A Dynamic Cellular Model as an Emerging Platform to Reproduce the Complexity of Human Vascular Calcification In Vitro.

Vascular calcification (VC) is a cardiovascular disease characterized by calcium salt deposition in vascular smooth muscle cells (VSMCs). Standard in vitro models used in VC investigations are based on VSMC monocultures under static conditions. Although these platforms are easy to use, the absence of interactions between different cell types and dynamic conditions makes these models insufficient to study key aspects of vascular pathophysiology.

Type-specific inflammatory responses of vascular cells activated by interaction with virgin and aged microplastics.

Microplastics (MPs) are recognized as a major environmental problem due to their ubiquitous presence in ecosystems and bioaccumulation in food chains. Not only humans are continuously exposed to these pollutants through ingestion and inhalation, but recent findings suggest they may trigger vascular inflammation and potentially worsen the clinical conditions of cardiovascular patients. Here we combine headspace analysis by needle trap microextraction-gas chromatography-mass spectrometry (HS-NTME-GC-MS) and biological assays to evaluate the effects of polystyrene, high- and low-density polyethylene MPs on phenotype, metabolic activity, and pro-inflammatory status of Vascular Smooth Muscle Cells (VSMCs) the most prominent cells in vascular walls.

Novel evidence on the beneficial effect of quercetin treatment in vascular calcification.

Vascular calcification is a pathological chronic condition characterized by calcium crystal deposition in the vessel wall and is a recurring event in atherosclerosis, chronic kidney disease, and diabetes. The lack of effective therapeutic treatments opened the research to natural products, which have shown promising potential in inhibiting the pathological process in different experimental models. This study investigated the anti-calcifying effects of Quercetin and Berberine extracts on vascular smooth muscle cells (VSMCs) treated with an inorganic phosphate solution for 7 days.

Echo state networks for the recognition of type 1 Brugada syndrome from conventional 12-LEAD ECG.

Artificial Intelligence (AI) applications and Machine Learning (ML) methods have gained much attention in recent years for their ability to automatically detect patterns in data without being explicitly taught rules. Specific features characterise the ECGs of patients with Brugada Syndrome (BrS); however, there is still ambiguity regarding the correct diagnosis of BrS and its differentiation from other pathologies. This work presents an application of Echo State Networks (ESN) in the Recurrent Neural Networks (RNN) class for diagnosing BrS from the ECG time series.