Unraveling Elastic Fiber-Derived Signaling in Arterial Aging and Related Arterial Diseases

Biomolecules, Journal Year: 2025, Volume and Issue: 15(2), P. 153 - 153

Published: Jan. 21, 2025

Arterial stiffening is a significant risk factor for the development of cardiovascular diseases, including hypertension, atherosclerosis, and arteriopathy. The destruction elastic fibers, accompanied by vascular inflammatory remodeling, key process in progression arterial related pathologies. In young, healthy arteries, intact fibers create resilient microenvironment that maintains quiescence cells. However, with advancing age, these undergo post-translational modifications, such as oxidation, glycosylation, calcification, leading to their eventual degeneration. This degeneration results release degraded peptides formation an inflammatory, stiffened niche. Elastic fiber profoundly impacts proinflammatory phenotypes behaviors various cells, endothelial smooth muscle macrophages, fibroblasts, mast Notably, elastin-derived (EDPs), which act potent molecules. EDPs activate cellular processes, secretion, cell migration, proliferation, interacting elastin receptor complex (ERC). These elastin-related events are commonly observed aging diseased arteries. findings suggest meshwork primary event driving inflammation, stiffening, adverse remodeling age. Therefore, preserving blocking EDP/ERC signaling pathways may offer promising therapeutic strategies mitigating age-related diseases.

Language: Английский

Integrating Artificial Intelligence in the Diagnosis and Management of Metabolic Syndrome: A Comprehensive Review

Diabetes/Metabolism Research and Reviews, Journal Year: 2025, Volume and Issue: 41(4)

Published: March 27, 2025

ABSTRACT Background Metabolic syndrome (MetS) is a progressive chronic pathophysiological state characterised by abdominal obesity, hypertension, hyperglycaemia, and dyslipidaemia. It recognised as one of the major clinical syndromes affecting human health, with approximately one‐quarter global population impacted. MetS increases risk developing cardiovascular diseases (CVDs), stroke, type 2 diabetes mellitus (T2DM), diverse metabolic diseases. Early diagnosis could potentially reduce prevalence these However, care for faces significant challenges due to (i) lack comprehensive understanding full spectrum associated diseases, stemming from unclear mechanisms (ii) frequent underdiagnosis or misdiagnosis in settings inconsistent screening guidelines, limited medical resources, time constraints practice, insufficient awareness training. The increasing availability healthcare data presents opportunities apply innovate artificial intelligence (AI) addressing challenges. This review aims summarise AI models applied syndrome‐related (MetSRD), where MetSRD collectively refers conditions directly MetS. Methods Our consists two phases. Initially, we conducted literature on narrow down based strength evidence. We then used terms ‘Metabolic Syndrome’ ‘Machine Learning’ combination identified further refinement. In total, 52 related first phase 36 articles second phase. Results total after phase, T2DM, CVDs, cancer being top three. Among obtained observed following: criteria were across studies. primary purpose applications was identify factors thereby improving predictions MetSRD. Traditional machine learning models, such Random Forest Logistic Regression, found be most effective. (iii) addition criteria, explored other factors, including demographic physiological variables, dietary influences, lipidomic proteomic indicators, more. Conclusion underscores link between particular focus underreported non‐alcoholic fatty liver disease stroke. Through analysis sources, diagnostic additional indicators beyond traditional measures have been identified, emphasising importance combining both non‐traditional markers enhance predictive capabilities shows great potential research, particularly through integration multi‐source data, metrics, genetic information, omics data. amalgamation modern promising, offering balanced approach model performance complexity. While international definitions provide applicability, they may not suitable all populations scenarios, necessitating flexible adaptive, explainable algorithms. Ultimately, will enable personalised diagnostics targeted interventions.

Language: Английский

Liposomal encapsulation of Chenopodium berlandieri extracts rich in oleanolic acid: Improved bioactivities targeting metabolic syndrome prevention

Food & Function, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Chronic inflammation and oxidative stress are major contributors to the development of metabolic syndrome conditions, including obesity, insulin resistance, dyslipidemia, hypertension.

Language: Английский