Search Results (47,881)

Diabetic kidney disease (DKD) remains a primary driver of end-stage kidney disease and cardiovascular morbidity despite the optimized use of renin–angiotensin system (RAS) inhibitors and sodium-glucose cotransporter-2 (SGLT2) inhibitors. Recent evidence identifies the overactivation of the mineralocorticoid receptor (MR) as a critical, independent pathway leading to persistent renal inflammation and fibrosis. In the diabetic milieu, MR overactivation—driven by both aldosterone and ligand-independent factors such as Rac1 GTPase and oxidative stress—triggers pro-inflammatory and pro-fibrotic gene networks. Unlike traditional steroidal mineralocorticoid receptor antagonists (MRAs), the novel non-steroidal MRA finerenone exhibits a distinct binding mode that more effectively blocks the recruitment of transcriptional co-activators, thereby silencing detrimental downstream signaling in podocytes, fibroblasts, and myeloid cells. Preclinical models have demonstrated that MR blockade significantly reduces albuminuria and preserves podocyte integrity independent of systemic blood pressure. These findings translated into landmark clinical trials; the FIDELIO-DKD and FIGARO-DKD trials established that finerenone significantly reduces the risk of kidney disease progression and cardiovascular events across a broad spectrum of chronic kidney disease stages in type 2 diabetes. Furthermore, recent data from the FINEARTS-HF and CONFIDENCE trials suggest a synergetic benefit when combined with SGLT2 inhibitors, offering more robust cardiorenal protection with a manageable risk of hyperkalemia. This review synthesizes the current understanding of MR pathophysiology and clinical evidence, providing a comprehensive framework for the integration of MRAs into the evolving standard of care for patients with diabetic kidney disease. Full article

Disease staging is a critical component of clinical diagnosis, treatment, and prognosis assessment. However, structured clinical data typically exhibit high-dimensional, nonlinear feature interactions; stage-specific dominant features; and threshold-based discontinuities. These characteristics make it challenging for a single model to achieve both global feature modeling capability and local discriminative power, thereby limiting further improvements in prediction accuracy. To address this limitation, we propose a novel deep ensemble learning framework, ETGB-SEF (Entmax-TabNet Gradient Boosting Stacked Ensemble Framework), for multiclass disease staging. First, at the base model level, Entmax-1.5 replaces Sparsemax in TabNet, thereby enabling an adjustable sparse feature selection mechanism that enhances the ability to model weakly correlated clinical features while preserving interpretability. Second, at the model-fusion level, a stacked ensemble architecture in the probability space is developed. This architecture integrates the modified TabNet with Gradient Boosting Decision Trees (GBDT) in a complementary way, enabling the former to capture global nonlinear semantic dependencies while the latter captures threshold-based discriminative boundaries among clinical features. Extensive experiments on real-world datasets demonstrate that the proposed method consistently outperforms existing state-of-the-art approaches. Full article

Cardiometabolic disease is increasingly shaped by the overlap among obesity, type 2 diabetes, chronic kidney disease, heart failure, and atherosclerotic cardiovascular disease, underscoring the need for prevention strategies that extend beyond glucose-centered care. This narrative review critically examines the mechanistic rationale, clinical evidence, guideline evolution, and practical implementation of sodium-glucose cotransporter-2 inhibitors (SGLT-2 inhibitors) and glucagon-like peptide-1 receptor agonists (GLP-1 receptor agonists) within the cardiorenal–metabolic continuum. A structured literature search was conducted in PubMed, Scopus, and Web of Science, focusing primarily on publications from January 2019 to March 2026, with selected landmark studies from earlier years included for context. Priority was given to randomized controlled trials, major cardiovascular and kidney outcome trials, meta-analyses, clinical practice guidelines, scientific statements, and expert consensus documents. The reviewed evidence indicates that SGLT-2 inhibitors show the most consistent benefits in reducing heart failure events, slowing chronic kidney disease progression, and lowering cardiorenal risk, whereas GLP-1 receptor agonists are more strongly associated with reductions in major adverse cardiovascular events, residual atherosclerotic risk, and body weight. Emerging data also support extension of this therapeutic paradigm beyond diabetes, particularly in obesity-associated cardiovascular risk. Contemporary care is increasingly moving toward phenotype-informed treatment selection, earlier organ-protective intervention, and multidisciplinary management, although cost, access, tolerability, and implementation barriers remain important limitations. SGLT-2 inhibitors and GLP-1 receptor agonists are therefore central to modern cardiovascular prevention across the cardiovascular–kidney–metabolic spectrum. In this context, the proposed Cardiometabolic 2.0 framework may serve as a clinically oriented model for integrating these therapies within contemporary organ-protective care. Full article

DNA methylation is a fundamental epigenetic modification that regulates gene expression, genome stability, and cell identity across vertebrate development. Disruption of DNA methylation homeostasis contributes to a wide spectrum of human diseases, including developmental disorders, neurological conditions, and cancer. Understanding how DNA methylation patterns are established, maintained, and dynamically remodeled during development is therefore essential for elucidating disease mechanisms and identifying therapeutic opportunities. The zebrafish (Danio rerio) has emerged as a powerful vertebrate model for investigating DNA methylation dynamics in vivo. Its external fertilization, optical transparency, rapid embryogenesis, and high fecundity enable direct observation and experimental manipulation of epigenetic processes at developmental stages that are difficult to access in mammalian systems. In addition, the core enzymatic machinery governing DNA methylation, including DNA methyltransferase (DNMT) and ten-eleven translocation (TET) protein families, is evolutionarily conserved between zebrafish and humans. In this review, we summarize current knowledge of the zebrafish methylome and the enzymatic regulators that control DNA methylation dynamics. We discuss how DNA methylation shapes early embryonic development, organogenesis, and cell fate decisions, and highlight insights gained from zebrafish models of human disease. Finally, we examine emerging technologies that are enabling increasingly precise interrogation of epigenetic regulation in vivo. Together, these advances position zebrafish as an important platform for bridging developmental epigenetics with human disease biology and therapeutic discovery. Full article

Human mesenchymal stem/stromal cells (MSCs) have attracted significant interest in regenerative medicine due to their self-renewal capacity, immunomodulatory functions, multipotency, and relative ease of isolation and expansion. However, several limitations restrict their clinical application, including cellular heterogeneity, challenges in large-scale expansion, and poor in vivo persistence after transplantation. Systemically administered MSCs are rapidly cleared because of limited adhesion, short survival time, and inefficient extravasation, resulting in suboptimal therapeutic efficacy. To overcome these challenges, various strategies have been developed, such as hypoxic preconditioning, biomaterial-based approaches, and genetic modification. Among these, genetic modification represents a particularly powerful and versatile strategy, as it enables targeted enhancement of specific functional properties of MSCs and even the introduction of novel therapeutic capabilities. In this review, we summarize recent advances in genetically engineered MSCs and categorize these approaches into four functional domains: migration, adhesion, secretion, and survival. We further discuss their therapeutic outcomes across diverse disease models in vivo. Collectively, genetic modification substantially enhances the intrinsic therapeutic potential of MSCs and represents a promising direction for the development of next-generation cell-based therapies. Full article

Functional foods enriched with bioactive compounds have attracted increasing attention for their potential to improve metabolic health and reduce the risk of chronic diseases. White tea, a minimally processed tea rich in polyphenols and antioxidant constituents, may exert beneficial effects on obesity-related metabolic disturbances through multiple molecular pathways. In this study, we investigated the effects of white tea in a high-fat diet-induced obesity model in rats, with particular emphasis on metabolic regulation and adipokine signaling. Body weight, lipid profile, glucose homeostasis, insulin resistance-related parameters, and circulating levels of apelin and irisin were evaluated. High-fat diet feeding impaired metabolic balance and altered obesity-associated biochemical parameters, whereas white tea administration ameliorated several of these changes. White tea was associated with improvements in body weight gain and selected metabolic parameters, together with modulation of adipokine-related markers. These findings suggest that white tea may function as a bioactive-rich functional food with beneficial effects on pathways involved in obesity and metabolic homeostasis. Our results support the potential contribution of white tea-derived compounds to nutrition-based strategies for the prevention and management of obesity. Full article

Pathological cardiac hypertrophy is an important risk factor for cardiovascular disease. Ganoderic acid A (GAA), the primary bioactive constituent of Ganoderma lucidum (G. lucidum), is known for its stable chemical properties and diverse biological activities. It has been shown to confer protection against myocardial ischemia–reperfusion injury in rat models, potentially through modulating inflammatory responses and inhibiting protein expression linked to both NF-κB and apoptosis pathways. Nevertheless, the role of GAA in cardiac hypertrophy has not yet been fully elucidated. Using transverse aortic constriction (TAC)-induced cardiac hypertrophy in mice, we analyzed the degree of hypertrophy using echocardiography and at the pathology and molecular levels. Our results demonstrate that GAA effectively attenuates Ang II-induced cardiomyocyte hypertrophy in vitro and reduces pressure overload-induced cardiac hypertrophy in vivo. Further investigation revealed that GAA exerts its anti-hypertrophic effects by downregulating the mRNA expression of hypertrophic and fibrotic markers and attenuating inflammatory responses, and that the protective effects of GAA may involve NF-κB signaling. This study provides valuable theoretical support for the potential therapeutic application of GAA in treating pathological myocardial hypertrophy and heart failure. Full article

Background: Despite significant advances in medical image analysis, automated diagnosis of Multiple Sclerosis (MS) from magnetic resonance imaging (MRI) remains challenging due to the complexity of 3D brain data and the variability of lesion appearance. Objective: In this work, we propose an efficient and optimized feature extraction framework for automated MS diagnosis using FLAIR, T1-, and T2-weighted MRI. The approach enhances Decimal Descriptor Patterns (DDP) by integrating local gradient information, producing a 3D texture representation that is more discriminative and expressive. Methods: The study is divided into two main parts: (i) detection of MS, and (ii) assessment of disease progression in affected patients. In each part, features are extracted from the relevant MRI data and classified using multiple classical machine learning classifiers, including Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Logistic Regression. Furthermore, the performance of the proposed handcrafted feature-based approach was compared to features extracted using a deep learning-based model (vision–language model, VLM), specifically CLIP (Contrastive Language–Image Pretraining), enabling a clear comparison of their performance. To assess robustness and generalizability, two complementary validation strategies were adopted: (i) controlled experiments on the BrainWeb dataset under varying T1/T2 contrast conditions, and (ii) validation on a the real-world FLAIR MRI dataset, reflecting clinically relevant lesion visibility. Results:Gradient-DDP features achieve the best overall performance for MS progression, reaching up to 97% accuracy on T2-weighted MRI with SVM, while LDA and Logistic Regression also remain strong with accuracies around 83–96% on T2. For binary MS detection, the proposed method attains near-perfect results, with up to 99% accuracy on FLAIR (SVM/KNN) and 98% on T2-weighted images across SVM, while other classifiers also maintain high performance above 90%. Conclusions: Gradient-DDP provides strong consistency and transparency, offering an interpretable link between texture patterns and diagnostic outcomes. While VLM features perform well when lesion patterns are clearly defined (e.g., in T2), Gradient-DDP demonstrates greater robustness in more challenging modalities such as Flair, where deep representations may be less stable. Full article

We systematically analyze the evolutionary characteristics of China’s public health policies, focusing on the dynamic changes in policy content, stage-specific differences, and inter-subject collaborative relationships. Based on 137 public health policy documents issued by the central government, the analysis is conducted from a dual perspective: first, the BERTopic model is employed to identify prominent policy themes and track their evolutionary paths; second, Social Network Analysis (SNA) is utilized to deconstruct the collaborative mechanisms and network structural characteristics among policy actors, goals, and tools. The findings indicate: (1) Collaboration among core policy actors is close, yet inter-departmental transparency and collaborative inclusivity remain limited for certain organizations. (2) Policy goals show a diversifying trend, with the strategic focus shifting from infectious disease prevention and control to comprehensive public health services. (3) There are significant preferences in the selection of policy tools for balancing rapid emergency response with sustainable long-term health governance. These findings reveal the evolutionary laws of the public health policy system and provide a theoretical basis for optimizing the policy framework and enhancing governance efficacy. Full article

Non-alcoholic fatty liver disease affects approximately thirty percent of the global population, yet public awareness remains dangerously low among young adults facing occupational risk factors. This study introduces the Fatty Liver Awareness Game (FLAG), an educational serious game designed to improve liver health literacy among software engineering students at the University of Guayaquil. While evaluated with this specific sample, FLAG is intended for the broader target population of young adults in developing nations who face occupational sedentary risk and limited access to preventive health education. Through a controlled experiment with fifty participants randomly assigned to game-based or traditional lecture instruction, the game demonstrated superior effectiveness, with a twenty-percentage-point advantage in post-test scores and a seventy-two percent reduction in incorrect responses compared to fifty percent in the lecture group. The large effect size (Cohen’s d = 1.43) and reduced performance variability among game participants indicate that interactive, feedback-rich learning environments can outperform passive instruction for this population and content domain. While the present design does not isolate the contribution of individual game elements—such as narrative framing, explanatory feedback, or mini-game interleaving—the results establish FLAG as a replicable model for digital health interventions targeting underserved populations at critical developmental junctures. Future component analyses are needed to determine which specific design features drive the observed advantages. Full article

Elevated coronary artery calcium (CAC) is associated with increased cardiovascular and all-cause mortality. Coronary artery calcium score (CACS) may aid cardiovascular risk assessment beyond traditional models, particularly in primary prevention populations. This study aimed to evaluate the association between CACS and major adverse cardiovascular events (MACE) and its relationship with conventional cardiovascular risk models in patients without established coronary artery disease. We conducted a retrospective analysis of patients aged >40 years with at least one cardiovascular risk factor who underwent computed tomography coronary angiography. Data on cardiovascular risk factors, medication use, atherosclerotic cardiovascular disease (ASCVD) score, MESA score, and CACS were collected. Among 100 patients (61% male; mean age 57.1 ± 10.7 years), 47% were at intermediate ASCVD risk. Median CACS was 65.0 (IQR 0.0–383.3). CACS was significantly associated with ASCVD and MESA scores. In multivariable analysis, glucose level, ASCVD score, and MESA score were significantly associated with MACE, while CACS showed a borderline association. These findings should be interpreted as exploratory and suggest that CACS reflects overall cardiovascular risk burden and may contribute to risk assessment when integrated with conventional approaches. Further prospective studies are needed to validate these findings. Full article

Differentiating Alzheimer’s disease (AD) from frontotemporal dementia (FTD) using resting-state electroencephalography (EEG) remains clinically challenging because of their overlapping electrophysiological characteristics. Although EEG suits large-scale dementia screening, current method often overestimates performance because of epoch-level data leakage and multiclass feature competition in unified models. We propose a task-decoupled, two-stage hierarchical deep learning framework utilizing multiband EEG microstate dynamics. Continuous microstate sequences, modeled via Hungarian matching to preserve fine-grained temporal information, are processed using a normalizer-free 1D convolutional neural network (1D-CNN-NFNet) integrated with multi-head attention. By decoupling the workflow, Stage 1 performs generalized dementia screening using alpha and delta microstates, achieving an area under the curve (AUC) of 0.851. Stage 2 disentangles AD from FTD using delta and theta dynamics, yielding an AD-locking specificity of 86.1%. Evaluated under a strict subject-level leave-one-subject-out (LOSO) cross-validation protocol, the two-stage framework achieved 63.9% balanced accuracy, outperforming the single-stage baseline (55.4%) with a negligible inference latency of 0.733 ms. Furthermore, attention-based interpretability analysis links frequency-specific microstate alterations to underlying cortical disconnection syndromes. These results demonstrate that the framework provides a reproducible and interpretable auxiliary reference for dementia screening and subtyping in clinical neurology. Full article

Background: GBC is an aggressive biliary tract malignancy with limited survival. Although actionable genomic alterations (AGAs) are increasingly recognized in GBC, their prognostic association in real-world practice remains incompletely defined because genomic status is often confounded by stage at presentation and treatment selection. We evaluated the association between documented AGA status and overall survival (OS) using a tiered matching strategy to account for major clinical confounders. Methods: Using the TriNetX Global Collaborative Network, we identified adults with GBC and stratified them into patients with at least one documented AGA in KRAS, TP53, ERBB2, IDH1, FGFR1, PIK3CA, or ARID1A, and a comparison cohort with no documented AGA (representing a real-world population of untested and wild-type patients). Two 1:1 propensity score-matched models were constructed: Model 1 matched for age, sex, and race/ethnicity; Model 2 additionally matched for metastatic disease, surgical resection, and chemotherapy history. Outcomes were evaluated using risk analysis and Kaplan–Meier survival methods. Results: A marked disparity in genomic documentation was observed before matching, with unknown race recorded in 51.0% of the comparison cohort versus 3.7% of the documented AGA cohort. In the demographic-matched analysis (Model 1), the documented AGA cohort had higher mortality (52.8% vs. 42.5%, p < 0.001) and shorter median OS (684 vs. 948 days; HR 1.23, p = 0.006). In the primary stage- and treatment-matched analysis (Model 2), mortality remained higher in the documented AGA cohort (56.2% vs. 43.0%), corresponding to an absolute risk difference of 13.2% (p < 0.001). Median OS was numerically shorter in the documented AGA cohort (750 vs. 784 days), although the proportional hazards assumption was violated, supporting interpretation based primarily on absolute risk measures. In exploratory subgroup analyses, KRAS alterations were associated with worse survival, whereas the TP53 subgroup was limited by small sample size. Conclusions: In this real-world matched analysis, the presence of documented AGAs in GBC were associated with higher mortality even after matching for major demographic, stage-related, and treatment-related variables. These findings support the prognostic relevance of genomic status in GBC and underscore the need to address disparities in access to genomic documentation and testing. Full article

Background/Objectives: Autophagy is an important cellular self-cleansing process whose normal functioning is essential for preventing many age-related diseases. The search for and study of new autophagy activators allows the proposal of novel therapeutic approaches for the treatment of age-related diseases. Medical plants are a rich source of bioactive compounds with variable functions. In this study, we propose an HR-LCMS/MS-based technique for identifying the principal autophagy activators in plant extracts. Methods: We performed a Western blot analysis of the autophagy-inducing activity of plant extract HPLC fractions on a model of SH-SY5Y cells. The composition of the fractions showing autophagy-activating potential was determined via HR-LCMS/MS. Results: We analyzed five plants known to produce autophagy activators and proved the ability of the method to detect the main bioactive compounds. Additional screening demonstrated for the first time that Astragalus dasyanthus is a producer of the autophagy-inducer glabrol. Conclusions: The described HR-LCMS/MS-based method for identifying autophagy activators in multicomponent plant extracts is effective and could be used for further high-throughput screening. Full article

The increasing prevalence of cardiovascular diseases highlights the need to develop vascular grafts that match the mechanics of native vascular tissue and offer functional adaptability. This study reports the development and systematic optimization of a shape-memory polyurethane acrylate (PUA)-based photocurable resin for digital light processing (DLP)-based four-dimensional printing (4DP) applications. Resin formulations were designed by controlling hard/soft segment ratios, reactive diluent content, and crosslink density to position the glass transition temperature (T_g) within the physiological range (25–40 °C). Thermomechanical characterization was performed via dynamic mechanical analysis (DMA) and tensile testing, while a full-factorial Design of Experiments (DoE) approach was applied to optimize DLP process parameters—namely layer thickness, exposure time, and post-curing time. The developed resin formulation yielded a T_g of 38 °C as determined by DMA. Following process optimization, regression models showed high statistical fit (R² > 99%), and experimental validation under optimal conditions (layer thickness: 82.83 µm, exposure time: 11 s, post-curing: 2 min) resulted in an elongation at break of 64.0 ± 3.4%, a Young’s modulus of 10.9 ± 0.1 MPa, and a tensile strength of 6.2 ± 0.3 MPa. The optimized system exhibited thermally triggerable shape memory behavior at near-body temperature, with mechanical properties consistent with natural arterial tissue benchmarks. These findings demonstrate a promising material design strategy for DLP-based 4D-printed vascular structures. Full article