To systematically screen and verify the active components in the Dong ethnic medicine Lengyuxiao Tang (LYXT; Semen Pharbitidis, Verbena officinalis L., and Zingiber officinale Roscoe) for treating fever and cough, this scheme established a methodology process integrating network pharmacology prediction and experimental verification. Firstly, using the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP), with an oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 as criteria, the key active components of LYXT were screened, and the related targets for fever and cough were collected from multiple disease databases. Subsequently, the "component-target-disease" interaction network was constructed, and, through topological analysis and gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment, the core action targets (CYP3A4, POR) and key signaling pathways (MAPK pathway) were identified. Molecular docking technology was used to verify the binding ability of key components to core targets. Finally, a chronic bronchitis mouse model was established by intranasal instillation of lipopolysaccharide (LPS), and the in vivo anti-inflammatory efficacy of the predicted main active components was verified. The results showed that components such as β-carotene and saparenol, screened by network pharmacology, exhibited varying degrees of anti-inflammatory effects in animal experiments, and their mechanisms may involve inhibition of the MAPK/p38 signaling pathway. This scheme provides a repeatable example for the screening and mechanism research of active components in traditional ethnic medicines.

Natural killer (NK) cells are critical for early antiviral immunity, yet their metabolic regulation during acute human viral infection remains incompletely understood. We analyzed NK cell activation and metabolic reprogramming in 47 vaccinated individuals with mild breakthrough SARS-CoV-2 infection and 20 matched healthy control subjects. COVID-19 patients exhibited elevated plasma interferon α and NK cell activation markers (CD69, CD38), alongside increased basal STAT5 phosphorylation, consistent with IL-15-mediated signaling. Functionally, NK cells from infected subjects displayed heightened cytotoxicity. Metabolic profiling at the single-cell level revealed increased cell size, translational activity, amino acid and glucose uptake, and mitochondrial membrane potential, indicating a globally activated metabolic state specific to NK cells. Using newly developed spectral cytometry panels targeting metabolic regulators, we identified CPT1a as the most discriminative marker between patient and control NK cells, with elevated expression in both CD56bright and CD56dim subsets. CPT1a levels correlated with CD38 expression and with uptake of the fluorescent palmitate analog BODIPY-FL C16, reflecting enhanced long-chain fatty acid oxidation. These changes were absent in B and T lymphocytes. Our findings support that during SARS-CoV-2 infection, human NK cells undergo coordinated cytokine-driven activation and metabolic remodeling, integrating glycolysis and lipid oxidation to support amplified effector function.

Acute heart failure (AHF) is a common but underrecognized cause of dyspnea. Chest computed tomography (CT) can accurately assess pulmonary congestion, but radiologist reporting capacity may limit clinical utility. We hypothesized that an artificial intelligence (AI) model could automatically detect imaging signs of AHF and aimed to prospectively validate an AI model in an independent emergency department cohort, benchmarking its performance against radiologists and cardiologists.

We prospectively validated a supervised machine-learning model in a single-center study of dyspneic patients undergoing low-dose, non-contrast chest CT and echocardiography. The primary analysis assessed diagnostic performance for CT-detected pulmonary congestion compatible with AHF, using radiologist-reported AHF as the reference and the area under the curve at receiver operating characteristic analysis (AUROC). Secondary analyses compared the AI model with blinded research radiologists and expert cardiologists.

Of 234 patients (56% males), aged 74 ± 10 years (mean ± standard deviation), 61 (26%) had radiologist-reported AHF. The AI model achieved high diagnostic performance (AUROC 0.95 [95% confidence interval 0.93-0.98]), with 89% sensitivity [78-95] and 89% specificity [83-93]. At prespecified thresholds, rule-out maximized sensitivity (97% [89-100]) at the expense of specificity (74% [67-81]), whereas rule-in yielded high specificity (96% [92-98]) but lower sensitivity (66% [52-77]). In secondary analyses, the AI model achieved a median AUROC of 0.94 (range 0.91-0.96).

The AI model demonstrated high diagnostic performance for detecting AHF on chest CT in dyspneic patients. Integration into emergency workflows may support more consistent diagnosis, independent of clinician experience or time constraints.

AI-based analysis of chest CT may enable earlier and more consistent detection of AHF, supporting timely triage and management, especially when specialist radiological expertise is limited or delayed.

An AI model prospectively detected AHF on chest CT in dyspneic emergency department patients. In a prospective single-center cohort, AI achieved high diagnostic performance (AUROC 0.91-0.96), comparable to that of radiologists and cardiologists. AI-based chest CT interpretation may improve diagnostic consistency in the absence of standardized CT criteria for AHF.

To compare the differences in the risk of cardiovascular disease (CVD) events between narcolepsy type 1 (NT1) and type 2 (NT2) among patients with narcolepsy, while accounting for real-world use of stimulants.

Using the 2005-2023 MarketScan Commercial and Medicare Supplemental databases, we identified patients newly diagnosed with narcolepsy, either NT1 or NT2, using International Classification of Diseases, Ninth or Tenth Revision, Clinical Modification diagnosis codes. Stabilized inverse probability of treatment weighting (IPTW) was applied to balance baseline characteristics between the NT1 and NT2 groups. Primary outcomes included time to first (1) composite CVD event and (2) major adverse cardiovascular event (MACE). We used multivariable Cox proportional hazards regression models following IPTW to estimate adjusted hazard ratios (AHRs), accounting for time-fixed and time-varying covariates, including stimulant use. Individual cardiovascular outcomes were assessed separately, and analyses were stratified by age and sex.

After IPTW, the overall effective sample size was 30,154 patients (NT1 = 3,068, NT2 = 27,086; mean [SD] age, 39.8 [16.5] years; 61.8% female). In models adjusting for baseline covariates and time-varying stimulant use, there was no difference in risk of CVD (AHR, 0.95; 95% CI, 0.75-1.22) or MACE (AHR, 0.98; 95% CI, 0.72-1.33) between NT1 and NT2. Results were consistent across individual CVD and MACE outcomes, as well as in subgroup analyses by age and sex.

Findings from this IPTW cohort study suggest that, after adjusting for medication use, there was no significant difference in cardiovascular risk between individuals with NT1 and NT2. Patients with narcolepsy are at elevated cardiovascular risk, but little is known about how this risk differs between narcolepsy type 1 (NT1) and type 2 (NT2). Previous studies have not directly compared cardiovascular risk by narcolepsy subtype while accounting for real-world use of medications, including stimulants, which may change over time and influence outcomes. In this large, national cohort of commercially insured individuals, we found no significant difference in cardiovascular disease risk for individuals with NT1 vs. NT2 after adjusting for baseline characteristics and the use of narcolepsy medications, including stimulants. These findings address the gap in the literature regarding cardiovascular risks across narcolepsy subtypes and may inform clinical management of the disorder.

Aphasia is among the most debilitating post-stroke deficits. Previous studies have suggested that bilingualism, the ability to use more than one language, may confer advantages in language recovery compared with monolingualism in patients with chronic stroke. Our aim was to determine whether early pre-scholar simultaneous bilingualism acts as a protective factor by examining language recovery within the first seven days after acute ischaemic stroke.

We retrospectively analyzed clinical and neuroimaging data from Italian monolingual and early bilingual (Slovenian- and Croatian-Italian) patients with anterior-circulation ischaemic stroke and aphasia who were consecutively admitted between January 2018 and April 2020. The two cohorts were compared, and a multivariate logistic regression model was used to identify variables associated with language improvement at 7 days, defined as a ≥ 1-point reduction or complete recovery.

The two groups did not differ in demographic or clinical characteristics, type of acute treatment, or extent of the ischaemic lesion. Early bilingual patients exhibited significantly greater improvement in NIHSS language scores at day seven. In multivariate analysis, early bilingualism (p = 0.005) emerged as independent predictor of early language recovery, with consistent effect sizes across sensitivity analyses.

Early bilingualism (eBL) is an independent and robust predictor of aphasia recovery within the first seven days after stroke.

The deleterious effect of intraoperative hypotension has been emphasised by many observational studies. However, vasopressor therapies to treat intraoperative hypotension, specifically the choice between phenylephrine and norepinephrine, have been debated. There is a crucial need for additional studies to determine the most appropriate choice for vasopressor therapy in the context of this variable use.

A sequential explanatory mixed-methods design is proposed to expand the knowledge base relating to vasopressor selection during non-cardiac surgery and inform the education surrounding and extrapolation of the results of a pragmatic clinical trial. The three phases will consist of an observational study using rigorously validated multicentre electronic health record data, a web-based survey targeting clinicians and a semistructured interview targeting a subsample of survey respondents. These research aims will address areas with insufficient data: (1) the quantification of practice patterns and variation of vasopressor selection within non-cardiac operating rooms and (2) the reasoning behind these clinical decisions. This study will serve as primary steps to develop and provide context for the future results of a multicentre pragmatic clinical trial comparing the impact of phenylephrine and norepinephrine on postoperative patient outcomes.

This study has received Institutional Review Board (IRB) exemption from the University of Michigan. We plan to disseminate results through peer-reviewed journal articles, conference proceedings and presentations.

To evaluate the cost-effectiveness of insertable cardiac monitors (ICMs) compared to standard of care (SoC) to detect atrial fibrillation (AF) in patients with stroke of presumed known cause of large-artery atherosclerotic disease (LAD) or small-vessel occlusive disease (SVD) from a US payer perspective.

A lifetime Markov model assessed cost-effectiveness of ICM versus SoC from a US payer perspective. Patient characteristics and AF detection rates were based on the STROKE AF trial (NCT02700945): 3-year diagnostic yield was 21.7% (95% CI 16.7% to 27.9%) for ICM and 2.4% (1.0%-5.7%) for SoC. AF detection resulted in a switch from aspirin to direct oral anticoagulant unless precluded by prior bleeding. Subsequent risks of ischaemic strokes (ISs) and bleeding events were modelled based on published literature. Costs and effects were discounted at 3% annually. Specific SoC short-term monitoring strategies (STMs) were explored as scenarios.

US healthcare system perspective.

Hypothetical cohort of patients with IS believed to be due to LAD or SVD.

Patients received an ICM within ten days of the index stroke or SoC involving conventional follow-up.

Stroke and bleeding risk, mortality, health-related quality of life and healthcare cost and utilisation.

ICM was associated with a gain of 0.176 quality-adjusted life years (QALYs) compared with SoC per patient, representing a reduction of 53 strokes per 1000 patients. The lifetime incremental cost of ICM was $6736 per patient. This resulted in an estimated incremental cost-effectiveness ratio (ICER) of $38 346 per QALY gained, making ICM a cost-effective intervention at willingness-to-pay thresholds of $50 000-$150 000 per QALY in the USA. ICMs were also cost-effective compared with various individual STMs, with ICERs ranging from $29 814 to $38 941 per QALY gained. The mean probabilistic ICER across 5000 samples was $46 910 per QALY (97.5% CI$45 421 to $53 523). Results were sensitive to anticoagulant uptake on AF detection and underlying stroke risk. Model findings were robust to both probabilistic sensitivity analysis and sensitivity analyses where inputs tested were considered within plausible ranges, as ICM was found cost-effective in these analyses.

ICMs are highly likely to be a cost-effective diagnostic tool for secondary prevention of stroke related to AF in US patients with prior stroke attributed to LAD or SVD. However, further research is needed to understand the efficacy of secondary stroke prevention treatments in patients with stroke attributed to LAD or SVD and subclinical AF.

Chronic kidney disease (CKD) and cardiovascular disease (CVD) are leading global causes of morbidity and mortality, often coexisting and sharing common risk factors. Despite their interconnection, clinical care and research for affected individuals remain siloed and fragmented. Recognising the need for integrated approaches, this study aimed to identify and prioritise key research questions at the intersection of CKD and CVD that can be addressed using real-world healthcare data to inform more cohesive and data-driven strategies for improving outcomes across both disease areas.

A three-round modified Delphi process was conducted: Round 1 online survey collected open-ended research questions about CKD-CVD priorities via BHF Data Science Centre, Kidney Research UK, UK Renal Health Data Network and HDR UK public involvement channels; Round 2 in-person workshop refined and consolidated items; Round 3 online survey prioritised items across urgency, feasibility and impact using 5-point scales.

Survey mean scores for each research question were calculated across the three prioritisation domains, each scored out of 5. The top-ranked questions were identified based on overall scores.

Six thematic domains emerged: risk prediction and early detection, treatment optimisation, health inequities, multimorbidity, disease mechanisms and data infrastructure. The highest-rated research priority was "What are the most effective strategies for prevention, early diagnosis and intervention in CKD?" with a mean score of 12.6 (SD 1.1). Other top priorities included evaluating the cost-effectiveness of early treatment, identifying predictors of kidney failure and assessing the benefits of treating cardiovascular and renal conditions independently.

Across domains, prevention/early detection and early treatment in CKD consistently ranked highest, indicating near-term opportunities for data-enabled cardio-renal research and service improvement; these priorities can inform funder calls, data linkage work and evaluation studies.

Atherosclerosis continues to represent a major global health burden, with morbidity and mortality that remain inadequately addressed by current therapeutic strategies. Traditional Chinese herbal medicines (CHMs) are deeply embedded in cardiovascular practice and increasingly recognized for their capacity to target multiple pathological processes, including inflammation, lipid dysregulation, endothelial dysfunction, and plaque instability.

This review critically evaluates the clinical and mechanistic evidence supporting CHMs in atherosclerosis, with particular attention to translational barriers and their integration into contemporary cardiovascular care.

A structured literature search was conducted across PubMed, Web of Science, and CNKI, prioritizing CHMs with documented clinical efficacy and mechanistic relevance in atherosclerosis.

Key bioactives such as berberine, salvianolic acids, ginsenosides, and astragaloside IV exert pleiotropic effects through NF-κB, PI3K/AKT, mTOR, and ferroptosis-related pathways. Approved CHM formulations such as Tongxinluo Capsule and Danhong Injection have been shown to improve lipid profiles, enhance plaque stability, and reduce inflammatory markers. However, challenges related to bioavailability, trial design, and regulatory cohesion continue to limit their widespread clinical adoption.

Future research is expected to prioritize multicenter trials, standardized protocols, and omics-guided profiling to bridge traditional practice with evidence-based medicine.

Cardiovascular magnetic resonance (CMR) is widely used across various cardiac conditions and systematically assesses cardiac anatomical structures and functional dynamics. Machine learning (ML) can accurately predict outcomes and understand the inherent features of clinical data.

This study aimed to derive CMR phenotypes related to cardiovascular aging, investigate the relationship between these phenotypes and stroke risk, and relearn these phenotypes using supervised ML.

We enrolled 36,467 participants without stroke and extracted CMR parameters from the UK Biobank, with follow-up data extending until September 30, 2023. Using the generative topographic mapping technique, we identified latent grid nodes among participants and then derived phenotypes through agglomerative hierarchical clustering. We used supervised ML models to predict cardiac function phenotypes and used Cox proportional hazards models to assess the association between these phenotypes and long-term stroke risk.

We enrolled 36,467 participants in the study. The mean age was 54.9 (SD 7.5) years, with 17,442 (47.8%) male participants. During a mean follow-up time of 14.7 (SD 1.1) years, 500 (1.4%) participants developed stroke and 664 (1.8%) participants died, respectively. After generative topographic mapping modeling, we identified 2 distinct phenotypes: phenotype 1, characterized by adverse cardiac function and an accumulation of cardiovascular risk factors, reflecting cardiovascular aging; and phenotype 2, associated with a lower risk of stroke (hazard ratio 0.695, 95% CI 0.559-0.864; P=.001), which remained significant after accounting for competing mortality (hazard ratio 0.578, 95% CI 0.484-0.691; P<.001). We selected the random forest model as the optimal model for the phenotypes, demonstrating high accuracy (area under the curve 0.914, 95% CI 0.911-0.918 for training and 0.867, 95% CI 0.858-0.876 for validation) and calibration ability (Brier score 0.111, 95% CI 0.109-0.113 for training and 0.132, 95% CI 0.127-0.137 for validation).

By integrating unsupervised and supervised ML methods, we identified cardiovascular aging-related phenotypes that demonstrate robust predictive ability for incident stroke, which may have the potential to improve preventive and therapeutic strategies for high-risk populations.