CHEST

Tobacco smoke shapes mutations, selection and clonal expansion in lung epithelial cells. Smoking cessation leads to divergent epidemiology in the two most common lung cancers: squamous cell carcinoma risk declines sharply, while adenocarcinoma risk is preserved. To investigate this discrepancy, we analysed 806 genomes of alveolar type II (AT2) cells and found persistently elevated mutation burdens after cessation. In contrast, in the proximal airway, rare basal stem cells with near- normal mutation burden expand after cessation, protecting against squamous cell carcinoma. Targeted single-molecule DNA sequencing of AT2 cells revealed positive selection for TP53 and cell cycle and MAPK genes, supporting continued cancer risk. A multistage carcinogenesis model emphasised the importance of a small population of hypermutated cells in the alveoli and reproduced the divergent epidemiological trajectories following cessation due to distinct regenerative dynamics. Our findings suggest that differences in mutational burden and clonal regeneration explain post-cessation trends in lung cancer subtypes.

Background: Biologically distinct asthma subgroups may obscure genetic effects when analyzed as a single phenotype. We examined whether asthma susceptibility signals are shared, heterogeneous, or stratum-specific across ancestry, obesity status, and sex. Methods: We performed ancestry-specific GWAS meta-analyses in African ancestry participants (9,965 asthma cases; 37,391 controls) and European ancestry participants (6,074 cases; 116,255 controls), followed by obesity- and sex-stratified analyses. Analyses used imputed dosages and fixed-effect meta-analysis within ancestry. Results: Stratification detected asthma association signals that were less apparent in the combined phenotype. Shared cross-ancestry loci implicated epithelial antiviral susceptibility and immune regulation, represented by signals near CDHR3 and FOXO1. An ancestry-heterogeneous signal at the 17q21 locus, harboring ORMDL3/GSDMB, supported population-dependent effects at an epithelial inflammatory locus. Obesity stratification mapped the genome-wide significant burden to asthma without obesity. Sex stratification detected genome-wide significant signals in AFR females with asthma and obesity and in both sex strata with asthma without obesity, with the strongest signal burden in EU females without obesity. Conclusions: Asthma genetic architecture differed by ancestry, obesity status, and sex. Stratified analyses identified group-specific susceptibility related to epithelial and immune regulation, airway inflammation, remodeling, and neural signaling, supporting precision approaches to asthma.

Background and Aims: Residual pulmonary vascular obstruction (RPVO) defined as the persistence of thrombotic material within the pulmonary arteries several months after an acute pulmonary embolism (PE) is associated with an increased risk of severe complications, including recurrent events and chronic pulmonary hypertension. However, the genomic architecture underlying RPVO in unprovoked PE remains poorly understood, and this study aims to address this gap. Method: By leveraging genetic and imaging RPVO data from three independent cohorts totaling 586 unprovoked PE patients, we conducted a meta-analysis of genome wide association study (GWAS) of RPVO using a dedicated statistical method to handle the semi-continuous distribution of RPVO. The meta-GWAS was complemented by haplotype association analyses and transcriptome wide association studies as well as Mendelian Randomization (MR) approaches based on plasma metabolites and proteins. Results: Through meta-GWAS, we identified one locus, OSTN, associated with RPVO (lead variant rs59109356 associated with a ~2-fold increase of RPVO, p=3.92x10-8). A second locus, CCN4, previously reported to associate with pulmonary fibrosis, was also identified, with evidence of association approaching genome-wide significance (p=6.7x10-8). We also identified a common haplotype spanning over AHSG/HRG/KNG1 associated with a ~3-fold increase of RPVO (p=2.96x10-8). Using plasma protein-based MR, we demonstrated that one unit increase in genetically determined plasma levels of IL-1 R AcP encoding IL1RAP was associated with a 28% (p=1.32x10-6) reduction in RPVO. We also observed statistical evidence that the CCN4 (p=0.06) and IL1RAP (p=0.02) loci associate with the risk of PE recurrence in a sample of 1,617 unprovoked PE patients. Conclusions: By identifying novel molecular determinants of RPVO that map to loci involved in inflammatory pathways and vascular remodeling, our study provides evidence that inflammation is the predominant, and likely the key mechanism underlying RPVO, whereas impaired fibrinolysis appears to play a more limited role.

Introduction: Standardizing fluid and vasopressor resuscitation in sep- tic shock is challenging due to patient heterogeneity. We trained a causal model to identify optimal dosing during the first six hours of intensive care unit (ICU) admission. Methods: Graphical causal inference models were applied to estimate het- erogeneous treatment effects. Grounding models in expert clinical knowl- edge minimizes bias from spurious correlations to generate robust, contextu- ally meaningful recommendations. Our model was trained on 1,702 MIMIC database admissions and externally validated on 1,434 eICU admissions. Pri- mary outcomes were in-hospital survival and 24-hour clinical improvement (SOFA score reduction of two points or more). Findings: The cohort comprised 3,136 participants (median age 65 years [IQR 53-75]; 42.7% female). Deviation from vasopressor recommendations was associated with increased in-hospital mortality (median OR 5.61, 95% CI 5.44-5.78) and failed clinical improvement (median OR 6.33, 95% CI 6.17-6.50). Fluid deviations yielded corresponding median ORs of 1.02 (95% CI 1.02-1.02) and 1.14 (95% CI 1.14-1.14). In external validation, the model achieved a median survival AUROC of 0.73 (95% CI 0.69-0.77) and clini- cal improvement AUROC of 0.69 (95% CI 0.66-0.72), matching predictive baselines. Treatment effects were heterogeneous: optimal fluids increased survival by up to 4% in low-severity subgroups, while vasopressor responses varied from 0.5% to 17% across acute severity levels. Sensitivity analyses across 36 scenarios confirmed primary associations in 33 cases (91.7%). Interpretation: Recommendations from expert-grounded causal models correlate with improved septic shock outcomes in external validation, cap- turing significant heterogeneity in patient response.

Background: Automatic tube compensation (ATC) was designed to compensate for the additional resistive load imposed by the endotracheal tube during spontaneous breathing. In ATC mode, the ventilator adds or subtracts the flow-dependent pressure drop across the tube during both inspiration and expiration so that tracheal pressure remains close to PEEP. Early prototype ventilators achieved true tracheal-pressure control and showed physiological and clinical benefits, but clinical studies with commercial systems have failed to confirm these earlier findings. A 2003 bench study found that commercial ventilators provided, at best, only partial tube compensation, unlikely to result in meaningful clinical benefit. We therefore tested whether this limitation has been remedied in contemporary ICU ventilators. Methods: We performed a bench comparison of five commercial ICU ventilators and an ATC prototype ventilator designed to accurately compensate for the flow-dependent resistance over a wide range of flow rates. An active lung simulator generated spontaneous breathing patterns with weak, moderate, and strong inspiratory efforts at different PEEP levels. We tested each breathing pattern through endotracheal tubes with inner diameters of 7 and 8 mm, and measured airway pressure, tracheal pressure, and flow during CPAP with and without ATC. Breathing through the tube against open atmosphere served as a zero-PEEP/T-piece reference. Results: In CPAP mode, the commercial ventilators showed flow-dependent airway-pressure deviations, amounting to a substantial added resistance of 1.5 - 6.5 mbar/(L/s), whereas the ATC prototype ventilator imposed an added resistance of only 0.6 mbar/(L/s). In ATC mode, the commercial ventilators reduced the resistive load by no more than by 25%, and large tracheal-pressure deviations remained, especially at higher inspiratory effort and during expiration. In some cases, the residual load during ATC was even greater than the load during unsupported breathing through the tube. By contrast, the ATC prototype ventilator maintained tracheal pressure close to PEEP throughout the breathing cycle and eliminated on average 79% of the tube-related resistive load. Conclusions: In the commercial ventilators evaluated in this study, the defining physiological objective of ATC was only partially achieved. Therefore, clinical benefits previously reported for tracheal-pressure control support should be interpreted with caution when applied to commercial ATC implementations, unless effective tube compensation has been demonstrated under relevant conditions. These findings suggest that more advanced control approaches, such as those implemented in the ATC prototype ventilator, may be required to achieve consistent and physiologically accurate tube compensation.

Background Literature investigating mediation of the association between child abuse and dementia has largely considered composite adverse childhood experience scores rather than individual adverse experiences, despite evidence that different experiences have different impacts on dementia risk. Additionally, prior studies consider mediators in isolation, despite known associations between mediators which may impact indirect pathways from child abuse to dementia. Objectives To investigate whether potentially modifiable health and lifestyle factors mediate the association between child abuse and dementia. Methods We used data from the English Longitudinal Study of Ageing to investigate associations between child abuse and dementia (N:5,448). Indirect pathways through four mediator categories (education, health behaviours, mental health and cardiovascular health) were examined. We used regression modelling to estimate associations between child abuse, mediators and dementia, and causal mediation analysis using the g-formula to estimate the joint indirect effect through the mediators. Results Individuals who experienced child abuse had, on average, an 80% higher hazard of dementia, compared to those who did not (RTE HR:1.80, 95% CI:1.21-2.39). Mental health mediators showed strong associations with both child abuse and dementia. Evidence for other mediators was weaker. Education, health behaviours, mental health and cardiovascular health mediated approximately 18% of the association. Sensitivity analysis revealed that almost all this mediation occurred through mental health. Conclusions Child abuse was associated with higher risk of dementia. Joint mediation analysis suggested that education, health behaviours, cardiovascular health, and mental health accounted for a relatively small proportion of the observed association, with most mediation occurring through mental health. Future research must focus on other potential pathways from child abuse to dementia, including biological and social mechanisms.

Background: Anti-synthetase syndrome (ASyS) is a subgroup of idiopathic inflammatory myopathies that is increasingly recognized as a distinct entity with features of myositis, interstitial lung disease, inflammatory arthritis, and Raynaud phenomenon. Co-reactivity with anti-Ro-52, an antibody directed against the Ro-52 E3 ubiquitin ligase, has been shown to be associated with progressive interstitial lung disease within this patient population. However, less is known regarding the association of anti-Ro-52 positivity with cardiovascular outcomes. Methods: A sub-cohort of patients with anti-synthetase antibodies at a large single institution center was retrospectively analyzed to define presence of anti-Ro-52 positivity (defined as anti-Ro-52 titer greater than or equal to 11 utilizing the line immunoblot platform, Euroline Autoimmune Inflammatory Myopathies, EuroImmun Diagnostics, Lubeck, Germany). Patients who did not meet 2017 ACR/EULAR classification criteria for idiopathic inflammatory myopathies were excluded from the final analysis. Cardiovascular outcomes ascertained via retrospective chart review included atrial fibrillation, left bundle branch block, right bundle branch block, pulmonary hypertension (confirmed via right heart catheterization), heart failure with reduced ejection fraction (HFrEF, defined as ejection fraction less than or equal to 40 percent), acute coronary syndrome (based on clinical diagnosis and angiography if available), and myocarditis (based on clinician diagnosis and either cardiac MRI or troponin elevation). When a pre-specified cardiac outcome was identified, the date of onset was recorded. Differences in proportions were analyzed via Chi-squared and Fishers exact tests, and time-to-event analyses were performed via Cox Proportional Hazards Models, incorporating a false discovery rate correction for multiple outcomes. All analyses were performed using SAS v9.4. Results: 88 patients were included in the final analysis, of whom 69 (78.4 percent) were categorized as anti-Ro-52 positive. Patients with anti-Ro-52 positivity had a higher maximum recorded serum creatine kinase (median 1297 vs 395 units per liter, p = 0.042). No significant associations between anti-Ro-52 positivity and the pre-defined cardiovascular outcomes were found over median follow up time of 12.5 years. Conclusions: In a large, single-center cohort of patients with ASyS, anti-Ro-52 positivity was not associated with an increased burden of negative cardiovascular outcomes, including the onset of pulmonary hypertension. Future studies may seek to further elucidate the mechanisms underlying the pleiotropic effects of anti-Ro-52 antibodies on the cardiopulmonary system.

Background: Explanations play a crucial role in helping clinicians understand how black-box machine learning models make predictions in clinical settings. Several different types of explanations have been developed, each corresponding to a unique approach for characterizing the relationships between model inputs and predictions. However, it remains unclear what types of explanations are the most valued by clinicians. Objective: To improve the utility of machine learning in clinical settings, we aimed to evaluate how different explanation methods are valued by clinicians across clinically important metrics, such as importance, trust, understanding, and how explanations affect clinicians' thinking about patients. Methods: We conducted a user study of 39 critical care and hospital medicine nurses and physicians to compare attribution, counterfactual, and rule-based explanations. We analyzed the impact of each type of explanation on clinicians' trust in and understanding of the predictions made by machine learning models, how well clinicians understood the explanation, and how the explanation affected what they thought were the most important features for determining patients' status. We also assessed clinicians' preferences for the representation of different types of explanations. Results: Clinicians consider explanations of clinical machine learning models important, with physicians perceiving explanations as more important after interacting with them than nurses. All explanation types affected clinicians across all measured dimensions, with attribution explanations having the most significant positive effects on all measured dimensions. Moreover, nearly half of clinicians preferred viewing multiple explanation types together. Conclusions: It is important to provide explanations for predictions made by machine-learning models in clinical settings. When implementing machine learning explanations in these settings, developers should prioritize attribution explanations while allowing for multiple types of explanations to be shown. Furthermore, the development of new explanation methods should be tailored towards specific clinical roles, as nurses and physicians may utilize explanations differently to support their respective workflows.

Importance: The transition from medical school to residency forms a national training network, yet its large-scale structure and implications for trainee outcomes remain poorly characterized. Objective: To evaluate the US residency match as a network and assess how institutional position relates to residency placement, educational debt, and specialty choice. Design: Cross-sectional analysis of publicly reported 2025 residency match outcomes. Setting: 107 US MD-granting medical schools and 301 residency institutions with available match data. Participants: 14,616 US MD students matching into residency in 2025 (convenience sample). Exposure: Institutional position within the residency match network, quantified using PageRank network centrality. The relative strength of each school's graduating class was defined as the median centrality of residency destinations across graduates (placement score). Main Outcomes and Measures: Residency placement outcomes, mean medical school debt at graduation, and specialty choice (primary care vs surgical specialties) in relation to institutional position within the residency match network. Network-derived measures were also compared with NIH funding, residency reputation, and student selectivity. Results: Among 14,616 US MD students matched across 107 medical schools and 301 residency institutions (approximately 73.5% of total US MD cohort), network-derived measures of institutional influence closely aligned with benchmarks of institutional standing such as NIH funding, residency reputation, and student selectivity (Spearman's Rho; = 0.72-0.86; all p < .001). Graduate outcomes varied systematically across institutions. Graduates of highly connected medical schools were more likely to match into highly connected residency programs (87.3% for top-quintile vs 41.0% for bottom-quintile schools). Schools with higher placement scores had graduates with lower educational debt, reduced entry into primary care, and increased entry into surgical or competitive specialties. Compared with bottom-decile schools, top-decile schools (stratified by placement score) had 37% lower mean graduate debt, 24% lower primary care entry, and 75% higher surgical specialty entry. Higher educational debt was not associated with entry into higher-compensated specialties. Conclusions and Relevance: The residency match network reflects a hierarchical structure of institutional standing. Graduates of higher- and lower-positioned medical schools experience systematically different residency placement outcomes. These findings provide a population-level, behavior-based perspective on institutional influence and its relationship to training pathways.

Background: Automatic sleep staging algorithms are increasingly applied in clinical and home-based recordings. However, their performance may degrade when transferred to new montages and clinical populations. This is particularly relevant in reduced-channel portable PSG and in disorders such as REM sleep behaviour disorder (RBD), where altered sleep architecture may challenge pretrained models. Objective: To evaluate and compare multiple open-source sleep staging algorithms on a minimal portable PSG setup in controls and patients with and without RBD, and to assess the impact of fine-tuning on clinic-ascertained data. Methods: Six open-source models were applied to 76 subjects recruited from three clinical sleep medicine sites. Performance was assessed using accuracy, F1 scores, and Cohen's kappa, both overall and per sleep stage. Each model was evaluated out-of-the-box and after fine-tuning on clinical data. Results: Out-of-the-box performance varied substantially across models (Cohen's kappa 0.21-0.54). Fine-tuning consistently improved agreement, with the best-performing model (GSSC) reaching Cohen's kappa = 0.58 indicating moderate to good agreement. Performance was highest in controls and lower in patient groups. N3 was the most reliably classified stage across models, whereas N1 remained consistently challenging. REM classification improved after fine-tuning in several architectures but remained model, and subgroup-dependent, particularly in RBD subjects. Conclusion: Fine-tuning substantially mitigates domain shift, updating model parameters to align with new data distributions, when applying automatic sleep staging algorithms to portable clinical recordings. Model architecture influences robustness, with feature-learning approaches demonstrating greater adaptability than fixed-feature models. Despite moderate agreement after adaptation, performance, especially for REM and N1 remains insufficient for fully automated diagnostic use in clinical populations.

High-dimensional Mendelian randomization (MR) screens can prioritize candidate dietary and immune pathways for insomnia, but their interpretation is constrained by multiple testing, cross-dataset instability, and limited correspondence between genetic constructs and measured population variables. We conducted an exploratory cross-design analysis that combined MR screening of 231 dietary traits and 731 immune phenotypes, targeted cross-release genetic follow-up in FinnGen R12 and R13, and population-based analyses in NHANES and CHARLS. The targeted R13 follow-up prioritised an omelette-related dietary signal (OR 0.773, 95% CI 0.651-0.917; within-layer FDR q=0.00783), a mixed-fruit signal (OR 1.285, 95% CI 1.102-1.498; within-layer FDR q=0.00683), and CD33- and HLA-DR-related immune-cell traits. In NHANES, mapped omelet/scrambled-egg intake was associated with lower odds of sleep problems in 2017-March 2020 (OR 0.746, 95% CI 0.600-0.927; FDR=0.033) and doctor-reported sleep disorder in 2005-2006 (any intake: OR 0.313, 95% CI 0.157-0.624; FDR=0.008; per 50 g: OR 0.721, 95% CI 0.569-0.914; FDR=0.019). Mixed-fruit proxies were not directionally concordant. Higher C-reactive protein (CRP) was associated with sleep problems in NHANES (OR 1.192, 95% CI 1.085-1.309; FDR=0.001) and frequent restless sleep in CHARLS (OR 1.097, 95% CI 1.049-1.147; FDR<0.001). These findings provide exploratory genetic prioritization and population-based association evidence for selected dietary constructs and systemic inflammatory proxies. They do not establish a causal diet-immune-insomnia mechanism, confirm flow-cytometry immune-cell phenotypes, or support dietary intervention recommendations.

Early screening for chronic obstructive pulmonary disease (COPD) is critical due to the progressive and debilitating nature. Preliminary diagnosis typically relies on pulmonary function tests, particularly the ratio of forced expiratory volume in one second (FEV1) to forced vital capacity (FVC). However, conventional spirometers are often bulky and non-portable, while most existing portable devices can only measure a single parameter, such as FVC, thereby limiting comprehensive assessment. To address these limitations, an integrated wearable system was proposed for both monitoring and rehabilitation training. This system is based on the innovative thermoelectric-airflow inversion (TAI) model, which quantitatively correlates convective heat transfer with thermoelectric voltage to reconstruct airflow velocity and volume in real time. The developed thermoelectric smart mask enables simultaneous measurement of two key obstructive indicators (FVC and FEV1) and automatically evaluates COPD risk via the FEV1/FVC ratio, alerting users to seek medical consultation when abnormalities are detected. In terms of performance, the device demonstrates a measurement accuracy of 99.10% and a coefficient of determination (R2) of 0.9947 compared to a commercial spirometer. Furthermore, the incorporated virtual reality assisted rehabilitation system was developed, yielding an average FVC improvement of 5.87% across three participants after one week of interactive training. Enabled by the TAI framework and a closed-loop multi-parameter design, this platform provides an intelligent, quantitative, and continuous solution for respiratory healthcare and rehabilitation.

Background. Diagnosis and risk stratification in rare hematologic malignancies such as myeloproliferative neoplasms (MPNs) - polycythemia vera (PV), essential thrombocythemia (ET), and myelofibrosis (MF) - require expert review of longitudinal, heterogeneous clinical records. This process is cognitively demanding, inconsistently applied, and difficult to scale beyond tertiary centers. No automated phenotyping workflow currently exists for hematologic malignancies. Methods. A HIPAA-compliant large language model (LLM) framework for phenotyping MPN was developed to integrate (i) rule-based retrieval of bone marrow biopsy reports, clinical notes, and structured laboratory results from the electronic health record (EHR); (ii) zero-shot extraction of diagnostic and prognostic variables from unstructured text using GPT-4 Turbo; (iii) a clinician-informed source-prioritization algorithm to reconcile conflicting multi-source data; (iv) WHO/ICC-criteria-based diagnostic classification; and (v) NCCN-based risk stratification using the conventional risk model for PV, IPSET-thrombosis for ET, and DIPSS, DIPSS-plus, and MIPSS70/MIPSS70+ v2 for MF. Patients were identified via MPN-related ICD-9/10 codes; cases met 2017 WHO criteria or had a hematologist-documented diagnosis, and controls did not. The cohort was split into a prompt-development set (n = 60) and a held-out test set (n = 450; 75 cases and 75 controls per disease). Ground truth was established by independent dual-clinician chart review with consensus adjudication. LLM performance was evaluated against the ground truth: variable-level extraction using accuracy, F1 score, and Cohen's kappa; patient-level diagnostic classification using sensitivity, specificity, and Cohen's kappa; and prognostic risk stratification (among confirmed cases) using accuracy, weighted F1 score, and quadratic-weighted Cohen's kappa. Wilson 95% confidence intervals (CIs) were used for proportions and bootstrap 95% CIs with 500 resamples for F1 scores. Results. The held-out test set included 450 patients (PV: 150; ET: 150; MF: 150) with pathology reports and structured laboratory results, and 172 patients (PV: 52; ET: 55; MF: 65) with clinical notes. From pathology reports, overall variable extraction accuracy and F1 score were 99% (95% CI, 98-100) and 1.00 (0.99-1.00) for PV, 100% (99-100) and 0.99 (0.96-1.00) for ET, and 100% (99-100) and 0.99 (0.97-1.00) for MF. From clinical notes, overall accuracy and F1 score were 96% (91-100) and 0.94 (0.85-1.00) for PV, 100% (100-100) and 1.00 (1.00-1.00) for ET, and 100% (99-100) and 0.98 (0.95-1.00) for MF. Diagnostic sensitivity was 100% (95% CI, 95.1-100.0) for PV, ET, and MF; specificity was 98.7% (92.8-99.8) for PV and 100% (95.1-100.0) for both ET and MF, with Cohen's kappa of 0.99 for PV and 1.00 for ET and MF. Risk stratification accuracy was 100% with weighted F1 score of 1.00 and quadratic-weighted Cohen's kappa of 1.00 across all three diseases. A pre-specified source-ablation analysis showed that pathology reports alone were sufficient for diagnosis (sensitivity 98.7% for PV, 100% for ET, 96.0% for MF; specificity 100% across all three subtypes) but inadequate for prognostication (accuracy 69.3% for PV, 93.3% for ET, 77.3% for MF). Adding clinical notes to pathology reports recovered full prognostic accuracy of 100% across all three diseases. Conclusions. This first-in-class automated framework achieved expert-level performance for MPN diagnosis and risk stratification from real-world EHR data, establishing a foundation for scalable, standardized phenotyping in rare hematologic malignancies. Prospective, multi-site validation is warranted before clinical deployment.

Peripheral whole-blood gene expression profiling offers a minimally invasive route to lung cancer detection, but high-dimensional transcriptomic data are prone to optimistic bias when preprocessing and model selection are not properly separated from performance evaluation. We applied L1-penalised (LASSO) logistic regression to 303 peripheral whole-blood microarray profiles (123 lung cancer cases and 180 healthy controls; Gene Expression Omnibus accession GSE252168; Illumina HumanHT-12 v4) within a leakage-free nested cross-validation framework (5 outer and 3 inner folds), in which all data-dependent steps (imputation, univariate feature screening by ANOVA F-test with k = 500, and standardisation) were confined strictly to training partitions. Statistical significance was assessed by permutation testing (B = 100), and feature selection stability was quantified across outer folds. LASSO was compared with ridge logistic regression, linear support vector machines, and random forest under the same framework. The LASSO model identified a sparse 29-probe signature with a pooled out-of-fold area under the ROC curve (AUC) of 0.990 (nested estimate 0.989 +/- 0.015), accuracy 97.4%, sensitivity 94.3%, and specificity 99.4% at a 0.50 threshold; permutation testing confirmed significance (p = 0.0099). Six probes, including CDC42, U2AF1, and RPS15A, were selected in all five outer folds, forming a stable core, and all classifiers exceeded AUC 0.987, indicating a strong, algorithm-independent signal. A leakage-free nested cross-validation framework enables unbiased performance estimation and reproducible feature selection in blood-based lung cancer classification. The 29-probe panel is an internally validated candidate requiring prospective, multicentre external validation before clinical use.

Deep learning reads 12 lead electrocardiograms at close to expert level on public benchmarks, yet most reports give one accuracy figure for the whole test set and stop there. We trained three architectures that are standard in this field, a 1D ResNet, a convolutional network with a bidirectional LSTM, and a convolutional network with a bidirectional LSTM followed by a transformer encoder, on the PTB-XL dataset to classify the five diagnostic superclasses, and then looked at how each one performed across sex and age. On the held out fold all three reached a macro AUC near 0.92, in line with the strongest published results on this benchmark, and the simplest model, the 1D ResNet, was marginally the best at 0.9241. The averages hid a steady pattern. Every model scored lower for female patients than for male patients, and every model scored lowest for patients aged 80 and over, where the 1D ResNet fell to 0.8878 and the transformer to 0.8693. Adding complexity did not close either gap and slightly widened the gap by age. Overall accuracy on PTB-XL is close to solved for these model families, but the benefit is not shared evenly, and a single headline number hides the patients a model serves worst. We release the full stratified evaluation to support fairness aware reporting.

Importance: Adverse drug events in older adults are a substantial public health burden, yet spontaneous reporting systems detect them poorly owing to underreporting and the lack of a defined population. These limitations are of particular concern for older adults, who are underrepresented in pre-approval trials yet at elevated risk owing to polypharmacy, multimorbidity, and age-related changes in drug metabolism. Objective: To develop and apply an active, claims-based pharmacovigilance framework using sequential target trial emulation to detect adverse drug event signals in older adults, with atorvastatin as the initial application. Methods: Using Medicare fee-for-service claims (2017-2019), we studied statin-naive beneficiaries aged 65 years or older following myocardial or cerebral infarction. We emulated up to 14 daily sequential trials from the discharge date, classifying patients as initiating atorvastatin (A1), initiating a different medication (A2), or no new medication (A0); the primary contrast was A1 versus A2. For each trial, incident outcomes were ascertained and classified into 552 outcomes based on the Clinical Classifications Software Refined categories. Per-protocol effects were estimated over a 6-month follow-up period using Fine-Gray regression models weighted by the inverse probability of treatment and censoring, treating death as a competing risk, with the false discovery rate controlled via the Benjamini-Hochberg procedure. A signal was declared when the q-value was 0.10 or lower and the subdistribution hazard ratio (sHR) was 1.20 or greater in any prespecified analytic stratum (sensitivity analyses used thresholds of q 0.20 or lower and sHR 1.20 or greater). Results: Of 70,130 eligible patients, 39,948 initiated atorvastatin (A1) and 19,182 initiated another new medication (A2); after weighting, baseline characteristics were closely balanced. After excluding outcomes with sparse cell counts, 295 outcomes were analyzed; five met the primary signal detection criteria: valve disorders (sHR 1.71, 1.20 to 2.43); sprains and strains (sHR 1.79, 1.26 to 2.54); general sensation/perception symptoms (sHR 1.23, 95 percent CI 1.11 to 1.36); abnormal findings without diagnosis (sHR 1.55, 1.18 to 2.05); and prediabetes (sHR 1.71, 1.24 to 2.36). In the sensitivity analysis, we additionally detected posthemorrhagic anemia, hemorrhagic stroke, varicose veins, and other circulatory and skin conditions. Conclusions: An active, claims-based framework using sequential target trial emulation detected both expected and previously unrecognized adverse drug event signals following atorvastatin initiation in older adults, offering a systematic alternative to passive surveillance that can be extended to other commonly prescribed medications.

Background: The UK Government aims to reduce smoking rates by implementing new, and investing in existing, tobacco control strategies including increased funding for Stop Smoking Services (SSS) in England. This study examined whether the additional funding starting in April 2024 was associated with a detectable increase in quit attempts supported by SSS and whether it was cost-effective. Methods: We used data from the Smoking Toolkit Study, a repeat cross-sectional survey conducted in 2021 to 2025. Adults aged [&ge;]18 years who smoked cigarettes and had made a quit attempt in the past year were included (weighted n=5,076). The outcome was monthly prevalence of past-year quit attempts supported by SSS. We fitted general additive models with a step change in April 2024 to represent the start of the increased funding. We adjusted for tobacco tax increases, the Swap-to-Stop scheme, age, gender, and a measure of socioeconomic position. In an unplanned analysis, we extended the time series back to 2006. For the cost-effectiveness, we estimated incremental cost-effectiveness ratios for the total population and age groups, accounting for future lifetime cessation. Results: In the primary model, the April 2024 step change was not statistically significant (adjusted odds ratio: 1.13; 95% CI: 0.52, 2.49). The cost-effectiveness analysis ranged from cost-effective to extremely ineffective (incremental cost-effectiveness ratio (ICER): GBP 104,126, 95% CI: 939,398 to 8,293). When using the extended time series, the adjusted odds ratio for the step change was 2.70 (95% CI: 2.03, 3.60) and the intervention was cost-effective (ICER: GBP 13,857; 21,393 to 9,620). Conclusions: Compared with the long-term trend, increased funding to SSS in England in 2024 appeared to lead to an increase in quit attempts supported by SSS at the population level. This result is somewhat uncertain because our primary pre-planned analyses assessing the impact relative to a more recent trend were insensitive.

Abdominal aortic aneurysm (AAA) patients in the ICU represent a heterogeneous, high-risk population with mortality risk evolving across distinct clinical phases. Existing prognostic tools rely largely on Cox proportional hazards (Cox PH) nomograms with narrow predictor sets and single time horizons, leaving the value of modern machine learning, extended features, and external generalizability uncharacterized. We extracted an ICD-coded AAA ICU cohort from MIMIC-IV v2.2 (858 patients with complete six-predictor admission data: age, BUN, sepsis, antihypertensive use, anion gap, mean SpO2) using a 24-hour admission window. An extended feature set added hemodynamic, laboratory, and comorbidity variables, with feature selection via LASSO and SVM-RFE intersection. Six models (Cox PH, logistic regression, random forest, gradient boosting, XGBoost, MLP) were trained on a 70% split and evaluated at 7-, 14-, and 28-day horizons using ROC-AUC, C-index, Brier score, calibration, and SHAP. External validation used a harmonized eICU-CRD cohort. In-hospital mortality was [~]11.8%. On the six-predictor set, logistic regression led at 7 days (AUC 0.866) and 14 days (AUC 0.872), with XGBoost competitive. Extended features yielded modest gains; random forest achieved the best 28-day AUC (0.892). The MLP consistently underperformed. Discrimination declined monotonically with longer horizons. External validation showed expected attenuation (best 7-day AUC 0.771). SHAP consistently identified anion gap, BUN, and age as top contributors. We conclude that regularized linear models excel under data scarcity, while tree ensembles gain advantage as features and horizons expand. External results motivate local recalibration before deployment.

Introduction The Tobacco and Vapes Act 2026 provides the UK government powers to ban vaping in public places. Proposals include extending existing indoor smoke-free legislation to also being vape-free and making public children's playgrounds and outdoor areas of education settings vape-free. We examined adults' and adolescents' views on vape-free places. Methods Two UK-wide cross-sectional online surveys were conducted in 2026, one with adult (18+) current and former nicotine users (n=2,851), and one with adolescents aged 11-17 years (n=2,123). We measured (1) perceived acceptability of vaping in public places, (2) views on whether vapes should/should not be allowed in public places, and (3) perceived likelihood of public compliance with vaping bans. Results Adults considered it unacceptable to vape on public transport (85.9%), on school grounds (outdoors) (83.6%), outside hospital entrances (59.2%), and inside pubs (57.8%) and nightclubs (52.5%). A minority considered it unacceptable to vape at open air playparks (40.6%). Most adolescents viewed vaping as unacceptable in all locations. Perceived acceptability of vaping in each place was associated with current vaping and/or smoking. Adults and adolescents believed vaping should not be allowed on public transport (88.9%; 93.8%) or outdoors on school grounds (85.8%; 93.5%). Adults and adolescents perceived likely compliance on public transport, school grounds and in pubs, but not in nightclubs, outside hospital entrances and at open air playparks. Conclusion The findings indicate public support for some vape-free places among adults and adolescents, particularly on public transport and school grounds, and less so for a ban at open air playparks.

Cystic fibrosis (CF) is characterized by defective CFTR-mediated chloride transport, resulting in elevated sweat chloride concentrations. As people with CF (PwCF) now live longer due to highly effective CFTR modulators, exercise has become integral to maintaining health, yet it introduces additional physiological demands on salt and fluid balance. In this study, we used a wearable microfluidic biosensor (CF Patch) to quantify sweat rate and chloride loss during exercise performed both in the supervised laboratory and remote free-living in PwCF and healthy volunteers (HV). Participants completed exercise sessions under both conditions, with continuous heart rate monitoring and sweat collection with real-time measurement of sweat characteristics. Sweat volume and chloride concentration were assessed by colorimetric image analysis, enabling estimation of total fluid and chloride loss at the end of each exercise session. PwCF exercised for a longer duration at a lower average heart rate during remote exercise compared to laboratory exercise though exercise volume (average heart rate x duration) was greater during remote exercise. There was a positive association between exercise volume and both fluid and chloride loss for both PwCF and HV. PwCF exhibited greater chloride loss for a given exercise volume compared to HV, though fluid loss was similar. Further, compared to HV, PwCF demonstrated significantly greater intra- and interindividual variability in sweat chloride loss across the remote exercise sessions. Collectively, these findings provide evidence for the feasibility and physiological validity of remote exercise assessment and establish the feasibility and physiological validity of wearable sweat sensing for remote monitoring of fluid and electrolyte dynamics during real-world exercise. In addition, the variability of chloride loss in response to exercise suggests utility of the CF Patch in providing personalized fluid and salt repletion data for PwCF and advances the translational potential of digital sweat diagnostics for personalized CF care.