Lesion-symptom mapping results can vary substantially as a function of specific analysis parameters, but the extent to which individual methodological choices interact to modulate the sensitivity and specificity of results is not clear. Here, we employed a large-scale simulation approach to inform practical recommendations for lesion-symptom mapping studies. Routine clinical imaging from 959 stroke survivors (mean age = 72.5, 49.3% female) was used to conduct 384,780 lesion-symptom mapping analyses based on simulated behavioural data. Each simulated analysis used different combinations of plausible sample inclusion criteria, analysis parameters (e.g., correction factors), analysis types (e.g., univariate vs. multivariate), and underlying target correlates. Simulated analysis accuracy (percent coverage of target correlates, Dice similarity coefficient, and false positives) was compared across designs. Overall, analysis accuracy varied widely and was substantially modulated by the specific design used. Analyses that maximised lesion coverage by including large and diverse samples reliably outperformed analyses using more restricted samples. Analyses using direct total lesion volume controls outperformed analyses using other (or no) volume corrections across all accuracy measures. False discovery rate corrections yielded the best performance in terms of target coverage, while permutation corrections yielded the best Dice coefficients. While multivariate approaches were more accurate than univariate analyses in terms of Dice coefficient, univariate analyses generated higher target hit rates and percent target coverage. These results identify specific analysis designs suitable for studies aiming to maximise their sensitivity and/or specificity to underlying critical correlates, while highlighting the inferential strengths and weaknesses of these complementary approaches.

Ischemic stroke is a major cause of death and disability, in which neuroinflammation exacerbates injury. Mesenchymal stem cell (MSC)-derived extracellular vesicles (EVs) offer therapeutic potential but face translational hurdles in scalable production, rapid systemic clearance, and inefficient targeted delivery.

We engineered an implantable alginate-microsphere system encapsulating EV-secreting MSCs and displaying the RGD-4C peptide (ACDCRGDCFC) on its surface. This platform functions as a bioreactor that sustains the release of functionalized EVs with enhanced targeting to the ischemic brain. Proteomics analyses compared EVs derived from 3D-microsphere cultures and conventional 2D cultures. Efficacy was evaluated in a mouse stroke model with intraperitoneal microsphere implantation, assessing biodistribution, neuroinflammation, microglial polarization, and recovery.

The system sustained the release of targeted EVs, demonstrating proteomic enrichment of anti-inflammatory cargo. In vivo, the platform enhanced EV accumulation in the ischemic brain, reduced neuroinflammation, shifted microglia toward a reparative phenotype, and significantly improved neuronal survival and functional recovery.

This integrated platform represents a promising preclinical strategy for treating ischemic stroke and has potential applications in other neuroinflammatory diseases. This system circumvents the need for EV extraction and storage while eliminating the peak-and-trough kinetics of bolus injections, and suggests potential for future translation pending further validation.

Neutrophil extracellular traps (NETs) have emerged as important mediators at the interface of innate immunity, thrombosis, and vascular remodeling. Aortic aneurysms and cerebrovascular diseases are now recognized as prototypical NET-driven conditions in which dysregulated immunothrombosis accelerates wall degeneration, promotes thrombus formation, and exacerbates tissue damage. This narrative review synthesizes current evidence on NET biology in the context of abdominal and thoracic aortic aneurysms, acute aortic syndromes, ischemic stroke, and intracerebral hemorrhage. First, we summarize the molecular mechanisms of NET formation, regulation, and clearance, outlining how NETs shape the immunothrombotic niche in aneurysm thrombi and the cerebral vasculature. Following this, experimental and clinical data on circulating and tissue NET markers are examined in relation to disease presence, lesion instability, hematoma expansion, and functional outcome, thereby highlighting their potential as diagnostic and prognostic biomarkers. Thereafter, pharmacological strategies that either inhibit NET formation or enhance NET degradation are discussed. We then examine how multilayered flow modulators, biodegradable scaffolds, and bioinspired stent coatings can modulate NET responses at the blood-device interface. Finally, we explore the emerging roles of multi-omics profiling, molecular imaging, artificial intelligence (AI)-based analyses in integrating NET-related signals into personalized risk prediction, device selection, and treatment monitoring. Collectively, these findings position NETs as central targets for biomarker development, immunomodulatory therapy, and immunobioengineered vascular devices. NET-informed precision management of aortic and cerebrovascular diseases is therefore a key area for further research.

Intracerebral hemorrhage (ICH) with thrombocytopenia is associated with poor outcomes, but early risk prediction tools for this subgroup are limited. We aimed to develop and externally validate an interpretable machine learning model for predicting 28-day all-cause mortality after ICU admission.

Internal data were derived from MIMIC-III/IV, eICU, and NWICU, and external validation data from a single tertiary academic teaching hospital in China. The internal cohort was randomly split 7:3 into internal training set and internal test set. Feature selection, hyperparameter optimization, and training of five machine learning models were performed in the internal training set. Performance was evaluated in the internal test set and external validation cohort. SHAP analysis was used for interpretation, and a web-based tool was developed.

Among 1190 included patients, 859 were in the internal cohort and 331 in the external validation cohort. Fifteen predictors were retained. LightGBM showed the best performance, with AUROCs of 0.840 and 0.764 in the internal test and external validation cohorts, respectively. Important predictors included GCS, diastolic blood pressure, glucose, and platelet count.

The developed LightGBM model showed good internal discrimination, acceptable external discrimination, and interpretable feature contributions, supporting its potential as a complementary early risk stratification aid for patients with ICH and thrombocytopenia. Prospective multicenter validation is required before clinical implementation.

Neurotrophic factors (NTFs), including nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), neurotrophin-3 (NT-3), glial cell line-derived neurotrophic factor (GDNF), ciliary neurotrophic factor (CNTF), and vascular endothelial growth factor (VEGF), play a central role in neuronal survival, plasticity, and regeneration. Despite their distinct etiologies and temporal profiles, stroke (both ischemic and hemorrhagic), traumatic brain injury (TBI), and neurodegenerative diseases (NDDs), including Alzheimer's disease (AD) and Parkinson's disease (PD), converge on a common pathophysiological phenotype characterized by excitotoxicity, oxidative stress, mitochondrial dysfunction, neuroinflammation, blood-brain barrier (BBB) disruption, and neuronal apoptosis. Neurotrophic factors modulate these pathological cascades through tropomyosin receptor kinase (Trk) receptors, p75 neurotrophin receptor (p75NTR), and related signaling pathways, thereby supporting neuroprotection, neurogenesis, and synaptogenesis. Experimental evidence from preclinical models demonstrates robust beneficial effects of neurotrophin-based interventions in stroke, TBI, AD, and PD across protein, gene, and cell-based strategies. However, clinical translation remains severely limited. Early-phase clinical trials of adeno-associated virus (AAV)-mediated GDNF and neurturin gene therapy for PD, ex vivo NGF gene therapy for AD, and BDNF gene therapy for AD have confirmed acceptable safety profiles but yielded modest or inconsistent efficacy, largely due to constraints in brain delivery, the need for invasive neurosurgical procedures, restricted target coverage, suboptimal control of expression, and marked patient heterogeneity. Consequently, the principal barrier to clinical success is not biological validity, but the lack of safe, effective and scalable delivery platforms capable of bypassing or functionally modulating the BBB. In this review we synthesize shared pathophysiological mechanisms linking stroke, TBI and NDDs; examine the biology, receptor systems, and signaling pathways of key neurotrophic factors; summarize preclinical evidence for their therapeutic potential; and critically evaluate current delivery strategies, including viral vectors, lipid nanoparticles, exosomes, cell-based therapies, small-molecule mimetics, and intranasal administration. We conclude that overcoming delivery barriers through development of improved viral and non-viral platforms, minimally invasive administration routes, controllable expression systems, and rational patient stratification based on disease stage and biomarkers will be essential to fully realize the neuroprotective and neuroregenerative potential of neurotrophin-based therapies for acute and chronic brain disorders.

PurposeTo determine the accuracy of nursing diagnoses at hospital admission and discharge for patients with heart failure (HF).MethodsThis comparative study examined the documentation in 155 medical records of patients with an admitting diagnosis of HF during August 2018 and July 2019. An audit tool was used to record the diagnoses made by nurses during routine care at the time of admission and discharge. Two researchers (L.S. and A.C.) examined the records and evaluated the documented nursing diagnoses using the Nursing Diagnosis Accuracy Scale version 2. Kappa was used for agreement between them. Patient social and clinical characteristics were described using percentages, absolute frequencies, means, and standard deviations.FindingsA total of 18 unique nursing diagnoses were identified across the 155 patients. Among the 754 nursing diagnoses recorded, 85% of those identified at admission (n = 644) were deemed highly accurate. At discharge, of the 527 diagnoses recorded, 66% (n = 349) were rated as highly accurate. Excess fluid volume was the most common diagnosis (85% at admission, 49% at discharge). Three risk diagnoses were frequent at both points: risk for infection, risk for falls, and risk for decreased cardiac output. Agreement between evaluators ranged from Κ = 0.234 to 1.00.ConclusionsGreater agreement in nursing diagnoses at discharge likely reflects ongoing patient monitoring. Persistent diagnoses at discharge highlight the need for continued nursing care post-discharge.Implications for nursing practiceThis study encourages nurses to improve clinical evaluation for HF patients from admission to discharge. As key clinical indicators are identified, nurses can improve the accuracy of their diagnoses and plan more effective interventions to achieve positive health outcomes and reduce unnecessary hospitalization.

Patients hospitalized for heart failure (HF) face a high-risk early postdischarge period.

We aimed to develop and internally validate the soluble ST2, age, and estimated glomerular filtration rate-heart failure (SAGE-HF) score, a simplified risk model incorporating soluble suppression of tumorigenicity 2 (sST2), age, and renal function, for predicting 30-day postdischarge major adverse cardiovascular events (MACE).

This two-center prospective cohort study included 218 patients hospitalized with acute decompensated heart failure (ADHF) and/or heart failure with reduced ejection fraction (HFrEF) and 59 non-HF controls in Vietnam (July 2025-January 2026). Serum sST2 concentrations were measured at admission using enzyme-linked immunosorbent assay and compared between the ADHF/HFrEF cohort and controls, with adjustment for clinical covariates. Among the ADHF/HFrEF cohort, the primary endpoint was 30-day MACE, defined as a composite of all-cause death or HF rehospitalization. Candidate predictors were considered based on clinical relevance, biological plausibility, and exploratory univariable associations, and prespecified parsimonious logistic regression models were evaluated using discrimination (area under the receiver operating characteristic curve [AUC]), calibration, Brier score, and Akaike information criterion. Internal validation was performed using 1000 bootstrap resamples, and model robustness was assessed using Firth penalized logistic regression. The SAGE-HF score was derived from the final model. All data were analyzed using the R environment (version 4.5.5; R Foundation for Statistical Computing).

Among 277 participants, sST2 concentrations were significantly higher in patients with HF than in non-HF controls after adjustment. Among 218 patients, 47 (21.6%) experienced 30-day MACE. In multivariable analysis, age (odds ratio [OR] 1.04 per year, 95% CI 1.01-1.07; P=.02), sST2 (OR 1.06 per ng/mL, 95% CI 1.01-1.11; P=.01), and estimated glomerular filtration rate (OR 0.98 per unit, 95% CI 0.97-1.00; P=.02) were independently associated with MACE. The final model demonstrated acceptable discrimination (AUC 0.741, 95% CI 0.664-0.819) and good calibration, with stable performance after bootstrap validation (corrected AUC 0.725). Firth analysis yielded consistent results. The SAGE-HF score showed progressive risk stratification, with predicted 30-day MACE ranging from 7.0% to 60.1% and maintained acceptable discrimination and calibration.

The SAGE-HF score, incorporating age, sST2, and estimated glomerular filtration rate, demonstrated acceptable performance for predicting 30-day MACE after HF hospitalization and may support early risk stratification, pending external validation.

The coexistence of acute ischemic stroke (AIS) and unruptured intracranial aneurysms (UIAs) is relatively common, but the safety of antithrombotic therapy in this population remains uncertain. This study evaluated whether different secondary prevention regimens influence UIA progression or rupture risk.

This single-center retrospective cohort study included patients with AIS and concomitant saccular UIAs between January 2016 and December 2021. Patients were categorized according to long-term antithrombotic exposure: single antiplatelet therapy (SAPT), anticoagulation, or no regular therapy. Dual antiplatelet therapy (DAPT) was analyzed separately. The primary outcome was aneurysm rupture; secondary outcomes included symptomatic events and radiographic progression (≥1 mm growth).

Among 197 patients (mean age 68.4 ± 10.2 years; 53.8% female), 140 received SAPT, 36 received anticoagulation, and 21 received no regular therapy. During a mean follow-up of 28.6 ± 18.4 months, aneurysm rupture occurred in two patients (1.0%). No ruptures occurred in the anticoagulation group. Firth's penalized logistic regression showed no significant association between antithrombotic regimen and aneurysm rupture (anticoagulation vs SAPT: OR 0.41, 95% CI 0.01-5.21; no therapy vs SAPT: OR 3.21, 95% CI 0.21-45.6; both p > 0.05).

In patients with small incidental saccular UIAs and AIS, standard secondary prevention regimens were not associated with an increased risk of aneurysm rupture. However, the limited number of rupture events and potential residual confounding warrant cautious interpretation. Secondary stroke prevention should remain the priority, with individualized assessment of aneurysm-related risks.

Refugees experience a disproportionately high burden of hypertension, with war-related displacement disrupting their social networks. Countries from the Middle East and North Africa (MENA) are among the largest producers of refugees globally. While previous research has established the role of social support in improving chronic disease management, little is known about its impact among refugees from Arabic-speaking MENA countries. This study focuses on refugees diagnosed with hypertension from two Arabic-speaking MENA countries, Syria and Iraq, to investigate perceived social connectedness and the influence of social support on hypertension management.

107 refugees from Iraq (n = 84) and Syria (n = 23) who resettled in San Diego, CA, were recruited from a federally qualified health center in a refugee-majority neighborhood. Participants completed quantitative surveys; a subsample of 54 completed in-depth interviews until thematic saturation was reached.

Mean age was 61.61 years (SD = 9.81), mean years since United States resettlement was 9.6 years (SD = 5.82), and 48.6% (n = 52) of participants were female. Overall, 60.7% (n = 65) reported social disconnection, and 61.7% reported an annual income <$15,000. Participants described 3 forms of social support (or lack thereof) that impact hypertension management. (1) Informational: reliance on close social networks for health information; (2) Instrumental: lack of transportation and language barriers in clinical spaces; and (3) Emotional: loss of social networks and social status, causing psychosocial stress.

Findings suggest that prioritizing family reunification during resettlement and integrating social support into public health interventions, such as family-centered care, shared medical appointments, and community-based peer support, may mitigate the loss of social support and related hypertension disparities among refugees.

Chronic heart failure (CHF) is a progressive and recurrent condition associated with high hospitalization rates, substantial healthcare burden, and impaired quality of life. Health-promoting behaviors are essential for long-term disease management in patients with CHF, yet their formation and maintenance are often constrained by individual capability, external support, and motivational factors.

This study aimed to examine the current status and associated factors of health-promoting behaviors among patients with chronic heart failure and to clarify the pathways among related factors.

A cross-sectional design was used to investigate health-promoting behaviors and their associated factors among patients with CHF. The study was conducted from December 2024 to November 2025 in two tertiary Grade A hospitals in Shanghai. Convenience sampling was used, and 320 questionnaires were distributed; 302 valid questionnaires were included, yielding a valid response rate of 94.38%. The instruments included a general information questionnaire, the Health-Promoting Lifestyle Profile, the Perceived Social Support Scale, the Heart Failure-Specific Health Literacy Scale, and the Health Activation Scale. SPSS version 26.0 was used for descriptive statistics, univariate analysis, and multiple linear regression. AMOS version 23.0 was used for path analysis, and Python version 3.9 was used for random forest-based feature importance ranking.

The total health-promoting behavior score was 126.00 (116.00, 137.00), and the mean item score was 2.42 (2.23, 2.63), indicating an overall moderate level; the largest proportion of patients was classified as moderate (52.32%). Among the dimensions, self-actualization had the highest score, whereas physical activity had the lowest score. Health literacy was at a moderate level, perceived social support was at a high level, and health activation was at a low level. Univariate analysis showed significant differences in health-promoting behavior scores by age, educational level, employment status, marital status, living arrangement, monthly per capita household income, type of medical insurance, disease duration, number of comorbidities, and number of hospitalizations in the past year (p < 0.05). Health-promoting behaviors were positively correlated with health literacy, perceived social support, and health activation (p < 0.01). Multiple linear regression analysis showed that monthly per capita household income, type of medical insurance, number of comorbidities, number of hospitalizations in the past year, employment status, health activation, perceived social support, and health literacy were independently associated with health-promoting behaviors, jointly explaining 68.7% of the variance. Mediation analysis showed that health activation partially mediated the associations of health literacy and perceived social support with health-promoting behaviors. Nonlinear analysis further suggested that number of hospitalizations in the past year, health activation, perceived social support, and health literacy were key features associated with health-promoting behaviors.

Health literacy, perceived social support, and health activation were key factors associated with health-promoting behaviors among patients with CHF. Health activation served as a significant mediator linking health literacy and perceived social support with health-promoting behaviors. Future interventions should focus on improving health literacy, strengthening social support systems, and enhancing health activation to promote the development and maintenance of health-promoting behaviors among patients with CHF.