Search Results (1,398)

Metabolic dysfunction-associated steatotic liver disease (MASLD) is increasingly recognized as a significant comorbidity in individuals with type 1 diabetes (T1D), despite its historical association with type 2 diabetes. This review focuses on summarizing current findings regarding the role of insulin resistance in the development of MASLD in T1D, as well as examining the relationship between MASLD and diabetes-related complications. We will also briefly discuss the prevalence, diagnostic challenges, associated complications, and potential mechanisms underlying MASLD in T1D. Although insulin resistance is well established in MASLD among those with type 2 diabetes, its role in T1D requires further clarification. Emerging markers, such as the estimated glucose disposal rate, offer early insight into this relationship. MASLD in T1D is linked to both microvascular and macrovascular complications, including nephropathy, retinopathy, neuropathy, and cardiovascular disease. Variability in prevalence estimates reflects inconsistencies among imaging modalities, emphasizing the need for standardized, non-invasive diagnostic approaches. Recognizing and addressing MASLD and its links to insulin resistance and diabetes complications in T1D is vital for mitigating long-term complications and enhancing clinical outcomes. Full article

Background/Objectives: Automatic classification of ECG signal arrhythmias plays a vital role in early cardiovascular diagnostics by enabling prompt detection of life-threatening conditions. Manual ECG interpretation is labor-intensive and susceptible to errors, highlighting the demand for automated, scalable approaches. Deep learning (DL) methods are effective in ECG analysis due to their ability to learn complex patterns from raw signals. Methods: This study introduces two models: a custom convolutional neural network (CNN) with a dual-branch architecture for processing ECG signals and demographic data (e.g., age, gender), and a modified VGG16 model adapted for multi-branch input. Using the PTB-XL dataset, a widely adopted large-scale ECG database with over 20,000 recordings, the models were evaluated on binary, multiclass, and subclass classification tasks across 2, 5, 10, and 15 disease categories. Advanced preprocessing techniques, combined with demographic features, significantly enhanced performance. Results: The CNN model achieved up to 97.78% accuracy in binary classification and 79.7% in multiclass tasks, outperforming the VGG16 model (97.38% and 76.53%, respectively) and state-of-the-art benchmarks like CNN-LSTM and CNN entropy features. This study also emphasizes interpretability, providing lead-specific insights into ECG contributions to promote clinical transparency. Conclusions: These results confirm the models’ potential for accurate, explainable arrhythmia detection and their applicability in real-world healthcare diagnostics. Full article

Growing interest in the future applications of nanotechnology in medicine has led to groundbreaking developments in nanosensors. Nanosensors are excellent platforms that provide reliable solutions for continuous monitoring and real-time detection of clinical targets. Nanosensors have attracted great attention due to their remarkable sensitivity, portability, selectivity, and automated data acquisition. The exceptional nanoscale properties of nanomaterials used in the nanosensors boost their sensing potential even at minimal concentrations of analytes present in a clinical sample. Along with applications in diverse sectors, the beneficial aspects of nanosensors have been exploited in healthcare systems to utilize their applications in diagnosing, treating, and preventing diseases. Hence, in this review, we have presented an overview of the disease-prognostic applications of nanosensors in chronic diseases through a detailed literature analysis. We focused on the advances in various nanosensors in the field of major diseases such as cancer, cardiovascular diseases, diabetes mellitus, and neurodegenerative diseases along with other prevalent diseases. This review demonstrates various categories of nanosensors with different nanoparticle compositions and detection methods suitable for specific diagnostic applications in clinical settings. The chemical properties of different nanoparticles provide unique characteristics to each nanosensors for their specific applications. This will aid the detection of potential biomarkers or pathological conditions that correlate with the early detection of various diseases. The potential challenges and possible recommendations of the applications of nanosensors for disease diagnosis are also discussed. The consolidated information present in the review will help to better understand the disease-prognostic potentials of nanosensors, which can be utilized to explore new avenues in improved therapeutic interventions and treatment modalities. Full article

Plaque erosion (PE) is now recognized as a common and clinically significant cause of acute coronary syndromes (ACSs), accounting for up to 40% of cases. Unlike plaque rupture (PR), PE involves superficial endothelial loss over an intact fibrous cap and occurs in a low-inflammatory setting, typically affecting younger patients, women, and smokers with fewer traditional risk factors. The growing recognition of PE has been driven by high-resolution intracoronary imaging, particularly optical coherence tomography (OCT), which enables in vivo differentiation from PR. Identifying PE with OCT has opened the door to personalized treatment strategies, as explored in recent trials evaluating the safety of deferring stent implantation in selected cases in favor of intensive medical therapy. Given its unexpectedly high prevalence, PE is now recognized as a common pathophysiological mechanism in ACS, rather than a rare exception. This growing awareness underscores the importance of its accurate identification through OCT in clinical practice. Early recognition and a deeper understanding of PE are essential steps toward the implementation of precision medicine, allowing clinicians to move beyond “one-size-fits-all” models toward “mechanism-based” therapeutic strategies. This narrative review aims to offer an integrated overview of PE, tracing its epidemiology, elucidating the molecular and pathophysiological mechanisms involved, outlining its clinical presentations, and placing particular emphasis on diagnostic strategies with OCT, while also discussing emerging therapeutic approaches and future directions for personalized cardiovascular care. Full article

Background: Metabolic syndrome (MetS) is a complex clinical condition characterized by the coexistence of interrelated metabolic abnormalities that significantly increase the risk of cardiovascular diseases and type 2 diabetes mellitus. Endocan—an endothelial cell-specific molecule—is considered a biomarker of endothelial dysfunction and inflammation. This study aimed to evaluate the relationship between serum endocan levels and the severity of MetS, assessed using the MetS-Z score. Methods: This study included 120 patients with MetS and 50 healthy controls. MetS was diagnosed according to the NCEP-ATP III criteria. MetS-Z scores were calculated using the MetS Severity Calculator. Serum levels of endocan, sICAM-1, and sVCAM-1 were measured using the ELISA method. Results: Serum levels of endocan, sICAM-1, and sVCAM-1 were significantly higher in the MetS group compared to the control group (all p < 0.001). When the MetS group was divided into tertiles based on MetS-Z scores, stepwise and statistically significant increases were observed in the levels of endocan and other endothelial markers from the lowest to highest tertile (p < 0.0001). Correlation analysis revealed a strong positive association between the MetS-Z score and serum endocan levels (r = 0.584, p < 0.0001). ROC curve analysis showed that endocan has high diagnostic accuracy for identifying MetS (AUC = 0.967, p = 0.0001), with a cutoff value of >88.0 ng/L. Conclusions: Circulating levels of endocan were significantly increased in MetS and were associated with the severity of MetS, suggesting that endocan may play a role in the cellular response to endothelial dysfunction-related injury in patients with MetS. Full article

Aim: To compare diabetic polyneuropathy (DPN) and cardiac autonomic neuropathy (CAN) between T1DM and T2DM patients. Methods: This study enrolled 66 T1DM and 79 T2DM patients. DPN was evaluated using three different methods: clinical examination, using neuropathy symptom score (NSS) and neuropathy disability score (NDS), current perception threshold (CPT) using Neurometer, and nerve conduction studies (NCSs). CAN was assessed by cardiovascular autonomic reflex tests (CARTs). Results: The prevalence of DPN did not differ between T1DM and T2DM (p > 0.05 for all), however, the proportion of DPN depended on the method used and was highest with CPT (53.0% vs. 46.8%), followed by NCSs (44.1% vs. 41.2%) and clinical examination (25.8% vs. 31.6%). T2DM vs. T1DM patients were more often diagnosed with painful DPN (51.9% vs. 27.3%, p = 0.004), reduced perception of vibration (72.2% vs. 48.5%, p = 0.006), and autonomic neuropathy (59.5% vs. 32.3%, p = 0.001), while NCSs revealed more prevalent motor nerve dysfunction in T1DM compared to T2DM (41.2% vs. 19.6%). Multivariate regression analysis showed increased DPN risk with age and CAN risk with worsening of eGFR in T1DM. No significant associations remained after multivariate adjustment for T2DM. Conclusions: The prevalence of DPN is highly varied and depends on the diagnostic method used. T2DM patients more often had symptoms and signs of diabetic neuropathy. However, stronger associations with risk factors were observed in T1DM. Full article

Background: Lysosomal storage diseases (LSDs) are a genetically and clinically heterogeneous group of inborn errors of metabolism caused by variants in genes encoding lysosomal hydrolases, membrane proteins, activator proteins, or transporters. These disease-causing variants lead to enzymatic deficiencies and the progressive accumulation of undegraded substrates within lysosomes, disrupting cellular function across multiple organ systems. While classical phenotypes typically manifest in infancy or early childhood with severe multisystem involvement, a combination of advances in molecular diagnostics [particularly next-generation sequencing (NGS)] and improved understanding of disease heterogeneity have enabled the identification of attenuated forms characterised by residual enzyme activity and later-onset presentations. These milder phenotypes often evade early recognition due to nonspecific or isolated symptoms, resulting in significant diagnostic delays and missed therapeutic opportunities. Objectives/Methods: This study characterises the clinical, biochemical, and molecular profiles of 10 adult patients diagnosed with LSDs, all representing attenuated forms, and discusses them alongside a narrative review. Results: Enzyme activity, molecular data, and phenotypic assessments are described to explore genotype–phenotype correlations and identify diagnostic challenges. Conclusions: These findings highlight the variable expressivity and organ involvement of attenuated LSDs and reinforce the importance of maintaining clinical suspicion in adults presenting with unexplained cardiovascular, neurological, ophthalmological, or musculoskeletal findings. Enhanced recognition of atypical presentations is critical to facilitate earlier diagnosis, guide management, and enable cascade testing for at-risk family members. Full article

Vascular smooth muscle cells (VSMCs) are central to the maintenance of vascular homeostasis and the progression of cardiovascular diseases (CVDs), owing to their remarkable phenotypic plasticity. This editorial introduces a Special Issue of Cells that compiles recent advances in our understanding of the molecular, epigenetic, metabolic, and mechanical mechanisms that govern VSMC behavior. Highlighted contributions explore the roles of RNA modifications, chromatin remodeling, lipid metabolism, and mechanotransduction in VSMC phenotypic switching, revealing new therapeutic targets and diagnostic opportunities. Together, these studies emphasize the multifactorial regulation of VSMC plasticity and its dual role in vascular repair and disease pathogenesis. Full article

Advances in plaque imaging have transformed cardiovascular diagnostics through detailed characterization of atherosclerotic plaques beyond traditional stenosis assessment. This review outlines the clinical applications of varying modalities, including dual-layer spectral CT, photon-counting CT, dual-energy CT, and CT-derived fractional flow reserve (CT-FFR). These technologies offer improved spatial resolution, tissue differentiation, and functional assessment of coronary lesions. Additionally, artificial intelligence has emerged as a powerful tool to automate plaque detection, quantify burden, and refine risk prediction. Collectively, these innovations provide a more comprehensive approach to coronary artery disease evaluation and support personalized management strategies. Full article

Primary aldosteronism (PA), the most common cause of secondary hypertension, is increasingly recognized as an independent driver of adverse cardiac remodeling, mediated through mechanisms beyond elevated blood pressure alone. Chronic aldosterone excess leads to myocardial fibrosis, left ventricular hypertrophy, and diastolic dysfunction via mineralocorticoid receptor activation, oxidative stress, inflammation, and extracellular matrix dysregulation. These changes culminate in a distinct cardiomyopathy phenotype, often underrecognized in early stages. Multimodality cardiac imaging, led primarily by conventional and speckle-tracking echocardiography, and complemented by exploratory cardiac magnetic resonance (CMR) techniques such as T1 mapping and late gadolinium enhancement, enables non-invasive assessment of structural, functional, and tissue-level changes in aldosterone-mediated myocardial damage. While numerous studies have established the diagnostic and prognostic relevance of imaging in PA, several gaps remain. Specifically, the relative sensitivity of different modalities in detecting subclinical myocardial changes, the long-term prognostic significance of imaging biomarkers, and the differential impact of adrenalectomy versus medical therapy on cardiac reverse remodeling require further clarification. Moreover, the lack of standardized imaging-based criteria for defining and monitoring PA-related cardiomyopathy hinders widespread clinical implementation. This narrative review aims to synthesize current knowledge on the pathophysiological mechanisms of aldosterone-induced cardiac remodeling, delineate the strengths and limitations of existing imaging modalities, and critically evaluate the comparative effects of surgical and pharmacologic interventions. Emphasis is placed on early detection strategies, identification of imaging biomarkers with prognostic utility, and integration of multimodal imaging into clinical decision-making pathways. By outlining current evidence and highlighting key unmet needs, this review provides a framework for future research aimed at advancing personalized care and improving cardiovascular outcomes in patients with PA. Full article

Cardiovascular diseases (CVDs) remain the leading cause of death globally, underscoring the urgent need for data-driven early diagnostic tools. This study proposes a multilayer artificial neural network (ANN) model for heart disease prediction, developed using a real-world clinical dataset comprising 13,981 patient records. Implemented on the Orange data mining platform, the ANN was trained using backpropagation and validated through 10-fold cross-validation. Dimensionality reduction via principal component analysis (PCA) enhanced computational efficiency, while Shapley additive explanations (SHAP) were used to interpret model outputs. Despite achieving 83.4% accuracy and high specificity, the model exhibited poor sensitivity to disease cases, identifying only 76 of 2233 positive samples, with a Matthews correlation coefficient (MCC) of 0.058. Comparative benchmarks showed that random forest and support vector machines significantly outperformed the ANN in terms of discrimination (AUC up to 91.6%). SHAP analysis revealed serum creatinine, diabetes, and hemoglobin levels to be the dominant predictors. To address the current study’s limitations, future work will explore LIME, Grad-CAM, and ensemble techniques like XGBoost to improve interpretability and balance. This research emphasizes the importance of explainability, data representativeness, and robust evaluation in the development of clinically reliable AI tools for heart disease detection. Full article

Chronic Coronary Syndrome (CCS) significantly impacts quality of life and increases the risk of adverse cardiovascular events, remaining the leading cause of mortality worldwide. The use of sensor technology in medicine is emerging as a promising approach to enhance the management and monitoring of patients across a wide range of diseases. Recent advancements in engineering and nanotechnology have enabled the development of ultra-small devices capable of collecting data on critical physiological parameters. Several sensors integrated in wearable and implantable devices, instruments for exhaled gas analysis, smart stents and tools capable of real time biochemical analysis have been developed, and some of them have demonstrated to be effective in CCS management. Their application in CCS could provide valuable insights into disease progression, ischemic events, and patient responses to therapy. Moreover, sensor technologies can support the personalization of treatment plans, enable early detection of disease exacerbations, and facilitate prompt interventions, potentially reducing the need for frequent hospital visits and unnecessary invasive diagnostic procedures such as coronary angiography. This review explores sensor integration in CCS care, highlighting technological advances, clinical potential, and implementation challenges. Full article

Continuous blood pressure (BP) monitoring provides valuable insight into the body’s dynamic cardiovascular regulation across various physiological states such as physical activity, emotional stress, postural changes, and sleep. Continuous BP monitoring captures different variations in systolic and diastolic pressures, reflecting autonomic nervous system activity, vascular compliance, and circadian rhythms. This enables early identification of abnormal BP trends and allows for timely diagnosis and interventions to reduce the risk of cardiovascular diseases (CVDs) such as hypertension, stroke, heart failure, and chronic kidney disease as well as chronic stress or anxiety disorders. To facilitate continuous BP monitoring, we propose an AI-powered estimation framework. The proposed framework first uses an expert-driven feature engineering approach that systematically extracts physiological features from photoplethysmogram (PPG)-based arterial pulse waveforms (APWs). Extracted features include pulse rate, ascending/descending times, pulse width, slopes, intensity variations, and waveform areas. These features are fused with demographic data (age, gender, height, weight, BMI) to enhance model robustness and accuracy across diverse populations. The framework utilizes a Tab-Transformer to learn rich feature embeddings, which are then processed through an ensemble machine learning framework consisting of CatBoost, XGBoost, and LightGBM. Evaluated on a dataset of 1000 subjects, the model achieves Mean Absolute Errors (MAE) of 3.87 mmHg (SBP) and 2.50 mmHg (DBP), meeting British Hypertension Society (BHS) Grade A and Association for the Advancement of Medical Instrumentation (AAMI) standards. The proposed architecture advances non-invasive, AI-driven solutions for dynamic cardiovascular health monitoring. Full article

Background: Magnesium (Mg) is an essential mineral that plays a vital role in various physiological processes, including enzyme regulation, neuromuscular function, and cardiovascular health. Dysmagnesemia has been associated with arrhythmias, neuromuscular dysfunction, and poor outcomes in intensive care unit (ICU) settings, representing diagnostic and therapeutic challenges. However, the relationship between dysmagnesemia and health outcomes in the ICU remains inadequately defined. Aim/Objective: This study aimed to assess the prevalence of dysmagnesemia and evaluate the correlation between total (tMg) and ionized magnesium (iMg) levels in a cohort of ICU and high dependency unit (HDU) patients. It also sought to evaluate patient characteristics and relevant health outcomes by comparing both concentrations of iMg and tMg. Methods: This prospective study was conducted among adult patients admitted to the ICU and the high dependency unit (HDU). Results: Among the 134 included patients, the median age was 63.5 years (IQR: 52.0–77.0). The majority, 91.0%, required mechanical ventilation. Additionally, 50.0% were diagnosed with diabetes, 28.4% had chronic kidney disease, and proton pump inhibitors (PPIs) were administered to 67.2% of the patients. The prevalence of hypomagnesemia, as measured by iMg, was 6.7%, while hypermagnesemia was at 39.6%. When measured by tMg, hypomagnesemia and hypermagnesemia were observed at rates of 14.9% and 22.4%, respectively. The iMg measurements showed an association between the incidence of atrial fibrillation and hypomagnesemia (p = 0.015), whereas tMg measurements linked hypomagnesemia with longer hospital stays. Notably, only a few patients identified with iMg-measured hypomagnesemia received magnesium replacement during their ICU stay. Conclusions: Dysmagnesemia is prevalent among critically ill patients, with discordance between iMg and tMg measurements. iMg appears more sensitive in detecting arrhythmia risk, while tMg correlates with length of stay. These findings support the need for larger studies and suggest considering iMg in magnesium monitoring and replacement strategies. Full article

Trimethylamine N-oxide (TMAO) is known to increase in human cardiovascular, metabolic, and renal diseases. In human medicine, TMAO has recently been utilized as a diagnostic and prognostic biomarker for renal dysfunction, and research is ongoing regarding its potential as a therapeutic target. This study aimed to evaluate the diagnostic and prognostic potential of TMAO as a supportive biomarker in dogs with chronic kidney disease (CKD). To assess its diagnostic utility, TMAO concentrations were compared between a CKD group (n = 32) and a healthy control group (n = 32). In addition, patients with CKD were subdivided into stages 2 (n = 12), 3 (n = 11), and 4 (n = 9) and compared individually with the healthy controls. For prognostic evaluation, the CKD group was monitored over six months, and the TMAO levels were compared between survivors (n = 18) and non-survivors (n = 14). The TMAO concentrations showed a highly significant difference between patients with CKD and healthy controls (p < 0.0001). Patients with each different CKD stage exhibited statistically significant differences compared with the healthy controls (p < 0.05). Furthermore, the median TMAO levels tended to increase with advancing CKD stage; however, the differences among stages were not statistically significant. In addition, within the CKD group, TMAO concentrations were significantly higher in non-survivors than in survivors at the six-month follow-up (p = 0.0142). This pilot study highlights the potential of TMAO as a supportive renal biomarker for diagnostic and prognostic evaluation in canine CKD. Full article