Heart failure (HF) is a growing public health concern, driven by the increasing prevalence of obesity, diabetes, and aging. Despite therapeutic advances, HF continues to be associated with high morbidity and mortality. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), originally developed for glycemic control in type 2 diabetes, have demonstrated cardiovascular benefits in clinical trials. Recent studies, including STEP-HFpEF and SUMMIT, have shown improvement in symptoms and weight loss in patients with HF with preserved ejection fraction (HFpEF). GLP-1 RAs are involved in multiple biological pathways relevant to heart failure pathophysiology. These include pathways related to sympathetic nervous system activity, inflammatory cytokine signaling, oxidative stress, calcium handling, natriuretic peptide signaling, and cardiac metabolism. GLP-1 receptor agonists modulate vascular pathways involving nitric oxide signaling, endothelial function, and renal sodium handling, contributing to improved hemodynamics and neurohormonal balance. Together, these actions intersect with key neurohormonal and cellular processes contributing to chronic heart failure progression. This review explores the mechanistic overlap between GLP-1 receptor signaling and heart failure pathophysiology. This mechanistic overlap suggests a plausible role for these agents as adjunctive treatments in heart failure, especially in metabolically driven phenotypes. While direct cardiac effects remain incompletely defined, systemic metabolic and anti-inflammatory actions provide a mechanistic basis for observed clinical benefits.

Background/Objectives: Cognitive impairment is a debilitating comorbidity affecting diverse patient populations, yet the cognitive effects of therapies like mineralocorticoid receptor antagonists (MRAs) remain underexplored. Preclinical evidence suggests that MRAs, particularly spironolactone, may reduce cognitive decline by modulating aldosterone-dependent pathways and targeting hippocampal receptors. However, evidence in humans is fragmented, and no systematic review has consolidated these findings. This review evaluates the cognitive effects of MRAs, synthesizes current data, and identifies research gaps. Methods: A literature search using terms related to MRAs and cognitive outcomes was performed in PubMed and Web of Science from 1979 to 2023. A total of 143 articles were identified and 85 were screened after removing duplicates. Ultimately, 44 studies were included and were classified based on study design and population focus (preclinical, healthy controls, patients with psychiatric disorders, and cardiovascular patients). Results: Spironolactone demonstrated mixed effects on cognition. In healthy participants, it improved spatial memory under stress and prevented stress-related suppression of medial temporal activity, but impaired working memory and selective attention. In patients with psychiatric conditions, spironolactone reduced cognitive empathy deficits in major depressive disorder and improved working memory in bipolar I disorder. In cardiovascular patients, spironolactone improved cognitive scores and hippocampal memory but had no effect on non-hippocampal memory. Conclusions: Spironolactone exhibits potential cognitive benefits across diverse populations. However, its effects on cognition are mixed, highlighting the need for further research to understand its mechanisms and therapeutic potential, particularly in patients with heart failure and other related conditions.

Heart failure (HF) affects over 55 million individuals globally, with prevalence projected to exceed 11 million in the United States by 2050 and is increasingly recognized as a systemic disorder extending beyond hemodynamic dysfunction to encompass profound alterations in neural and gut physiology. Cognitive impairment affects nearly half of HF patients and represents a major determinant of morbidity, self-care capacity, and mortality. Recent advances suggest that the gut microbiome serves as a pivotal intermediary in the heart–brain crosstalk, influencing neurocognitive outcomes through inflammatory, metabolic, and neurohumoral pathways. Dysbiosis in HF disrupts intestinal barrier integrity, facilitating translocation of endotoxins and microbial metabolites such as trimethylamine-N-oxide (TMAO), short-chain fatty acids (SCFAs), and bile acids, which in turn modulate neuroinflammation, cerebral perfusion, and neuronal signaling. The gut–heart–brain axis provides an integrative framework linking HF and cognitive impairment pathophysiology through dysbiosis-driven systemic inflammation and metabolite dysregulation. Gut-derived biomarkers and microbiome-targeted interventions represent promising strategies for detection of early alterations and precision treatment, highlighting the urge for prospective, multi-omics studies to establish causality and therapeutic efficacy. This review synthesizes current evidence connecting gut microbiome dysbiosis and metabolite alterations to both HF and cognitive impairment pathophysiology and proposes translational strategies for integrating microbiome-targeted therapies in HF patients with cognitive dysfunction.

Heart failure (HF) affects over 55 million individuals globally, with prevalence projected to exceed 11 million in the United States by 2050 and is increasingly recognized as a systemic disorder extending beyond hemodynamic dysfunction to encompass profound alterations in neural and gut physiology. Cognitive impairment affects nearly half of HF patients and represents a major determinant of morbidity, self-care capacity, and mortality. Recent advances suggest that the gut microbiome serves as a pivotal intermediary in the heart–brain crosstalk, influencing neurocognitive outcomes through inflammatory, metabolic, and neurohumoral pathways. Dysbiosis in HF disrupts intestinal barrier integrity, facilitating translocation of endotoxins and microbial metabolites such as trimethylamine-N-oxide (TMAO), short-chain fatty acids (SCFAs), and bile acids, which in turn modulate neuroinflammation, cerebral perfusion, and neuronal signaling. The gut-heart-brain axis provides an integrative framework linking HF and cognitive impairment pathophysiology through dysbiosis-driven systemic inflammation and metabolite dysregulation. Gut-derived biomarkers and microbiome-targeted interventions represent promising strategies for detection of early alterations and precision treatment, highlighting the urge for prospective, multi-omics studies to establish causality and therapeutic efficacy. This review synthesizes current evidence connecting gut microbiome dysbiosis and metabolite alterations to both HF and cognitive impairment pathophysiology and proposes translational strategies for integrating microbiome-targeted therapies in HF patients with cognitive dysfunction.

Background: Wound healing progresses through phases of homeostasis, inflammation, proliferation, and remodeling, regulated by signaling pathways. The root extract of Onosma microcarpum (OM), traditionally used in Western Iran for wound care, contains bioactive compounds like alkannin, shikonin, and polyphenols, potentially enhancing healing. This study evaluates OM's effects on wound healing through the signaling pathways in each phase. Methods: Dried OM root was formulated into ointments, and male Wistar rats with excisional wounds were treated with OM (0.2% or 1%) or vehicle. Wound closure rate, histological and gene expression analyses were conducted on days 3, 8, and 14, measuring inflammation, granulation, angiogenesis, and collagen formation. The mRNA expression of cytokines (Interleukin-1β (IL-1β), IL-6, tumor necrosis factor-α (TNF-α)), growth factors (Transforming Growth Factor-β1 (TGF-β1), platelet-derived growth factor (PDGF)), and cell regulators (extracellular signal-regulated kinase (ERK), c-Jun N-terminal kinases (JNK), p38 mitogen-activated protein kinase (p38 MAPK)) were analyzed. Results: OM at 0.2%, significantly modulated key pathways in wound healing. By day 3, OM reduced TNF-α ( P = 0.003) and IL-1β ( P = 0.008), decreasing inflammation and JNK activity ( P = 0.009). On day 8, OM further reduced IL-6 ( P = 0.002) and p38 MAPK ( P = 0.005) while increasing TGF-β1 ( P = 0.008) and PDGF ( P = 0.001), promoting proliferation. By day 14, increased ERK ( P = 0.008) and p38 MAPK ( P = 0.002) activity facilitated tissue repair. Wounds treated with 0.2% OM showed better outcomes than 1%, with more organized collagen (mean score 3.0 vs 2.3, P = 0.021), improved angiogenesis (3.4 vs 2.5, P = 0.038), and higher wound closure (87% vs 69%, P = 0.023). Conclusion: OM enhances wound healing by targeting each phase: reducing early inflammation, promoting mid-phase fibroblast proliferation, and supporting collagen organization in remodeling. These targeted effects suggest OM's therapeutic potential for wound repair.

Background Diffusion-weighted imaging (DWI) map the microenvironment of breast cancer (BC) into cellular density and membrane integrity, and captures the effects of capillary microcirculation and intracellular structures through multi b-value analyses. Amidst potential biases in the radiomics pipeline, we aim to discern clinically relevant features from artifacts, improving machine learning (ML) classifier applicability in BC diagnostics through informed feature selection. Methods We prospectively enrolled 148 BC patients for ML classifier training, with an additional 98 patients included retrospectively for validation, ensuring consistent imaging and post-processing standards. Tumor subtypes were classified based on hormone receptor (HR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 levels. Utilizing a wide range of ML classifiers, we pinpointed an optimal feature count range of 8–13 for maximal training efficacy and generalizability, given our training and validation cohort sizes. Specifically, 12 domain-specific multi b-value DWI features were selected, focusing on entropy and first-order statistics of the of apparent diffusion coefficient (ADC), and higher-order statistical features (intravoxel incoherent motion (IVIM) parameters Dt, fp, Dp; diffusion kurtosis imaging (DKI) metrics MD, MK). Classifier stability was gauged by the interfold range of 4-fold cross-validation area under the curve (AUC) for the training dataset, while performance was assessed based on validation dataset AUC. Significant DWI features for molecular-based stratifications were identified based on a combined criterion applied to the ML classifier with the highest validation AUC, prioritizing the top three features ranked by importance and with a stability score over 0.7 in subsampling. Results Among linear classifiers, Stochastic Gradient Descent (SGD) stood out by showing distinct predictive power for HR status, contrasting with the generally limited effectiveness of other linear models. Non-linear classifiers significantly outperformed linear models across other categories. Random Forest excelled in Ki67 and luminal A subtype, AdaBoost in triple-negative subtyping, and XGBoost in HER2 status and subtype. SVM with Radial Basis Function kernels and Feed-Forward Neural Network jointly showed proficiency in classifying luminal HER2. Notably, XGBoost and Random Forest demonstrated stable feature selection processes. The entropy and first-order features of ADC was pivotal across molecular-based prognostic stratifications. IVIM features significantly influenced HR and Ki67 statuses, along with their attributed subtypes (luminal A, luminal B, and triple-negative). Conversely, DKI features were uniquely predictive in the HER2 domain, highlighting their distinctive contributions to the stratification of luminal HER2 and HER2 subtypes. Conclusions Non-linear machine learning classifiers excel in BC stratification, leveraging complex DWI features to deepen insights into cancer subtypes and molecular characteristics, marking a strategic evolution towards precision diagnostics.