European Respiratory Journal

Background The European Association for the Study of the Liver (ESAL) - Steatotic Liver Disease (SLD) screening algorithm involves two steps; initial screening with FIB-4 followed by referral for vibration-controlled transient elastography (VCTE) in patients likely to have significant fibrosis (SF). However, VCTE is not widely available in resource-limited settings. Aim To optimise the EASL SLD screening algorithm for resource-poor settings using machine learning (ML). Methods We analysed data from 964 adults aged [≥]35 years who underwent VCTE at a tertiary referral centre in Sri Lanka between November 2024 and 2025. Multiple ML models using different methods and variable combinations were trained on 80% of the dataset and tested on the remaining 20%. Best models were selected based on performance and externally validated using data from 430 patients who underwent VCTE before November 2024. Model performance was compared with the FIB-4 using confusion matrices. Results A Random Forest model incorporating age, AST, ALT, and platelet count separately, rather than using FIB-4, outperformed. The all-variable ML model showed the best predictive performance for SF, with accuracy of 77.2%, recall of 0.762, precision of 0.778, and AUC-ROC of 0.818. The variables used in the model, in descending order of feature importance, were AST, platelet count, BMI, ALT, age, diabetes mellitus, hypertension, dyslipidaemia, sex, family history, hypothyroidism, diabetes complication and smoking. External validation demonstrated 75.1% accuracy and an AUC of 0.779. When used as the first step of the SLD screening algorithm, the all-variable ML model identified 37 (17.1%) additional true positives and reduced false-negative diagnoses by 50% compared with FIB-4. Conclusions ML-based models were more effective than the FIB-4 score as the first-line screening tool for VCTE referral, substantially improving the identification of patients with significant fibrosis in this South Asian cohort.

ABSTRACT Background: Corticosteroids reduce mortality in severe COVID-19 requiring oxygen or invasive mechanical ventilation, yet emerging data suggest that SARS-CoV-2-associated acute lung injury is biologically heterogeneous and that treatment response may vary across molecularly defined disease states. Lung-derived molecular endotypes of severe COVID-19-associated acute lung injury have been described, but direct molecular profiling is not routinely available at the bedside. We evaluated whether a clinical predictor of previously defined lung molecular endotype identifies heterogeneity in corticosteroid treatment effect among mechanically ventilated patients with COVID-19. Methods: We utilized a single-center cohort of 5,000 patients with COVID-19 treated at the University of North Carolina Hospital between January 1, 2020, and December 31, 2022, to emulate a target trial assessing the effect of corticosteroid receipt on mortality, length of stay, and incident organ support. Confounding was addressed through inverse probability of treatment weighting (IPTW). Outcomes for severely ill patients requiring mechanical ventilation were compared to the RECOVERY trial results, with subsequent moderation analysis and stratified analysis by clinically predicted lung molecular endotype and vaccination status. The primary outcome was 28-day mortality. Secondary Outcomes were time to discharge alive and progression to additional organ support. Results: This emulated target trial showed a directionally favorable but non-statistically significant association between corticosteroid treatment and reduced 28-day mortality in patients requiring mechanical ventilation for SARS-CoV-2 infection. A clinical predictor of lung molecular endotype moderated the effect of corticosteroids on 28-day mortality (p-value for interaction 0.038) and identified distinct predicted endotype-specific treatment effect. Corticosteroid treatment was associated with lower 28-day mortality in the predicted Hyper-Inflammatory endotype (OR 0.62, 95% CI 0.39, 0.99) but not in the predicted Metabolic Dysregulation endotype (OR 1.15, 95% CI 0.82, 1.61). We did not detect significant effect modification by vaccination status (p-value for interaction 0.65), although inference was limited by the small, vaccinated subgroup (28-mortality OR 0.78, 95% CI 0.37, 1.65 in vaccinated vs 0.94, 95% CI 0.70, 1.26 in unvaccinated). Conclusions: In this target trial emulation of mechanically ventilated patients with severe COVID-19, corticosteroid treatment showed a directionally favorable but non-statistically significant association with reduced 28-day mortality in the overall cohort. However, a clinical predictor of lung molecular endotype identified significant heterogeneity in treatment effect, with benefit concentrated in the predicted Hyper-Inflammatory endotype and no apparent benefit in the predicted Metabolic Dysregulation endotype. These findings support prospective validation of clinically deployable endotype-guided corticosteroid treatment strategies in acute lung injury and ARDS.

Background: Alpha-1 antitrypsin deficiency (AATD) caused by the PI*ZZ mutation (Glu342Lys) results in hepatic accumulation of misfolded AAT-Z protein and reduced circulating AAT levels, leading to progressive liver disease and emphysema. Gene correction therapy represents a potentially curative approach by directly correcting the underlying genetic defect. We report the first case of successful hepatic gene correction with early histological and functional assessment. Methods/Case presentation: We report the case of a 66-year-old male patient with PI*ZZ AATD who underwent gene correction therapy within the YOLT-202 phase I/Ia clinical trial (clinical trial.gov ID NCT07193615). Ten weeks post treatment a liver biopsy was performed to re-evaluate pre-existing F2 liver fibrosis as measured by elastography before entering the study. Serum samples allowed functional assessment of the AAT-mediated elastase inhibition. Results: Liver biopsy did not show signs of hepatic inflammation and demonstrated 54% (Sanger) and 57% (Illumina) gene correction rate of the PI*ZZ variant on the DNA level with no bystander edits or off-target effects. Following a transient elevation of transaminases during the early post-treatment period, liver enzymes normalized. Monthly serum AAT measurements demonstrated biologically active and stable therapeutic levels throughout follow-up. Conclusions: This case demonstrates efficient and precise hepatic gene correction without concerning histological alterations and with substantial improvement of functional parameters, supporting the feasibility and safety of gene editing approaches for AATD.

Background World Symposium on Pulmonary Hypertension (WSPH) Group 2 pulmonary hypertension (PH) is a clinically integrated phenotype attributed to left heart disease, whereas pre- versus post-capillary classification is operationalized primarily by pulmonary capillary wedge pressure (PCWP). Although current recommendations emphasize contextual interpretation and provocative testing for intermediate PCWP values, the relationship between PCWP-based classification and underlying phenotype has not been systematically evaluated. We aim to quantify phenotype-hemodynamic discordance across the PCWP spectrum and evaluate a staged physiology-guided framework incorporating inhaled nitric oxide (iNO), ventricular geometry, and provocative testing. Methods We studied 1,032 participants from the NHLBI-sponsored PVDOMICS cohort with multidisciplinary adjudicated phenotypes integrating clinical, imaging, physiologic, and hemodynamic data. Stage-specific PCWP thresholds classified pre- versus post-capillary physiology at rest, during iNO, and during provocation (fluid challenge or invasive cardiopulmonary exercise testing [iCPET]). Echocardiographic right ventricular-to-left ventricular (RV/LV) ratio was evaluated as a marker of ventricular interdependence. Restricted cubic spline and staged concordance analyses defined certainty-based PCWP ranges and incremental diagnostic yield. Results Adjudicated Group 2 phenotype was present in 37.0% of participants. Resting PCWP demonstrated good discrimination (AUC 0.86), but substantial bidirectional phenotype-hemodynamic discordance persisted across intermediate PCWP ranges. At a resting PCWP of 12 mmHg, 25% of participants classified as pre-capillary had adjudicated Group 2 PH, whereas at 18 mmHg, 35% classified as post-capillary remained discordant non-Group 2. Concordance did not approach 90% until PCWP values were <9 mmHg or >24 mmHg. Dynamic testing incrementally improved concordance within these overlap zones. Nearly half of adjudicated Group 2 PH participants (46.5%) were not identified by resting PCWP alone; incorporation of iNO and provocative testing increased cumulative Group 2 identification by 63.4% and improved sensitivity from 79.9% to 83.7%. Model discrimination improved from an AUC of 0.863 to 0.908 (likelihood-ratio P<0.001). iNO increased PCWP in discordant Pre/G2 participants, unmasking latent left-sided limitation, while lowering PCWP in discordant Post/NonG2 participants, consistent with ventricular interdependence. RV/LV ratio [&ge;]0.94 reduced discordant Post/NonG2 classification by 70.5%, and incorporation of PCWP/cardiac output slope improved physiologic specificity during exercise. Conclusions Group 2 PH is a dynamic, load-dependent phenotype inadequately characterized by resting PCWP alone. Intermediate PCWP values represent continuous probabilities of bidirectional discordance rather than discrete diagnostic states. A staged physiology-guided approach integrating iNO, ventricular geometry, and provocative testing improves concordance between hemodynamic classification and clinically integrated phenotype assignment.

Background: Pathologic aldosteronism induces oxidative stress, tissue injury, and increases in hemoglobin. Conversely, aldosterone antagonist therapy decreases hemoglobin. Whether these effects are attributable to aldosterone-mediated changes in iron and oxygen metabolism is unknown. Methods: The plasma proteome of participants with overt primary aldosteronism (PA) (n=50) was compared with participants without overt PA (n=61). To isolate aldosterone-dependent effects, participants without overt PA underwent oral sodium suppression testing to quantify the magnitude of renin-independent aldosterone production, enabling monotonic dose-response analyses across the continuum of renin-independent aldosteronism (subclinical to overt PA). Differential abundance testing was performed using empirical Bayes linear modeling, followed by Reactome pathway enrichment analysis and covariate-adjusted sensitivity analyses. To validate clinical relevance, aldosterone dose-response trends with blood count parameters were examined in this cohort, and an independent population-based cohort of 5,713 people with hypertension. Results: 903 proteins in the peripheral circulation were differentially abundant in overt PA versus participants without PA. The most significantly increased protein in overt PA was CYBRD1, involved in iron reduction and absorption. Pathway enrichment identified 16 iron- and heme-related pathways, including erythropoietin signaling, heme biosynthesis and mitochondrial iron-sulfur cluster biogenesis, with increases in heme and erythroid proteins and decreases in mitochondrial iron-sulfur proteins. Linear aldosterone dose-dependent trend analyses across the PA continuum further supported this signature, identifying progressive increases in hemoglobin subunits (HBA1/HBB), heme-related proteins (HMBS, UROS, AMBP, HPX, GLO1) and erythrocyte oxygen handling enzymes (CA1/CA3), alongside progressive reductions in mitochondrial electron transport chain subunits (CYCS, ETFA). These proteomic changes corresponded with aldosterone dose-dependent increases in red blood cell count, hemoglobin, and hematocrit, in this cohort and another population-based cohort. Conclusion: The continuum of PA is characterized by a progressive shift away from mitochondrial oxidative phosphorylation and toward increased intestinal iron absorption, preferential iron transport over storage, and enhanced heme synthesis and recycling, possibly reflecting cellular pseudohypoxia and systemic adaptations to increase oxygen delivery. These findings provide a novel mechanistic basis for aldosterone-mediated tissue injury and the benefits of aldosterone-directed therapy.

Background Tuberculosis remains the leading infectious cause of death worldwide. In the WHO African region, declining incidence has coincided with antiretroviral therapy (ART) scale-up, though whether this reflects reduced progression to disease or reduced transmission is unclear. We evaluated how ART and symptom status influence within-household Mycobacterium tuberculosis complex (MTBC) transmission risk. Methods We conducted a case-contact household study in rural South Africa, enrolling index adults with bacteriologically-confirmed pulmonary tuberculosis. MTBC immunoreactivity was measured in all child household contacts (aged 2-14 years) as a proxy measure of within-household transmission. We assessed the influence of index person ART status and symptom status, and explored effect-measure modification of the association between index person HIV status and transmission risk by sex. Results Among 755 child contacts of 296 index persons, effective ART was not associated with within-household MTBC transmission risk (risk ratio [RR], 1.07; 95% CI, 0.66-1.74). Among PLHIV engaged in ART care, WHO TB four-symptom screen (WHO4SS) status was not associated with transmission risk (RR, 0.80; 95% CI, 0.43-1.47), although absence of reported cough reduced risk (RR, 0.61; 95% CI, 0.38-0.96). A pronounced interaction between sex and HIV status was observed: HIV-negative women had the highest within-household MTBC transmission risk (30.5% vs. 14.3% in women with HIV) whereas risks were similar between HIV-positive and HIV-negative men. Conclusions We found no evidence that effective ART or WHO4SS status influenced within-household MTBC transmission risk, though confidence intervals were wide. Absence of reported cough was associated with lower risk, and transmission risk was highest among child contacts of HIV-negative women. These findings suggest reported cough is a useful marker of transmission risk and that routine tuberculosis screening within ART care may reduce transmission from PLHIV; intensified efforts are nonetheless needed to achieve earlier tuberculosis detection in HIV-negative individuals.

High-frequency physiological monitoring in ICUs can identify impending deterioration hours before clinical recognition yet extracting reliable early-warning signals from noisy vital-sign streams remains challenging. We present SIgnose, an interpretable prediction framework for early detection of abnormal shock index (SI), built from routinely monitored vital signs using physiologic variability and nonlinear time-series features. SIgnose was developed on the eICU Collaborative Research Database and externally validated on the MIMIC-III adult database and a pediatric SafeICU cohort (AIIMS New Delhi), with additional prospective validation in the pediatric ICU. We benchmarked three representation strategies: (i) engineered physiologic variability and nonlinear time-series features, (ii) deep learning, and (iii) Llama-3.1-8B embeddings with low-rank adaptation. Physiologic variability features consistently demonstrated superior cross-cohort generalization. The final model used 3,970 features from five vital signs to predict abnormal SI up to 8 hours ahead, achieving AUROC 0.861 (95% CI 0.859-0.863) and AUPRC 0.927 (95% CI 0.925-0.929) on eICU. External validation yielded AUROC 0.870 (95% CI 0.863-0.876) and AUPRC 0.935 (95% CI 0.930-0.940) on MIMIC-III, and AUROC 0.875 (95% CI 0.863-0.888) and AUPRC 0.915 (95% CI 0.898-0.930) on SafeICU; prospective pediatric validation (n = 88) achieved AUROC 0.885 (95% CI 0.868-0.902) and AUPRC 0.911 (95% CI 0.882-0.936). SHAP interpretability analysis identified heart rate variability, respiratory trend dynamics, and multi-scale blood pressure variability as key early-warning signatures. These findings establish SIgnose as a reproducible, low-compute, early-warning framework and demonstrate that physiologic variability features provide robust, generalizable representations for early deterioration detection across adult and pediatric critical care.

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.

Background. Tobacco smoke exposure, quantified by serum cotinine, is associated with cardiovascular, metabolic, and sleep-related health risks. The relationship between biomarker-verified tobacco smoke exposure and objectively measured, free-living wrist-worn ambient light patterns has not been examined in a nationally representative U.S. adult sample. Methods. We analyzed NHANES 2011-2014 cross-sectional data from 6,937 adults aged >20 years with valid serum cotinine and wrist-worn Physical Activity Monitor (PAM) ambient light data. Seven light outcomes were modeled using survey-weighted linear regression with log2(cotinine+1) as the continuous exposure across four covariate adjustment levels. Benjamini-Hochberg false discovery rate (FDR) correction was applied across the 7 outcomes within each model. Results. In Model 2 (adjusted for age, sex, race/ethnicity, education, poverty-income ratio, BMI, and survey cycle; N = 6,350), higher serum cotinine was associated with significantly higher nighttime light (beta = +0.024, 95% CI: 0.010, 0.038; p-FDR = 0.014) and lower evening light (beta = -0.031, 95% CI: -0.055, -0.008; p-FDR = 0.042). In exploratory behavioral models without alcohol (Model 3a; N = 5,766), both nighttime and evening associations remained FDR-significant. After additional adjustment for alcohol, which substantially reduced the sample due to 37.6% missingness (Model 3b; N = 3,866), the nighttime association attenuated below the FDR threshold, while the evening association remained FDR-significant. Categorical analyses showed progressively higher nighttime light across cotinine groups, and a hypothesis-generating sex interaction was identified (p-interaction = 0.001). Conclusions. Higher serum cotinine concentrations were associated with higher nighttime and lower evening ambient light after sociodemographic adjustment. Attenuation after behavioral adjustment and the cross-sectional design preclude causal inference. Longitudinal studies with formal mediation analyses are needed to clarify the temporal ordering and mechanisms linking tobacco smoke exposure, smoking-related behaviors, and personal light-dark cycle patterns.

Objective: Clinical phenotyping methods that rely on clinical and informatics expertise can be time-intensive and costly. We tested both manual and highly automated approaches using electronic health record (EHR) data to identify an FDA Sentinel Initiative health outcome of interest, acute pancreatitis. Materials and Methods: We trained and evaluated machine learning algorithms using EHR data with two approaches: a custom approach that included manually curated features and trained on outcomes data validated with medical record review, and a highly automated approach that greatly simplifies and automates feature engineering and relies on low-cost silver-standard outcomes for model training. Results: Custom algorithms using manually curated structured claims data discriminated cases from non-cases with a high degree of accuracy (cv-AUC 0.89 [95%CI 0.84-0.94]); the inclusion of natural language processing (NLP)-derived covariates from clinical notes increased performance slightly (cv-AUC 0.91[95%CI 0.86-0.97]). The automated algorithm trained on the outcome count of diagnosis codes performed less well (AUC 0.80 [95% CI 0.75-0.85]) but improved using maximum lipase value as an outcome (AUC 0.88 [95% CI 0.84-0.92]). At a positive predictive value of 90%, the custom algorithm had a sensitivity of 92%, the automated algorithm trained on diagnosis code count had a sensitivity of 45%, and the automated algorithm trained on maximum lipase value had a sensitivity of 84%. However, a prediction rule derived by clinicians during chart review was nearly as accurate (maximum lipase value [&ge;] 3 times upper limit of normal; AUC 0.86, PPV 85%, sensitivity 92%). Discussion: Machine learning algorithms with manually curated structured data and NLP features trained on validated outcomes data successfully identified validated events. Use of an outcome in the automated model based on specific phenotype knowledge (maximum lipase value) allowed for performance similar to the custom model and with considerably less resources.

Background Continuous health monitoring enables early detection of diseases and improves therapeutic outcomes. Non-intrusive biosignal sensors, such as capacitive ECG (cECG), offer a practical solution for daily monitoring in private environments, such as smart homes and vehicles. However, artifacts reduce signal quality and compromise reliability. Methods Following a registered report protocol (Warnecke JM et al. Plos One. 2021; 16(7):e0254780), we record data of 44 subjects and develop an artifact index for cECG. We use three signal quality indices (SQIs): the correlation of QRS complexes (corSQI), the R-peak detection consistency (bSQI) and the absolute amplitude ratio (aSQI). Our index classifies overlapping 10s segments with a step-width of 2s into clean or artifact segments. We label a 2s interval as artifacts if all five overlapping segments indicate artifacts. We record cECGs using an armchair with integrated electrodes in a single-arm study involving 44 subjects performing two activities -- reading and watching television (TV); for 11 minutes each. We record a time-synchronized reference ECG with skin electrodes on the chest. To evaluate the artifact index, we compare it with manually generated ground truth. Moreover, we evaluate the clothing materials cotton, linen, jeans, and polyester in 5 subjects. Results Watching TV results in longer, continuously clean signal durations than reading. On average, 88.3% of the signal has a minimum continuous clean duration of 10s, versus 79.8% during reading. All clothing configurations achieve a clean signal duration exceeding 10s. Among the SQI metrics, bSQI performs best, achieving an accuracy of 90.7% and an F1 score of 79.9%. Combining the three SQI metrics in a voting approach improves accuracy to 92.0% and F1 score to 82.1%. Discussion Our artifact index automatically distinguishes clean from artifact cECG segments, promoting health monitoring in unsupervised real-world settings, earlier disease detection, and preventive health management. A limitation is the investigation of only two scenarios (reading and watching TV).

Pooled testing programs were introduced during the COVID-19 pandemic to expand surveillance capacity while preserving testing resources, but evidence on their epidemiological impact in schools under real-world conditions remains limited. We analyzed data from the pooled testing program implemented in public primary schools of the canton of Basel-Landschaft, Switzerland, during the Fall-Winter 2021 Delta wave. We used an agent-based transmission model informed by pooled and individual testing results, school characteristics, contact networks, and community incidence. The model was fitted to pooled positivity ratios in four clusters of administrative areas with similar epidemic trajectories. We compared pooled testing with alternative protocols in terms of school transmission, testing volume, and student-days lost. During the study period, pooled testing was offered to 21'187 students across 62 public primary schools, with high and stable participation across clusters (mean 71-79%). The fitted model reproduced observed pool positivity trends well. Compared with pooled testing, reactive class closure, reactive screening, and symptomatic testing were associated with higher in-school transmission, with excess ranging from 50% to 87%, 63% to 104%, and 72% to 133% across clusters. Weekly individual screening achieved similar reductions in transmission but required 15-25 times more tests. Relaxing class closure after depooling substantially reduced student-days lost without increasing transmission. Under real-world conditions, pooled testing provided an effective and resource-efficient strategy to reduce SARS-CoV-2 transmission in primary schools. Combining early detection of asymptomatic infections with low testing demands, pooled testing offers a scalable approach to school surveillance and control for pandemic response in educational settings.

Background. Capillary refill time is a resuscitation target in septic shock,1-4 but bedside measurement is examiner-dependent. An ICU monitor co-records a photoplethysmogram on the pulse oximeter and intermittent noninvasive blood pressure cuff cycles; if the probe and the cuff share a limb, each cycle is an unplanned vascular occlusion test on the distal microvascular bed. Standard practice places the two on opposite limbs. Objective. To measure how often, in MIMIC-IV-WDB v0.1.0, charted cuff cycles show the photoplethysmographic morphology expected of a same-limb cuff and probe, and to characterize the candidate capillary refill-like signal when that morphology is present. Methods. MIMIC-IV-WDB v0.1.05 was linked to the MIMIC-IV clinical database.6 A pre-registered rule-based detector identified candidate occlusion-reperfusion signatures on the 1-Hz perfusion-index envelope around each charted cuff timestamp. The primary endpoint was the proportion of cuff cycles suitable for analysis that were detector-positive at a 15-second reperfusion threshold, with 95% confidence intervals estimated by resampling patients at a fixed seed. A secondary analysis used a locally hosted multimodal language model (a Gemma-3 derivative on a non-device server) to adjudicate the same signature on perfusion-index plots; no MIMIC-IV-WDB content left the workstation. Results. Of 9,224 charted cuff cycles, 8,909 had a usable pulse-oximeter waveform, and 268 cycles in 15 patients (4.30% of the 6,236 cuff cycles suitable for analysis, 95% CI 2.60 to 6.03) met the primary 15-second threshold. The language model adjudicated the same cycles and called 1,367 of the 8,909 cycles with a usable waveform (15.34%) signature-present, roughly five times the detectors count. Because no laterality ground truth exists, agreement with a single blinded reader served as the comparator rather than accuracy. The two methods were about equally concordant with the reader: precision was 0.25 (95% CI 0.14 to 0.39) for the detector and 0.24 (95% CI 0.10 to 0.35) for the language model, although reweighting to the full population of cycles with a usable waveform lowered the language model to 0.030 (95% CI 0.009 to 0.053). These estimates are reference-limited: a blinded re-read of a 150-card subsample showed only moderate intra-rater reliability (Cohen {kappa} 0.46 to 0.59) with systematic undercalling on the first pass, and rescoring against the corrected re-read roughly doubled precision for both methods. Conclusions. Opportunistic extraction of capillary refill-like signals from archived ICU pulse oximetry is limited in two distinct ways. First, sensor geometry limits how often the signal is recordable: cuff cycles rarely show the morphology expected of a same-limb cuff and probe pair, consistent with opposite-limb placement, so the bottleneck is geometry rather than signal processing. Second, the modest reliability of morphology adjudication limits how well any single flagged cycle can be confirmed: against a blinded reader the detector is a usable screen but a noisy confirmer, the reference is itself only moderately reliable, and the language model is no more concordant despite flagging many more cycles. The minority of cycles in which the morphology appears contain a candidate signal that may merit prospective study under controlled placement with laterality recorded.

Background and Aims: MASLD has become the most prevalent chronic liver disease globally. Although MVPA and plasma fatty acids have been individually studied in relation to metabolic health, their independent and combined associations with MASLD incidence remain unclear. We aimed to investigate these associations. Methods: This study included 51,717 UK Biobank participants free of liver disease at baseline, with MVPA measured using wrist-worn accelerometers and plasma fatty acids quantified via NMR. Multivariable-adjusted Cox models and restricted cubic splines were used. Results: Over a median follow-up of 7.8 years, 472 incident cases were identified. In fully adjusted models, meeting recommended MVPA levels together with higher n-6 PUFA concentrations was associated with a 71% lower risk (HR 0.29, 95% CI 0.18-0.45). The MVPA-MASLD association was nonlinear, with risk reduction plateauing at approximately 189 minutes per week. Higher n-6 PUFA was associated with reduced risk, whereas n-3 PUFA showed no significant association. Conclusions: These findings suggest that behavioral and metabolic factors may jointly influence MASLD risk. Further studies in diverse populations are needed to confirm these associations.

Background. Capillary refill time, an examiner-dependent bedside test of distal microvascular perfusion, has become a resuscitation target in septic shock,1,2,3,4 motivating a continuous surrogate computed from the photoplethysmogram (PPG, the optical waveform the pulse oximeter on every ICU patient already records).5,6,7,8 Objective. We attempted three PPG-derived candidate measures on the MIMIC-IV Waveform Database (MIMIC-IV-WDB v0.1.0) and asked, by inspecting randomly drawn examples, whether each captured its intended physiology before any downstream modeling. Methods. MIMIC-IV-WDB v0.1.09 was linked to MIMIC-IV.10 The signals were a cuff-anchored perfusion-index recovery (reactive hyperemia when the cuff shares an arm with the probe), a slow Mayer-wave-band power ratio of the perfusion index (sympathetic vasomotor tone), and a per-beat diastolic exponential decay time constant (a refill-like recovery time). For each signal we drew 10 random examples at a fixed seed and checked them against a checklist fixed in advance. Each was read by the author and, separately, by MedGemma 1.5, a multimodal medical language model run locally. A synthetic test with a known time constant checked the third signal. Results. The cuff-anchored signal showed the expected occlusion-reperfusion shape on 268 of 6,236 evaluable cuff cycles (4.30%) in 15 of 19 patients, consistent with opposite-limb placement of the probe and cuff. The slow-band ratio returned a stable cohort value, but a clear, stationary peak appeared in only4 of 10 random windows. The per-beat fit met its goodness-of-fit threshold in 10 of 10 beats, yet a cardiac-frequency heuristic flagged a possible fit on the heart-rate oscillation in 7 of 10, and in 5 of 17 patients the time constant lay where an exponential is indistinguishable from a straight line. A 0.5Hz high-pass pre-filter implanted its own approximately 318 ms time constant regardless of truth. The language model tracked the human on clear positives but reported the pattern present on every call it returned, never absent. Conclusions. Two of the three candidate signals did not reflect their intended physiology in most examples, and the third was constrained by sensor placement. Inspecting a few random raw inputs against a checklist written in advance is an inexpensive upstream check before downstream inference on PPG-derived microvascular signals.

Use of fungal mycelia, which has antiviral properties, constitutes a novel strategy for addressing existing and newly emerging viral diseases. We evaluated safety and feasibility of fungal mycelia (Fomitopsis officinalis and Trametes versicolor, FoTv) for treatment of COVID-19 and assessed its antiviral effects and potential to reduce symptoms. In a randomized, double-blind, placebo-controlled, dual site (UCSD/UCLA medical centers) clinical trial we examined non-hospitalized patients who contracted mild-to-moderate COVID-19 [&le;] 96 hours, and experienced symptom onset [&le;] nine days, before enrollment. FoTv was safe, well-tolerated, and feasible for COVID-19 treatment. Minor differences in biochemical markers were observed between groups (26 FoTv, 24 Placebo). FoTv significantly reduced the number and severity of symptoms, particularly sore throat/cough, and in vitro SARS-CoV-2 (pseudovirus) cellular infection. In conclusion, FoTv was safe and reduced COVID-19 symptoms and cellular viral infection. Future studies should investigate therapeutic benefits of fungal mycelia for SARS-CoV-2 and other viruses. Clinicaltrials.gov registration:NCT04667247.

Background: Artemisinin-based combination therapies remain the mainstay of malaria control strategies; nevertheless, the advent of genetic markers linked to partial artemisinin resistance in Plasmodium falciparum has elicited substantial concern across African settings. To assess the prevalence, geographic distribution, and clinical associations of these molecular markers, we undertook a systematic review and meta-analysis of observational cohort studies.Methods: We conducted a search of cohort studies published between January 2015 and June 2025, following PRISMA 2020 guidelines. We queried databases including PubMed/MEDLINE, Scopus, Web of Science, and CINAHL. Eligibility required prospective enrollment of patients, longitudinal monitoring (therapeutic efficacy studies), and pfkelch13 propeller domain genotyping.Results: A meta-analytical synthesis of 888 isolates from six core prospective cohorts revealed a pooled prevalence of 6% (95% CI: 2.1%-11.8%) for validated pfkelch13 mutations. A profound geographic dichotomy was identified: while West and Central African cohorts maintained a 0% prevalence, East African hotspots showed significant expansion, with prevalence reaching 12.8% in Rwanda and up to 25.5% in Northern Uganda; high statistical heterogeneity (, ) reflects this biological divergence. Conclusions: These findings highlight the established and expanding presence of artemisinin partial resistance in East Africa. Standardized surveillance is essential to adapt malaria control policies across the continent. Keywords: Africa; artemisinin resistance; clinical indicators; pfkelch13 gene; molecular markers; partial resistance; Plasmodium falciparum.

Objective. To introduce PsiBench, a clinically validated medication-safety benchmark for evaluating large language models (LLMs) against the standards used to certify hospital computerized provider order entry (CPOE) and electronic health record (EHR) systems, and a non-overlapping three-tier evaluation framework separating highest-stakes discrimination, the operational CDS regime, and category-correct alerting. Materials and Methods. PsiBench comprises 492 medication-safety scenarios across 11 safety categories, created by clinical pharmacology experts whose work underpins an annualized testing procedure used by more than 2,000 U.S. hospitals. The three-tier framework partitions the scenarios non-overlappingly: Discrimination (98 scenarios, 50 fatal vs 48 deception, near-balanced 51%/49%); Operational (394 scenarios, 261 serious unsafe plus 133 safe including 41 Excessive Alerts reclassified as operational negatives); and Attribution (311 alert-required scenarios). We evaluated 40 frontier LLMs from 10 providers over 3 runs per scenario at temperature 0.2 (or the provider default where temperature is not configurable), yielding 59,040 evaluations conducted April 21-23, 2026. Results. Headline binary performance on the full benchmark spans a wide range across the 40 models: F1 78.5%-92.3%, accuracy 65.4%-89.8%, sensitivity 81.4%-100.0%, specificity 6.1%-81.8%. Leading models by F1 (o4-mini 92.3%; o3 92.2%) pair high sensitivity with meaningful specificity; three models saturate sensitivity at 100% but fall below 25% specificity, indistinguishable from a naive always-alert classifier. The wide spread on a single headline metric motivates tier-specific analyses, developed in a separate clinical paper. Discussion and Conclusion. PsiBench and the three-tier framework operationalize a rigorous evaluation rubric for LLM medication safety, grounded in two decades of national hospital audit experience. The framework generalizes to any binary medication-safety classifier (rule-based, conventional ML, or LLM-driven), supporting tier-aware model selection and post-deployment surveillance.

Background Coeliac disease (CD) diagnosis on duodenal biopsies is limited by interobserver variability. We have previously demonstrated pathologist-level performance with our artificial intelligence (AI) model for the histopathological diagnosis of adult CD, but not in paediatric practice. As paediatric CD screening programmes expand internationally, accurate and scalable diagnostic tools are needed. We investigated whether an AI model trained exclusively on adult whole-slide images (WSIs) can generalise to paediatric CD diagnosis across independent centres. Methods A training and validation dataset of 9,958 WSIs from 8,421 adult patients (961 CD) from five centres was used to develop an ensemble of multiple-instance learning models using features from a foundation model. Testing was performed on 708 consecutive paediatric patients (86 CD) from two centres (Edinburgh and Southampton) not included in training. Model calibration was assessed, and probability outputs were grouped into clinically interpretable categories. Findings In adult cross-validation, the AI model achieved an area under the receiver operating characteristic curve (AUC) of 98.7%, sensitivity of 84.9%, specificity of 99.0%, and negative predictive value (NPV) of 98.1%. On testing (paediatric) datasets, performance remained high (AUC 98.8%, sensitivity 80.2%, specificity 98.4%, NPV 97.3%). Restricting analysis to predictions outside the intermediate-probability range (predicted CD probability <10% or [&ge;]65%; 85.3% of cases) improved sensitivity to 100% and specificity to 98.7%. No misclassifications were observed among high-confidence predictions (<2% or [&ge;]85%; 66.0% of cases). The expected calibration error was 0.03. Performance improved significantly when biopsies from both duodenal sites (bulb [D1] and descending [D2/3]) were considered. Interpretation Our AI model, trained on adult biopsies, generalises to paediatric CD diagnosis across centres and scanner platforms. Well-calibrated probability outputs provide clinically interpretable measures of diagnostic confidence and could support safe identification of CD-negative biopsies within defined thresholds. These findings demonstrate the feasibility of applying adult-derived AI models in paediatric populations and reinforce the importance of multi-site (D1 & D2) biopsy sampling.

AIMS Cardiometabolic risk factors may impair health by altering the autonomic modulation of the cardiovascular system, a physiological process described by heart rate (HR) circadian oscillations. However, the impact of cardiometabolic health determinants on HR circadian oscillations remains scarcely characterized in real-world, population-based settings. To address this, we applied digital health technologies to investigate how cardiometabolic health determinants shape HR circadian oscillations in a real-world cohort of individuals free of cardiometabolic diseases. METHODS First, a 10-fold cross-validation of a model was performed, aiming at mitigating wearables measurement error caused by motion artifacts. This process was informed by 10,056 epochs of concurrent wearable-derived and polysomnographic HR assessment, yielding an average 1.3 bpm reduction in wearables measurement error. We subsequently applied this model to over 2 million 1-minute epochs of HR data, derived from 7-day continuous actigraphic recordings of 245 individuals free of cardiometabolic disorders. Functional-on-scalar regression modelling and both parametric and nonparametric analyses characterized HR circadian profiles and their relationships with demographics, lifestyle, chronotype, sleep health, and chronic insomnia diagnosis. A 6-dimension sleep health index was calculated. RESULTS Sex, chronotype, and sleep health predominantly shaped HR circadian oscillations. In detail, females consistently showed higher HR across the 24 hours. Moreover, chronotype was associated to a phase shift in HR circadian profiles, with later timings corresponding to eveningness. Notably, sleep health impacted HR circadian oscillations in a dose-dependent fashion: each additional impaired sleep dimension was associated with a 1.2 bpm HR increase during nighttime, alongside reduced circadian robustness and delayed oscillation timings. Finally, the earlier occurrence of morning HR peaks served as a digital biomarker of insomnia (80% specificity, 74% sensitivity). CONCLUSIONS This work provides a digital health framework to characterize HR circadian oscillations in free-living populations and supports its clinical utility in capturing the autonomic disruptions related to cardiometabolic health determinants.