BackgroundCovert brain infarction (CBI) is a frequent incidental neuroradiological finding increasingly recognized as clinically relevant due to its association with stroke, cognitive decline, and other adverse outcomes. However, substantial uncertainty remains regarding its optimal assessment and management, and existing guidelines provide limited and heterogeneous recommendations. We aimed to develop an international expert consensus on the evaluation and management of CBI using a modified Delphi methodology.MethodsA modified Delphi process was conducted with an international, multidisciplinary panel of 21 experts from 15 countries, including neurologists, neuroradiologists, neuroepidemiologists, and geriatricians. An initial questionnaire was developed from a structured literature review and identified clinical practice gaps. The survey included 91 statements across four domains: (1) prognosis, etiology, and screening; (2) neuroimaging-based management and follow-up; (3) cardiac evaluation; and (4) pharmacological management. Two iterative rounds were conducted, with anonymized responses, controlled feedback, and structured discussion. A five-point Likert scale was used. Consensus was defined a priori as ≥70% agreement.ResultsConsensus was reached on 33/91 statements (36.3%), moderate agreement on 43/91 (47.2%), and no consensus on 15/91 (16.5%). The panel agreed that CBI is associated with increased risk of ischemic stroke, cognitive impairment, and dementia, and that risk is higher with multiple lesions. Consensus supported targeted clinical evaluation, including a cardiac workup with transthoracic echocardiography in all patients and vascular imaging in selected cases. Routine population screening and systematic post-procedural imaging were not supported. Regarding management, consensus favored optimizing vascular risk factors, particularly strict blood pressure control in hypertensive patients. There was no consensus on routine antiplatelet therapy for incidental CBI, and anticoagulation was generally not supported in the absence of cardioembolic indications. Lipid-lowering showed limited consensus regarding specific benefits for CBI-related outcomes.ConclusionThis Delphi consensus highlights substantial variability in the perceived management of CBI and identifies areas of convergence, particularly in risk stratification and control of vascular risk factors. Key uncertainties remain regarding antithrombotic strategies, lipid-lowering therapy, and long-term surveillance. These results provide a pragmatic, expert-based framework to support clinical decision-making and further highlight the need for prospective studies and randomized trials in this rapidly evolving field.

Type 2 cardiorenal syndrome (CRS) is a complex clinical syndrome characterized by chronic kidney injury resulting from chronic cardiac dysfunction. Its underlying pathophysiological mechanisms remain incompletely understood, highlighting the urgent need for disease models that better mimic human pathology and accurately simulate the progression of cardiac-induced renal injury. Existing models possess significant limitations and often fail to adequately replicate the onset and progression observed in clinical Type 2 CRS patients. We therefore hypothesized that a double-hit approach combining left anterior descending (LAD) ligation (to induce acute myocardial infarction) with transverse aortic constriction (TAC) (to increase afterload) would produce a model that highly simulates the development and progression of clinical Type 2 CRS, characterized by heart failure and subsequent renal injury. A double-hit strategy combining LAD ligation and TAC was performed in mice to induce heart failure followed by secondary renal injury. At four post-operative time points (2 weeks, 4 weeks, 6 weeks, and 8 weeks), we assessed cardiac function, renal function, inflammatory damage, energy metabolism dysfunction, and renal/cardiac fibrosis. Proteomic analysis was additionally performed to characterize molecular changes and identify the optimal model configuration. At week 2, mice developed early-stage heart failure accompanied by mild kidney inflammation and abnormal energy metabolism. By week 4, these abnormalities progressed to marked inflammation, fibrosis, chronic heart failure, and overt kidney damage. The impairments in energy metabolism, inflammation, and fibrosis peaked at Weeks 6 and 8, leading to severe dysfunction of both the heart and kidneys. This double-hit model stably recapitulates the natural progression from chronic cardiac dysfunction to chronic kidney injury observed in Type 2 CRS. It therefore serves as a powerful and innovative research platform for in-depth exploration of the molecular mechanisms underlying Type 2 CRS, screening of potential therapeutic targets, and evaluation of novel interventional strategies.

Tranexamic acid (TXA) has been widely used in primary total knee arthroplasty (TKA) to reduce perioperative blood loss, but the pooled evidence regarding blood loss, transfusion, and deep vein thrombosis (DVT) remains to be further clarified. The purpose of this meta-analysis was to evaluate the efficacy and safety of TXA in primary TKA.

PubMed, Embase, Web of Science, the Cochrane Library, and relevant additional sources were searched to identify randomized controlled trials (RCTs) comparing TXA with a control regimen in primary unilateral TKA. The primary outcomes were blood loss, transfusion, and DVT. Continuous outcomes were pooled as mean differences (MDs) with 95% confidence intervals (CIs), and dichotomous outcomes were pooled as risk ratios (RRs) or odds ratios (ORs) with 95% CIs. Risk of bias was assessed using the Cochrane RoB 2 tool. Random-effects models were applied for meta-analysis. For the blood-loss outcome, an additional robustness analysis was performed using an empirical Bayes random-effects model with Knapp-Hartung standard error adjustment to evaluate whether the result was sensitive to model assumptions, given that methodological choices in random-effects meta-analysis may influence statistical significance when substantial heterogeneity is present.

A total of 17 studies were included in the systematic review and meta-analysis. TXA was associated with significantly reduced blood loss compared with control (MD = -423.28 mL, 95% CI -536.90 to -309.66; P < 0.001), although substantial heterogeneity was observed (I² = 93.44%). Leave-one-out sensitivity analysis showed that the pooled effect remained significant after exclusion of individual studies; exclusion of one influential study reduced heterogeneity to 81.88%. The Knapp-Hartung-adjusted robustness analysis yielded a similar significant effect estimate. TXA also significantly reduced transfusion risk (RR = 0.55, 95% CI 0.45 to 0.68; P < 0.001), with no significant heterogeneity (I² = 0.0%). No significant difference was found between the TXA and control groups in the incidence of DVT (OR = 0.75, 95% CI 0.47 to 1.22; P = 0.25), and heterogeneity was low (I² = 0.0%). Funnel plot inspection suggested possible asymmetry for blood loss and transfusion; however, Egger's test was not significant for blood loss (P = 0.842), whereas significant small-study effects were detected for transfusion (P = 0.002).

The current evidence indicates that TXA in primary TKA significantly reduces blood loss and transfusion requirements with no detected increase in DVT risk in the available randomized evidence. The blood loss analysis showed substantial heterogeneity, whereas the transfusion and DVT analyses were more consistent across studies.

This review aimed to map the effects of sodium-glucose cotransporter 2 inhibitors (SGLT2i) on gout-related and cardiometabolic outcomes in patients with type 2 diabetes mellitus (T2DM) and gout.

A scoping review was conducted following the Joanna Briggs Institute methodology and the PRISMA-ScR checklist. Searches were performed in PubMed, EMBASE, Scopus, Web of Science, LILACS/BVS, and gray literature sources. Eligible studies included patients with T2DM and gout treated with SGLT2i, assessing outcomes such as mortality, gout flares, serum uric acid (SUA) levels, emergency care visits, and hospitalizations due to gout. The PRISMA-ScR checklist is provided in Additional file 1, and the full search strategy in Additional file 2.

Of 282 articles initially identified, 22 met the inclusion criteria. Most studies were retrospective cohort studies (n = 12) or post hoc analyses (n = 8). Only three studies evaluated patients with both T2DM and gout exclusively. The main findings included reductions in SUA levels (n = 8), incidence of gout (n = 12), gout flares (n = 8), initiation of urate-lowering therapy or colchicine (n = 8), and gout-related emergency care or hospital visits (n = 1). Reductions in all-cause (n = 5) and cardiovascular (CV) mortality (n = 4) were also reported. Across the included studies, SGLT2i were frequently associated with favorable effects on the analyzed outcomes.

SGLT2i appears to be associated with multiple favorable effects in patients with T2DM and gout, including urate-lowering effects, reduced gout flares, and lower mortality (all-cause and CV-related). Although limited by the small number of studies focusing specifically on this population, these findings support the potential therapeutic relevance of SGLT2i in patients with coexisting T2DM and gout, contributing to the management of both conditions and the reduction of CV-related events associated with them.

Myocardial infarction (MI) triggers persistent inflammatory dysregulation, wherein macrophages undergo glycolytic reprogramming and adopt an inflammatory phenotype that impairs cardiac repair. However, the specific metabolic drivers underlying post-MI macrophage dysfunction remain unclear. Here, we identify enolase 1 (ENO1)-a glycolytic enzyme up-regulated in post-MI macrophages-as a critical regulator of macrophage metabolic fate and inflammatory function. To intervene, we engineered an acid-responsive nanocarrier (PECS) that externalizes phosphatidylserine within the acidic infarct microenvironment, facilitating selective macrophage uptake. Intracellular delivery of siENO1 suppressed glycolytic flux, thereby curbing lactate accumulation and relieving its metabolic inhibition of KLF4, which initiated anti-inflammatory reprogramming. In vivo, PECS-loaded siENO1 (siENO1@PECS) treatment attenuated infarct size, fibrosis, and cardiomyocyte apoptosis while improving ventricular function. Collectively, these findings identify ENO1 as a critical metabolic checkpoint in post-MI macrophage regulation and demonstrate a bioresponsive nanocarrier strategy for precise gene modulation to enhance cardiac repair.

Following the publication of the above paper, it has been drawn to the Editor's attention by a concerned reader that, for the immunohistochemical images shown in Fig. 3 on p. 1070, the 'SOCS3/HD/16W' and 'SOCS3/CD/28W' images were apparently the same, albeit with differently coloured shading, suggesting that this figure had been assembled incorrectly. In addition, upon analyzing the data in this paper independently in the Editorial Office, it came to light that one of the histological images featured in Fig. 2A (the 'Oil Red O/12W' image) apparently reappeared in a paper written by different authors at different research institutes in the journal Redox Biology several years after the publication of this article, albeit the image in that paper did not feature the same level of clarity. The authors have been contacted by the Editorial Office to offer an explanation for the apparently incorrect assembly of the data in Fig. 3, and we are awaiting their response. Owing to the fact that the Editorial Office has been made aware of potential issues surrounding the scientific integrity of this paper, we are issuing an Expression of Concern to notify readers of this potential problem while the Editorial Office conitnues to investigate this matter further. [International Journal of Molecular Medicine 31: 1066‑1074, 2013; DOI: 10.3892/ijmm.2013.1323].

Understanding the influence of cardiovascular diseases (CVD) on human regulatory functions is a major challenge in modern healthcare. This study explores whether the autonomic response to sleep arousals can serve as a diagnostic marker of the health of physiological networks. We introduce a novel method for the robust calculation of information transfer, based on the optimization of embedding parameters for transfer entropy (TE) calculation. Using this approach, we analyzed the information transfer dynamics of 8,136 sleep arousals from 1,956 subjects from the SHHS follow-up visit. Compared to three state-of-the-art approaches, our newly introduced method, max. TE, achieved a 17.9% more stable TE estimate than the second-best approach. Our results indicate a significantly ( p < 0.001 ) lower TE in patients with CVD ( 85.4 ± 0.8  mnats) than that in healthy controls ( 87.9 ± 0.5  mnats), indicating an impairment of regulatory control mechanisms. Furthermore, the TE decreases linearly with increasing age and shows significant differences between sleep stages, with the highest TE occurring during REM sleep ( p < 0.001 ) . These findings demonstrate that sleep arousals, as a physiological stimulus, can reveal sleep stage- and disease-related changes in cardiovascular networks and that our approach provides a robust group-level embedding strategy for physiological information-transfer analysis.

With the rapid advancement of China's aging population, the mental health problems of the elderly, especially depression, have become an important concern in the field of public health. However, research on the combined effect of education level and widowhood on depression is still relatively limited. The aim of this study is to explore the association between education level, widowhood status, and depression symptoms among elderly people in China, and further analyze the moderating effects of health status, demographic characteristics, and behavioral variables in this association. Data from the 2018 China Health and Retirement Longitudinal Study were used. A total of 3243 participants aged 50 and above were analyzed. A simplified 10 item CES-D scale was used to assess depression. Logistic regression models were used to examine the independent and interactive effects of education level and widowhood on depression, while adjusting for demographic variables (age, gender, type of residence), health status (chronic disease, self-rated health), and behavioral factors (physical activity, smoking). Even after controlling for confounding factors, lower levels of education were significantly associated with a higher risk of depressive symptoms (odds ratio [OR] = 1.52, P < .01). Widowhood is also a strong predictor of depression (OR = 1.79, P < .01), especially in women. Interaction analysis indicates that poor self-rated health and a sedentary lifestyle exacerbate the negative impact of low education and widowhood on depression. In addition, rural residents and individuals with lower socioeconomic status face a higher risk of depression symptoms. Low education level and widowhood are independent factors contributing to the high incidence of depression among elderly people in China. Health status and lifestyle factors play a crucial mediating role in this association. These findings emphasize the necessity of targeted intervention measures, such as community-based education programs, sports promotion, and strengthening social support networks, to reduce the risk of depression among vulnerable elderly people in China.

Training mental health clinicians to conduct standardized clinical assessments is challenging due to a lack of scalable, realistic practice opportunities. Traditional methods often fail to prepare trainees for the variability and complexity of real-world patient interactions, potentially impacting data quality in clinical trials. This paper introduces a novel approach to address this training gap using large language model (LLM)-based interview simulations.

This study aimed to develop and validate a voice-enabled virtual patient simulation system as a proof of concept. We described the development of the system and evaluated whether it could generate virtual patients that (1) accurately adhered to predefined clinical profiles, (2) maintained a coherent and consistent narrative, and (3) produced dialogue that is perceived as realistic.

We implemented a system that used an LLM to simulate patients with specified symptom profiles, demographic backgrounds, and distinct communication styles. The system's performance was analyzed through a mixed methods evaluation, which included a formal assessment by 5 experienced clinical raters who conducted simulated structured Montgomery-Åsberg Depression Rating Scale (MADRS) interviews with 4 virtual patient personae, scored them on the scale, and provided qualitative feedback on the system's clinical plausibility, narrative cohesion, and dialogue realism.

Across 20 interviews, the virtual patients demonstrated reasonable adherence to their configured clinical profiles, with human rater scores falling near their predefined score configurations. The mean item difference between MADRS rater scores and configured scores was 0.52 (SD 0.75); interrater reliability for the total score was 0.90 (95% CI 0.68-0.99). Expert raters consistently gave average ratings of "agree" to "strongly agree" when asked to evaluate the qualitative realism and cohesion of the virtual patients.

LLM-powered virtual patient simulations offered a promising, scalable tool for training clinicians in standardized clinical assessment. This pilot study provides initial evidence for the system's ability to produce clinically relevant practice scenarios with reasonable fidelity.

Globally, the mental health of children and adolescents is a public health concern. Schools are a primary access point for youth mental health support, yet traditional delivery models remain constrained by limited resources, shortage of specialised professionals, and high counsellor caseloads. Digital mental health interventions may help address these gaps by offering scalable, accessible, and less-stigmatising support.

This review aimed to map and characterise school-based digital mental health interventions for children and adolescents by examining intervention characteristics, implementation, and intervention outcomes.

A comprehensive search of peer-reviewed studies (2008-2025) was conducted across PsycNet, ERIC, and PubMed, using the PRISMA-ScR Checklist. Eligible studies evaluated a digital mental health intervention delivered in a school setting among K-12 students and reported at least one implementation and/or mental health outcome.

Across 40 studies, most were conducted in high-income countries and used randomised designs. Web-based, structured cognitive behavioural therapy (CBT) modules in classroom settings predominated. Intervention delivery ranged from teacher-led to self-directed use, with some hybrid clinician-supported models. Findings were heterogeneous for internalising symptoms (e.g., depression and anxiety), with evidence of benefit in some CBT-oriented interventions but mixed effects in universal programmes. In contrast, mental health literacy, stigma, and well-being outcomes showed more consistent positive effects. Engagement and implementation quality appeared closely linked to effectiveness.

School-based digital mental health interventions can improve mental health literacy and stigma-related outcomes when grounded in CBT. Future research should prioritise standardised outcome measurement (e.g., fidelity, adoption), theory-to-component linkages, co-design elements, and hybrid effectiveness-implementation, particularly in low-and middle-income settings.