Frontiers in Physiology

Objective: Several endotypes contribute to the development of Obstructive Sleep Apnea (OSA). However, efforts to measure these endotypes have been challenging. In this paper, we propose a new method that overcomes some of these challenges. Methods: To test the feasibility of this new method, data from the Sleep Heart Health Study (SHHS) were analyzed and two oxygen-based endotypes were identified and plotted on a graphical model: the steady-state SpO2 and the SpO2 arousal threshold. The first is the oxygen saturation that would occur during sleep if there were no arousals, and it is a measure of upper airway collapsibility (a more collapsible airway produces a lower SpO2). The latter is the oxygen saturation that triggers arousals. These endotypes were validated by assessing their ability to detect positional and state-related changes in airway collapsibility and arousal threshold. Results: The study showed that it was feasible to measure oxygen-based endotypes in 95% of SHHS participants. As expected, steady-state SpO2 was lower during supine vs. non-supine sleep, as well as during REM vs. NREM sleep. Also, the SpO2 arousal threshold was similar between supine and non-supine sleep. However, SpO2 arousal threshold was not lower in REM sleep vs. NREM sleep. Therefore, in 3 of the 4 conditions, the oxygen-based endotypes moved in the expected direction due to positional or sleep state changes. Conclusion: Although further validation experiments are required, this study indicates that OSA endotyping using the pulse oximetry signal is feasible. The oxygen-based endotypes could be used to aid therapeutic decision making.

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.

Objective To verify the reliability of a self developed bowel sound monitoring device under real biological tissue acoustic propagation conditions using a controllable sound source, and to establish quantitative evidence for its translational applicability. Methods Freshly euthanized six month old Bama miniature pigs were used as an experimental model. A high fidelity Bluetooth audio playback device was implanted into the abdominal cavity to deliver manually annotated bowel sound recordings as controllable acoustic stimuli. A self developed bowel sound monitoring device was fixed on the abdominal surface for continuous signal acquisition. Playback timestamps were defined as the ground truth, and event level matching was performed within a predefined temporal tolerance window. Four performance indicators were evaluated: (1) bowel sound acquisition and energy amplification, (2) event matching accuracy, (3) acoustic feature consistency, and (4) subjective agreement assessed by blinded auscultation from gastroenterologists with different levels of clinical experience. Results The monitoring device exhibited stable detection capability and effectively covered the full spectral range of the original signals. It significantly enhanced bowel sound energy while preserving temporal and spectral characteristics, demonstrating high consistency in time and frequency domain features. Blinded clinician assessments showed a subjective agreement rate of 88.9% between original and surface recorded bowel sound events. Conclusions Under real tissue acoustic propagation conditions, the self-developed bowel sound monitoring device reliably captures bowel sound events with high temporal accuracy, acoustic fidelity, and clinical perceptual consistency. This controllable sound source based validation provides robust technical evidence for subsequent in vivo studies and clinical translation, supporting the development of objective and continuous gastrointestinal function monitoring.

Postoperative resilience varies widely among older adults, yet the biological drivers of recovery remain unclear. We evaluated whether preoperative immune profiles, measured in plasma and through ex vivo whole blood stimulation, predict resilience to the acute stress of total knee arthroplasty. A total of 152 adults (greater or equal to 60 years) in the PRIME KNEE cohort underwent elective total knee arthroplasty and had available blood samples for measurement of 45 immune biomarkers, quantified in plasma and in whole blood stimulated ex vivo for 24 hours with lipopolysaccharide (LPS) or influenza antigen (FLU). Resilience was assessed using Expected Recovery Differential (ERD) and Resilience Trajectory (RT) across pain severity, pain interference, lower extremity physical activities of daily living (LE PADLs), and step counts. An exploratory stability selection framework using LASSO identified biomarker predictors of postoperative outcomes. Plasma and stimulated biomarkers showed broadly similar predictive performance. A shared set of biomarkers, including LBP, leptin, TNFR1, CD30, and LIF, was consistently selected across models. Immune predictors explained ~12-24% of the variance in resilience outcomes. Distinct immune signatures emerged for pain versus functional recovery: pain related predictors mapped to local inflammatory and neuroimmune pathways, whereas function related predictors reflected systemic inflammatory load and cytokine signaling. Preoperative immune biomarkers, whether measured in plasma or after ex vivo stimulation, capture meaningful variance in postoperative resilience. The divergence between pain related and function related immune signatures highlights biologically distinct pathways underlying different dimensions of recovery and supports further development of immune based perioperative risk assessment.

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

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.

Caveolinopathies caused by CAV3 mutations present with heterogeneous clinical phenotypes ranging from asymptomatic hyperCKemia to limb-girdle-type muscular dystrophy. Although prior imaging studies have described commonly affected muscles, structured modeling of muscle involvement patterns in caveolinopathy has not been established. We analyzed whole-body skeletal muscle computed tomography imaging in eight patients with pathogenic or likely pathogenic CAV3 variants, comprising 14 imaging study samples. Fat infiltration across 43 muscles was graded using modified Mercuri scores. Computational multivariate analysis,including principal component analysis, clustering, and pseudotime modeling,was applied to characterize severity staging and distribution patterns. A statistically supported, stage-dependent continuum of muscle involvement was identified. Most samples demonstrated a distributed limb-girdle-predominant pattern with coordinated progression across muscle clusters. In contrast, one patient (three samples in longitudinal series) exhibited a compartment-restricted thigh-dominant pattern characterized by early posterior and medial thigh involvement. Rectus femoris showed consistent stage-dependent progression, while greater medial gastrocnemius involvement was associated with advanced severity. None of the patients exhibited clinical evidence of rippling muscle disease. These findings suggest that integrating semi-quantitative imaging with computational modeling may provide an objective framework for characterizing muscle involvement patterns in CAV3-related myopathy.

Background Chronic pain is common in adults aged 85 years and older (85+) and is associated with detrimental outcomes. Chronic pain guidelines advise first line management with non-pharmacological measures; paracetamol and non-steroidal anti-inflammatory drugs are the preferred analgesics. Challenges in accessing non-pharmacological therapies for adults aged 85+, and the presence of multimorbidity and polypharmacy, mean that opioid medication is often prescribed for chronic pain despite the potential for opioid-related adverse effects and guidance identifying long-term opioids for chronic pain as a potentially inappropriate prescription. Aim This study aims to explore patient, caregiver, and healthcare professional perspectives on the prescription of opioid medications for pain management for chronic pain in adults aged 85+ to support development of resources for optimising opioid prescribing. Design and Setting In this qualitative study, participants were recruited through primary care, in the community or in care home settings. Method 36 semi-structured interviews were conducted with care home residents and community dwellers aged 85+ (n=12), caregivers (informal and care home staff) (n=12), and healthcare professionals (n=12). Interviews were transcribed and analysed using reflexive thematic analysis. Results Four themes were developed: contextual complexity, satellite influences, balancing act, and pragmatic prescribing. Using opioids in adults aged 85+ is a balancing act to support patients best possible quality of life within their unique circumstances whilst using the pain management tools available. Conclusion Opioids continue to have an important role in pain management in adults aged 85+ largely due to paucity of alternatives and the drive to support quality of life.

**Abstract** **Background:** Transcatheter edge-to-edge repair (TEER) is an established treatment for mitral regurgitation but remains highly dependent on operator experience and complex transesophageal echocardiography (TEE)-guided intraprocedural imaging. Artificial intelligence (AI)-based semantic segmentation may improve procedural reproducibility and intraprocedural guidance; however, no TEER-specific segmentation framework has been reported. **Objectives:** To develop and evaluate AutoClip, a clinician-driven AI-guided TEE semantic segmentation model designed for simultaneous delineation of mitral valve anatomy and in-vivo TEER device components. **Methods:** A retrospective proof-of-concept study was conducted using 987 intraprocedural TEE frames derived from 10 video clips in 3 patients undergoing MitraClip G4 implantation. Seven semantic labels, including mitral leaflets and device components, were manually annotated using ITK-SNAP. Following standardized preprocessing and region-of-interest extraction, an Attention U-Net architecture was trained frame-wise on bicommissural and corresponding X-plane TEE views. Model performance was assessed using mean intersection-over-union (IoU) and Dice coefficient on an independent test set. **Results:** The Attention U-Net demonstrated improved sensitivity to small device structures compared with conventional U-Net architectures. Preliminary training performance achieved a mean IoU of approximately 0.93, while independent test performance reached a mean IoU of 0.46 across foreground classes. Qualitative assessment demonstrated feasible simultaneous segmentation of mitral leaflets, clip arms, grippers, and delivery shaft during TEER procedures. **Conclusions:** AutoClip represents a proof-of-concept TEER-specific TEE semantic segmentation framework initiated through a clinician-oriented workflow without formal computer science expertise. Although preliminary accuracy remains modest due to limited sample size, this study establishes a reproducible pathway for future AI-assisted intraprocedural guidance systems and larger multicenter development efforts in structural heart interventions.

Background: Wheeled walkers can improve safety during walking, but improper use may increase fall risk among frail older adults. No suitable tool exists to assess safe indoor wheeled walker use in this population. This study aimed to develop and validate a video-based expert assessment tool. Methods: Based on the literature and expert consensus, seven problematic indoor situations were identified, and an assessment tool with five safety criteria per situation was developed (maximum score = 35). Fifty participants (mean age 83.9 years, 64% women) from a geriatric rehabilitation clinic and a nursing home were video-recorded while using a rollator. Expert ratings were compared with nursing staff ratings, self-ratings, and the Timed Up and Go test to evaluate validity. Intra- and inter-rater reliability were determined from independent ratings by two physiotherapists and a repeated expert rating after seven days. Sensitivity to change was assessed after two weeks of rehabilitation, and feasibility by the time required for assessment. Results: The expert score of rater 1 at baseline was 28.5 points, and assessment required a mean of 17.5 minutes. Intra-rater reliability was excellent (ICC = 0.98) and inter-rater reliability was good (ICC = 0.80). Validity analyses showed the strongest association with nursing staff assessments (r = 0.74) and a moderate association with the Timed Up and Go test (r = -0.45). After two weeks, patients improved by an average of 2.38 points (8.4% of baseline score). Conclusions: The new instrument demonstrated high reliability, acceptable validity, sensitivity to change, and good feasibility for assessing safe wheeled walker use in frail older adults. Trial registration number and date of registration: DRKS00038358, 07/11/2025

Background Slow walking in older adults with mild parkinsonian signs (MPS) is a complex, multifactorial phenomenon arising from the cumulative burden of subclinical age-associated pathologies. This decline reflects age-associated neuronal loss in the dopaminergic system. A recent study suggests that levodopa treatment may enhance gait parameters. The goal of this small pilot study is to explore the effect of levodopa treatment on slow walking gait in older adults with MPS. Method This study was a randomized, placebo-controlled clinical pilot trial. Slow walking older adults without clinical evidence of PD were recruited and randomized into 2 groups (active treatment group or placebo control group). Participants in the active group were pre-treated with carbidopa for three days, followed by carbidopa-levodopa for seven days. Spatiotemporal gait parameters were evaluated at baseline and post-intervention. Results Gait factor analysis identified three main factors explaining gait characteristics at baseline, which included gait efficiency, gait rhythmicity, and gait turning.No effect of treatment was observed in the placebo group (p=0.111, p=0.616), no group difference was observed between the placebo and active group at baseline ({beta}=0.310, p=0.547), but a strong trend for a treatment-related increase was observed in the active treatment group ({beta}=0.506, p=0.076). Conclusion Our preliminary data suggest that sustained levodopa treatment (one week) in conjunction with carbidopa pre-treatment and concomitant carbidopa supplementation is feasible in slow walking older adults with MPS. Moreover, the data indicate potential efficacy, showing improvements in cadence, and step durations.

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).

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.

Abstract Background: Heart Failure is a complex clinical syndrome of growing public health concern in sub-Saharan Africa, yet the data from Sierra Leone are absent. The aim of the study is to characterise the clinical profile, etiological and temporal trends of hospitalised HF patients at Choithrams Memorial Hospital (CMH), Freetown, Sierra Leone, to confirm specific management strategies. Methods: This single-center, retrospective observational cohort study analysed data on HF patients (>18years) admitted at the CMH between January 2021 to 31 December 2025. The clinical definition of HF was based on the Framingham criteria and the European Society of Cardiology (ESC) guidelines , including standard echocardiographic parameters. All variables, including patients demographics, HF. phenotype, aetiology, medical history and hospital outcomes were extracted from the digital record. Non-parameteric tests, multivariable logistic regression to identify variables associated with etiology, Wilcoxon rank-sum test to compare groups and Kruskal-Wallis test to analyse trends over time were utilised. Result: A total of 765 patients were included in the study, with a median age of 53 years (IQR 42-61) and male predominance of 55.3%. Patients with recurrent HF (60.9%) were more common than those with de novo HF (39.1%), were older (54 years vs 53 years), had a higher comorbidity burden (34% vs 4%, p < 0.001), and presented with a cold-wet hemodynamic profile (18.4% vs 8.4%, p < 0.001). HFrEF (61.3%) was the most predominant phenotype, though HFpEF increased with age. Dilated Cardiomyopathy (37.0%), Hypertensive Heart Disease (31.2%) and Valvular Heart Failure (17.1%) were the leading etiologies, while ischemic heart disease (6.3%) was relatively uncommon. A majority of the patients were referred (77.9%), and 50.8% presented with NYHA IV. The strongest independent predictor for HF was hypertensive heart disease [AOR = 17.81; C.I 95%: (3.13-48.76), p <0.001]. An analysis of the trends in etiologies and demographics over the five-year period demonstrated no significant changes (all p-values > 0.05 for age, sex, aetiology, and most comorbidities). Conclusion: HF affects the younger adult population in Sierra Leone and is mainly caused by DCM and HHD. The late case presentations, the high prevalence of recurrent HF, and the associated high burden of comorbidities emphasize an urgent need to develop and implement improved strategies for the prevention, early detection, and long-term management of HF within Sierra Leone's healthcare system.

Background Pulse-field ablation (PFA) is regarded as a non-thermal ablation modality, but there is an increasing range of complications that could be due to thermal effects. Methods The hydrogel undergoes permanent colour change when a target temperature is reached allowing direct visualisation of the surface thermal footprint and depth. Comparative lesion sets using a variable loop circular catheter (VP), circular over-the-wire catheter (PS) and pentaspline catheter (FP) were performed. Protocols included single and stacked applications with variation of force, irrigation, and voltage. The hydrogel lesions were analysed en-face and by section using digital image analysis. Results All 3 PFA catheters tested had significant thermal footprints. The VP catheter had the largest mean surface footprint (156.1mm2) and thermal depth (1.31mm) compared to the other two catheters (PS 55.4mm2 & 1.1mm, FP 29.8mm2 & 1.05mm, p<0.005). Increasing irrigation showed a trend to reduce thermal footprint but did not achieve statistical significance. Increasing voltage increased thermal footprint, but increasing force had negligible effect. Stacked lesions incrementally increased thermal lesion footprint and depth in all catheters. Thermal depths of up to 2.4mm were observed. Areas of darkening and degradation of the hydrogel were observed with the VP and FP catheters, consisting of up to 47% of lesion area. No darkening was observed with the PS catheter. Conclusions There are significant thermal footprints in all the systems tested. Temperatures exceeding 60oC have been demonstrated, comparable to radiofrequency ablation, and this may explain the mechanism of injury in some reports of collateral damage during PFA.

Background: Histological examination of mucosal tissue in inflammatory bowel diseases (IBD) is a sensitive tool to measure disease activity, and histological remission is emerging as a potentially important treatment target. There are several existing histopathological indices, but they often encompass caveats such as not primarily having been designed to measure the degree of inflammation, encompassing subjective components with poor intra- and interindividual reproducibility, and requiring expert pathologists who are scarce, thus resulting in extended response times. Aim: To construct a new computerized, automated index to objectively measure histological disease activity in the ileal and colonic mucosa, applicable to both Crohn's disease (CD) and ulcerative colitis (UC). Materials and methods: Ileocolonic biopsies were collected from control subjects and patients with CD or UC. A group of CD patients was sampled before and after 12 weeks of anti-TNF therapy. Another group of CD and UC patients functioned as a small validation cohort. Epithelial cells, neutrophils, macrophages, and T cells were immunohistochemically stained, followed by digitalization of the color signal and computerized delineation of the epithelial and lamina propria compartments. The various immune cell types within the epithelium and the lamina propria, respectively, were enumerated, and the numbers were compared between control subjects and patients with CD or UC. Results: The numbers of neutrophils and macrophages in the epithelium, and neutrophils in the lamina propria, showed the highest sensitivity and specificity for distinguishing control-subject tissues from CD and UC tissues. These three parameters were thus chosen to construct a new index, named QiC3 1.0, that could separate tissues from control subjects and patients with CD or UC with high precision. It performed equally well in a small validation cohort of patients. The QiC3 index correlated well with previously described histopathological indices, fecal calprotectin, and endoscopic scores in UC, but showed worse correlation with endoscopic scores in CD and symptomatic scores. When applying the new index to tissues from CD patients before and after therapy, it showed good responsiveness, demonstrating a distinct amelioration in the microscopic inflammatory status that corresponded well to improvements in histopathological scores. Conclusion: We describe a new quantitative, computerized, automated, non-subjective, and response-sensitive immunohistological index (QiC3) for measuring disease activity in ileal and colonic mucosal biopsies, suitable for both CD and UC.

Background: Red cell distribution width (RDW), a readily available hematological parameter reflecting erythrocyte size heterogeneity, has been increasingly recognized as a prognostic marker in congestive heart failure (CHF), with elevated levels independently associated with adverse outcomes. However, RDW-derived composite indices-particularly the RDW-to-platelet ratio (RPR) and RDW-to-hemoglobin ratio (RHR), which integrate inflammatory, hemostatic, and oxygen-delivery pathways-remain largely unexplored in CHF populations. Whether these indices provide incremental prognostic value beyond RDW alone in critically ill patients with CHF has not been established. Methods: This retrospective cohort study included 30,409 participants from the MIMIC-IV and eICU-CRD databases. Multivariable logistic regression, restricted cubic spline (RCS) analysis, and subgroup analyses were employed to evaluate the associations between RDW, RDW-derived indices (RPR and RHR), and in-hospital mortality in patients with congestive heart failure. Results: Based on a pooled cohort of 30,409 patients with CHF from the MIMIC-IV and multi-center eICU-CRD databases (15,983 and 14,426, respectively), 16,295 (53.6%) were male and 14,114 were female, with a median age of 71.7 years. The mean RDW was 16.0 {+/-} 2.5, and the overall in-hospital mortality rate was 12.6%. Higher RDW quintiles were associated with progressively increased in-hospital mortality. In the fully adjusted model, RDW, RPR, and RHR were all significantly associated with increased in-hospital mortality, with adjusted odds ratios (ORs) of 2.46 (95% CI: 2.17-2.79) for RDW, 1.55 (95% CI: 1.38-1.73) for RPR, and 2.43 (95% CI: 2.09-2.82) for RHR. Sensitivity analyses using restricted cubic splines demonstrated that the association between RDW and RHR with in-hospital mortality was linear (P for nonlinearity > 0.05), whereas that for RPR exhibited a non-linear pattern (P = 0.02 for non-linearity). Conclusions. Elevated RDW, RPR, and RHR were independently associated with increased in-hospital mortality in patients with congestive heart failure. Notably, RPR exhibited a non-linear threshold association with in-hospital mortality.

Aims: Sarcopenia and sarcopenic obesity are associated with increased risks of cardiovascular (CV) disease and mortality. This study examined the associations of body composition and daily physical activity with mortality, CV events and cancer in patients with diabetes. Methods: This prospective cohort study included patients with diabetes treated at a specialised clinic in Japan between January 2018 and March 2023. Body composition, including visceral adipose tissue (VAT), was assessed by bioelectrical impedance analysis. Daily physical activity was evaluated using the non-exercise activity thermogenesis (NEAT) questionnaire, and handgrip strength (HGS) was measured by dynamometry. Cox proportional hazards models were used to assess associations with mortality, CV events, and cancer. Results: Among 2,024 patients (mean age 63.0 years, BMI 24.6 kg/m^2, HbA1c 7.8%), NEAT, HGS, and VAT were not independently associated with all-cause mortality. Higher VAT was associated with increased cancer risk (HR 1.485; 95% CI 1.101-2.003; p = 0.009). Higher HGS was inversely associated with CV event risk (HR 0.951; 95% CI 0.919-0.984; p = 0.004). NEAT was not associated with any outcome. Conclusions: Higher VAT was associated with increased cancer risk, whereas higher HGS was protective against CV events. Incorporating body composition and HGS assessments into clinical practice may improve risk stratification and management in patients with diabetes.

Background: Aortic stenosis (AS) is a progressive valvular disease associated with poor prognosis once symptoms develop, yet routine echocardiographic screening is impractical. While artificial intelligence (AI)-based electrocardiogram (ECG) models have shown promise for AS detection, it remains unclear whether they primarily reflect conventional left ventricular hypertrophy (LVH) voltage criteria or capture additional ECG features. Methods and Results: We developed a deep learning model using 244,816 ECGs from 51,713 patients across six academic institutions in Japan (CLIDAS database). AS labels were derived from inpatient Diagnosis Procedure Combination (DPC) codes. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.849 (95% confidence interval 0.832-0.865) in the independent test cohort, with consistent performance across institutions, sex, and age. At a threshold of 0.1, sensitivity was 79.1%, specificity was 73.9%, and negative predictive value (NPV) was 98.0%. Conventional LVH voltage criteria (Sokolow-Lyon AUC 0.706; Cornell AUC 0.692) showed lower performance, and adding them to the AI model conferred no incremental benefit (AUC 0.849 vs. 0.847). Gradient-weighted class activation mapping (Grad-CAM) revealed predominant attention around QRS complexes in limb leads, beyond regions typically assessed in LVH evaluation. Conclusions: This multicenter AI-ECG model demonstrated strong discrimination for AS and captured ECG features beyond conventional LVH voltage criteria. The high NPV supports its use as a rule-out pre-screening tool.

This study aims to assess women's perceptions of artificial intelligence (AI) used in breast cancer screening in France by examining their knowledge of AI and the barriers to their participation in organized screening. The results of a survey conducted in June 2025 among a national sample of 2000 women (aged 40-75) reveal limited participation and persistent concerns among women. Nevertheless, despite a low awareness of specific AI applications, a large majority of the women surveyed are very favorable to the use of AI in breast cancer diagnosis, even considering it a lever to increase screening participation.