Communications Medicine

This study analyzes 794,811 digitized medical examina- tions from Indian life-insurance applicants, a working-age, urban-skewed demographic often undersampled by national surveys. The cohort exhibits a pronounced South-Asian car- diometabolic risk profile: among valid adult records, 41.9% met the criteria for dyslipidemia (driven heavily by low HDL and elevated triglycerides), and 61.4% met AHA 2017 crite- ria for stage 1 hypertension. However, canonicalizing this dataset across 33,244 diagnostic centers revealed significant heterogeneity in laboratory reference ranges. At the clinical prediabetes threshold of 110 mg/dL for fasting blood sugar, the record-pair disagreement rate across laboratories was 49.7%, with similar variance across other common tests. This structural inconsistency materially affects patient classi- fication and the tracking of disease prevalence, underscoring a critical need for the national standardization of laboratory reporting in India

Electronic health record (EHR) prediction models often summarize longitudinal histories as static patient-level features, which may omit potentially informative event ordering. We developed a simplified spike-timing-dependent plasticity (STDP)-inspired framework that represents asynchronous EHR data as sparse, directional transition features. The approach encodes whether one clinical event precedes another within prespecified temporal windows, preserving event identity, directionality, and approximate timing while retaining feature-level interpretability. We evaluated this framework in two retrospective prediction tasks with different temporal scales: incident acute kidney injury (AKI) prediction in 17,351 MIMIC-IV ICU stays and early postoperative recurrence prediction in 713 CUMC patients with pancreatic ductal adenocarcinoma (PDAC). Models were compared with static burden features (demographics, comorbidities, raw lab measurements) and in addition with STDP transitional feature sets using patient-level cross-validation and rolling prediction horizons. In AKI, a calibrated STDP ensemble model showed higher discrimination than static burden alone at the 24-hour decision snapshot for AKI by 72 hours, with AUROC 0.838 versus 0.800, and at 48 hours for near-term AKI prediction, with AUROC 0.868 versus 0.827. In PDAC, STDP transition features modestly improved Day -30 preoperative recurrence prediction, with AUROC 0.611 versus 0.587 and AUPRC 0.323 versus 0.318 for static burden and showed similar performance at Day 0 (7 days before recorded surgery date), with AUROC 0.681 and AUPRC 0.363. Decision-curve and feature analyses suggested that selected temporal transitions were clinically interpretable across renal, inflammatory, hepatobiliary, hematologic, glycemic, and nutritional trajectories. These findings suggest that STDP-inspired transition features may provide a practical, interpretable way to incorporate temporal ordering into EHR-based risk prediction across both acute and longitudinal settings

Insulin therapy in type 1 diabetes requires constant dose adjustment based on blood glucose, meals, physiological states, and physical activity. This demanding self-management imposes a substantial burden and increases dosing-error risk, underscoring the need for automated insulin delivery (AID) systems that reduce user intervention. However, many current systems depend on fixed, individualized parameters and may not fully adapt to rapid or unobserved physiological changes. We developed the Dynamic Physiology-Aware Reinforcement learning Controller (DPARC), a zero-shot insulin optimizer that infers latent physiological dynamics from recent continuous glucose monitoring (CGM) and insulin-delivery history without prior personalization, carbohydrate announcements, or preset subject-specific parameters. DPARC uses a rolling 24-hour CGM and insulin-history window, but closed-loop operation can begin after 1 hour of observed data by initializing unobserved history with neutral normalized padding and progressively replacing it with observations. In silico, a single frozen DPARC policy adapted within 1 hour, improved time in range compared with a total daily insulin-conditioned reinforcement learning baseline, and approached the upper-bound performance of a fully personalized model under stochastic unannounced meals with randomized timing, carbohydrate amounts, absorption variability, and meal skipping. In supervised porcine studies under unannounced meals, DPARC maintained high time in range without manual configuration, supporting large-animal feasibility while prospective human evaluation is needed before clinical efficacy can be established. Learned latent representations correlated with physiological markers including insulin sensitivity and plasma insulin concentration, supporting physiological alignment and explanatory anchors. Collectively, these findings support DPARC as a preclinical proof-of-concept zero-shot AID framework for future supervised human evaluation.

Although GLP1/GIP receptor agonists demonstrate unprecedented weight loss efficacy, their rapid clinical adoption has revealed significant real-world tolerability challenges. To evaluate their dynamic safety profiles, we developed a macro to micro pharmacovigilance framework by combining global FAERS reports with local UT Physician EHR. Macroscopically, we distilled 17 shared adverse events across the drug class from FAERS with disproportionality analysis. Microscopically, local EHR data (289,655 longitudinal treatment sessions across 71,316 patients) revealed 51.6% of GLP1 sessions terminated within 90 days. Furthermore, temporal stratified logistic regression demonstrated that initial exposure (0 to 30 days) correlated strongly with nausea and vomiting, which attenuated in extended sessions, whereas extended exposure (>2 years) uncovered late onset risks, notably incident hepatic steatosis. Ultimately, this time aware framework reveals that GLP1 safety profiles are profoundly duration dependent, providing critical insights into both acute intolerances and long-term medication safety.

Background. Despite the decline of the 2022 global outbreak, mpox remains an ongoing public health concern, with persistent transmission and emerging viral clades sustaining resurgence risk. Improving preparedness and response is a priority, yet it remains unclear how best pre-exposure vaccination and community response can effectively limit transmission under realistic conditions and whether behavioral adaptation is critical. Methods. We used a data-driven network model of mpox transmission among men who have sex with men in the Paris region, parameterized with sexual behavioral data and calibrated to surveillance data from the 2022 outbreak. We evaluated counterfactual scenarios by varying vaccination timing, rollout speed, prioritization, and behavioral responses. Results. Here we show that, with respect to the 2022 epidemic in the Paris region, vaccination alone delivered at the observed rollout speed would not have reproduced the observed epidemic decline, even if initiated the day of the first European alert, corresponding to 12 days before the first case was reported in France. Achieving comparable control through vaccination alone would have required more than a fourfold increase in rollout speed. Large-scale and long-term reductions in sexual contacts remain instrumental to limit the epidemic size, although earlier vaccination reduces the proportion of MSM needing to change behavior. In contrast, short-term behavioral measures adopted by the vaccinees, such as sexual abstinence during the 14-day immunity-building period, combined with moderately faster vaccine rollout, (+68% for 50% compliance; +34% for 75% compliance) could achieve comparable epidemic control. Targeting individuals with higher sexual activity further improved intervention efficiency. Conclusions. Under realistic reactive vaccination scenarios, mpox control still requires strong behavioral responses. Combining timely vaccination with short-term behavioral change guidance at vaccine administration offers a feasible path to limit transmission and strengthen outbreak preparedness and response.

The increasing availability of portable ultrasound systems motivates exploration of novel approaches to respiratory signal assessment. In this in-vitro study, we investigate whether pulsed-wave (PW) Doppler ultrasound can capture structured spectral patterns from replayed lung sound recordings. Digitized respiratory sounds were replayed through a tissue-mimicking ultrasound phantom, generating 1,478 PW Doppler spectral images from recordings associated with healthy subjects and several externally labeled disease categories. Exploratory classification experiments using a ResNet-18 architecture demonstrated that these Doppler representations contain learnable differences under controlled conditions. These findings motivate further investigation into PW Doppler as a potential representation of respiratory acoustics.

Background: Diabetes mellitus (DM) worsens pulmonary tuberculosis (TB) and drives systemic hyper-inflammation, but the underlying mechanisms remain unknown. Neutrophils have key roles in TB immunopathology and lung cavitation. Here, we determine the role of neutrophils in DMTB patients and in driving TB immunopathology. Methods: Sputum and plasma from 30 TB and 30 DMTB patients were analysed for proteases and cytokines using Luminex bead array. Whole blood transcriptomics identified transcriptional differences. Single-cell RNA sequencing characterised neutrophil subsets and dysregulated pathways. Neutrophil function of poorly-controlled DM patients (HbA1c>8%) and healthy controls (HC) were examined following Mycobacterium tuberculosis stimulation, including reactive oxygen species (ROS), neutrophil extracellular traps (NETs), and phagocytosis. Pathways were interrogated using chemical inhibitors, protein array and western blot. Results: Compared to non-diabetic TB patients, poorly-controlled DMTB patients showed up-regulated sputum MMP-8 and MMP-9, associated with increased collagen-destruction and lung cavity formation. Circulating neutrophil count and neutrophil-derived plasma MMP-8 were up-regulated, alongside transcriptional enrichment of extracellular matrix degradation and inflammatory pathways including TNF and RAGE. Single-cell profiling identified reduced cycling neutrophil subset and myelocytes in DMTB, with overall reduced antibacterial and cell-killing signatures. Ex vivo mycobacterial stimulation of DM neutrophils increased ROS and MMP-9 with impaired NETs and delayed phagocytosis. TNFR1, TNFR2, and RAGE were up-regulated. RAGE inhibition with rosiglitazone mitigated Mtb-induced ROS and MMP-8 release. Conclusion: DM worsens neutrophil-driven tissue destruction and inflammation in TB via dysregulated TNF and RAGE-signalling, priming neutrophils towards immunopathology. Targeting RAGE alongside tight glycaemic control may dampen neutrophil hyper-inflammatory responses to limit tissue destruction.

Objective: Despite the complex and non-linear progression of diabetes, its shared pathways with atherosclerotic cardiovascular disease (ASCVD) are conventionally described using models based on single time points. We identified longitudinal diabetes clusters before diagnosis using deep learning and studied their association with ASCVD events and mortality. Methods: We analyzed 157,670 visits from 15,871 adults (25-65 years) without diabetes from four pooled U.S. cohorts (median follow-up: 22 years [IQR: 9-30]). A gated recurrent unit model with decay (GRU-D) was used to predict 1-year risk of diabetes or censoring within 10 years, by learning longitudinal embeddings across 25 clinical characteristics and biomarkers. Parallel Factor Analysis-2 (PARAFAC-2) and Gaussian mixture models (GMM) were used to group longitudinal participant representations as clusters. Landmark time Cox proportional hazards regressions, relative to last observation in the training window, were used to study covariate-adjusted associations of clusters with ASCVD and mortality. Prognostic utility of clusters beyond the PREVENT risk score was assessed using Harrell's C-index. Findings were replicated in a fifth cohort. Results: The analytic sample was aged 49 years [SD: 11], 58% female, and 68% white; 1,202 (8%) developed diabetes within the first 10 years. We identified five clusters (Cluster A to E) that differed in their clinical characteristics over time. Cluster E (46%) had the highest cumulative incidence of diabetes in the study period, followed by Cluster C (40%) and Cluster A (38%). Cluster C, which was defined by older age, high blood pressure, and suboptimal renal function at the first visit, had higher rates of ASCVD (HR: 1.09, 95%CI: 0.98-1.21) and mortality (HR: 1.08, 95%CI: 1.00-1.16), relative to Cluster A despite being similar in age and BMI at the first visit. Relative to Cluster A, all other clusters had similar or lower rates of ASCVD and mortality. We observed substantial cluster effects for three clusters (Clusters C to E), which were based on only two cohorts. The two clusters (Clusters A and B) that included participants from all four cohorts were reproduced in the fifth cohort and showed similar rates of outcomes. Clusters did not improve ASCVD prognosis, relative to a model that included only the PREVENT risk score. Conclusions: Longitudinal clusters reveal substantial heterogeneity in the period before diabetes diagnosis, and their risk for ASCVD and mortality. However, clusters discovered may, in part, be explained by cohort effects from variations in recruitment and visit patterns after recruitment.

Multimorbidity, the co-existence of two or more long-term conditions, is up to three times more prevalent among people with cancer than in the general population and is associated with poorer survival, particularly for cancers with a more favourable prognosis such as colorectal cancer. In Scotland, multimorbidity is the norm among older adults, emerges earlier in socioeconomically deprived populations, and may contribute to comparatively low cancer survival rates. Despite this, the influence of multimorbidity on the colorectal cancer pathway remains poorly understood. We conducted a Scottish data-linkage study of adults diagnosed with colorectal cancer between 2010 and 2014, linking the Scottish Cancer Registry to national prescribing, hospital admissions, death registration, and bowel screening datasets. Prescribing data were used to derive overall and system-specific comorbidity measures as a proxy for multimorbidity and active disease burden. Associations with stage at diagnosis, treatment, survival, and screening uptake were examined using logistic regression and Cox proportional hazards models adjusted for demographic and clinical covariates. Among 19,043 patients, 87% had at least one prescribing-based comorbidity, most commonly cardiovascular, nervous system, and gastrointestinal conditions. Overall comorbidity burden was not associated with stage at diagnosis, although laxative-related prescribing was associated with later-stage disease. Increasing comorbidity burden reduced the likelihood of receiving any treatment and surgery, while associations varied across system-specific comorbidities. Higher comorbidity burden was also associated with increased all-cause and colorectal cancer-specific mortality, particularly among patients with respiratory, nervous system, and haematological/nutritional conditions. Screening uptake was not associated with overall comorbidity burden but did differ by system-specific comorbidity. Prescribing-based multimorbidity was highly prevalent and strongly associated with treatment patterns and mortality among patients with colorectal cancer. System-specific multimorbidity measures provided greater discrimination than overall morbidity counts, highlighting the importance of considering distinct multimorbidity profiles when assessing cancer pathways and designing targeted interventions for optimising treatment and survival. Keywords (primary health care, general practice, multimorbidity, comorbidity, colorectal cancer, early diagnosis, cancer treatment, survival)

Background. Blood-based biomarkers are increasingly proposed for identifying high-risk individuals before clinical disease and for making prevention-oriented trials more efficient. Prognostic enrichment can increase event rates, but trial efficiency also depends on whether the intervention effect is preserved in the enriched population. Methods. Using the UK Biobank Pharma Proteomics Project, we trained disease-specific proteomic risk scores (ProRS) from 2,916 plasma proteins with elastic-net Cox models. We compared ProRS, polygenic risk scores (PRS), and combined PRS--ProRS scores across ten incident diseases. We estimated cumulative incidence and theoretical two-arm time-to-event trial sample sizes across risk strata. To evaluate effect preservation, we examined six intervention-analogue exposure--outcome pairs spanning genetic (PCSK9/coronary artery disease, APOE/Alzheimer's disease, PPARG/type 2 diabetes, IL23R/Crohn's disease), behavioural (physical activity/all-cause mortality), and pharmacological (RAAS inhibitors versus calcium channel blockers/coronary artery disease) examples. Results. ProRS outperformed PRS for 9 of 10 diseases (median C-index 0.75 versus 0.61). ProRS and PRS were weakly correlated (median Pearson |r| = 0.04), and joint PRS--ProRS stratification identified groups with higher observed incidence than either score alone for several endpoints. In the top risk quartile, combined-score enrichment reduced theoretical required sample sizes by 32--74\% under a fixed 20\% relative hazard reduction. These gains were not always preserved when stratum-specific intervention-analogue effects were used. Effects were broadly preserved for APOE/Alzheimer's disease and physical activity/mortality. The PPARG/type 2 diabetes effect attenuated toward the null under all three score types, showing that event-rate enrichment does not guarantee effect preservation. For IL23R/Crohn's disease and the antihypertensive comparison, point estimates differed across score types -- preserved under polygenic but attenuated under proteomic enrichment -- but confidence intervals were wide and overlapping. Conclusions. Proteomic risk scores can identify high-event-rate populations for prevention-oriented trials, but event-rate enrichment alone is insufficient for trial design. Biomarker-guided enrichment should evaluate mechanism-specific effect preservation and may be preferable as a stratification or adaptive-design variable rather than as a restrictive eligibility criterion.

In February 2026, the FDA announced that a single pivotal phase 3 (P3) trial would become the new default standard for drug approval - a regulatory direction that had been legally enabled since the FDA Modernization Act of 1997. This announcement has strategic, scientific, and economic implications for drug developers, contract research organizations (CROs), and biotech investors. We argue that the expansion of this framework, originally reserved for various niche submissions, represents a paradigm change, dramatically increasing the value of rigorous early phase (P1 and P2) trial design, requiring sponsors to establish both statistical efficacy signals and mechanistic biological understanding before entering phase 3. Using a CNS indication cost model, we show that single P3 approval can reduce total development expenditure from approximately $447 million over 14 years to $297 million over 12 years - a savings of $150 million and providing two years of additional commercial runway for a modeled CNS drug. Case examples including lecanemab, omaveloxolone, and tofersen illustrate how biomarker-informed early phase strategies can establish the confirmatory evidence necessary for single-trial approval. We provide practical guidance for maximizing the value of P1 and P2 under this evolving framework.

One in four adults has insulin resistance (IR), a modifiable driver of type-2 diabetes that can precede diagnosis by a decade. However, IR assessment remains clinic- and laboratory-based, limiting repeated population screening. We tested whether free-living wearable data can detect IR in adults with normoglycemia or prediabetes. Machine-learning models using continuous glucose monitor (CGM)-based glucose dynamics and smartwatch-based heart rate/heart rate variability were developed in Study 1 (N = 97) and externally validated without retraining in Study 2 (N = 61, 31% IR prevalence). The best-performing CGM-based model achieved AU-ROC = 0.873 [0.756-0.967] and AU-PRC = 0.816 [0.640-0.934], outperforming an anthropometrics-only baseline (AU-ROC = 0.749, AU-PRC = 0.593). Findings are the first to detect IR from wearables without blood tests or structured glucose challenges, with state-of-the-art comparable performance. By enabling continuous at-home screening, this approach can identify undetected at-risk individuals and trigger confirmatory blood tests to close detection gaps.

Personalized oncology treatment recommendation is a critical clinical task that requires in-tegrating complex, multi-modal patient data with established medical knowledge to ensure both accuracy and safety. While deep learning models excel at capturing latent patterns from high-dimensional data, their opaque decision-making processes and inability to strictly enforce clinical constraints hinder their adoption in high-stakes medical domains. Conversely, traditional rule-based systems offer high interpretability but struggle to scale with complex, heterogeneous data. To address these challenges, we propose the Knowledge-driven Neuro-Symbolic Network (K-NeSyNet), a novel framework for personalized oncology treatment recommendation. K-NeSyNet is grounded in a newly constructed Multi-Modal Oncology Knowledge Graph (MM-OKG) that unifies genomic mutations, medical imaging features, clinical text, and structured medical guidelines from publicly available sources including TCGA, DGIdb, KEGG, and NCCN guidelines. The core innovation of K-NeSyNet is a three-channel differentiable symbolic reasoning mechanism that explicitly models guideline recommendations, mutation-target matching, and contraindication penalties for each patient-drug pair. These symbolic signals are dynamically fused with the outputs of a knowledge-aware graph attention neural reasoning module via an adaptive gated fusion network. Crucially, the fusion gate learns to balance neural and symbolic confidence in a patient-specific manner, while contraindication evidence enters both the symbolic score and a dedicated safety objective to down-weight clinically risky drugs. Extensive experiments on a real-world multi-modal oncology dataset comprising 4,781 patients across 10 cancer types demonstrate that K-NeSyNet consistently outperforms eight state-of-the-art baselines. Specifically, K-NeSyNet achieves the highest F1@10 of 0.9227, NDCG@10 of 0.9656, and Jaccard similarity of 0.9366, while maintaining competitive Clinical Guideline Consistency. Ablation studies confirm the indispensable role of each component, with the removal of the fusion gate causing the most significant performance degradation. Furthermore, K-NeSyNet provides transparent, score-decomposed explanations for its recommendations, offering a crucial step toward trustworthy AI-assisted clinical decision support.

Schistosomiasis causes a complex, difficult to diagnose form of liver fibrosis with high rates of life-threatening morbidity in resource-poor settings where there are often no trained sonographers. Protocols for diagnosis of schistosomiasis-related liver fibrosis have focused on difficult-to-acquire and subjective ultrasound images dependent on extensive expertise. Here we present SchistoTrackVideoNet, the first deep learning-based video model trained on easy-to-acquire standardised ultrasound video sweeps for classification of schistosomiasis-related liver fibrosis. This video-based classification model was trained and evaluated on video sweeps from 2140 participants aged 5--87 years from three districts in rural Uganda. We tested the model at a clinically-relevant sensitivity threshold ($\geq$90\%) and achieved positive predictive values of 0.0968--0.5556 for diverse presentations of liver fibrosis. Our findings show potential for the use of easy-to-acquire video sweeps for diagnosis of schistosomiasis-related liver fibrosis and our model provides a proof-of-concept for deep learning applied to liver ultrasound video for diagnosis of schistosomiasis-related liver morbidity.

Liver fibrosis is a major cause of death in low- and middle-income country contexts. In rural, poor areas of sub-Saharan Africa, schistosomiasis is an underestimated cause of liver fibrosis. Despite the need for increased diagnostic capacity for schistosomiasis-related liver fibrosis, there are no automated, clinically-validated tools to diagnose schistosomiasis-related liver fibrosis. We present SchistoTrackNet which is, to our knowledge, the first deep learning-based model for distinguishing distinct presentations of schistosomiasis-related liver fibrosis of varying severity. Ultrasound images from 1533 participants aged 5--84 years from three districts in rural Uganda were used to train and evaluate the presented models. The models were evaluated by assessing failure cases and by comparing results with re-readings performed by sonographers experienced in diagnosis of schistosomiasis morbidity. Our models show potential to enable automated reading of ultrasound images for schistosomiasis-related liver fibrosis to allow large-scale surveillance of schistosomiasis morbidity and contribute towards the World Health Organization target to eliminate schistosomiasis as a public health problem.

Background. The ongoing Andes hantavirus outbreak linked to the cruise ship MV Hondius (April-May 2026, seven confirmed cases, three deaths, patients hospitalised across six countries including France) highlights the urgent need for mechanistic tools to predict which hantavirus pulmonary syndrome (HPS) patients will progress to fatal cytokine storm. Methods. We present a 14-variable antigen-gated ordinary differential equation (ODE) model integrating viral dynamics, CD8+ cytotoxic T lymphocyte (CTL) expansion, four cytokines (TNF-alpha, IFN-gamma, IL-6, IL-10), VEGF-mediated vascular permeability, and platelets. We derive two reproduction numbers: the viral invasion number R0 and the immunopathological loop gain Rip. We apply Villani's hypocoercivity theory and the HWI optimal transport inequality to prove that the spectral gap of the CTL-IFN-gamma feedback loop collapses to zero at a critical infected endothelial cell count Ic*, providing a computable early-warning threshold. We define a Wasserstein patient stratification score from six clinically observable variables. Results. At default parameters (R0 = 0.396, Rip = 1.875): (1) the disease-free equilibrium is locally asymptotically stable - the virus self-limits - but the CTL-IFN-gamma loop has sufficient gain to amplify autonomously once established; (2) the storm-block spectral gap collapses exactly to zero at Ic* = 2.23 cells/uL, a threshold attained within hours of infection onset, confirming that immunopathological amplification is essentially unavoidable; (3) the Wasserstein score rises 1-2 days before vascular permeability reaches clinical threshold, providing an early-warning window; (4) exogenous IL-10 supplementation is the single most effective intervention (predicted 40% reduction in peak permeability), outperforming corticosteroid immunosuppression and ECMO; the combination of all three applied at day 7 reduces peak permeability below the fatal threshold. Conclusions. This framework predicts that HPS cytokine storm is a structural consequence of Rip > 1 rather than excessive viral load, explaining death after viral clearance. For clinicians managing MV Hondius Andes virus patients, the model identifies a six-variable triage score and a day-7 IL-10-centred therapeutic window as the highest-priority clinical targets. A live simulator and bedside triage tool are available at xvirus.org.

Background Gastric cancer management is heterogeneous, and although the treating surgeon leads decisions across the pathway, surgeon level outcome variation remains poorly quantified. This study assessed surgeon identity as an independent predictor of survival after risk adjustment, introducing the Surgical Assessment and Healthcare (SAH) Index. Methods This single institution retrospective study (Ruijin Hospital, Shanghai Jiao Tong University; NCT07180966) included 692 patients undergoing curative-intent resection for gastric adenocarcinoma (pStage I ,II, III) in 2019 by eight consultant surgeons. Overall survival was modelled by multivariable Cox regression (primary model, 199 events, EPV 16.6; complete-case sensitivity model, N = 647). The SAH Index expressed surgeon * stage observed-to-expected ratios for five-year mortality and major morbidity (Clavien Dindo [≥] IIIa). Median follow up was 74.3 months. Results Independent predictors of survival were tumour stage (HR 2.979/step), age (HR 1.030/year), and non-distal gastrectomy (HR 1.498; all p [≤] .006). After full adjustment, surgeon identity remained significant (Wald = 14.58, df = 7, p = .042): two surgeons carried roughly double the reference hazard S6 (HR 2.219, p = .003) and S8 (HR 2.034, p = .031) both with the cohort's lowest neoadjuvant chemotherapy rates (3.0% and 7.0% versus 17.6%), implicating pre-operative pathway decisions. The effect persisted in the sensitivity model (MSI also prognostic, HR 3.162, p = .007). Morbidity benchmarking flagged no surgeon for excess complications (no Tier 2 flags) and one survival-outlier cell (S6, Stage II; Tier 3). Conclusion Surgeon identity is independently associated with survival in gastric cancer beyond measurable case mix. The SAH Index offers a reproducible tool for institutional and inter-hospital benchmarking, with tier assignments stable across all four prespecified weighting scenarios confirming tier classification is independent of weight specification.

Background: Colon capsule endoscopy (CCE) has been proposed as a non-invasive alternative to colonoscopy for colorectal cancer (CRC) screening, offering greater patient comfort and potentially reducing healthcare burden. However, its cost-effectiveness in population-based screening remains uncertain. Methods: This study used a state-transition (Markov) model to simulate lifetime outcomes of CRC screening in Denmark, Scotland, and Spain, comparing the standard pathway based on fecal immunochemical testing (FIT) followed by colonoscopy with an alternative pathway replacing colonoscopy with CCE after a positive FIT result. The model incorporated costs (2024 euros), quality-adjusted life-years (QALYs), and CRC cases avoided, applying a yearly discount rate of 3%. Deterministic sensitivity analyses explored uncertainty in capsule cost, adherence, and reinvestigation rates for non-advanced polyps. Results: Across all settings, CCE resulted in higher costs but slightly increased effectiveness and utility (mean QALYs 28.7 vs. 28.8; CRC detected 0.032-0.034 vs. 0.035-0.037 per person). Incremental cost-effectiveness ratios (ICER) ranged from 43,538EUR in Spain to 136,930EUR in Denmark per additional CRC detected. Capsule cost was the main driver of ICER variation, whereas adherence rates had minimal effect on cost-effectiveness. Changes in the prevalence of non-advanced polyps had a modest impact, except when capsule prices were high. Conclusions: Overall, replacing colonoscopy with CCE slightly increases detection and health gains at the expense of higher costs. Cost-effectiveness largely depends on capsule price and adherence. Artificial intelligence-assisted CCE interpretation may further improve diagnostic and economic performance, potentially supporting adoption in large-scale CRC screening programs.

AO_SCPLOWBSTRACTC_SCPLOWAlcohol-associated liver disease (ALD) has been steadily increasing in the United States for many years, as attested by increases in ALD deaths and liver transplant demand. Direct measurement of ALD incidence is challenging as diagnosis often occurs late (or not at all). This study employs a demographically-aware backcalculation method, based on mortality data, to reconstruct latent, age-structured ALD risk and incidence trends in the US population from 2008 to 2022 and uses this information to forecast future ALD trends through 2030. We find that ALD incidence has risen steadily since 2008, with a sharp increase during the 2020 COVID-19 pandemic, and that the average age at onset has also increased over time, with demographic factors playing a substantial role. While our forecasts suggest a continuation of the pre-2020 growth in ALD incidence for most age and sex groups, we also predict marked increases among younger men, a generational shift toward older age cohorts, and substantial rises among older females. Most concerning, between 2022 and 2030, incidence is expected to double among younger men and older females and by 2030 the number of new male ALD cases is projected to be more than twice that of females for all age groups. Our results provide a clearer understanding of evolving ALD trends, highlighting the role of demographic and birth cohort effects. We underscore the urgent need for targeted interventions, particularly among younger men, to reduce ALD-related behaviors and future burden.

Background: ADTKD-MUC1 is one of the major entities of ADTKD caused by frameshift variants in the MUC1 VNTR that standard short-read sequencing fails to detect. Existing 59dupC-targeted probe-extension assays do not allow for broad screening and cannot detect atypical non-dupC variants. Recently, VNtyper, a Kestrel-based genotyping pipeline with optional code-adVNTR cross-validation for MUC1 VNTR genotyping from short-read sequencing data allowed to circumvent this diagnostic limitation, but needed further development for easy access and rapid sample processing. Methods: We developed VNtyper 2, by refactoring VNtyper into a modular, production-grade tool with a companion web platform, VNtyper-Online (https://vntyper.org), for freely available browser-based analysis with short turnaround time and without local bioinformatics infrastructure. We validated VNtyper 2 on 400 simulated samples generated with MucOneUp and 142 clinical exomes with independently confirmed genotypes. Results: In simulation, VNtyper 2 detected the canonical 59dupC variant with 96% sensitivity and 100% specificity. Reference-standard validation on 142 samples yielded 90.6% sensitivity and 98.2% specificity overall, with cohort-dependent performance across the Twist Exome v2 French-German cohort (98% sensitivity, 87.5% specificity) and the KAPA HyperExome V2 (Roche) Czech-US cohort (79.4% sensitivity, 100% specificity). Screening of 3582 exomes and targeted panels from international CKD referral programmes identified 51 positive individuals, including 9 with atypical non-dupC frameshift variants that would have been missed by 59dupC-targeted probe-extension assays. In unselected CKD cohorts, a descriptive random-effects summary estimated a detection rate of 1.4% (95% CI 0.6 to 3.1%). Conclusions: VNtyper 2 and VNtyper-Online are open-source tools for MUC1 VNTR genotyping from short-read data and can support locally validated workflows when VNTR coverage is adequate. By improving accessibility and turnaround time, these tools democratize MUC1 diagnostics at global scale. For its integration into routine diagnostics, we propose an expert-informed two-pathway workflow developed through European ADTKD-Net consortium consensus.