Frontiers in Physiology

BackgroundIn patients requiring respiratory support, clinicians rely on physical exam, radiologic, laboratory, and ventilator-derived measures for the provision of sufficient support while minimizing ventilator and "work of breathing" induced lung injury. Point of care lung ultrasound (LUS) is a widely available tool in hospital and clinic environments. To date, LUS has not been used to evaluate lung strain. MethodsWe collected LUS images in four anesthetized, neuromuscularly blocked, and mechanically ventilated pigs being used for another experiment. A feature tracking tool was developed which tracked echo-bright lung structures in ten second clips obtained in triplicate of the right and left, upper and lower lung fields using tidal volumes of 4, 6, 8, 10, and 12 mL/kg. Pleural lines were manually drawn and a program for quantifying lung strain developed with assistance from Anthropic Claude Artificial Intelligence tool. Structures were identified in inspiratory and expiratory frames and tracked bidirectionally with median strain per frame used for calculations. ResultsTriplicate measures of lung ultrasound images in four pigs had a median coefficients of variation of 35% (23-47% IQR) and linear modeling of strain with tidal volumes of 4-12 mL/kg showed positive correlation with R2 value ranging from 0.89 to 0.97. Strain measurements were similar after bronchial administration of 1.5M hydrochloric acid. ConclusionsRegional lung strain quantification using LUS is a viable and potentially useful tool for respiratory support management.

PurposeObstructive sleep apnea (OSA) is a common comorbidity in heart failure (HF) patients with prevalence increasing as HF severity worsens. While CPAP/BiPAP has been shown to reduce disease burden and mortality in the general HF population, it is unclear whether these benefits extend to patients with left ventricular assist devices (LVADs). We sought to determine whether OSA affects long-term survival in newly implanted LVAD patients and whether CPAP/BiPAP treatment confers mortality benefits. MethodsThis single-center retrospective study included patients who underwent LVAD implantation between January 2007 and February 2022. Recipients were stratified by OSA status (OSA vs No-OSA), and those with OSA were further categorized based on CPAP/BiPAP compliance. Comparative statistics and Kaplan-Meier survival analyses were performed, with log-rank tests used to compare groups and assess survival differences. A Cox proportional hazards model was conducted to evaluate the association between risk factors and survival among patients with OSA and No-OSA. ResultsBefore LVAD implantation, patients with OSA had higher body mass index, hypertension, and a higher rate of implantable cardioverter-defibrillator placement than those without OSA. OSA was not associated with increased postoperative complications. Although survival did not differ significantly between OSA and No-OSA patients (p=0.33), CPAP/BiPAP-compliant OSA patients had significantly better survival than noncompliant patients (p=0.0099). ConclusionsLVAD patients with OSA who consistently use CPAP/BiPAP have better survival than those who do not. CPAP/BiPAP is a simple, low-risk treatment that can reduce mortality in this population. Therefore, increased perioperative screening for OSA should be considered for patients receiving LVADs. Multicenter studies are needed to confirm our findings further.

BackgroundChronic exposure to high-altitude hypoxia imposes sustained cardiovascular stress, yet hemodynamic adaptation among healthy high-altitude dwellers is heterogeneous and remains poorly characterized. This study aimed to identify distinct hemodynamic phenotypes in a healthy high-altitude population using unsupervised machine learning and to evaluate their association with multi-system subclinical target organ damage. MethodsThis cross-sectional study enrolled 694 healthy adults permanently residing at [&ge;]3300 m on the Qinghai-Tibet Plateau. Unsupervised K-means clustering was performed on nine hemodynamic variables, including peripheral and central blood pressures, augmentation index (AIx), pulse pressure amplification ratio (pPP/cPP), and systolic pressure amplification (pSBP-cSBP). Differences across phenotypes in carotid intima-media thickness (IMT), estimated glomerular filtration rate (eGFR), left ventricular mass index (LVMI), and pulse wave velocity (PWV) were assessed using one-way ANOVA with Bonferroni-corrected post-hoc tests. ResultsThree distinct hemodynamic phenotypes were successfully identified. The C2 (Balanced Adaptation) phenotype (n = 245) demonstrated the most favorable hemodynamic profile, characterized by the lowest blood pressure and augmentation index (AIx) values, along with the highest peripheral-to-central pulse pressure ratio (pPP/cPP). The C1 (Vascular Stress) phenotype (n = 267) presented with normal peripheral systolic blood pressure (125.9 {+/-} 11.3 mmHg) but exhibited markedly elevated wave reflection indices, including the highest heart rate-adjusted augmentation index (AIx@HR75: 31.9 {+/-} 9.7%) and the lowest pPP/cPP ratio (1.29 {+/-} 0.08). The C3 (High-Load Decompensation) phenotype (n = 182) displayed significantly elevated blood pressures and the greatest overall hemodynamic load. Regarding target organ damage, a clear gradient was observed across the three phenotypes. The C3 phenotype showed the highest carotid intima-media thickness (IMT: 1.162 {+/-} 0.23 mm) and left ventricular mass index (LVMI: 69.18 {+/-} 40.73 g/m{superscript 2}). Conversely, the C2 phenotype exhibited the highest estimated glomerular filtration rate (eGFR: 97.38 {+/-} 16.38 mL/min/1.73m{superscript 2}) and the lowest IMT (0.994 {+/-} 0.26 mm). The C1 phenotype consistently displayed intermediate values for all organ damage indicators. After Bonferroni correction, all pairwise comparisons for LVMI and pulse wave velocity (PWV) reached statistical significance (all P < 0.05). ConclusionsHealthy high-altitude individuals manifest three distinct hemodynamic phenotypes arrayed along a cardiovascular risk continuum. The novel Vascular Stress (C1) phenotype represents a "masked" high-risk state characterized by normal peripheral blood pressure but elevated arterial stiffness and wave reflection, challenging sole reliance on brachial pressure for risk assessment. This phenotype-based stratification provides a framework for precision prevention and early intervention in high-altitude populations.

Continuous, non-invasive cardiovascular monitoring is limited by the superficial sensing depth of Photoplethysmography (PPG), which is susceptible to peripheral artifacts. This study evaluates a wearable dual-modality prototype integrating dryelectrode Impedance Plethysmography (IPG) and PPG within a smartwatch form factor. Results from a pilot study (N=2) demonstrate that IPG signals exhibit a temporal lead over PPG across ventral and dorsal sites, supporting its greater penetration depth. During brachial artery modulation, IPG showed superior sensitivity to arterial recovery on the ventral forearm. Furthermore, 60-minute napping sessions revealed that while PPG remained morphologically stable, IPG signals underwent significant evolution, capturing distinct pulsewave archetypes. These findings suggest that wearable IPG provides a high-fidelity window into deep systemic hemodynamics typically reserved for clinical instrumentation.

PurposeColonic motility disorders, including diarrhea-predominant irritable bowel syndrome and slow-transit constipation, impose a major clinical burden. Although high-resolution colonic manometry reveals characteristic spatiotemporal motor patterns, such as high-amplitude propagating contractions and cyclic motor pattern in healthy individuals, these patterns are often altered or absent in disease. Understanding how these patterns arise from underlying pacemaker, neural, and mechanical mechanisms is essential for improving treatment strategies. MethodsWe developed a biophysical whole-colon model that integrates an Interstitial Cells of Cajal-inspired oscillator network, enteric nervous system reflexes, a pressure-gated modulation element motivated by rectosigmoid brake behavior, and a nonlinear tube law describing colon wall mechanics. The model simulates spatiotemporal pressure patterns along the colon and allows systematic variation of physiological parameters associated with pacemaker activity, neural reflex control, and distal gating. ResultsA small set of parameters reproduces three illustrative motility patterns corresponding to healthy motility, diarrhea-predominant irritable bowel syndrome, and slow-transit constipation. The simulated pressure maps recapitulate key features observed in high-resolution manometry, including propagation direction, regional patterning of contractions, and case-specific changes in amplitude and coordination. Sensitivity analysis suggests that proximal excitation strength and waveform morphology strongly influence global motility metrics. ConclusionOur study presents a simple, biophysical framework for reproducing clinically observed colonic motor patterns and exploring their disruption in disease. More broadly, the model may help interpret clinical manometry in mechanistic terms and support hypothesis-driven in silico studies of colonic motility disorders.

The recently published EHRA/EACVI consensus statement on a standardized bi-atrial regionalization provides new opportunities for consistent regional analyses across patients, imaging modalities and clinical centers. To make this standardized regionalization widely accessible, we developed the open-source software DIVAID, which automatically divides bi-atrial geometries according to the proposed regions, ensuring consistency, reproducibility and operator independence. We evaluated the accuracy of the algorithm by comparing its results to manual expert annotations across 140 geometries from multiple modalities and centers. Veins were automatically clipped correctly in 81% and orifices annotated correctly in 100% of cases. The median (interquartile range; IQR) Dice similarity coefficient (DSC) for left atrial regions was 0.98 (0.96-1.00) for DIVAID-expert and 0.98 (0.94-1.00) for inter-expert comparisons. For right atrial geometries, DSC was higher for DIVAID-expert than for inter-expert comparisons at 0.90 (0.80-0.95) and 0.88 (0.74-0.94), respectively. To assess the accuracy of regional boundaries, we computed the mean average surface distance (MASD) for boundaries derived from automatic or manual annotations. The median (IQR) MASD between DIVAID and experts was 0.17 mm (0.03-0.78) and 1.93 mm (0.65-3.96) in the left and right atrium, respectively. To conclude, DIVAID robustly divides anatomically diverse bi-atrial geometries according to the 15-segment model, while outperforming cardiac experts in both speed and consistency, and demonstrating an accuracy of regional boundaries comparable to the spatial resolution of cardiac imaging modalities. By providing automated, consistent atrial regionalization, DIVAID enables large-scale, standardized regional analyses and data-driven investigation of harmonized, multi-dimensional datasets, which may advance atrial arrhythmia research and personalized treatment strategies.

PurposeThis study evaluates the Visual Hemofilter, a novel decision-support and information transfer tool designed to assist with regional citrate anticoagulation (RCA) in hemofiltration. By representing hemofilter parameters and patient blood constituents as animated icons, the tool aims to improve clinicians interpretation of blood gas results and RCA reference tables. We hypothesized that the Visual Hemofilter would enhance clinical decision-making by enabling faster and more accurate therapy adjustments, increasing clinicians confidence in their decisions, and reducing cognitive workload compared to conventional methods. MethodsWe conducted a prospective, randomized, computer-based simulation study across four intensive care units at the University Hospital Zurich. Twenty-six critical care professionals participated, each managing regional citrate anticoagulation (RCA) scenarios using either the Visual Hemofilter or conventional methods involving blood gas analysis and reference tables. Following each scenario, participants made therapy adjustments and rated their decision confidence and cognitive workload. ResultsUse of the Visual Hemofilter significantly improved decision accuracy (odds ratio [OR] 3.96; 95% CI 2.03-7.73; p < 0.0001) and reduced decision time by an average of 33 seconds (mean difference -33.3 seconds; 95% CI -39.4 to -27.2; p < 0.0001). Participants also reported greater confidence in their decisions (OR 5.41; 95% CI 2.49-11.77; p < 0.0001) and experienced lower cognitive workload (mean difference -15.05 points on the NASA-TLX scale (National Aeronautics and Space Administration-Task Load Index); 95% CI -18.99 to -11.13; p < 0.0001). ConclusionsThe Visual Hemofilter enhances clinical decision-making in RCA by increasing accuracy and speed, boosting decision confidence, and reducing cognitive workload. This technology has the potential to reduce errors and better support critical care professionals in managing complex treatment scenarios.

Background. Patients with heart failure and reduced ejection fraction (HFrEF) commonly show signs of systemic inflammation. Interleukin-1 (IL-1) is a pro-inflammatory cytokine, known to modulate cardiac function. We aimed to determine the effects of treatment with anakinra, recombinant IL-1 receptor antagonist (IL-1Ra), on plasma IL-1Ra levels. Methods. We measured IL-1Ra levels at baseline and longest available follow-up to 24 weeks in 63 patients (44 males, 40 self-identified Black-Americans) with recent hospitalization for HFrEF, and systemic inflammation (C reactive protein [CRP] levels >2 mg/L) who were assigned to anakinra (N=42 [66.7%]) or placebo (N=21 [33.3%]) as part of the REDHART2 clinical trial (NCT0014686). Cardiorespiratory fitness was measured as peak oxygen consumption (peak VO2). Results. Baseline plasma IL-1Ra levels were 380 pg/ml (290 to 1046). On-treatment IL-1Ra levels were significantly higher in the patients treated with anakinra vs placebo (3,994 pg/ml [3,372 to 5,000] vs 492 pg/ml [304 to 1370], P<0.001). The longest available follow-up was 6 weeks in 10 patients (15.9%), 12 weeks in 12 patients (19%) and 24 weeks in 41 patients (65.1%). On-treatment IL-1Ra levels and interval change in IL-1Ra showed a modest inverse correlation with on-treatment CRP levels (R=-0.269, P=0.033 and R=-0.355, P=0.004, respectively) and no statistically significant correlations with peak VO2 values (P>0.05). Conclusions. Patients with recently decompensated HFrEF and systemic inflammation treated with recombinant IL-1Ra, anakinra, have a significant several-fold increase in plasma IL-1Ra levels. On-treatment IL-1Ra levels however show only a modest correlation with CRP levels and not with peak VO2.

AimsCaveolae are plasmalemmal microdomains regulating stretch-dependent, nitric oxide (NO), and other signalling pathways governing myocardial structure, function and resilience. We have reported that global deletion of the scaffold protein cavin-1 disrupts caveolar biogenesis and impairs ventricular compliance and tolerance to ischaemic injury. However, cardiomyocyte-specific and sex-dependent roles of cavin-1 and caveolar complexes remain unresolved. Methods and ResultsWe generated a floxed Cavin-1 transgenic mouse, enabling cardiomyocyte-specific knockdown via adeno-associated virus (AAV) mediated expression of iCre recombinase driven by a cardiac-specific troponin T promoter. Knockdown was confirmed by RNA, protein, and immunofluorescence analyses, and cardiac function was assessed via echocardiography, left ventricular pressure-volume (PV) catheterisation, and ex vivo PV analysis of perfused hearts. Conditionally deleted hearts and myocytes exhibited up to 50% knockdown of Cavin-1 mRNA together with 15% deficiency in muscle-specific Caveolin-3, 70% depletion of caveolae, and mislocalisation of NO synthase (NOS) within cardiomyocytes. This was associated with elevated heart rate and shortened PR interval; reduced intraventricular and systolic blood pressures and peripheral resistance; and sex-dependent impairment of ventricular filling (females only). Diastolic dysfunction was detectable ex vivo, to a greater extent in male vs. female hearts. Mechanisms were sex-dependent, linked to interstitial fibrosis in females and NOS overactivity (inhibited by 100 {micro}M L-NAME) in males. Female hearts also exhibited increased susceptibility to ischaemia-reperfusion injury. Coronary function appeared preserved in both sexes, with intact reactive hyperaemic responses. ConclusionThis model identifies cardiomyocyte caveolae and cavin-1 as key determinants of myocardial function and compliance, involving sex-dependent remodelling and NOS signalling. By linking cardiomyocyte disruption to whole-organ and -body dysfunction, this model provides mechanistic insight into impaired function in heart failure and ageing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/717104v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@1aabf7forg.highwire.dtl.DTLVardef@1026839org.highwire.dtl.DTLVardef@108ad11org.highwire.dtl.DTLVardef@9a6dfd_HPS_FORMAT_FIGEXP M_FIG C_FIG

Functional spinal cord repair in zebrafish is governed by regeneration-favorable biochemical and mechanical cues within the lesion microenvironment. Alterations in extracellular matrix composition and stiffness are closely associated with axon regeneration. However, experimentally dissecting the interplay between mechanical signals and axonal regrowth in vivo remains technically challenging. Here, we present an agent-based modeling framework to simulate stiffness-mediated axonal growth trajectories across the lesion. We use this model to explore potential mechanisms underlying the characteristic growth patterns observed during zebrafish spinal cord regeneration. Computational predictions were qualitatively compared with confocal imaging data obtained from larval zebrafish. These phenomenological comparisons revealed a close agreement between simulated and experimentally observed axon growth, indicating that experimentally observed patterns could be governed by transient changes in the stiffness profile of the spinal cord and lesion microenvironment. Hence, our computational framework provides an in silico platform for investigating the role of mechanical cues in axon regeneration in the injured spinal cord.

Backgound: Accurate assessment of diastolic function and left ventricular (LV) filling pressure is central to heart failure diagnosis and risk stratification. Contemporary guideline algorithms rely on complex parameters that are not consistently available in routine clinical practice. Objective: To compare the diagnostic and prognostic performance of the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging (ASE/EACVI) and 2025 ASE guidelines with a deep learning model based on routinely acquired echocardiographic variables. Methods: This study evaluated the guideline-based algorithms and a deep learning model in participants from the Atherosclerosis Risk in Communities (ARIC) cohort (n=5450) for prognostication and two invasive hemodynamic validation cohorts from the United States (n=83) and Japan (n=130) for detection of elevated left ventricular filling pressure. Results: In the ARIC cohort, the deep learning model demonstrated superior prognostic performance compared with the 2016 and 2025 guidelines (C-index: 0.676 vs. 0.638 and 0.602, respectively; both p<0.001). Similar findings were observed among participants with preserved ejection fraction (C-index: 0.660 vs. 0.628 and 0.590; both p<0.001), with improved performance compared with the H2FPEF score (C-index: 0.660 vs. 0.607; p<0.001). In the US hemodynamic validation cohort, the deep learning model showed higher diagnostic performance than the 2025 guidelines (AUC: 0.879 vs. 0.822; p=0.041) and similar performance compared with the 2016 guidelines (AUC: 0.879 vs. 0.812; p=0.138). In the Japanese hemodynamic validation cohort, the deep learning model outperformed both guidelines (AUC: 0.816 vs. 0.634 and 0.694; both p<0.05). Conclusions: A deep learning model leveraging routinely available echocardiographic parameters demonstrated improved diagnostic and prognostic performance compared with contemporary guideline-based approaches, potentially offering a scalable alternative for assessing diastolic function and left ventricular filling pressures.

Junctional adhesion molecule-like (JAML) is an adhesion molecule known to promote T cell activation and T cell-mediated tumor rejection. In the current study, we show that JAML expression is enriched on mouse intratumoral NK cells compared with splenic NK cells. JAML+ NK cells were associated with tissue residency and co-expressed the immune checkpoints PD-1 and LAG3. JAML expression could be induced on splenic NK cells by IL-2 and further enhanced by IL-21. JAML levels were inversely correlated with inhibitory signaling, as NK cells expressing self-recognizing Ly49 receptors had reduced JAML expression, suggesting regulation of JAML expression by MHC class I molecules. Interaction with the JAML ligand CXADR also reduced JAML surface expression, indicating that tumor-mediated membrane stripping may represent a mechanism of immunoediting. Although JAML RNA transcripts were detectable in human NK cells, JAML protein was found only intracellularly. Together, these findings identify the JAML-CXADR interaction as a potential regulatory pathway in NK cell-mediated killing of tumors.

Aims The oral glucose tolerance test (OGTT) is effective for detecting post-load dysglycemia, but it is burdensome and therefore not routinely used. Continuous glucose monitoring (CGM) offers a convenient way to capture real-world glucose patterns, yet it remains unclear whether CGM-derived metrics reflect OGTT-defined dysglycemia. We therefore aimed to evaluate CGM-derived and clinical metrics for predicting OGTT 2-hour glucose, classifying OGTT-defined dysglycemia, and assessing day-to-day repeatability. Methods We analyzed a cohort with paired free-living CGM and OGTT. Multiple CGM-derived metrics and clinical measures were compared for prediction of OGTT 2-hour glucose, classification of OGTT-defined dysglycemia, and day-to-day stability. Predictive performance was assessed primarily by leave-one-out (LOO) R^2, and day-to-day repeatability by intraclass correlation coefficients (ICC). Results The glycemic persistence index (GPI), a metric integrating the magnitude and duration of glycemic elevation, was the strongest single predictor of OGTT 2-hour glucose (LOO R^2 = 0.439). GPI also showed strong day-to-day repeatability (ICC = 0.665) and ranked first on a combined prediction-stability score. For classification of OGTT-defined dysglycemia, HbA1c had a slightly higher AUC than GPI, but GPI plus HbA1c performed best overall, indicating complementary information. Conclusions GPI was a strong predictor of OGTT 2-hour glucose and showed a favorable balance between predictive performance and day-to-day stability, supporting its potential utility as a CGM-derived marker of dysglycemia.

Conflicting evidence on scatter correction (SC) methods plagues quantitative myocardial perfusion SPECT (MPI), hindering standardized clinical protocols. This simulation study, utilizing the SIMIND Monte Carlo program and a highly realistic 4D XCAT phantom, systematically evaluates Dual Energy Window (DEW, with k=0.5) and Triple Energy Window (TEW) SC techniques. We uniquely investigate their performance across various photopeak window widths (2, 4, and 6 keV) and novel overlapped/non overlapped configurations specifically for Tc 99m MPI parameters largely unexplored in realistic cardiac models. Images were reconstructed with OSEM under uncorrected (UC), SC, and combined attenuation and scatter corrected (ACSC) conditions. Quantitative analysis focused on signal to noise ratio (SNR), contrast to noise ratio (CNR), defect contrast, and relative noise to background (RNB). Our findings consistently show ACSC's superior performance in CNR, SNR, and defect contrast, confirming its critical role. Interestingly, SC alone reduced noise but compromised defect contrast relative to UC, highlighting a potential trade-off without attenuation correction. Crucially, this study reveals minimal influence of photopeak window width and overlap configuration on image quality, and no significant difference between DEW and TEW across most metrics. These results provide essential evidence for optimizing quantitative MPI protocols, suggesting that for Tc 99m, the choice between DEW and TEW, and specific window settings, may be less critical than ensuring robust attenuation correction.

BackgroundGlobal cerebral anoxia is a leading cause of death and resuscitated patients often remained persistently affected by neurological deficits. While previous studies suggest that brain-heart electrophysiological interactions may predict severity and prognosis after hypoxic brain injury coma, little is known about the brain-heart dynamics at near-death. Gaining insight into these mechanisms is crucial for developing targeted interventions in critical conditions. ResultsUsing a rodent model of reversible systemic anoxia (n=29, male and female rats), we investigated whether brain-heart interactions during the asphyxia onset could predict the return of brain electrical activities after resuscitation. Electrophysiological recordings confirmed that cerebral activity declines following asphyxia, coinciding with increased heart rate variability. Notably, the strong coupling between cardiac parasympathetic activity and high-frequency brain activity in the somatosensory cortex and hippocampus serves as a key predictor of a favorable outcome. ConclusionOur study underscores the involvement of the brain-heart axis mechanisms in the physiology of dying and the potential prognostic significance of these mechanisms, paving the way for translational research into critical care, based on new characterizations of cardiac reflexes and brain-heart interactions.

AbstractO_ST_ABSBackgroundC_ST_ABSLeft ventricular (LV) remodeling in chronic Chagas cardiomyopathy (CCC) is progressive, but whether population-level LV mass dynamics follow nonlinear patterns and whether the loss of dynamic complexity tracks mortality is unknown. MethodsFifty outpatients from SEARCH-Rio cohort were followed-up for 10 years. Serial echocardiography provided paired LV mass measurements fitted to the logistic equation x = {middle dot}x{middle dot}(1-{gamma}{middle dot}x). Lyapunov exponents (LE) were computed from consecutive inter-patient derivatives. A clinical risk score was developed using Firth penalized logistic regression with bootstrap validation (B = 1,000) and Cox-Firth sensitivity analysis. ResultsLV mass remodeling adjusted the logistic equation with = 3.91 {+/-} 0.18 and {gamma} = 1.27 {+/-} 0.06, which was compatible with dynamics near the complexity threshold ( {approx} 3.57). Survivors showed positive LE (+0.339 {+/-} 0.543), and nonsurvivors showed negative LE (-0.825 {+/-} 0.972; p = 0.015). The fixed-point equilibrium of {approx} 280 g was approached by 63 % of patients at follow-up (p = 0.0003), a pattern indistinguishable from regression to the mean in the present design. Firth regression identified EF < 51.7 % and maximum heart rate < 109 bpm as independent predictors (optimism-corrected AUC = 0.959); the derived score showed a monotonic mortality gradient accompanied by lower LE across strata (Spearman {rho} = -0.369, p = 0.004). ConclusionsThese exploratory findings are compatible with nonlinear LV mass remodeling in Chagas disease and the association between loss of dynamic complexity and mortality. Replication in larger cohorts, formal model comparisons, and prospective validation of the score are warranted. Nonstandard Abbreviations and AcronymsASE, American Society of Echocardiography; AUC, area under the receiver operating characteristic curve; CCC, chronic Chagas cardiomyopathy; CHF, congestive heart failure; CI, confidence interval; EF, ejection fraction; HRV, heart rate variability; IQR, interquartile range; IVS, interventricular septum; LA, left atrial; LAHB, left anterior hemiblock; LBBB, left bundle branch block; LE, Lyapunov exponent; LV, left ventricular; LVEDD, left ventricular end-diastolic diameter; LVESD, left ventricular end-systolic diameter; NSVT, nonsustained ventricular tachycardia; NYHA, New York Heart Association; OR, odds ratio; PSVT, paroxysmal supraventricular tachycardia; PVC, premature ventricular complex; PW, posterior wall; RBBB, right bundle branch block; ROC, receiver operating characteristic; SAECG, signal-averaged electrocardiogram; SD, standard deviation; SDNN, standard deviation of normal-to-normal intervals; SEM, standard error of the mean; SVE, supraventricular ectopy

Ambulatory electrocardiograms (ECG) provides continuous monitoring of the hearts electrical activity. However, many existing machine learning and artificial intelligence models for analyzing ambulatory ECG traces are often unimodal and do not incorporate patient clinical context. In this study, we propose a multimodal framework integrating ambulatory ECG-derived representations with clinical text embeddings to predict two cardiac outcomes: sudden cardiac death and pump failure death. Ambulatory ECG traces are preprocessed, segmented, and encoded via a multiple instance learning and temporal convolutional neural network framework. In parallel, patient clinical features are parsed into structured prompts, which are passed through a large language model to generate clinical reasoning; this reasoning passes through a biomedical language encoder to generate a text embedding. With the ECG and text embeddings, we systematically evaluate multiple fusion strategies, including concatenation- and gating-based approaches, to integrate these two data modalities. Our results demonstrate that multimodal models consistently outperform unimodal baselines, with adaptive fusion mechanisms providing the greatest improvements in predictive performance. Decision curve analysis highlights the potential clinical utility of the proposed framework for risk stratification. Finally, we visualize model attention across modalities, including ECG attention patterns, segment-level saliency, heart rate variability features, and clinical reasoning, to contextualize patient-specific predictions.

We are pleased to submit our Original article entitled "Assessing medication-related burden and medication adherence among older patients from Central Nepal: A machine learning approach" for consideration in your esteemed journal. In this paper, we assessed medication burden using validated Living with medicines Questionnaire (LMQ-3) and medication adherence using Adherence to Medication refills (ARMS) Scale. In this paper we analysed our result through machine learning approach in spite of traditional statistical approach to identify the complex factors influencing both. Six ML architectures (Ordinary Least Square, LightGBM, Random Forest, XGBoost, SVM, and Penalized linear regression) were employed to predict ARMS and LMQ scores using various socio-demographic, clinical and medication-related predictive features. Model explainability was provided through SHAP (Shapley Additive exPlanations). Our study identified the moderate medication burden with moderate non-adherence among older adults. Requiring assistance for medication and polypharmacy were the strongest drivers for the medication burden and non-adherence. The high predictive accuracy by ML suggests the appropriate clinical intervention like deprescribing to cope with the high prevalent medication burden and non-adherence among older adults in Nepal.

O_LICardiac rate and rhythm reveal how animals adapt physiologically to day-to-day challenges, with consequences for health and fitness. However, these data remain difficult to collect in wild animals, despite their relevance for individual health and fitness. C_LIO_LIHere, we present a system for collecting and transmitting long-term, fine-scaled physiological data in wild animals. We implanted Bluetooth-enabled cardiac and physiological monitor devices in three wild adult female baboons in the Amboseli ecosystem in Kenya and paired these devices with collars that enabled remote data downloads over long-range wide area network (LoRaWAN). C_LIO_LIThe system performed well over >10 months, providing the first long-term cardiac data in wild primates. The baboons showed strong circadian patterns in heart rate, heart rate variability, and activity. We also present data on one female who left her social group for unknown reasons; while alone she exhibited higher heart rate variability, lower activity, and evidence of disrupted sleep. C_LIO_LIIn sum, physiologgers paired with low-energy methods of remote data retrieval are powerful tools for investigating physiology in wild animals on timescales that extend over many months, with minimal disruption to their behavior. C_LI

Coronary artery disease is a leading cause of morbidity and mortality. Invasive coronary angiography is currently the gold standard in disease diagnosis. Several studies have attempted to use artificial intelligence (AI) to automate their interpretations with varying levels of success. However, most existing studies cannot generate detailed angiographic reports beyond simple classification or segmentation. This study aims to fine-tune and evaluate the performance of a Vision-Language Model (VLM) in coronary angiogram interpretation and report generation. Using twenty-thousand angiogram keyframes of 1987 patients collated across four unique datasets, we finetuned InternVL2-4B model with Low-Rank Adaptor weights that can perform stenosis detection, anatomy labelling, and report generation. The fine-tuned VLM achieved a precision of 0.56, recall of 0.64, and F1-score of 0.60 for stenosis detection. In anatomy segmentation, it attained a weighted precision of 0.50, recall of 0.43, and F1-score of 0.46, with higher scores in major vessel segments. Report generation integrating multiple angiographic projection views yielded an accuracy of 0.42, negative predictive value of 0.58 and specificity of 0.52. This study demonstrates the potential of using VLM to streamline angiogram interpretation to rapidly provide actionable information to guide management, support care in resource-limited settings, and audit the appropriateness of coronary interventions. AUTHOR SUMMARYCoronary artery disease has heavy disease burden worldwide and coronary angiogram is the gold standard imaging for its diagnosis. Interpreting these complex images and producing clinical reports require significant expertise and time. In this study, we fine-tuned and investigated an open-source VLM, InternVL2-4B, to interpret and report coronary angiogram images in key tasks including stenosis detection, anatomy identification, as well as full report generation. We also referenced the fine-tuned InternVL2-4B against state-of-the-art segmentation model, YOLOv8x, which was evaluated on the same test sets. We examined how machine learning metrics like the intersection over union score may not fully capture the clinical accuracy of model predictions and discussed the limitations of relying solely on these metrics for evaluating clinical AI systems. Although the model has not yet achieved expert-level interpretation, our results demonstrate the potential and feasibility of automating the reporting of coronary angiograms. Such systems could potentially assist cardiologists by improving reporting efficiency, highlightning lesions that may require review, and enabling automated calculations of clinical scores such as the SYNTAX score.