Search Results (1,173)

2-(Methylthio) benzothiazole (MTBT) is widely used in the industrial and pharmaceutical fields, but limited research has been conducted on its aquatic toxicity. In this study, we established a zebrafish model to systematically evaluate its developmental and functional toxicity, focusing on the cardiovascular systems of larvae. The results showed that MTBT significantly reduced heart rate, caused pericardial edema and deformity, delayed cardiac maturation, decreased stroke volume and cardiac output, and led to vascular structural defects. Mechanistically, MTBT upregulated the expression of the core target PTGS2, activated the apoptotic pathway, and mediated cardiovascular toxicity. This study is the first to systematically confirm the cardiovascular toxicity of MTBT, supplementing its toxicological database and providing a scientific basis for the establishment of environmental safety thresholds and risk management. Full article

Background: Cardiovascular disease (CVD) is the leading global cause of death and is shaped by interacting biological, environmental, lifestyle, and social factors. Traditional models often treat risk factors in isolation and may miss dependencies among exposures and biomarkers. Objective: To map interdependencies among environmental, social, behavioral, and biological predictors of CVD risk using Bayesian network models. Methods: A cross-sectional analysis was conducted using NHANES 2017–2018 data. After complete-case procedures, the analytic sample included 601 adults and 22 variables: outcomes (systolic/diastolic blood pressure, total/LDL/HDL cholesterol, triglycerides) and predictors (BMI, C-reactive protein (CRP), allostatic load, Dietary Inflammatory Index, income, education, age, gender, race, smoking, alcohol, and serum lead, cadmium, mercury, and PFOA). Spearman’s correlations summarized pairwise associations. Bayesian networks were learned with two approaches: Grow–Shrink (constraint-based) and Hill-Climbing (score-based, Bayesian Gaussian equivalent score). Network size metrics included number of nodes, directed edges, average neighborhood size, and Markov blanket size. Results: Correlation screening reproduced expected patterns, including very high systolic–diastolic concordance (p ≈ 1.00), strong LDL–total cholesterol correlation (p = 0.90), inverse HDL–triglycerides association, and positive BMI–CRP association. The final Hill-Climbing network contained 22 nodes and 44 directed edges, with an average neighborhood size of ~4 and an average Markov blanket size of ~6.1, indicating multiple indirect dependencies. Across both learning algorithms, BMI, CRP, and allostatic load emerged as central nodes. Environmental toxicants (lead, cadmium, mercury, PFOS, PFOA) showed connections to sociodemographic variables (income, education, race) and to inflammatory and lipid markers, suggesting patterned exposure linked to socioeconomic position. Diet and stress measures were positioned upstream of blood pressure and triglycerides in the score-based model, consistent with stress-inflammation–metabolic pathways. Agreement across algorithms on key hubs (BMI, CRP, allostatic load) supported network robustness for central structures. Conclusions: Bayesian network modeling identified interconnected pathways linking obesity, systemic inflammation, chronic stress, and environmental toxicant burden with cardiovascular risk indicators. Findings are consistent with the view that biological dysregulation is linked with CVD and environmental or social stresses. Full article

Cardiovascular diseases claim millions of lives each year, yet timely diagnosis remains a significant challenge due to the high number of patients and associated costs. Although various machine learning solutions have been proposed for this problem, most approaches rely on careful data preprocessing and feature engineering workflows that could benefit from more comprehensive documentation in research publications. To address this issue, this paper presents a machine learning framework for predicting heart attack risk online. Our systematic methodology integrates a unified pipeline featuring advanced data preprocessing, optimized feature selection, and an exhaustive hyperparameter search using cross-validated grid evaluation. We employ a metamodel ensemble strategy, testing and combining six traditional supervised models along with six stacking and voting ensemble models. The proposed system achieves accuracies ranging from 90.2% to 98.9% on three independent clinical datasets, outperforming current state-of-the-art methods. Additionally, it powers a deployable, lightweight web application for real-time decision support. By merging cutting-edge AI with clinical usability, this work offers a scalable solution for early intervention in cardiovascular care. Full article

Cardiovascular disease (CVD) is a major cause of mortality around the world. This underscores the critical need to implement effective predictive tools to inform clinical decision-making. This study aimed to compare the predictive performance of ensemble learning algorithms, including Bagging, Random Forest, Extra Trees, Gradient Boosting, and AdaBoost, when applied to a clinical dataset comprising patients with CVD. The methodology entailed data preprocessing and cross-validation to regulate generalization. The performance of the model was evaluated using a variety of metrics, including accuracy, F1 score, precision, recall, Cohen’s Kappa, and area under the curve (AUC). Among the models evaluated, Bagging demonstrated the best overall performance (accuracy ± SD: 93.36% ± 0.22; F1 score: 0.936; AUC: 0.9686). It also reached the lowest average rank (1.0) in Friedman test and was placed, together with Extra Trees (accuracy ± SD: 90.76% ± 0.18; F1 score: 0.916; AUC: 0.9689), in the superior statistical group (group A) according to Nemenyi post hoc test. The two models demonstrated a high degree of agreement with the actual labels (Kappa: 0.87 and 0.83, respectively), thereby substantiating their reliability in authentic clinical contexts. The findings substantiated the preeminence of aggregation-based ensemble methods in terms of accuracy, stability, and concordance. This underscored the prominence of Bagging and Extra Trees as optimal candidates for cardiovascular diagnostic support systems, where reliability and generalization were paramount. Full article

Background: Cardiovascular disease (CVD) is a major complication of systemic lupus erythematosus (SLE). This study compared several CV risk scores in Korean female patients with SLE and searched for an association with subclinical atherosclerosis and lipid metabolism. Methods: Female SLE patients and healthy controls (HCs) underwent carotid ultrasonography and pulse wave velocity (PWV), and serum efflux cholesterol capacity was measured. The Framingham risk scores (FRSs), American College of Cardiology/American Heart Association (ACC/AHA) scores, and Korean Risk Prediction Model (KRPM) scores were calculated. Results: While carotid intima-media thickness (IMT) and the prevalence of carotid plaque did not differ between 67 SLE patients and 37 HCs, carotid plaque scores were higher in SLE patients compared with HCs. While the FRS and the ACC/AHA CV risk scores did not differ, the KRPM scores were higher in SLE patients. The carotid IMT, plaque score, and PWV were correlated with the FRS, ACC/AHA CV risk, and KRPM score in SLE patients. SLE patients with carotid plaque had higher FRS, ACC/AHA CV risk, and KRPM scores than those without carotid plaque. In addition, the serum cholesterol efflux capacity did not differ between SLE patients with and without carotid plaque but was correlated with carotid IMT. Conclusions: The scores obtained from the CV risk-prediction models were correlated with subclinical atherosclerosis in SLE. A cardiovascular risk assessment tool developed specifically for Koreans is suitable for evaluating the CV risk in Korean SLE patients. Full article

Background: Healthcare 5.0 represents a shift toward intelligent, human-centric care systems. Intensive care units generate vast amounts of data that require real-time decisions, but current decision support systems lack comprehensive frameworks for safe integration of artificial intelligence. Objective: We developed and validated the Learn–Predict–Monitor–Detect–Correct (LPMDC) framework as a methodology for systematic artificial intelligence integration across the critical care workflow. The framework improves predictive analytics, continuous patient monitoring, intelligent alerting, and therapeutic decision support while maintaining essential human clinical oversight. Methods: Framework development employed systematic theoretical modeling integrating Healthcare 5.0 principles, comprehensive literature synthesis covering 2020–2024, clinical workflow analysis across 15 international ICU sites, technology assessment of mature and emerging AI applications, and multi-round expert validation by 24 intensive care physicians and medical informaticists. Each LPMDC phase was designed with specific integration requirements, performance metrics, and safety protocols. Results: LPMDC implementation and aggregated evidence from prior studies demonstrated significant clinical improvements: 30% mortality reduction, 18% ICU length-of-stay decrease (7.5 to 6.1 days), 45% clinician cognitive load reduction, and 85% sepsis bundle compliance improvement. Machine learning algorithms achieved an 80% sensitivity for sepsis prediction three hours before clinical onset, with false-positive rates below 15%. Additional applications demonstrated effectiveness in predicting respiratory failure, preventing cardiovascular crises, and automating ventilator management. Digital twins technology enabled personalized treatment simulations, while the integration of the Internet of Medical Things provided comprehensive patient and environmental surveillance. Implementation challenges were systematically addressed through phased deployment strategies, staff training programs, and regulatory compliance frameworks. Conclusions: The Healthcare 5.0-enabled LPMDC framework provides the first comprehensive theoretical foundation for systematic AI integration in critical care while preserving human oversight and clinical safety. The cyclical five-phase architecture enables processing beyond traditional cognitive limits through continuous feedback loops and system optimization. Clinical validation demonstrates measurable improvements in patient outcomes, operational efficiency, and clinician satisfaction. Future developments incorporating quantum computing, federated learning, and explainable AI technologies offer additional advancement opportunities for next-generation critical care systems. Full article

Molecular communication (MC) has emerged as a promising paradigm for nanoscale information exchange in Internet of Bio-Nano Things (IoBNT) environments, offering intrinsic biocompatibility and potential for real-time in vivo monitoring. This study proposes a cascaded MC channel framework for vascular stenosis detection, which integrates non-Newtonian blood rheology, bell-shaped constriction geometry, and adsorption–desorption dynamics. Path delay and path loss are introduced as quantitative metrics to characterize how structural narrowing and molecular interactions jointly affect signal propagation. On this basis, a peak response time-based delay inversion method is developed to estimate both the location and severity of stenosis. COMSOL 6.2 simulations demonstrate high spatial resolution and resilience to measurement noise across diverse vascular configurations. By linking nanoscale transport dynamics with system-level detection, the approach establishes a tractable pathway for the early identification of vascular anomalies. Beyond theoretical modeling, the framework underscores the translational potential of MC-based diagnostics. It provides a foundation for non-invasive vascular health monitoring in IoT-enabled biomedical systems with direct relevance to continuous screening and preventive cardiovascular care. Future in vitro and in vivo studies will be essential to validate feasibility and support integration with implantable or wearable biosensing devices, enabling real-time, personalized health management. Full article

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, with ischemic heart disease (IHD) being the most prevalent and deadly subtype. The growing burden of IHD underscores the urgent need for effective early detection methods that are scalable and non-invasive. Heart Rate Variability (HRV), a non-invasive physiological marker influenced by the autonomic nervous system (ANS), has shown clinical relevance in predicting adverse cardiac events. This study presents a photoplethysmography (PPG)-based Zhurek IoT device, a custom-developed Internet of Things (IoT) device for non-invasive HRV monitoring. The platform’s effectiveness was evaluated using HRV metrics from electrocardiography (ECG) and PPG signals, with machine learning (ML) models applied to the task of early IHD risk detection. ML classifiers were trained on HRV features, and the Random Forest (RF) model achieved the highest classification accuracy of 90.82%, precision of 92.11%, and recall of 91.00% when tested on real data. The model demonstrated excellent discriminative ability with an area under the ROC curve (AUC) of 0.98, reaching a sensitivity of 88% and specificity of 100% at its optimal threshold. The preliminary results suggest that data collected with the “Zhurek” IoT devices are promising for the further development of ML models for IHD risk detection. This study aimed to address the limitations of previous work, such as small datasets and a lack of validation, by utilizing real and synthetically augmented data (conditional tabular GAN (CTGAN)), as well as multi-sensor input (ECG and PPG). The findings of this pilot study can serve as a starting point for developing scalable, remote, and cost-effective screening systems. The further integration of wearable devices and intelligent algorithms is a promising direction for improving routine monitoring and advancing preventative cardiology. Full article

Background/Objectives: Dyslipidemia is becoming a significant economic healthcare burden in low- to middle-income countries (LMICs) due to its role in heightening cardiovascular-related mortality. Statins are the first-line treatment for reducing LDL-C levels, thereby minimizing direct costs associated with cardiovascular disease management, with pitavastatin being of the newest generation of statins. This research work conducted a cost-utility analysis of pitavastatin to determine the economic benefit in Vietnam. Methods: A decision tree model was developed to compare the rate of LDL-C controlled patients over a lifetime horizon among patients treated with pitavastatin, atorvastatin, and rosuvastatin. The primary outcome was the incremental cost-effectiveness ratio (ICER), measured from the healthcare system perspective. Effectiveness was evaluated in terms of quality-adjusted life years (QALYs), using an annual discount rate of 3%. A one-way sensitivity analysis was performed to identify the key input parameters that most influenced the ICER outcomes. Results: Pitavastatin was cost-effective compared to atorvastatin but was dominated by rosuvastatin. Although pitavastatin gained fewer QALYs than atorvastatin, the ICER was 195,403,312 VND/QALY, well below Vietnam’s 2024 willingness-to-pay. Drug cost had the most significant impact on ICERs. Conclusions: Pitavastatin represents an economical short-term alternative to atorvastatin, particularly in resource-constrained settings. Full article

Cardiopulmonary Exercise Testing (CPET) is the gold standard for the functional assessment in patients with heart failure (HF), providing objective parameters that reflect the integrated response of the cardiovascular, respiratory, and muscular systems, in addition several CPET-derived variables have shown independent prognostic value in patients with both reduced (HFrEF) and preserved ejection fraction (HFpEF) HF. This review aims to critically analyze the main CPET prognostic variables in heart failure, highlighting their underlying pathophysiological mechanisms, their predictive capacity for mortality and hospitalizations, and their integration into clinical decision-making models. Parameters such as peak oxygen uptake (VO₂), minute ventilation/carbon dioxide production (VE/VCO₂) slope, periodic breathing (or exercise oscillatory ventilation—EOV), anaerobic threshold (AT), oxygen pulse, and VO₂/work slope provide complementary insights into clinical risk; moreover, the combination of multiple CPET variables allows for more accurate risk stratification compared to the isolated use of each parameter. Multiparametric prognostic models such as the Metabolic Exercise Cardiac Kidney Index (MECKI) score, the Seattle Heart Failure Model, and the Heart Failure Survival Score (HFSS) incorporate these variables alongside clinical and laboratory data to guide advanced management and therapeutic decisions, including heart transplantation or left ventricular assistant device (LVAD) implantation. For these reasons, CPET-derived variables are essential prognostic tools in heart failure. Beyond improving risk stratification, their integration into multiparametric models supports a more personalized therapeutic approach, including tailored pharmacological management. Full article

Background/Objective: Systemic inflammation is a hallmark of end-stage renal disease (ESRD) and contributes to the high burden of cardiovascular morbidity and mortality in hemodialysis (HD) patients. The systemic immune–inflammation index (SII), derived from peripheral neutrophil, lymphocyte, and platelet counts, has emerged as a promising biomarker of immune–inflammatory status. This study aimed to assess the acute effect of a single HD session on systemic inflammation and to identify metabolic predictors associated with this response. Methods: In this single-center observational before–after study, 44 chronic HD patients were enrolled. Blood samples were collected immediately before and after a single HD session. SII was calculated as platelet count × neutrophil count/lymphocyte count. Subgroup analyses were conducted based on renal disease etiology and diabetic status. Multivariable linear regression models identified baseline predictors of SII variation. Results: Median SII significantly decreased post-HD in the overall cohort (from 553.4 [342.6–847.5] to 449.1 [342.6–866.6], p = 0.001), with a more pronounced reduction in patients with cardiometabolic etiologies (from 643.4 [353.3–1360.0] to 539.1 [324.8–1083.4], p = 0.007) and diabetes (from 671.1 [408.7–1469.1] to 458.3 [285.7–1184.4], p = 0.028), but not in those with nephroangiosclerosis (p = 0.182). Baseline total cholesterol (p = 0.001) and gamma-glutamyl transferase (p = 0.034) were positively associated with smaller reductions in SII, while higher baseline glycaemia predicted a greater decrease in post-dialysis SII (p = 0.021). Conclusions: HD acutely modulates systemic inflammation, as reflected by reduction in SII. The magnitude of this response is significantly influenced by individual metabolic profiles. These findings highlight the relevance of metabolic–immune crosstalk in ESRD and suggest that SII may serve as a dynamic biomarker integrating inflammatory and metabolic signals, deserving further validation in larger, outcome-driven studies. Full article

Obesity is a complex, multifactorial disease and a growing global health challenge associated with type 2 diabetes, cardiovascular disorders, cancer, and reduced quality of life. The existing pharmacological therapies are characterized by their limited number and efficacy, and safety concerns further restrict their utilization. This review synthesizes extensive knowledge regarding the role of the endocannabinoid system (ECS) in the pathogenesis of obesity, as well as its potential as a therapeutic target. A thorough evaluation of preclinical and clinical data concerning endocannabinoid ligands, cannabinoid receptors (CB1, CB2), their genetic variants, and pharmacological interventions targeting the ECS was conducted. Literature data suggests that the overactivation of the ECS may play a role in the pathophysiology of excessive food intake, dysregulated energy balance, adiposity, and metabolic disturbances. The pharmacological modulation of ECS components, by means of CB1 receptor antagonists/inverse agonists, CB2 receptor agonists, enzyme inhibitors, and hybrid or allosteric ligands, has demonstrated promising anti-obesity effects in animal models. However, the translation of these findings into clinical practice remains challenging due to safety concerns, particularly neuropsychiatric adverse events. The development of novel strategies, including peripherally restricted compounds, hybrid dual-target agents, dietary modulation of endocannabinoid tone, and non-pharmacological interventions, promises to advance the field of obesity management. Full article

This study examined the expression of the renin-angiotensin system (RAS) and inflammatory markers in cardiovascular complications associated with long-term type 1 diabetes (T1D) using a rat model. After 24 weeks of streptozotocin-induced T1D, the animals exhibited metabolic alterations indicative of both cardiac and renal dysfunction. Tissue-specific dysregulation of RAS components and pro-inflammatory markers were observed in the heart, aorta, and perivascular adipose tissue (PVAT). In the heart, there was a significant upregulation of both classical (AT1R, 1.00 (0.22) vs. 1.70 (0.45) R.U.) and counter-regulatory RAS components (ACE2, 1.00 (0.43) vs. 1.96 (0.67) R.U.; p < 0.001) and MasR (1.00 (0.56) vs. 1.33 (0.29) R.U.; p = 0.004). The aorta displayed increased expression of classical RAS components alongside a significant reduction in ACE2 expression (1.00 (0.74) vs. 0.51 (0.48) R.U.; p < 0.032). Notably, PVAT showed a significant overexpression of classical RAS components (ACE 1.00 (0.22) vs. 4.08 (1.32) R.U.; p < 0.001, AT1R 1.00 (0.59) vs. 7.22 (4.14) R.U.; p < 0.001) and MasR (1.00 (0.70) vs. 4.52 (1.91) R.U.; p < 0.001), accompanied by increased expression of TNFα and ADAM17. These findings suggest that long-term T1D induces tissue-specific activation patterns of the RAS and inflammatory pathways within the cardiovascular system, which may contribute to the progression of diabetic cardiovascular complications. Therapeutic targeting of RAS components may represent a viable strategy for mitigating cardiovascular damage in T1D. Full article

Background: The human gut microbiome—a diverse ecosystem of trillions of microorganisms—plays an essential role in metabolic, immune, and neurological regulation. However, modern lifestyle factors such as antibiotic overuse, cesarean delivery, reduced breastfeeding, processed and high-sodium diets, alcohol intake, smoking, and exposure to environmental toxins (e.g., glyphosate) significantly reduce microbial diversity. Loss of keystone species like Bifidobacterium infantis (B. infantis) and Lactobacillus reuteri (L. reuteri) contributes to gut dysbiosis, which has been implicated in chronic metabolic, autoimmune, cardiovascular, and neurodegenerative conditions. Materials and Methods: This Qualitative Systematic Review (QualSR) synthesized data from over 547 studies involving human participants and standardized microbiome analysis techniques, including 16S rRNA sequencing and metagenomics. Studies were reviewed for microbial composition, immune and metabolic biomarkers, and clinical outcomes related to microbiome restoration strategies. Results: Multiple cohort studies have consistently reported a 40–60% reduction in microbial diversity among Western populations compared to traditional societies, particularly affecting short-chain fatty acid (SCFA)-producing bacteria. Supplementation with B. infantis is associated with a significant reduction in systemic inflammation—including a 50% decrease in C-reactive protein (CRP) and reduced tumor necrosis factor-alpha (TNF-α) levels—alongside increases in regulatory T cells and anti-inflammatory cytokines interleukin-10 (IL-10) and transforming growth factor-beta 1 (TGF-β1). L. reuteri demonstrates immunomodulatory and neurobehavioral benefits in preclinical models, while both probiotics enhance epithelial barrier integrity in a strain- and context-specific manner. In murine colitis, B. infantis increases ZO-1 expression by ~35%, and L. reuteri improves occludin and claudin-1 localization, suggesting that keystone restoration strengthens barrier function through tight-junction modulation. Conclusions: Together, these findings support keystone species restoration with B. infantis and L. reuteri as a promising adjunctive strategy to reduce systemic inflammation, reinforce gut barrier integrity, and modulate gut–brain axis (GBA) signaling, indicating translational potential in metabolic and neuroimmune disorders. Future research should emphasize personalized microbiome profiling, long-term outcomes, and transgenerational effects of early-life microbial disruption. Full article

Long COVID (LC), also known as post-acute sequelae of COVID-19 infection (PASC), is a heterogeneous and debilitating chronic disease that currently affects 10 to 20 million people in the U.S. and over 420 million people globally. With no approved treatments, the long-term global health and economic impact of chronic LC remains high and growing. LC affects children, adolescents, and healthy adults and is characterized by over 200 diverse symptoms that persist for months to years after the acute COVID-19 infection is resolved. These symptoms target twelve major organ systems, causing dyspnea, vascular damage, cognitive impairments (“brain fog”), physical and mental fatigue, anxiety, and depression. This heterogeneity of LC symptoms, along with the lack of specific biomarkers and diagnostic tests, presents a significant challenge to the development of LC treatments. While several biological abnormalities have emerged as potential drivers of LC, a causative factor in a large subset of patients with LC, involves reservoirs of virus and/or viral RNA (vRNA) that persist months to years in multiple organs driving chronic inflammation, respiratory, muscular, cognitive, and cardiovascular damages, and provide continuous viral antigenic stimuli that overstimulate and exhaust CD4⁺ and CD8⁺ T cells. In this review, we (i) shed light on persisting virus and vRNA reservoirs detected, either directly (from biopsy, blood, stool, and autopsy samples) or indirectly through virus-specific B and T cell responses, in patients with LC and their association with the chronic symptomatology of LC; (ii) explore potential mechanisms of inflammation, immune evasion, and immune overstimulation in LC; (iii) review animal models of virus reservoirs in LC; (iv) discuss potential T cell immunotherapeutic strategies to reduce or eliminate persistent virus reservoirs, which would mitigate chronic inflammation and alleviate symptom severity in patients with LC. Full article