Background and Objective: Obstructive sleep apnea (OSA) is a fatal widespread sleep-related breathing disorder and a major risk factor for cardiovascular and cerebrovascular diseases, significantly impacting older adults' health worldwide. Due to the risk of such complications, timely and accurate identification of OSA is crucial. Polysomnography is considered the most accurate technique for detecting OSA; however, it is limited by its complexity and multi-channel requirements. A promising alternative is electrocardiogram (ECG)-based diagnosis, which continuously monitors heart rhythm and captures subtle cardiac changes associated with OSA. Nevertheless, existing ECG-based approaches still face challenges related to complex feature engineering, limited capture of complementary temporal-spectral information and global dependencies, along with inadequate feature recalibration and fusion, which can restrict OSA detection. Thus, further improvements are still required to achieve clinically reliable performance. Methods: To address these challenges, this study proposes SApneaNet, a novel advanced deep learning method for detecting OSA events using ECG signals. The proposed approach employs the continuous wavelet transform (CWT) to convert ECG signals into RGB log-scalograms, enabling the simultaneous analysis of temporal and frequency-domain features. The generated RGB log-scalograms are then fed into a deep CNN encoder with adaptive squeeze-and-excitation (ASE), followed by a transformer and an adaptive gated feature fusion (AGFF) architecture. In this framework, to improve OSA detection performance, the CNN extracts rich local features, the ASE module performs channel-wise recalibration to enhance feature representations, the transformer performs data-parallel processing and captures global contextual dependencies, and the AGFF mechanism adaptively emphasizes informative features while suppressing less relevant ones. Results: The experimental results on the Apnea-ECG dataset showed that the model achieved a sensitivity of 94.7%, specificity of 95.2%, F1-score of 93.5%, accuracy of 95.1%, Cohen's kappa of 89.4%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.989 for per-segment classification. Furthermore, for per-recording classification, the model achieved an accuracy of 100.0%, a mean absolute error (MAE) of 2.025, and a Pearson correlation coefficient (PCC) of 0.992. Overall, the experimental results demonstrated that the proposed model achieved excellent and competitive performance compared with other advanced state-of-the-art methods for OSA classification. Conclusions: The proposed model demonstrates strong efficacy in OSA detection, providing a novel and robust alternative to conventional diagnostic methods. The model's reliable and consistent diagnostic performance highlights its potential for integration into practical OSA diagnostic systems, including home-based health monitoring devices and clinical decision-support tools.

In major public-health emergencies, universities constitute core nodes within a multi-actor intersecting governance network composed of governments, universities, students, media and social-sector organizations. While uniform closed-campus management can maximize the protection of students' physical health, it frequently overlooks students' mental-health needs and value-recognition concerns, which may exacerbate tensions between universities and students. Taking political costs associated with university pandemic governance as the research subject, this study integrates systems theory and cost-benefit theory to construct a tripartite evolutionary-game model covering universities, social-media platforms and students. Evolutionarily stable strategies are derived by solving replicator-dynamic equations. A small-signal model is further introduced to analyze the stability, root-locus characteristics and participation factors of all eight equilibrium points. Python-based numerical simulation is also adopted to reveal the feedback effect of political costs on system stability. This paper presents a case analysis of responses and proactive adjustment outcomes of two universities during the 2022 Omicron wave. The findings are as follows: (1) University governance behavior serves as the dominant participation factor driving instability across different equilibrium states, whereas student (public) participation acts as the core stabilizing factor and the primary driver of political-cost consumption; (2) Under the optimal equilibrium, a moderate political-cost signal can both warn universities against exploiting information asymmetry and empower them to respond decisively to public-health crises, fostering a win-win tripartite synergy among universities, media and students; (3) When political costs are severely depleted, students will set aside economic considerations and prioritize health-related concerns by adopting negative behavioral responses, which destabilizes the governance system. On this basis, this paper puts forward a three-dimensional "system-process-value" framework. It optimizes the supply pathway of university pandemic-prevention policies through structural reorganization, process re-engineering and value allocation. This framework is not limited to COVID-19-related prevention and control. It also offers transferable guidance for educational institutions to implement routine emergency management when confronted with emerging infectious diseases and future public-health emergencies.

Voice and related laryngeal disorders may affect communication, occupational functioning and readiness in military populations, but their prevalence remains poorly characterized. We estimated prevalence across general military populations, high-vocal-demand personnel and chemically exposed military or veteran cohorts.

This systematic review and meta-analysis followed PRISMA 2020. PubMed, Embase, Scopus, Web of Science, ClinicalTrials.gov and Google Scholar were searched in September 2024. Eligible studies included active-duty personnel, recruits, reservists or veterans and reported voice or related laryngeal outcomes. Clinically referred or symptom-selected cohorts were synthesized narratively. Subgroup-specific random-effects meta-analyses used logit-transformed proportions, restricted maximum likelihood estimation and Hartung-Knapp adjustment. Prediction intervals and generalized linear mixed-model sensitivity analyses were calculated. Risk of bias and certainty of evidence were assessed using design-appropriate appraisal tools and GRADE.

Sixteen studies were included in the systematic review, and 11 independent datasets from nine studies contributed to the meta-analysis. In general military populations, pooled prevalence was 3.3% (95% CI 0.2-37.5%; five datasets; n = 1,613,921; I² = 99.8%; 95% prediction interval 0.001-99.1%). Prevalence was 40.0% in high-vocal-demand personnel (95% CI 0.8-98.2%; three datasets; n = 3,708; I² = 99.5%; prediction interval 0-100%) and 72.8% in chemically exposed cohorts (95% CI 43.3-90.4%; three datasets; n = 145; I² = 57.5%; prediction interval 0.6-99.9%). An exploratory meta-regression identified an overall subgroup association (p = 0.0300), but pairwise findings were inconsistent and statistical power was limited. Certainty of evidence was Very Low for all three estimates.

Reported prevalence varies substantially across military populations and exposure contexts. Because heterogeneity was high and the certainty of evidence was Very Low, the pooled estimates should be interpreted with caution and should not be considered precise prevalence figures or evidence of causation. Standardized prospective studies are needed to quantify the occupational-health burden and evaluate targeted surveillance, prevention and early-referral approaches.

PROSPERO https://www.crd.york.ac.uk/PROSPERO/view/CRD420250640753, identifier: CRD420250640753.

Severe abdominal Henoch-Schönlein purpura (HSP) can cause intense abdominal pain and gastrointestinal bleeding. Glucocorticoid therapy has limited efficacy and a high relapse rate after withdrawal. Telitacicept blocks BLyS/APRIL and inhibits the production of pathogenic IgA immune complexes, but its use in HSP has not been reported.

This was a single-center retrospective matched cohort study. Children hospitalized with severe abdominal HSP (abdominal pain VAS ≥3, hematochezia, and extensive rash) between January 2025 and December 2025 were included. Patients were divided into a telitacicept plus glucocorticoid group (telitacicept group) and a glucocorticoid-alone group (steroid group). Individual 1:1 matching was performed based on age, sex, and disease severity. The primary outcome was time to abdominal pain relief. Secondary outcomes included time to rash resolution, disease relapse rate within 3 months after treatment, and safety. Intergroup comparisons used Wilcoxon rank-sum test or Fisher's exact test, and relapse-free time was analyzed by Kaplan-Meier curves and the log-rank test.

A total of 26 children (13 per group) were included. Baseline characteristics were balanced between the two groups (age, sex, age strata, all P > 0.05). The median time to abdominal pain relief was 3.0 days (IQR 3.0-4.0) in the telitacicept group, significantly shorter than the 5.0 days (IQR 4.0-6.0) in the steroid group (P = 0.013). Median time to rash resolution was 9.0 days in both groups, with no significant difference (P = 0.113). The 3-month relapse rate was 7.7% (1/13) in the telitacicept group versus 46.2% (6/13) in the steroid group; the log-rank test showed a significant difference in relapse-time distribution (P = 0.022). The number needed to treat (NNT) was 2.6. For safety, mild injection-site redness occurred in 2 patients (15.4%) in the telitacicept group, and transient hyperglycemia occurred in 1 patient (7.7%) in the steroid group. No serious adverse events were observed.

In children with severe abdominal HSP, adding telitacicept to glucocorticoid significantly shortens the time to abdominal pain relief and reduces the short-term relapse rate, with a favorable safety profile. This study provides preliminary clinical evidence for telitacicept as an adjunctive therapy to glucocorticoids, which warrants further validation in prospective randomized controlled trials.

To analyze the risk factors of disability in readmitted patients with recurrent stroke and construct a nomogram model, so as to provide references for clinical medical staff to formulate targeted intervention measures for stroke patients.

From September 2023 to February 2025, a total of 415 readmitted patients with recurrent stroke were enrolled from the neurology departments of tertiary hospitals in Liaoning and Shanxi Provinces by convenience sampling method. Chi-square test and t-test were used for univariate analysis, and binary logistic regression analysis was performed to establish the risk prediction model. A nomogram was plotted to visualize the risk, and the model was validated via the area under the ROC curve and calibration curve.

The detection rate of disability among readmitted patients with recurrent stroke was 60%. Complicated hypertension, depressive symptoms, cognitive impairment (CI), occasional or no physical exercise were independent risk factors for disability (p < 0.05), while social support and self-efficacy were protective factors (p < 0.05). The AUC value was 0.993. The calibration curve was well fitted with the ideal curve, and the clinical decision curve confirmed favorable clinical application value of the model.

The constructed nomogram model presents satisfactory predictive efficiency. It can assist clinical staff in screening disability risks, and carry out targeted intervention and rehabilitation management according to relevant risk factors, so as to reduce the incidence of disability in readmitted patients with recurrent stroke.

Cardiovascular disease (CVD) is the leading cause of death in the United States. We examined whether a model that classifies prevalent CVD using clinical risk factor precursors (social, environmental, and behavioral factors), age, and sex, organized according to the Vital Conditions for Health framework, demonstrates good discrimination and calibration.

In this cross-sectional study, we analyzed the 2021 Medical Expenditure Panel Survey (MEPS) Social Determinants of Health (SDOH) dataset (N = 18,435 adults). Cardiovascular disease prevalence was defined using self-reported diagnoses and International Classification of Diseases, 10th Revision (ICD-10), codes. Candidate predictors included variables from the seven Vital Conditions domains, age, and sex. Models included least absolute shrinkage and selection operator (LASSO) regression and extreme gradient boosting (XGBoost). Internal validation used stratified k-fold cross-validation and 2022 MEPS data. Discrimination and calibration were assessed using the area under the receiver operating characteristic curve (AUC), Brier score, calibration slope, calibration-in-the-large (CITL), and observed-to-expected (O:E) ratio. Subgroup performance was evaluated by age, sex, and race or ethnicity.

Both LASSO and XGBoost performed well. LASSO achieved an AUC of 0.822 (95% confidence interval [CI], 0.815-0.831), with a calibration slope of 1.022 and an O:E ratio of 1.001. XGBoost achieved an AUC of 0.822 (95% CI, 0.814-0.831), with a calibration slope of 1.026 and an O:E ratio of 1.002. Discrimination was stable across sex and race or ethnicity subgroups, although some calibration drift occurred among Hispanic participants. Performance declined among adults aged ≥ 65 years (XGBoost AUC, 0.67) compared with those aged < 65 years (AUC, 0.812). Key predictors included age, sex, exercise, social isolation, healthcare access, food insecurity, financial strain, adverse childhood experiences (ACEs), smoking, and transportation barriers.

Models using upstream social determinants of health, age, and sex demonstrated good discrimination when classifying prevalent CVD. External and prospective validation is needed before these models can be considered for clinical or population-level screening.

Direct balloon dilatation angioplasty (DBDA) is an established treatment for intracranial atherosclerotic stenosis (IAS) without acute ischemia; however, its role in IAS-induced acute ischemic stroke (ICAS-LVO) remains poorly defined. This retrospective single-arm study aimed to evaluate the technical feasibility, safety, and 90-day functional outcomes of DBDA in a selected cohort of patients with ICAS-LVO.

Data from 28 consecutive patients with ICAS-LVO who underwent DBDA as the primary recanalization strategy at Huzhou Central Hospital (October 2018-December 2020) were retrospectively analyzed. Safety endpoints included intraprocedural dissection, plaque retraction, distal embolism, the need for remedial stenting, symptomatic intracranial hemorrhage (sICH), and 90-day mortality. Technical success was defined as postprocedural modified Thrombolysis in Cerebral Infarction (mTICI) grade 2b-3. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days.

Among the 28 patients (mean admission NIHSS 16.5 ± 5.6), 23 (82.1%) received intravenous thrombolysis prior to intervention. Intraprocedural dissection occurred in one patient, and plaque retraction with flow failure occurred in another patient, with both patients requiring remedial stenting. sICH was observed in one patient (3.6%); no distal embolization or 90-day mortality was observed. Successful reperfusion (mTICI 2b-3) was achieved in 26 patients (92.9%; 95% CI 77.4-98.0%), with mTICI 2b achieved in 9 (32.1%; 95% CI 17.2-51.6%) and mTICI 3 in 17 (60.7%; 95% CI 42.2-76.8%) patients. The median puncture-to-revascularization time was 65 min (IQR 55-78). The median NIHSS at discharge was 4 (IQR 2-8). At 90 days, 18 patients (64.3%; 95% CI 45.8-79.8%) achieved functional independence (mRS 0-2).

In this highly selected cohort of ICAS-LVO patients, DBDA was associated with a high rate of successful reperfusion and favorable 90-day functional outcomes. However, given the single-arm retrospective design, the absence of a control group, and the high rate of intravenous thrombolysis use, these findings are hypothesis-generating only. The observed outcomes cannot be attributed to DBDA alone and require validation in multicenter randomized trials.

Large-scale plasma proteomics offers opportunities to characterize disease susceptibility and improve prospective risk prediction, but transferring proteomic predictors across datasets remains challenging because measured protein sets differ across cohorts, study phases and assay configurations. Here we developed a self-supervised protein-token Transformer that maps the proteins observed in each sample to a fixed-dimensional participant representation, allowing unavailable proteins to be omitted rather than imputed. Using plasma proteomic profiles from 53,014 participants in the UK Biobank Pharma Proteomics Project, we pretrained the encoder by masked-protein reconstruction and evaluated whether disease models developed from comprehensive 2,920-protein profiles could be reused with a predefined subset of 1,460 proteins. Across 144 diseases, median AUC was 0.679 with comprehensive coverage and 0.637 when the same encoder and disease models were applied to partial-coverage representations without refitting; retraining only the disease-specific models increased median AUC to 0.673. Under partial coverage, protein-token proteomic risk scores (ProRS) exceeded coefficient-truncated LASSO ProRS by a median paired AUC difference of 0.027 and were comparable to LASSO ProRS refitted using outcome labels, with a median difference of 0.003. Performance was relatively stable across cardiovascular-kidney-metabolic diseases but more heterogeneous across autoimmune diseases, while protein-token representations improved discrimination beyond clinical covariates for 10 of 12 focused diseases under partial coverage without refitting. These results support self-supervised protein-token representations as a strategy for building proteomic prediction models that remain usable across heterogeneous measurement settings.

Antithrombotic therapy undertreatment includes omission, nonadherence, underdosing, and clinical circumstances that result in subtherapeutic antithrombotic levels. Antithrombotic undertreatment has profound implications for patient safety, public health, and healthcare costs. Due to deviation from evidence-based clinical practice, misperceptions of bleeding versus thrombotic risk, and inequities in healthcare access, many patients with atrial fibrillation, venous thromboembolism, or peripheral artery disease remain untreated or receive suboptimal anticoagulant dosing. Antithrombotic undertreatment has been shown to increase rates of ischemic stroke, systemic embolism, recurrent thrombosis, and mortality. Real-world data continue to highlight substantial gaps in care, which appear to be magnified in patients with inequity in social determinants of health. The emergence of antithrombotic stewardship, in concert with growing bodies of literature on antithrombotic agent underdosing, represents a transformative opportunity to address these gaps through structured, interprofessional, and evidence-based approaches. Through interdisciplinary collaboration, healthcare professionals are well positioned to enhance patient health and safety by recognizing and recommitting to reducing antithrombotic undertreatment.

Heart failure (HF) remains a major cause of morbidity, mortality, and increased healthcare costs worldwide, and is recognized as the result of progressive cardiovascular-kidney-metabolic (CKM) syndrome. Obesity, type 2 diabetes, chronic kidney disease, hypertension, and atherosclerotic cardiovascular disease frequently coexist and interact through shared pathophysiological mechanisms that promote HF onset, especially with preserved ejection fraction (HFpEF). This European Journal of Heart Failure (EJHF) Expert Consensus Statement synthesizes contemporary evidence supporting pharmacological strategies to prevent HF across the CKM spectrum. Foundational therapies, including renin-angiotensin system inhibitors, sodium-glucose cotransporter 2 inhibitors, non-steroidal mineralocorticoid receptor antagonists, and incretin-based agents, demonstrate complementary benefits in reducing HF events, cardiovascular death, and chronic kidney disease (CKD) progression in high-risk populations. Early, parallel initiation of these therapies may modify disease trajectory and attenuate progression from at-risk and pre-HF stages to overt clinical HF. This document further emphasizes the need for an up-front, accelerated implementation strategy supported by structured monitoring, multidisciplinary CKM care pathways, and proactive management of laboratory abnormalities. Overcoming barriers such as clinical inertia, safety misperceptions, polypharmacy, cost constraints, and inequities in access is essential to translate evidence into population-level impact. Harmonization of prevention frameworks across cardiology, nephrology, endocrinology, and primary care societies is urgently required. Integrated, cross-disciplinary implementation of evidence-based CKM management offers a realistic opportunity to reduce incident HF and mitigate its growing global burden.