Molecular Pathways of Vulnerable Carotid Plaques at Risk of Ischemic Stroke: A Narrative Review.

The concept of vulnerable carotid plaques is pivotal in understanding the pathophysiology of ischemic stroke secondary to large-artery atherosclerosis. In macroscopic evaluation, vulnerable plaques are characterized by one or more of the following features: microcalcification; neovascularization; lipid-rich necrotic cores (LRNCs); intraplaque hemorrhage (IPH); thin fibrous caps; plaque surface ulceration; huge dimensions, suggesting stenosis; and plaque rupture. Recognizing these macroscopic characteristics is crucial for estimating the risk of cerebrovascular events, also in the case of non-significant (less than 50%) stenosis. Inflammatory biomarkers, such as cytokines and adhesion molecules, lipid-related markers like oxidized low-density lipoprotein (LDL), and proteolytic enzymes capable of degrading extracellular matrix components are among the key molecules that are scrutinized for their associative roles in plaque instability. Through their quantification and evaluation, these biomarkers reveal intricate molecular cross-talk governing plaque inflammation, rupture potential, and thrombogenicity. The current evidence demonstrates that plaque vulnerability phenotypes are multiple and heterogeneous and are associated with many highly complex molecular pathways that determine the activation of an immune-mediated cascade that culminates in thromboinflammation. This narrative review provides a comprehensive analysis of the current knowledge on molecular biomarkers expressed by symptomatic carotid plaques. It explores the association of these biomarkers with the structural and compositional attributes that characterize vulnerable plaques.

Microorganisms that produce enzymes active on biodegradable polyesters are ubiquitous.

Biodegradability standards measure ultimate biodegradation of polymers by exposing the material under test to a natural microbial inoculum. Available tests developed by the International Organization for Standardization (ISO) use inoculums sampled from different environments e.g. soil, marine sediments, seawater. Understanding whether each inoculum is to be considered as microbially unique or not can be relevant for the interpretation of tests results. In this review, we address this question by consideration of the following: (i) the chemical nature of biodegradable plastics (virtually all biodegradable plastics are polyesters) (ii) the diffusion of ester bonds in nature both in simple molecules and in polymers (ubiquitous); (iii) the diffusion of decomposers capable of producing enzymes, called esterases, which accelerate the hydrolysis of esters, including polyesters (ubiquitous); (iv) the evidence showing that synthetic polyesters can be depolymerized by esterases (large and growing); (v) the evidence showing that these esterases are ubiquitous (growing and confirmed by bioinformatics studies). By combining the relevant available facts it can be concluded that if a certain polyester shows ultimate biodegradation when exposed to a natural inoculum, it can be considered biodegradable and need not be retested using other inoculums. Obviously, if the polymer does not show ultimate biodegradation it must be considered recalcitrant, until proven otherwise.

Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review.

The correct recognition of the etiology of ischemic stroke (IS) allows tempestive interventions in therapy with the aim of treating the cause and preventing a new cerebral ischemic event. Nevertheless, the identification of the cause is often challenging and is based on clinical features and data obtained by imaging techniques and other diagnostic exams. TOAST classification system describes the different etiologies of ischemic stroke and includes five subtypes: LAAS (large-artery atherosclerosis), CEI (cardio embolism), SVD (small vessel disease), ODE (stroke of other determined etiology), and UDE (stroke of undetermined etiology). AI models, providing computational methodologies for quantitative and objective evaluations, seem to increase the sensitivity of main IS causes, such as tomographic diagnosis of carotid stenosis, electrocardiographic recognition of atrial fibrillation, and identification of small vessel disease in magnetic resonance images. The aim of this review is to provide overall knowledge about the most effective AI models used in the differential diagnosis of ischemic stroke etiology according to the TOAST classification. According to our results, AI has proven to be a useful tool for identifying predictive factors capable of subtyping acute stroke patients in large heterogeneous populations and, in particular, clarifying the etiology of UDE IS especially detecting cardioembolic sources.