The clinical heterogeneity of atrial fibrillation (AF) challenges current classifications and risk scores, limiting their real-world applicability. AI-driven methods may enhance phenotyping and risk stratification.

This study aimed to apply a generative topographic mapping (GTM)-based clustering approach to a large, real-world, prospective AF cohort to identify clinically relevant phenotypes and assess their associations with clinical outcomes.

We conducted a prospective observational cohort study including consecutive adult outpatients with newly diagnosed AF who initiated oral anticoagulation between January 2016 and November 2021. Cardiometabolic risk was modeled as a multidimensional construct integrating cardiovascular and metabolic comorbidities. GTM was applied to project high-dimensional clinical data into a low-dimensional latent space, enabling probabilistic patient representation and visualization of phenotypic structure. Unsupervised hierarchical clustering using Ward's method was performed on the latent space to identify AF phenotypes, and patients were assigned to clusters based on their highest posterior probability. Clinical outcomes, including thromboembolic events, major bleeding, major adverse cardiovascular events (MACE), cardiovascular death, and all-cause death, were assessed over a maximum follow-up of 2 years. Nonfatal outcomes were analyzed using Fine-Gray competing-risk models and reported as subdistribution hazard ratios (sHRs), whereas cardiovascular and all-cause death were analyzed using adjusted Cox proportional hazards models and reported as adjusted hazard ratios (aHRs).

Among 3259 patients with AF (median age 77, IQR 70-83 years; 1721/3259, 52.8% female; mean follow-up 1.81 years), four phenotypes emerged: (1) older with highest cardiometabolic burden, (2) older with lower cardiometabolic risk, (3) comparatively younger with intermediate-high cardiometabolic risk, and (4) comparatively younger with intermediate cardiometabolic risk. Compared with phenotype 1, phenotype 4 demonstrated lower risks of thromboembolic events (sHR 0.70, 95% CI 0.50-0.98), major bleeding (sHR 0.61, 95% CI 0.42-0.89), and MACE (sHR 0.67, 95% CI 0.47-0.94). Regarding cardiovascular and all-cause death, all phenotypes demonstrated a lower risk compared with phenotype 1: phenotype 2 (aHR 0.52, 95% CI 0.33-0.85; and aHR 0.66, 95% CI 0.50-0.87, respectively), phenotype 3 (aHR 0.57, 95% CI 0.32-0.99; and aHR 0.44, 95% CI 0.30-0.65, respectively), and phenotype 4 (aHR 0.49, 95% CI 0.33-0.72; and aHR 0.56, 95% CI 0.44-0.71, respectively). However, these associations were attenuated after further adjustment for age, sex, and major comorbidities, with phenotype 4 retaining a significant association with lower cardiovascular death and phenotypes 3 and 4 retaining significant associations with lower all-cause death.

GTM-based clustering analysis identified prognostically distinct AF phenotypes, highlighting the potential of machine learning approaches to support personalized AF care. Further external validation is needed to establish the generalizability of these findings.

Poststroke cognitive impairment (PSCI) is a common and disabling complication after stroke; however, early screening remains challenging due to limited access to neuropsychological testing and the high cost of neuroimaging. Portable, tablet-based eye-tracking technology may offer a scalable, low-cost solution for early PSCI detection.

This study aimed to evaluate the clinical utility of a tablet-based, AI-driven eye-tracking system for early screening of PSCI at 3 months after acute ischemic stroke. We sought to quantify oculomotor-cognitive associations and develop a practical nomogram for individualized risk prediction.

We prospectively enrolled 142 hospitalized patients with acute cerebral infarction between May 2023 and October 2024, of whom 122 completed the 3-month follow-up and were included in the final analysis, along with 20 healthy community-dwelling controls. All patients underwent tablet-based eye tracking (visual paired comparison and antisaccade tasks) during the acute phase, as well as baseline and 3-month neuropsychological assessments. PSCI was defined using validated cutoffs. Multivariable logistic regression was used to identify independent predictors, and a nomogram was constructed. Internal validation was performed using bootstrap resampling (1000 samples).

At 3 months, out of 122 patients, 47 (38.5%) met PSCI criteria. Compared with patients with non-PSCI (n=75), patients with PSCI showed significantly prolonged correct saccade latency (median 322.96, IQR 209.45-445.59 ms vs 194.55, IQR 141.50-299.75 ms; Z=-4.03, P<.001), increased uncorrected error rate (median 30.00%, IQR 15.00%-42.00% vs 5.00%, IQR 0.00%-28.00%; Z=-4.24, P<.001), and reduced novelty preference ratio (median 1.44, IQR 0.97-1.70 vs 2.12, IQR 1.27-4.56; Z=-3.44, P=.001). Multivariable analysis identified 4 independent predictors of 3-month PSCI: older age (odds ratio [OR] 1.067 per year, 95% CI 1.009-1.129; P=.02), lower education level (OR 0.841 per year, 95% CI 0.708-0.999; P=.049), higher NIHSS (National Institutes of Health Stroke Scale) scores (OR 1.557 per point, 95% CI 1.075-2.256; P=.02), and prolonged correct saccade latency (OR 1.004 per ms, 95% CI 1.000-1.007; P=.04). A nomogram incorporating these 4 factors achieved good discriminative performance (area under the receiver operating characteristic curve 0.86, 95% CI 0.793-0.927) with satisfactory calibration.

Age, education, admission NIHSS, and correct saccade latency were identified as possible independent predictors of 3-month PSCI in this cohort. The tablet-based eye-tracking system, when combined with clinical variables, may represent a feasible approach for early PSCI screening. A nomogram based on these variables demonstrated high accuracy and potential clinical utility for early PSCI identification. This approach may facilitate early identification of high-risk patients and enable timely, personalized interventions in resource-limited settings.

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, underscoring the need for effective risk prediction and early detection. Although the electrocardiogram (ECG) is a widely available and low-cost diagnostic tool, its traditional interpretation is limited by subjectivity. Artificial intelligence (AI) has emerged as a promising approach, capable of extracting hidden prognostic information from ECG signals. This systematic review aimed to assess original studies applying AI techniques to ECGs for cardiovascular risk prediction and mortality. Original studies that used ECG signals as the sole input variable for AI models, focusing on cardiovascular risk outcomes, were included. A systematic search was conducted in different databases, and data were synthesized narratively. Eleven studies were included, predominantly retrospective cohorts applying convolutional neural networks (CNNs) to predict cardiovascular risk or mortality. The sample primarily consisted of adult populations in high-income countries. Primary outcomes included all-cause mortality, cardiovascular death, and major adverse cardiovascular events (MACE). Reported AUROC values ranged from 0.63 to 0.961 in training sets, with some models outperforming traditional risk scores. AI-ECG models demonstrated the potential to detect subclinical disease, enabling early risk stratification even in normal ECGs. However, challenges remain regarding population diversity, model interpretability, and prospective validation. The application of AI to ECG analysis represents a promising advancement in personalized cardiovascular risk assessment. Nonetheless, further research is needed to ensure the safety, effectiveness, and equitable clinical integration of these technologies.

The aim of this study is to examine the independent and combined associations of physical activity (PA) and sedentary behavior (SB) with metabolic syndrome (MS) components in older adults. This cross-sectional study included 473 older adults aged ≥60 years, living in the urban area of ​​the municipality of Alcobaça, Brazil, and enrolled in the municipality's Family Health Strategy. Sociodemographic information, anthropometric measurements, laboratory and behavioral data were collected. The prevalence of MS was 52.8%. Longer time spent in SB was associated with central obesity (OR 2.85; 95% CI: 1.52-5.25) and hypertension (OR 1.86; 95% CI: 1.23-2.85). In the combined analysis, insufficiently active older adults with longer SB time were more likely to have two (OR 3.90; 95% CI: 1.46-10.42) to four (OR 4.18; 95% CI: 1.12-15.62) components. Even sufficiently active older adults with longer SB time had higher odds of central obesity (OR 3.44; 95% CI: 1.51-7.87), hypertension (OR 2.30; 95% CI: 1.30-4.05), and low HDL-c levels (OR 1.84; 95% CI: 1.03-3.28). SB is a determining factor in the prevalence of MS in older adults, with implications for public health policies that promote frequent interruptions of sedentary time and encourage regular PA.

Ischemia-reperfusion injury (IRI) is a major determinant of initial graft function after liver transplantation (LT), directly influencing the incidence of early dysfunction and short-term clinical outcomes. Macrophage migration inhibitory factor (MIF), a pleiotropic cytokine involved in inflammatory pathways and mechanisms of cellular adaptation to oxidative stress, may play a modulatory role in IRI, although its immediate tissue behaviour in grafts remains poorly characterised.

We retrospectively evaluated adult LT recipients who underwent post-reperfusion biopsies suitable for histological and immunohistochemical analysis. Immunohistochemical expression of MIF was quantified using the IHC Profiler plugin (ImageJ), whereas IRI was graded according to validated histopathological criteria.

Among 153 biopsies analysed, 103 met the eligibility criteria, with most cases showing absent or mild IRI (70.9%). Hepatocellular expression of MIF was predominantly weak or moderate. Stronger immunohistochemical staining for MIF was associated with lower IRI severity (p<0.05). MIF expression correlated with pre-transplant laboratory parameters, without association with steatosis or early outcomes, including retransplantation or death within 15 days.

The findings suggest that greater immunohistochemical expression of MIF at reperfusion is associated with lower IRI intensity, indicating a possible adaptive role for this cytokine during this critical phase. MIF emerges as a potential tissue marker complementary to traditional histopathological scoring, with relevance for graft assessment and use in contemporary liver perfusion strategies.

Chagas cardiomyopathy is associated with a higher risk of severe cardiovascular events and increased mortality. Among the factors associated with Chagas cardiomyopathy, impairment in functional capacity-commonly assessed using the New York Heart Association (NYHA) functional classification-is notable and may contribute to risk stratification. In this review, we assessed the prognostic value of the NYHA classification on cardiovascular events and mortality in patients with Chagas cardiomyopathy. The search was performed across EMBASE, LILACS, MEDLINE, and Web of Science databases. Prospective and retrospective cohort studies in which the prognostic value of the NYHA classification was assessed were eligible. Risk of bias was assessed using the Quality in Prognostic Studies tool. Evidence was classified using the adapted GRADE system. Eighteen studies were included in the systematic review. The meta-analysis results indicate that the NYHA classification is an important prognostic factor for mortality. Patients with NYHA class III and IV showed a higher risk of death than those with class I and II (hazard ratio, 2.63; 95% confidence interval: 2.00-3.45; moderate GRADE evidence). Patients with NYHA class IV had a higher risk of death than those with class I, II, and III (hazard ratio, 2.80; 95% confidence interval: 1.06-7.43; low GRADE evidence). Very low evidence suggests that the NYHA classification does not predict heart transplantation or stroke, but may predict cardiac pacemaker implantation. Overall, the NYHA classification is a key prognostic indicator of mortality in Chagas cardiomyopathy, although its ability to predict stroke, heart transplantation, and pacemaker implantation is inconsistent.

This study aimed to evaluate the protective effects of enoxaparin on ovarian tissue subjected to experimental ischemia/reperfusion (I/R) injury in rats.

Thirty female rats were divided into three groups: control, ischemia/reperfusion (I/R), and ischemia/reperfusion plus enoxaparin (I/R+E). Ovarian I/R injury was induced by 1 hour of ischemia followed by 1 hour of reperfusion. Rats in the I/R+E group received subcutaneous enoxaparin (0.5 mg/kg) prior to ischemia. Histopathological injury was assessed using a standardized scoring system, and total antioxidant status (TAS), total oxidant status (TOS), and oxidative stress index (OSI) were measured.

Histopathological injury scores were significantly higher in the I/R group compared with controls (p < 0.05), with no significant difference between the I/R and I/R+E groups (p > 0.05). Biochemical analysis showed significantly higher TAS and lower OSI levels in the I/R+E group compared with the I/R group (p < 0.05), while TOS levels were comparable between groups.

Although enoxaparin did not significantly improve histopathological injury, its beneficial effects on oxidative stress parameters suggest a protective role against oxidative stress in ovarian I/R injury.

to verify the association between adherence to the Mediterranean diet (MedDiet), sociodemographic, clinical, and biochemical factors, and hospital readmission in patients with coronary artery disease.

prospective cohort study in a tertiary hospital. MedDiet adherence was analyzed at admission and at three and six months after discharge using the Mediterranean Diet Adherence Screener. C-reactive protein, total cholesterol, LDL cholesterol, and triglycerides were analyzed at admission and six months after discharge; readmission was assessed at three and six months. Associations were verified using the generalized estimating equation model, with p-values ≤0.05 considered significant.

a total of 146 patients participated, mostly men, with a mean age of 66 years. MedDiet adherence increased from 3% to 41% and 47% during follow-up and was associated with age, female gender, complete higher education, economic activity, and previous myocardial revascularization.

MedDiet adherence was associated with sociodemographic and clinical factors.

to analyze the relationship between anxiety and depressive symptoms and social support in patients with atrial fibrillation.

a descriptive, cross-sectional, correlational study was conducted with 102 participants. Data were collected using the Hospital Anxiety and Depression Scale and the Medical Outcomes Study Social Support Survey. Descriptive statistics and Spearman's rank correlation were used to analyze the data.

the median anxiety score was 8.0 (interquartile range [IQR] 7-11), and the median depression score was 6.0 (IQR 3-10). Anxiety symptoms were identified in 60.8% of participants and depressive symptoms in 38.2%. Inverse correlations were observed between depressive symptoms and affectionate support, emotional/informational support, and positive social interaction. No associations were found between anxiety symptoms and social support.

higher levels of affectionate and emotional/informational support and positive social interaction were associated with lower depressive symptom scores.

Pulp stones are radiopaque calcified masses located within the coronal or radicular pulp tissue. They are typically asymptomatic and detected incidentally on radiographic examination. Global prevalence estimates vary widely, and methodological inconsistencies across studies hinder a comprehensive understanding of their epidemiology. Therefore, this systematic review aimed to answer the following question: "What is the global prevalence of pulp stones, and what factors are associated with their occurrence?" Aim: To systematically review and synthesize the evidence on the global prevalence of pulp stones and to identify factors associated with their occurrence.

Six electronic databases were searched up to March 2024. Data extraction included participant demographics, diagnostic methods, tooth type, and the presence of systemic diseases. Risk of bias was assessed using the JBI checklist, and prevalence was pooled using random-effects meta-analyses at individual and tooth levels. Meta-regression was performed to explore potential sources of heterogeneity, and the certainty of evidence was assessed using GRADE.

Twenty-four studies comprising 10,738 individuals and 94,416 teeth met the inclusion criteria. Pulp stones were present in 46% of individuals and 20% of teeth, with substantial heterogeneity across studies. Meta-regression identified systemic conditions and tooth type as significant contributors to prevalence variability. Individuals with both cardiovascular and renal diseases exhibited a higher prevalence (β=0.31, p=0.04), whereas those with cardiovascular diseases alone showed a lower prevalence (β=-0.33, p=0.04) than healthy participants (R2= 26.0). Risk of bias was moderate in most studies, and the overall certainty of evidence was very low.

Pulp stones are highly prevalent, and their occurrence may be influenced by systemic conditions and tooth-related factors. However, these findings are based on very-low-certainty evidence and are limited by heterogeneity and methodological inconsistencies across the included studies.