This study aimed to identify key biomarkers for obstructive sleep apnoea (OSA) by analysing RNA-seq (GSE75097: 6 control and 42 OSA) and single-cell RNA-seq (GSE214865: 11 control and 11 OSA) datasets. Differential expression analysis revealed 575 DEGs between OSA and control groups. Using weighted gene co-expression network analysis (WGCNA) combined with machine learning approaches (Random Forest, SVM-RFE and LASSO), we identified four hub genes (C17ORF91, HBEGF, PGBD2 and ZNF616) as candidate biomarkers, showing exploratory discriminatory performance (AUC > 0.7). Gene set enrichment analysis (GSEA) revealed that these genes are associated with pathways related to hypoxia response, atherosclerosis and metabolic reprogramming (NES as low as -2.049, adj. p < .05), as well as immune regulation (NES up to 1.932, adj. p < .05). Single-cell RNA sequencing analysis further demonstrated that cMonocytes play a central role in OSA progression, exhibiting the highest scores for hypoxia, metabolic dysfunction and atherosclerosis risk. An exploratory nomogram incorporating these hub genes showed favourable internal performance within the discovery cohort. These findings provide hypothesis-generating insights into the molecular features associated with OSA and identify these genes as candidate biomarkers that require independent external and functional validation. Further validation of these genes may provide a basis for evaluating their potential diagnostic and therapeutic relevance in OSA.

The COVID-19 pandemic presented severe challenges in understanding and predicting the spread of infectious diseases, necessitating innovative approaches beyond traditional epidemiological models. This study introduces an advanced method for automated model discovery using the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, leveraging a dataset from the COVID-19 outbreak in Thuringia, Germany, encompassing more than 400,000 patient records and vaccination data. We develop a flexible, data-driven model capturing pandemic dynamics, incorporating external factors and interventions into the mathematical framework. The fixed coefficient values globally determined by SINDy were not accurate for local modelling of the data, a limitation of prior SINDy-based epidemiological applications that our framework directly addresses. We therefore refined our technique based on the differential equations as found by SINDy, by investigating three modifications that account for recent local data, each offering different strengths for short-term prediction, scenario analysis, and capturing nonlinear effects, achieving an R² of 0.87 at a one-week horizon. In a first approach, we re-optimized the coefficient values using seven days of past data, without changing the globally determined differential equation. In a second approach, we allowed a temporal dependence of the coefficient values, fitted using all previous data, in combination with regularization. As a last method, we kept the coefficients fixed to the original values but augmented the differential equation with a small neural network, locally optimized to the data of the past week. Our results link vaccination and public health measures to the pandemic's trajectory. The proposed model allows simulation of intervention scenarios, such as vaccination strategies and public health interventions. While the current study is based on retrospective data from a single region, the framework could serve as a basis for exploring responses to future outbreaks, subject to further validation.

To examine socio-demographic inequalities in depressive symptoms among older adults in China during and after the COVID-19 pandemic and assess whether these inequalities remained stable or changed over time.

This systematic review and meta-analysis followed PRISMA guidelines. Nine English and Chinese databases were searched from 1 January 2020 to 15 June 2026. Eligible studies were conducted among community-dwelling adults aged ≥60 years in mainland China, used probability sampling and standardized measures of depressive symptoms, and reported cross-sectional data. Study quality was assessed using the 11-item Agency for Healthcare Research and Quality checklist. Potential publication bias was assessed using Begg's and Egger's tests and trim-and-fill analysis. Year-stratified prevalence and pooled odds ratios (ORs) were estimated for disparities by gender, marital status, urban-rural residence, living arrangement and educational attainment.

Seventy-nine studies involving 199,116 older adults from 21 provincial-level regions were included; 52 studies were rated as high quality and 27 as moderate quality, with none rated as low quality. Pooled prevalence fluctuated across investigation years, from 16.6% (95% CI 12.1-22.4) in 2022 to 22.9% (95% CI 18.3-28.3) in 2024-2025, with substantial between-study heterogeneity (overall I² = 99.55%). Higher odds of depressive symptoms were observed among women, unmarried older adults, rural residents, those living alone and those with lower educational attainment. The largest disparities were related to education (OR 1.693, 95% CI 1.388-2.060), living arrangement (OR 1.671, 95% CI 1.437-1.945) and marital status (OR 1.652, 95% CI 1.467-1.860). Year-level analyses suggested limited temporal variation overall: marital-status-related inequality showed a small upward trend, whereas living-arrangement-related inequality showed a non-significant tendency toward attenuation. No broad systematic restructuring was observed.

Depressive symptoms among older adults in China fluctuated across the pandemic and post-pandemic periods, while socio-demographic inequalities remained broadly stable. Major public health disruption appeared more likely to expose and prolong pre-existing inequalities than fundamentally reshape them in the short term. These findings provide a useful comparative reference for understanding mental health vulnerability in aging societies facing public health emergencies.

https://www.crd.york.ac.uk/PROSPERO/view/CRD42025642200; identifier: CRD42025642200.

Circulating microRNAs (miRNAs) are promising non-invasive biomarkers for coronary artery disease (CAD). We conducted an updated meta-analysis evaluating their diagnostic accuracy through December 2025. PubMed, Embase, and Scopus were searched for diagnostic accuracy studies of circulating miRNAs in angiography-confirmed CAD. Accuracy was pooled using a bivariate random-effects model yielding sensitivity, specificity, and SROC-AUC. Twenty-four studies (64 comparisons, 3,879 participants) were included. Pooled sensitivity was 0.777 (95% CI: 0.737-0.816), specificity 0.832 (95% CI: 0.801-0.862), and AUC 0.872. Multi-miRNA panels outperformed single miRNAs (AUC 0.901 vs. 0.868). Substantial heterogeneity was present (Sensitivity; I²=92.1%; τ²=0.738); however, τ² remained substantial (sens:0.738, tfpr:0.434), indicating persistent heterogeneity; Holling-adjusted I² (5.1-7.7%) reflects sensitivity to study precision, not resolved heterogeneity. Meta-regression identified panel-based assay type and plasma sample source as modifiable drivers. Circulating miRNAs show moderate-to-good diagnostic accuracy for CAD as investigational complementary biomarkers. Prospective validation with standardized protocols is required before clinical deployment. PROSPERO Registration ID: CRD420261279673. Not Applicable (As this is a Systematic Review and Meta-analysis and not a Clinical Trial).

Subthalamic nucleus-deep brain stimulation (STN-DBS) reliably improves cardinal motor symptoms in Parkinson's disease (PD). However, gait disorders and non-motor symptoms (NMS) may persist or emerge following surgery, diminishing quality of life and complicating clinical management.

To characterize the dominant axes of clinical covariance across motor and non-motor domains before and after STN-DBS, and to determine whether non-motor symptom severity moderates post-DBS gait and mobility change.

100 PD patients undergoing STN-DBS (mean age 60.2 ± 7.6 years; 67% male) were studied. Principal component analysis (PCA) was performed on thirteen motor and non-motor sub-scores at pre-DBS, 1-year follow-up (1-y FU), and a subset (n = 33) at 3-5-year follow-up. Moderation was tested using analysis of covariance (ANCOVA): each 1-year motor outcome was regressed on its pre-DBS baseline, a non-motor moderator, and their interaction, adjusting for age and disease duration, with a significant interaction indicating that baseline non-motor severity was associated with the degree of subsequent motor recovery.

PCA identified a core motor triad: gait, mobility, and freezing of gait as the primary source of clinical variance at all timepoints (PC1: 35% pre-DBS, declining to 28% at 3-5-y FU), PC1 was the only component to exceed a permutation null (p < 0.001), with memory-related impairment increased at longer-term follow-up. Moderation analysis showed that higher pre-operative mood burden specifically for psychomotor and psychosomatic symptoms were associated with poorer recovery of mobility (Psychomotor β = 1.16, p < 0.05). At 1-y FU anxiety and psychosomatic symptoms showed concurrent associations with mobility and overall motor burden (Anxiety β = 2.30, p < 0.05).

STN-DBS outcomes extend beyond global motor improvement and may be shaped by associations spanning the motor, cognitive, and mood domains. Higher NMS baseline burden was associated with poorer mobility recovery, and mood symptoms at follow-up were associated with concurrent motor burden. These exploratory findings support considering multi-domain pre-operative assessment and post-operative mood monitoring, as potential targets for optimizing motor outcomes.

Consensus is lacking on which endocardial borders to include when measuring left ventricular wall thickness (LVWT) in cats.

The number of endocardial borders included can influence whether an individual cat is classified as having hypertrophic cardiomyopathy (HCM) or not.

Fifty-two cats that previously were diagnosed as either healthy, equivocal for HCM, or having an HCM phenotype.

Single-center retrospective cross-sectional study. Three observers measured LVWT using 4 different LVWT measurement protocols: leading edge-to-leading edge (L-L); leading edge-to-trailing edge, trailing edge-to-leading edge, and trailing edge-to-trailing edge for the interventricular septum and L-L and trailing edge-to-leading edge methods for the left ventricular free wall (LVFW). Measurements were made using stored echocardiographic images on 2 occasions by 2 observers. A Friedman test and post-hoc Wilcoxon signed rank test with Bonferroni correction were performed to detect differences between measurement techniques.

Interventricular septum (IVS) wall thickness was significantly larger for leading-edge-to-trailing-edge measurement (median and [interquartile range (IQR)]: 5.5 [3.8-7.2] millimeters) compared with other methods (P < .001). The trailing-edge-to-leading-edge method produced significantly smaller results (4.3 [IQR, 3.4-6.2] millimeters for the interventricular septum and 4.8 [IQR, 3.4-6.2] millimeters for the LVFW) compared with other methods (P < .001). Median IVS thickness measured as leading-edge-to-trailing-edge was 1.2 millimeters larger than when measured as trailing-edge-to-leading-edge.

LVWT varies depending on the number of endocardial borders included. The number of endocardial borders included in LVWT measurements should be standardized.

Preclinical drug development has long relied on animal models to predict safety and efficacy before agents enter human trials, despite critical differences between human and animal model physiology. The withdrawal of rosiglitazone, rofecoxib, and terfenadine due to cardiovascular toxicity exemplifies the translational cost of this mismatch. Alternative, human-based systems enable more accurate modeling of cardiometabolic diseases in a dish; in 2025, the US FDA's new approach methodologies (NAMs) roadmap authorized the submission of results from human-relevant models. The roadmap encourages utilizing biological and digital twins as part of an integrated, context-specific, fit-for-purpose strategy. A "biological twin" is a human-derived in vitro system that captures the physiology of a patient and can be used to assess potential cardiotoxicity by drug metabolites. A "digital twin" is the computational counterpart trained on clinical drug response results that can further interpret biological twin data at the patient scale and predict pharmacological parameters. NAMs are no longer experimental but are not yet fully validated as replacements for animal models; major challenges remain before they can be effectively incorporated into the cardiometabolic disease drug discovery pipeline. Addressing these challenges head-on is essential for improving drug development and prediction of their cardiovascular safety.

The 2024 American Heart Association and American College of Cardiology multisociety peripheral artery disease (PAD) guideline updates the 2016 guideline by clarifying clinical terminology, using trial evidence to guide pharmacotherapy, and addressing equity and access to care. This article summarizes the main clinical recommendations and how to implement them.

Cellular ageing of the immune system has been suggested to play a role in the pathogenesis of rheumatoid arthritis (RA). RA onset is preceded by a preclinical phase of systemic autoimmunity without overt tissue inflammation, allowing the investigation of predisease cellular dysfunction in at-risk individuals. Lymph nodes (LNs) are crucial for (auto)antibody development, with LN fibroblasts playing a key role in regulating immune homeostasis. Here, we investigated age-associated alterations in LN fibroblast differentiation capacity during the at-risk and established stages of RA.

Primary LN fibroblasts were isolated from inguinal LN core biopsies obtained from healthy controls, at-risk individuals and patients with RA. Adipogenic differentiation assays served as a functional readout of cellular plasticity and bulk RNA sequencing was used to evaluate ageing-related transcriptional changes.

LN fibroblasts from at-risk individuals and patients with RA exhibited reduced adipogenic capacity, reflected by fewer lipid droplet-positive cells on differentiation. Transcriptomic analysis revealed downregulation of cell cycle, DNA repair and differentiation pathways. These fibroblasts also failed to upregulate the cell cycle switch G0S2 on stimulation.

Together, these findings indicate a disrupted coordination between cell cycle processes and differentiation in at-risk and RA LN fibroblasts, consistent with features of cellular ageing. Notably, these alterations were already present in at-risk individuals prior to clinical disease onset, suggesting that reduced fibroblast fitness may contribute to impaired LN tissue homeostasis in disease-prone individuals.

To evaluate trends, early clinical outcomes and surgeon-variation and hospital-variation in trainees performing isolated aortic valve replacement (AVR) and mitral valve (MV) surgery, including replacement (MVR) and MV repair (MVr) in the UK.

All adults undergoing elective or urgent isolated AVR or isolated MVr/MVR in the National Adult Cardiac Surgery Audit between 1996 and 2019 were included. Temporal trends and early clinical outcomes were compared between consultants and trainees as primary operators. Three-level multilevel logistic regression was used to quantify hospital-level and consultant-level variation in the proportion of cases performed by trainees.

Among 71 710 isolated AVR and 25 197 isolated MV surgery cases, 13 432 (18.7%) and 2472 (9.8%) were performed by trainees, respectively. MV surgery included 9893 MVR and 15 292 MVr, of which 1226 (12.4%) and 1246 (8.1%) were performed by trainees, respectively.Propensity matching generated balanced cohorts of 13 432 AVR, 1223 MVR and 1243 MVr pairs. Trainee-led procedures were not associated with higher in-hospital morbidity or mortality.Multilevel modelling demonstrated substantial variation in trainees being the primary operator, with 59.6%, 57.1% and 44.8% of variation at hospital-level and surgeon-level for AVR, MVr and MVR, respectively.

In this national UK cohort, trainees performed approximately one in five isolated AVR operations and one in ten isolated MV operations. Trainee exposure was the lowest in MVr. Trainee-led procedures were not associated with an increase in in-hospital mortality or early morbidity. There was a marked hospital-level and consultant-level variation in trainees' operating opportunities. These findings support continued structured exposure to isolated aortic and MV surgery, with particular attention to a specific MVr training pathway.