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Helicobacter (H.) pylori is characterized by a high degree of genomic diversity, with regional differences in virulence determinants. This study aims to explore genomic composition, phylogeography and accessory-gene relationships of Iraqi H. pylori isolates in a global contextualized dataset. A total of 198 H. Pylori genomes were reviewed, including 41 isolates sequenced from gastric samples of patients undergoing diagnostic endoscopy at Al-Yarmouk Teaching Hospital, Baghdad from June 2024 to February 2025. Illumina MiSeq was used to sequence genomes, which were quality-filtered and assembled using SPAdes. Prokka was used to perform annotation and Roary to infer pan-genome structure. FastTree was used to reconstruct core-genome phylogeny. Anatomical micro-niche (corpus vs. other gastric sites) were explored with pan-genome-wide association study (pan-GWAS) with Pyseer (linear mixed model, kinship based on the core alignment). The H. Pylori pan-genome showed 955 core gene families and 9,400 accessory genes. Isolates from Iraq were polyphyletic, mixing with European and Middle Eastern lineages. In the primary Pyseer linear mixed-model analysis, Benjamini-Hochberg correction across 3,259 valid lrt-pvalue tests identified 151 FDR-significant associations; after excluding rows with problematic Pyseer diagnostic notes, 70 unflagged loci remained significant. The strongest unflagged positive association was group_2036, whereas a co-occurring block including cagS, cagT, and virB4_1 was strongly depleted in corpus-derived isolates. These signals implicate accessory-genome variation in gastric micro-niche adaptation while also underscoring the need to interpret flagged Pyseer rows cautiously. The genomic variation of squamous H. Pylori isolates in Iraq corresponds to the global recombination trends and to the regional admixture. The corpus sampling-related accessory-gene cluster implies possible micro-niche adaptation. These preliminary results highlight the necessity of large, stratified Middle Eastern cohorts and long-read sequencing to dispel functional genetic constructions of tissue tropism and virulence.

Metabolic reprogramming is a substantial obstacle for anticancer drug screening, as targeted therapeutics often lose efficiency due to the dynamic adaption of cancer cells. Glutamine metabolism in cancer profoundly impacts tumor initiation, progression and metastasis. The existing agents are compromised by resistance and off-target toxicity. In this study, a real-time NMR tracking method for intracellular glutamine metabolic flux was established. This method enables comprehensive profiling of nitrogen metabolism and serves as a valuable tool for characterizing specific cancer metabolic phenotypes and screening drugs against targeted cancer cells. Applying this approach to traditional Chinese medicine (TCM) discovery, we identified Astragalus membranaceus as a potent regulator of glutamine metabolism. Through virtual screening via molecular docking, 12 potential compounds from Astragalus membranaceus were initially flagged as candidate binders toward the allosteric pocket of glutaminase 1 (GLS1). Crucially, subsequent in vitro recombinant human GLS1 enzyme activity assays successfully ruled out computational false positives and demonstrated that Compound 2 (quercetin) acts as the exclusive, direct enzymatic inhibitor among the tested monomers, capable of effectively suppressing GLS1 activity. Overall, this work provides a robust platform for real-time metabolic profiling of glutamine metabolism and drug screening at the living cell level, and offers new insights into the mechanisms of TCMs in anticancer therapy.

Chagas disease affects millions worldwide and remains a leading cause of cardiomyopathy in Latin America. Early diagnosis remains challenging in endemic regions. Artificial intelligence (AI)-based electrocardiography (ECG) analysis may offer a low-cost strategy for large-scale screening in resource-limited settings. To evaluate the performance of an AI-ECG algorithm combined with clinical data for detecting Chagas disease in a community-based screening program conducted in a highly endemic region in Northeastern Brazil. In August 2024, 1,115 adults underwent standardized 12-lead ECG acquisition during a field campaign in Feira de Santana, Bahia, Northeast Brazil. A previously trained AI model analyzed ECG tracings and incorporated three clinical variables: i) prior residence in triatomine-infested areas, ii) poor housing conditions, and iii) family history of Chagas disease. Individuals flagged as AI-positive were classified as suspected cases. Suspected cases and matched controls (2:1) underwent point-of-care serological testing. The algorithm flagged 121 individuals (10.9%), corresponding to an estimated AI-based prevalence of Chagas disease of 7.8% (95%CI: 6.2-9.6). Among the 112 individuals who completed serological testing, 13 tested positive, all within the AI-positive suspected group; no seropositive cases were identified among controls. Sensitivity was 100% (95%CI: 69-100), specificity 40% (95%CI: 30-50), negative predictive value 100% (95%CI: 91-100), and positive predictive value 12% (95%CI: 11-14), with a diagnostic odds ratio of 6.6. An AI-ECG algorithm combined with simple clinical variables demonstrated excellent sensitivity for the detection of Chagas disease and may represent a valuable triage tool in endemic, resource-constrained settings. A doença de Chagas afeta milhões de pessoas em todo o mundo e permanece como uma das principais causas de cardiomiopatia na América Latina. O diagnóstico precoce ainda representa um desafio em regiões endêmicas. A análise de eletrocardiografia (ECG) aprimorada por inteligência artificial (IA) pode oferecer uma estratégia de baixo custo para rastreamento em larga escala em contextos com recursos limitados. Avaliar o desempenho de um algoritmo de ECG aprimorada por IA combinado com dados clínicos para a detecção da doença de Chagas em um programa de rastreamento comunitário realizado em uma região altamente endêmica do Nordeste do Brasil. Em agosto de 2024, 1.115 adultos foram submetidos à aquisição padronizada de ECG de 12 derivações durante uma campanha de campo em Feira de Santana, Bahia, nordeste do Brasil. Um modelo de IA previamente treinado analisou os traçados de ECG e incorporou três variáveis clínicas: i) residência prévia em áreas infestadas por triatomíneos, ii) condições habitacionais precárias e iii) histórico familiar de doença de Chagas. Indivíduos identificados como positivos pela IA foram classificados como casos suspeitos. Casos suspeitos e controles pareados (2:1) foram submetidos a testes sorológicos no ponto de cuidado. O algoritmo identificou 121 indivíduos (10,9%), correspondendo a uma prevalência estimada de doença de Chagas baseada em IA de 7,8% (intervalo de confiança de 95% [IC95%]: 6,2-9,6). Entre os 112 indivíduos que completaram o teste sorológico, 13 apresentaram resultado positivo, todos pertencentes ao grupo suspeito positivo pela IA; nenhum caso soropositivo foi identificado entre os controles. A sensibilidade foi de 100% (IC95%: 69-100), a especificidade de 40% (IC95%: 30-50), o valor preditivo negativo de 100% (IC95%: 91-100) e o valor preditivo positivo de 12% (IC95%: 11-14), com uma razão de chances diagnóstica de 6,6. Um algoritmo de ECG aprimorada por IA combinado com variáveis clínicas simples demonstrou excelente sensibilidade para a detecção da doença de Chagas e pode representar uma ferramenta valiosa de triagem em contextos endêmicos com recursos limitados.

(1) Background: Digital nature, delivered through different media, has been proposed as a scalable substitute for physical nature exposure to support psychological well-being, especially in high-income countries. This scoping review aimed to map the evidence on digital nature interventions and positive psychological outcomes. (2) Methods: Following PRISMA-ScR guidelines, we searched PubMed, ScienceDirect, and Google Scholar, supplemented by AI search and citation tracking, for studies published between 2020 and 2025. The first search was conducted between 15 November 2025 and 25 December 2025 and revisited between 5 June 2026 and 10 June 2026. We included articles published in English in peer-reviewed journals with the full text available. (3) Results: Our search yielded 67 studies from diverse cultural settings in three categories: primary studies (N = 46), review articles (N = 19) and conceptual articles (N = 2). Digital nature was associated with reduced stress and anxiety, better mood, greater restorativeness, and stronger nature connectedness, across healthy individuals, older adults, and clinical populations; reviews supported these findings at a larger scale, though some flagged mixed results for specific outcomes and technologies. Two conceptual articles explained these patterns through biophilic design and digital placemaking. (4) Conclusion: Overall, digital nature appears to be a promising approach for promoting psychological well-being, particularly where access to natural environments is limited. However, the evidence is mixed, geographically concentrated in Europe, East Asia, and North America, and methodologically heterogeneous, with no studies from low-resource settings. Further research is needed in these underrepresented regions.

BeanGPT is a domain-specific retrieval augmented generation system designed to support research and breeding decisions in common bean (Phaseolus vulgaris L.) by transforming natural language questions into citation-backed, verifiable answers. The platform integrates a large, curated corpus of legume-focused peer-reviewed literature with structured multi-year agronomic trial records collected across diverse environments, climate projections extending to 2090 under multiple emission scenarios, and standardized cultivar nomenclature to resolve naming inconsistencies across datasets and publications. BeanGPT combines semantic retrieval from a vector database with intent-based query routing and structured parameter extraction to direct questions to genetics, field performance analytics, or climate modules. To reduce errors that commonly occur in general-purpose language models, BeanGPT incorporates a genomic index that enables constant time membership lookup of gene and protein identifiers against authoritative resources, ensuring that molecular entities are either validated or clearly flagged as literature-derived. The system is implemented with a streaming web interface and an asynchronous backend that supports concurrent users and can generate interactive visualizations through automated Plotly code generation. Beta testing demonstrated strong retrieval relevance, low response latency, reliable gene verification, and high citation precision, indicating that domain-grounded RAG can improve accuracy and usability for Phaseolus vulgaris research.

Blood smear examination involves classifying cells by morphology under a microscope, a labour-intensive process prone to subjective variation. Recent deep learning models achieve strong performance in blood cell classification but remain as "black boxes", offering little clinical transparency. We propose a dual-model framework that pairs a deep YOLO (You Only Look Once) classifier with a shallow, interpretable explainer. YOLO performs classification and segmentation, while clinically informed features are extracted from segmented images to train the explainer on YOLO predictions. SHapley Additive exPlanations (SHAP) quantify feature importance, with discrepancies flagged as "other reasons" to enhance transparency. Evaluated on proprietary and public datasets (PBC, Raabin), YOLO achieved AUCs of 0.997, 0.994, and 1.000 respectively, with the explainer demonstrating strong alignment: Score-CAM activation maps agreed with SHAP-identified features. In a user study with 9 trained and qualified haematologists and laboratory medical technologists, predictions achieved 96.9% concurrence. This work shows that the coupling of deep learning with interpretable models improves the confidence of predictions while providing clinical meaningful insights.

The extent and characteristics of Large Language Model (LLM) utilization in arthroplasty literature remain undefined. In this study, we aimed to quantify the extent of LLM utilization in manuscripts across major arthroplasty journals. Additionally, we sought to assess temporal trends in LLM utilization, as well as associations with author productivity, geographic origin, and citation impact. A cross-sectional analysis of 3,352 original research articles from six arthroplasty journals from the era before advanced Large Language Models (LLMs) (Pre-AI) (2018 to 2022) and the era after advanced LLMs (post-AI) (2023 to 2025) was performed. The text was processed using a detection algorithm. Journal-specific thresholds for significant AI involvement were established (mean + two standard deviations of pre-AI scores). Author productivity, primary language of affiliate country, and citation counts were analyzed. In the post-AI era, five of six journals demonstrated significantly higher odds of AI involvement (P < 0.05). The proportion of AI-flagged articles rose from less than 4.2% (2018 to 2021) to 20.4% in 2025, which was a notable nonlinear increase compared to 2024. The first authors in the 90th percentile of the dataset for authorship demonstrated significantly greater odds of exceeding AI thresholds compared to authors who only had one publication in the dataset (odds ratio (OR) = 1.83, P < 0.001). Conversely, non-English affiliate country authors (P = 0.436) and zero-citation-count articles (P = 0.882) did not have higher odds. Unsurprisingly, detectable AI assistance in arthroplasty research has increased significantly since the public release of LLMs. The AI tools are disproportionately utilized by high-productivity authors but not non-English-speaking country authors, suggesting adoption is driven by research efficiency and scalability rather than language barriers. Higher rates of LLM use were not found in zero citation articles.

To develop and evaluate a framework for human-AI interaction. This approach, SHARE (Synergistic Human-Agent REasoning system) was designed to support scalable phenotyping of complex outcomes accurately, robustly and reproducibly from real-world electronic health record (EHR) data to support real-world evidence (RWE) generation. Using rheumatoid arthritis (RA) disease activity as the use- case, we studied a multi-institutional EHR-based RA cohort of 3,167 patients. Expert reviewers and a disease activity agent labeled notes using the same review guideline. The agent combined embedding-based informative-note filtering, structured evidence extraction, and evidence-based integrated reasoning to assign disease activity categories with supporting evidence, rationale, confidence, and ambiguity flags. To support scalable deployment, we evaluated a budget-tiered configuration using GPT-5 Nano for high-volume evidence extraction, o4-mini for final reasoning, benchmarking against a GPT-5.4 high reasoning effort configuration applied at every step. Note-level discrepancies were adjudicated by reviewers into final co-produced labels that were used to refine labels and inform agent development. The main outcome measure was the mean absolute error (MAE) of the initial and final agent vs the final co-produced labels. The agreement between agent- and reviewer-flagged ambiguous notes, per- note cost and compute time across configurations were also tested. Expert reviewers labeled 626 notes from 273 patients; human-AI adjudication revised 127 (20%) of these initial labels and added 60 newly labeled notes, yielding a 686-note co-produced reference. Against this reference, the final agent's accuracy improved from a mean absolute error of 0.406 to 0.291 with co-learning, and its ambiguity flag agreed with expert ambiguity designations with 92.1% accuracy. Applied across the cohort, the agent labeled 101,691 notes; the budget tiered configuration matched the accuracy of GPT-5.4 at high reasoning effort while reducing estimated cost by 69% and compute time by 70%. Adopting a framework for human-AI co-learning, SHARE, improved the overall quality of gold-standard labels, identified ambiguous cases for further review, and supported accurate and standardized chart reviews of disease activity at a scale infeasible for manual review. SHARE's resource efficiency provides a transferable approach to incorporate complex phenotypes in RWE studies. What is already known on this topic: Defining disease states from electronic health record (EHR) data is central to generating real-world evidence (RWE), but complex phenotypes require extensive review of narrative clinical notes that are difficult to standardize, audit, and scale. Out-of-the-box large language model (LLM) prompting can support review, but accurate annotation with face validity requires workflows that preserve supporting evidence, recognize uncertainty, while keeping the clinical experts in the adjudication loop.What this study adds: We developed and evaluated the Synergistic Human-Agent REasoning system (SHARE), a multi-stage human-artificial intelligence (AI) co-learning framework in which clinical experts define phenotype guidelines and the agent identifies informative notes, extracts supporting evidence, assigns labels, and flags ambiguity for focused review. With rheumatoid arthritis disease activity as a use-case, adjudication improved and expanded the reference labels, while selective use of lower- and higher-cost models supported internally evaluated, resource-efficient scaling.How this study might affect research, practice or policy: SHARE introduces a framework for human-AI workflows for research, shifting review of complex EHR phenotypes from broad manual abstraction towards a scalable, resource- efficient, targeted expert adjudication, with clinical experts defining the guidelines, overseeing local validation and the final interpretation.

Response to neoadjuvant PD-1 inhibitor plus chemotherapy in locally advanced esophagogastric adenocarcinoma varies widely, and tumor biomarkers alone do not fully explain the variation. We developed an integrated Body Composition and Immunonutritional Signature (BCIS) from pretreatment CT body composition and routine blood markers, and tested whether it predicts pathological response, immune-related adverse events (irAEs), and survival. From four tertiary hospitals in Hebei Province, China, we enrolled 720 patients with histologically confirmed gastric or gastroesophageal junction (Siewert II/III) adenocarcinoma treated between 2019 and 2023 with neoadjuvant PD-1 inhibitor plus SOX or XELOX followed by D2 gastrectomy. BCIS was a 0-10 additive score from ten prespecified adverse host features. Analyses included logistic regression, restricted cubic spline (RCS) modeling, multivariable Cox regression, DeLong tests for nested AUC comparisons, decision-curve analysis, eleven sensitivity scenarios, and collinearity checks by Spearman correlation and variance inflation factors. BCIS classified 243 (33.8%) patients as favorable, 303 (42.1%) as intermediate, and 174 (24.2%) as unfavorable. Major pathological response (MPR) rates dropped sharply across strata (64.2%, 39.6%, 21.8%; P-trend<0.001). After adjusting for age, sex, cT/cN, PD-L1 CPS, MMR status, EGJ origin, and regimen, every BCIS point cut the odds of MPR by roughly 40% (adjusted OR&#xa0;=&#xa0;0.61, 95% CI 0.55-0.68, P<0.001) and pCR by close to half (adjusted OR&#xa0;=&#xa0;0.55, 95% CI 0.46-0.66, P<0.001). RCS modeling flagged CRP and CAR as nonlinear; BCIS itself was strictly linear. Adding BCIS to a clinical baseline lifted overall AUC for MPR from 0.597 to 0.710 (DeLong P<0.001) and external AUC from 0.588 to 0.704. At 42.5 months' median follow-up, each BCIS point raised the hazard of progression by 53% (adjusted HR&#xa0;=&#xa0;1.53, 95% CI 1.44-1.63) and of death by 38% (adjusted HR&#xa0;=&#xa0;1.38, 95% CI 1.29-1.47; both P<0.001). Direction of effect held across every prespecified subgroup (P-interaction>0.10 throughout) and all eleven sensitivity scenarios. BCIS is independently associated with pathological response and survival under neoadjuvant PD-1-based immunochemotherapy and adds discrimination beyond tumor-centered biomarkers and clinical staging. Because every component comes from routine pretreatment workup at no extra cost, BCIS could feasibly inform prehabilitation, toxicity surveillance, and shared decision-making.

In a single US health system, fewer than one in three adults flagged by spinal muscular atrophy-associated diagnostic codes had molecularly confirmed spinal muscular atrophy (positive predictive value 27%), with the majority miscoded across clinically distinct categories.

Respiratory sound monitoring is a promising non-invasive tool for precision pig farming, but practical evidence from calibrated room-level deployment under degraded air-quality conditions remains limited. This study reports a 28-day exploratory room-level case study in which 52 growing pigs were housed in two rooms: one standard-ventilation room and one ventilation-restricted room, and monitored with one microphone per room emphasizing mixed room-level soundscape monitoring rather than individual pig cough counts or replicated treatment inference. Because the design lacked independent room-level replication, all room contrasts and p-values were interpreted as exploratory descriptive screening summaries rather than causal treatment effects. Airflow verification, playback calibration at multiple pen positions, and background-noise spectral analysis were performed to address measurement bias. Signal inspection showed that biologically relevant vocal energy was retained after 16 kHz resampling, while class imbalance was handled by inverse-frequency weighting and macro-F1-based model selection. The Audio Spectrogram Transformer (AST) pipeline was subjected to five-fold group-blocked cross-validation, and temporal validation. The model achieved a test macro-F1 of 0.937, five-fold macro-F1 of 0.928 &#xb1; 0.019, and three-day deployment validation macro-F1 of 0.914. In this two-room dataset, the ventilation-restricted room displayed higher room-level cough-like detections, aggressive vocalizations, normal vocalizations, lower silence, reduced growth, and poorer air quality. Cough-like detections showed recurring clock-time clustering, with the most sustained elevation during 19:00-22:00 and a smaller peak around 10:00 with the highest occurrences at 20.00 (2.84 room-level cough-like detections standardized to group size). Audio-only early-warning analysis flagged deteriorated air-quality windows with AUROC = 0.91 and AUPRC = 0.88 and provided a median 34 min lead time before environmental threshold exceedance, highlighting practical utility as an early inspection cue for farmers before air-quality deterioration becomes more pronounced. Cough-like events descriptively co-varied positively with NH3, temperature, and CO2. Overall, calibrated AST-based monitoring can summarize group-level acoustic changes associated with degraded room environments, while multi-room and multi-farm replication remains necessary for causal inference and generalization.

Membrane proteins constitute approximately 20-30% of all proteomes and represent over 60% of current drug targets. Although protein-lipid interactions play important structural and regulatory roles in membrane-associated proteins, most existing structural resources focus on identifying whether a residue lies within a membrane region, typically inferred from computational hydrophobicity-based positioning algorithms. This approach does not directly address a distinct biological question: which residues at the protein surface make direct physical contact with lipid molecules? Answering this question from experimental data is critical for understanding lipid-mediated allostery, designing lipid-mimetic therapeutics, and training accurate machine learning models for lipid binding site prediction. We present MPLID (Membrane Protein-Lipid Interaction Database), a curated residue-level dataset comprising 4,704 membrane proteins representing 813 sequence clusters at 30% identity, 8,055,325 residues, and 80,439 experimentally validated lipid contact annotations (1.00% observed positive rate). Labels are derived exclusively from crystallized lipid molecules resolved in Protein Data Bank structures using a 4.0&#xa0;&#xc5; all-atom heavy-atom distance cutoff. Because most native lipid interactions are lost during purification and crystallization, this observed rate represents a lower bound, and the non-contact class inevitably contains false negatives. The dataset uses a curated list of 117 candidate lipid identifiers across ten functional categories, including 90 PDB-derived ligand codes audited against the RCSB Chemical Component Dictionary and 27 CHARMM-style lipid identifiers encountered in cryo-EM depositions. These identifiers span phospholipids, cardiolipin, sphingolipids, sterols, fatty acids, glycerolipids, detergent mimetics (explicitly flagged), lipid A components, and CHARMM simulation nomenclature. To prevent data leakage, proteins are clustered at 30% sequence identity using MMseqs2, yielding 813 clusters partitioned into training (2,578), validation (1,051), and test (1,075) splits. Amino acid composition analysis reveals biologically consistent enrichment at lipid contact sites: tryptophan (1.88&#xd7;), arginine (1.44&#xd7;), glycine (1.36&#xd7;), lysine (1.33&#xd7;), and phenylalanine (1.23&#xd7;) are enriched, while proline (0.51&#xd7;), isoleucine (0.57&#xd7;), and aspartate (0.59&#xd7;) are depleted. MPLID addresses a distinct biological question compared to existing resources (OPM, MemBlob, BioDolphin/PLIP): identifying residues that directly contact experimentally resolved lipid molecules rather than those positioned within computationally defined membrane boundaries. With 4,704 proteins and over 8 million annotated residues, MPLID provides the scale needed for training deep learning models for lipid contact prediction, with direct applications in structure-guided drug design and membrane protein engineering. The dataset adheres to FAIR principles and is freely available under a CC0 public domain dedication. Structurally resolved contacts represent only a subset of biological protein-lipid interactions, and MPLID is intended as an experimentally grounded resource rather than a complete catalog of lipid binding sites.

To evaluate the effectiveness of an intelligent clinical decision support system (CDSS) for neonatal hypoglycemia management in mother-infant rooming-in settings, and to dissect the differential hypoglycemia risk conferred by individual high-risk factors and their specific combinations under standardized surveillance. A multidisciplinary team developed a knowledge-driven CDSS grounded in national expert consensus, integrating automated maternal-neonatal risk identification, dynamic tiered monitoring reminders, and structured stratified management recommendations. Effectiveness was assessed using a pre-post self-controlled analysis (historical control: January-March 2024, n&#x2009;=&#x2009;522; CDSS-implemented: April-June 2024, n&#x2009;=&#x2009;417) and a concurrent parallel controlled analysis (non-CDSS wards: n&#x2009;=&#x2009;389; CDSS wards: n&#x2009;=&#x2009;352). Neonatal hypoglycemia was defined as blood glucose <2.2&#x2005;mmol/L. Risk factor combination patterns were explored among 6,667 system-flagged high-risk neonates. CDSS implementation significantly reduced hypoglycemia incidence in both the pre-post (5.76% vs. 11.88%, P&#x2009;<&#x2009;0.05) and parallel (5.40% vs. 9.25%, P&#x2009;<&#x2009;0.05) analyses. Under CDSS-managed surveillance, the overall hypoglycemia incidence in the high-risk cohort was 6.3%. Marked heterogeneity was observed: preterm birth (15.4%) and low birth weight (25.0%) carried the highest independent risks, while risk escalated non-linearly with specific factor combinations, reaching 18.8% in neonates with five concurrent factors. Serial monitoring demonstrated a sharp decline in hypoglycemia incidence from 6.1% at first measurement to &#x2264;0.4% thereafter. The intelligent CDSS effectively reduces neonatal hypoglycemia in rooming-in settings. Hypoglycemia risk depends more on the specific types and combinations of high-risk factors than on their quantity alone, providing evidence for precise risk stratification. This closed-loop, guideline-driven workflow enhances clinical standardization and patient safety. Future multicenter studies incorporating machine learning and long-term neurodevelopmental follow-up are warranted.

Health-related quality of life is a key secondary end point in stroke trials. Differential item functioning (DIF) occurs when individuals with the same underlying health-related quality of life interpret and respond differently to questionnaire items, potentially biasing treatment comparisons. This study evaluates DIF in the patient-reported 5-level EuroQOL questionnaire among patients with acute ischemic stroke across age, sex, and treatment groups. Data were from the AcT trial (Alteplase Compared to Tenecteplase), a registry-based randomized comparison of alteplase and tenecteplase conducted at 22 stroke centers across Canada (December 2019-January 2022). Patients with acute ischemic stroke presenting within 4.5 hours of symptom onset and eligible for thrombolysis completed the 5-level EuroQOL questionnaire at 90 days poststroke. DIF was assessed using multigroup graded response models with the Wald-based sweep procedure, which accounts for between-group differences in latent trait distributions. We quantified effect sizes using signed weighted area between curves (sWABC); |sWABC| <0.10=negligible. Of 1577 patients enrolled in the trial, 1264 survived to 90 days with complete 5-level EuroQOL questionnaire data (51.2% tenecteplase; 46.5% female; 30.1% aged &#x2265;80). Omnibus testing revealed significant DIF only for age (&#x3c7;2=86.9, P<0.001); neither sex (&#x3c7;2=31.7, P=0.063) nor treatment (&#x3c7;2=22.4, P=0.379) showed evidence of DIF. Four items flagged for age-related DIF: self-care, usual activities, pain/discomfort, and anxiety/depression. However, only self-care (sWABC=-0.46) and usual activities (sWABC=-0.34) showed moderate effects, while pain/discomfort (sWABC=-0.002) and anxiety/depression (sWABC=0.09) were negligible. Importantly, factor scores from models with and without DIF adjustment correlated (correlation coefficient=0.98). The 5-level EuroQOL questionnaire appears to function equivalently across sex and treatment groups in this stroke population. Age-related DIF, though statistically detectable in physical functioning items, had little practical consequence for individual scores, supporting the instrument's use for health-related quality of life comparisons in stroke trials. URL: https://www.clinicaltrials.gov; Unique identifier: NCT03889249.

Mirikizumab, a first-in-class interleukin-23p19 antagonist, was approved for ulcerative colitis (2023) and Crohn's disease (2025). The US Food and Drug Administration (FDA) identified a hepatotoxicity signal during pre-approval review, mandating post-marketing surveillance. No independent pharmacovigilance analysis has been published. To characterise the post-marketing safety profile of mirikizumab using multi-database pharmacovigilance, with a focus on hepatotoxicity and IL-23 inhibitor class comparison. Disproportionality analysis of the FDA Adverse Event Reporting System (FAERS; Q4 2023-Q4 2025) and Japanese Adverse Drug Event Report database (JADER) was performed using four algorithms (reporting odds ratio, proportional reporting ratio, Bayesian confidence propagation neural network, empirical Bayesian geometric mean). Signals of disproportionate reporting were defined by concordance of all four methods. Active comparator analysis against risankizumab, guselkumab and ustekinumab, Weibull time-to-onset modelling and hepatotoxicity case characterisation were conducted. Reporting followed READUS-PV guidelines. We identified 564 mirikizumab reports in FAERS and 123 in JADER. Nine signals met all four criteria in FAERS, including spontaneous abortion (Reporting odds ratio (ROR) 10.16, 95% CI 5.16-20.02), pulmonary embolism (ROR 5.56, 2.93-10.56) and injection site reactions. Hepatotoxicity showed no disproportionate reporting in either FAERS (ROR 1.19, 0.74-1.92; n = 17) or JADER (ROR 0.24, 0.05-1.19; n = 1). Comparator analysis identified cytomegalovirus infection and interstitial lung disease as mirikizumab-specific versus the IL-23 class. Weibull analysis (&#x3b2; = 0.65) indicated early-onset adverse event clustering. This first multi-database pharmacovigilance study of mirikizumab did not confirm the FDA-flagged hepatotoxicity signal. Potential signals warranting further investigation include thromboembolic events and pulmonary toxicity.

Previous research examining the relationship between psychological resilience and job performance has provided important insights; however, the psychosocial pathways underlying this relationship remain insufficiently understood in maritime settings. This study aimed to examine the relationship between psychological resilience and job performance, as well as the indirect pathways linking these variables through the serial mediation of perceived stress and loneliness among marine engineers. A sample of 400 marine engineers employed on Turkish-flagged commercial vessels participated in the study. Participants completed validated self-report measures of psychological resilience, perceived stress, loneliness, and job performance. The hypothesized relationships were tested using a serial mediation model with bias-corrected bootstrapping. The results indicated that psychological resilience was positively associated with job performance and negatively associated with perceived stress. Lower perceived stress, in turn, was associated with lower loneliness. Perceived stress independently mediated the relationship between psychological resilience and job performance, whereas loneliness did not emerge as a significant independent mediator. The serial mediation analysis supported an indirect pathway linking psychological resilience to job performance through perceived stress and loneliness. These findings contribute to a better understanding of the psychosocial pathways through which psychological resilience is associated with job performance in isolated and safety-critical maritime work environments. Given the cross-sectional and self-report nature of the study, the findings should be interpreted as associations consistent with the proposed theoretical model rather than evidence of causal or temporal relationships.

Long-term changes in forest management are documented across reports, plans, and scientific papers written for different purposes and with changing vocabularies. This makes it difficult to show how a documentary record was converted into a temporal claim. FORM-TRACE is a formula-based workflow that records corpus decisions, extraction quality, domain terms, and calculations before interpretation. We demonstrate it with Harvard Forest and New England documents dated 1908-2026. Of 257 PDFs inspected, 215 met the analytical criteria; 201 were extracted and scored, 14 were flagged as unreadable, scanned, or corrupted, and 42 methods-support references were kept outside the scored corpus. The method provides: &#x2022; A corpus manifest and extraction log that expose inclusion, exclusion, and coverage gaps; &#x2022; A keyword-domain matrix and five numbered equations that produce document- and period-level indicators; &#x2022; Saved score tables, plot data, and validation records that allow independent checking without redistributing copyrighted PDFs. FORM-TRACE measures documented attention rather than management performance and keeps interpretation separate from scoring.

Peripheral facial palsy is often attributed to idiopathic Bell's palsy, but secondary structural causes should be considered when local red flags are present. A 61-year-old man with a 40-pack-year smoking history and former betel quid chewing presented with a verrucous-appearing mass involving the left oral commissure and buccal mucosa, intermittent purulent discharge from the lesion, progressive left facial swelling, and ipsilateral lower-motor-neuron facial palsy with lagophthalmos. Magnetic resonance imaging demonstrated a left buccal/oral-cavity lesion with ipsilateral parotid duct obstruction. He underwent tracheostomy, wide excision, left supraomohyoid neck dissection, and radial forearm free-flap reconstruction. Pathology confirmed squamous cell carcinoma, pT2N0, cM0 (stage II), with perineural invasion; surgical margins were negative, and lymphovascular invasion was not identified. At follow-up, wound healing was satisfactory and purulent discharge had resolved, but lower-motor-neuron facial palsy persisted; adjuvant radiotherapy was recommended. This case emphasizes that lower-motor-neuron facial palsy with an oral mass, purulent discharge, facial swelling, or salivary-duct obstruction should prompt careful oral examination and head-and-neck imaging.

ICU-to-ward transfers are high-risk transitions marked by information loss and burdensome handoff preparation. We developed PAUSE-Agents, a clinician-in-the-loop multi-agent LLM pipeline that drafts source-attributed handoff briefs from structured ICU data and clinical notes using the clinician-developed ICU-PAUSE template. Mirroring ICU team structure, PAUSE-Agents routes each record through a scribe extractor, 6 role-specialized agents, explicit conflict surfacing, and deterministic safety checks before synthesis, producing an editable first draft rather than an autonomous note. In a single-center medical ICU cohort, 5 physicians completed 100 reviews of 84 agent-drafted briefs. Among adjudicable claims, 98.8% were verified and 1.2% were incorrect; 88% of briefs had no pertinent omission, and mean PDSQI-9 quality was 4.20/5. PAUSE-Agents surfaced 118 conflict warnings and 421 safety flags, making documentation inconsistencies visible before handoff. An o4-mini PDSQI-9 judge showed limited case-level discrimination but supported aggregate monitoring. We release PAUSE-Agents and its clinician evaluation application.