTS: Among 376 patients, 372 with baseline estimated TBV data were analyzed. In the high TBV group, Sac/Val was associated with greater NT-proBNP reduction than ACEI/ARB (-56% vs -32%; ratio of change, 0.67; 95% confidence interval, 0.53-0.84; P = .001), whereas no significant difference was observed in the low TBV group (P for heterogeneity = 0.063). In patients with LVEF <40%, Sac/Val was associated with greater NT-proBNP reduction in both TBV groups. In patients with LVEF &#x2265;40%, Sac/Val was associated with greater NT-proBNP reduction in the high TBV group, whereas the point estimate in the low TBV group numerically favored ACEI/ARB. CONCLUSIONS: In this exploratory post hoc analysis, higher estimated TBV was associated with greater NT-proBNP reduction after Sac/Val, particularly among patients with LVEF &#x2265;40%. These findings are hypothesis-generating and require external validation. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05164653; Japan Registry of Clinical Trials, jRCTs021210046.

Humans

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

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

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

Characterization and application potential of two newly isolated phages targeting the prevalent multidrug resistant Salmonella serovars in China.

The escalating global threat of multidrug resistant (MDR) Salmonella, a foodborne pathogen with animal-derived foods serving as the primary transmission vehicle, underscores the urgent need for effective lytic phages for biocontrol. From 142 environmental and farm samples in Shandong Province, we isolated 103 phages active against MDR S. Enteritidis and S. Typhimurium, which were the most prevalent Salmonella serovars in China. Two Siphoviridae phages vB-SenS-S1 and vB-SenS-SEC2 were selected for further study. With optimal multiplicities of infection (MOIs) of 10-2 (vB-SenS-S1) and 10-5 (vB-SenS-SEC2), both phages exhibited a 20&#xa0;min latent period, yielding burst sizes of 52 and 37 PFU/cell, respectively. They also demonstrated stability across a range of temperatures (50-60&#xa0;&#xb0;C), pH levels (5-11), and after 1&#xa0;h of UV exposure. Genomic analysis identified vB-SenS-S1 (43,002&#xa0;bp, 47.04% GC) and vB-SenS-SEC2 (42,948&#xa0;bp, 47.65% GC) as novel double-stranded DNA phages. Functional annotation confirmed the presence of genes essential for structural assembly, host lysis, and DNA replication/metabolism, and also verified the absence of resistance, virulence, and lysogeny-associated genes. Both phages vB-SenS-S1 and vB-SenS-SEC2 exhibited synergy with colistin and tetracycline. The synergy with colistin was particularly potent, leading to complete bacterial eradication in vitro. The in vivo therapeutic efficacy was further validated in both Galleria mellonella larvae and murine models of MDR Salmonella infection. Combination therapy with vB-SenS-SEC2 and colistin not only dramatically increased survival but also achieved a significant reduction in bacterial burden across multiple visceral organs of infected mice. Moreover, vB-SenS-S1 (108 PFU/mL) completely inhibited MDR Salmonella on chicken meat at 4&#xa0;&#xb0;C and -20&#xa0;&#xb0;C when initial contamination was &#x2264;103&#xa0;CFU/mL. This study not only expands the diversity of Salmonella phages but also highlights their potential as biocontrol agents in both clinical veterinary use and food decontamination, thereby enhancing food quality and safety at both the meat production source and the terminal product.

Animals

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

BACKGROUND AND OBJECTIVES: Pseudouridine (&#x3a8;) represents one of the most abundant and conserved RNA modifications. &#x3a8; 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 &#x3a8; 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 &#x3a8; site predictor. METHODS: New long-sequence datasets were constructed as benchmarks for RNA &#x3a8;-site prediction. The &#x3a8; 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 &#x3a8;-site prediction. Meta-PseU offers a new framework for robust &#x3a8;-site identification by using long sequences.

Pseudouridine

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

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&#x202f;=&#x202f;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

Daily low-dose carboplatin or weekly carboplatin plus nab-paclitaxel for concurrent chemoradiotherapy in older patients with locally advanced non-small cell lung cancer (JCOG1914): A randomized phase 3 trial.

BACKGROUND: Daily low-dose carboplatin with concurrent thoracic radiotherapy is the standard treatment for older patients with unresectable locally advanced non-small cell lung cancer (LA-NSCLC) in Japan. METHODS: This open-label phase 3 trial was conducted at 38 institutions in Japan. Patients aged&#xa0;&#x2265;&#xa0;75&#xa0;years with LA-NSCLC were randomly assigned (1:1) to receive daily carboplatin (30&#xa0;mg/m2) or weekly carboplatin (area under the curve, 2&#xa0;mg&#xb7;min/mL) plus nab-paclitaxel (30&#xa0;mg/m2) with thoracic radiotherapy. Durvalumab maintenance therapy was recommended after treatment completion. The primary endpoint was overall survival, which was used to assess the non-inferiority of weekly carboplatin plus nab-paclitaxel compared to daily low-dose carboplatin. RESULTS: From December 2020 to March 2024, 124 patients were enrolled (carboplatin arm, 61 and carboplatin plus nab-paclitaxel arm, 63). In the planned interim analysis, the Bayesian predictive probability indicating the non-inferiority of carboplatin plus nab-paclitaxel compared with carboplatin in the final analysis was 8.0%, leading to early study termination for futility. The median overall survival was not estimable in the carboplatin arm; the estimated value in the carboplatin plus nab-paclitaxel arm was 26.1&#xa0;months (hazard ratio, 1.56; 95% confidence interval, 0.79-3.11; p&#xa0;=&#xa0;0.200). Two treatment-related and seven non-cancer-related deaths occurred in the carboplatin plus nab-paclitaxel arm. Patients in the carboplatin arm had better quality of life than those in the carboplatin plus nab-paclitaxel arm at 6&#xa0;weeks (odds ratio, 0.39; 95% confidence interval, 0.18-0.81; p&#xa0;=&#xa0;0.012). CONCLUSIONS: Daily low-dose carboplatin with concurrent thoracic radiotherapy remains the standard treatment for older patients with unresectable LA-NSCLC in Japan.

Humans

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&#xa0;=&#xa0;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&#xa0;=&#xa0;35), colorectal cancer (n&#xa0;=&#xa0;21), and pancreatic cancer (n&#xa0;=&#xa0;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

The Association Between NT-Pro BNP, Nephropathy and Endothelial Dysfunction in Patients With Type 2 Diabetes Mellitus.

BACKGROUND: N-terminal pro-B-type natriuretic peptide (NT-Pro BNP) is an established biomarker of heart failure and has been recommended for cardiovascular risk stratification in type 2 diabetes mellitus (T2DM). However, its relationship with diabetic nephropathy and endothelial dysfunction across varying stages of kidney impairment remains unclear. This study examined the associations of NT-Pro BNP, renal impairment, albuminuria and endothelial dysfunction in patients with T2DM without overt heart failure. METHODS: A comparative cross-sectional study was conducted among 192 adults with T2DM. Participants were stratified by KDIGO eGFR groups (&#x2265;&#x2009;90, 60-89, 30-59&#x2009;mL/min/1.73m2). NT-Pro BNP was considered abnormal at a cut-off of &#x2265;&#x2009;125&#x2009;pg/mL. Albuminuria was categorized using the urinary albumin-to-creatinine ratio (uACR). Endothelial function was assessed by brachial artery flow-mediated dilatation (FMD). Logistic regression analysis was performed to identify independent factors of elevated NT-Pro BNP, with p-values <&#x2009;0.05 considered statistically significant. RESULTS: NT-Pro BNP levels were significantly higher in the lower eGFR groups compared with normal eGFR (204.3 vs. 96.8 vs. 62.2&#x2009;pg/mL, p&#x2009;<&#x2009;0001). A weak but significant positive correlation was observed between NT-Pro BNP and uACR (r&#x2009;=&#x2009;0.31, p&#x2009;<&#x2009;0.001). However, no significant association was found between NT-Pro BNP and FMD (p&#x2009;=&#x2009;0.388). Following multivariable adjustments, older age (adjusted OR 1.14, 95% CI: 1.06-1.23, p&#x2009;<&#x2009;0.001), higher systolic blood pressure (adjusted OR 1.05, 95% CI: 1.02-1.08, p&#x2009;=&#x2009;0.010), lower eGFR (adjusted OR 0.97, 95% CI: 0.95-0.99, p&#x2009;=&#x2009;0.003) and beta-blocker use (adjusted OR 4.65, 95% CI: 1.61-13.45, p <&#x2009;0.001) were independently associated with elevated NT-Pro BNP. CONCLUSION: In patients with T2DM without overt heart failure, elevated NT-Pro BNP showed a statistically significant association with lower eGFR and higher albuminuria. Moderate to severe albuminuria becomes an independent factor for elevated NT-Pro BNP after adjustment excluding eGFR. The lack of association with endothelial dysfunction suggests that NT-Pro BNP may reflect different pathophysiological pathways. NT-Pro BNP may serve as a useful biomarker for early cardiovascular risk stratification and identification of individuals at risk of pre-heart failure in diabetic kidney disease.

NT&#x2010;Pro BNP

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

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

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