Pre- and post-surgery: advancements in artificial intelligence and machine learning models for enhancing patient management in infective endocarditis.

Infective endocarditis (IE) is a severe infection of the inner lining of the heart, known as the endocardium. It is characterized by a range of symptoms and has a complicated pattern of occurrence, leading to a significant number of deaths. IE poses significant diagnostic and treatment difficulties. This evaluation examines the utilization of artificial intelligence (AI) and machine learning (ML) models in addressing information extraction (IE) management. It focuses on the most recent advancements and possible applications. Through this paper, we observe that AI/ML can significantly enhance and outperform traditional diagnostic methods leading to more accurate risk stratification, personalized therapies as well and real-time monitoring facilities. For example, early postsurgical mortality prediction models like SYSUPMIE achieved 'very good' area under the curve (AUROC) values exceeding 0.81. Additionally, AI/ML has improved diagnostic accuracy for prosthetic valve endocarditis, with PET-ML models increasing sensitivity from 59% to 72% when integrated into ESC criteria and reaching a high specificity of 83%. Furthermore, inflammatory biomarkers such as IL-15 and CCL4 have been identified as predictive markers, showing 91% accuracy in forecasting mortality, and identifying high-risk patients with specific CRP, IL-15, and CCL4 levels. Even simpler ML models, like Naïve Bayes, demonstrated an excellent accuracy of 92.30% in death rate prediction following valvular surgery for IE patients. Furthermore, this review provides a vital assessment of the advantages and disadvantages of such AI/ML models, such as better-quality decision support approaches like adaptive response systems on one hand, and data privacy threats or ethical concerns on the other hand. In conclusion, Al and ML must continue, through multi-centric and validated research, to advance cardiovascular medicine, and overcome implementation challenges to boost patient outcomes and healthcare delivery.

Vascular multiple sclerosis: addressing the pathogenesis, genetics, pro-angiogenic factors, and vascular abnormalities, along with the role of vascular intervention.

Dysfunction in the epithelium, breakdown of the blood-brain barrier, and consequent leukocyte and T-cell infiltration into the central nervous system define Vascular Multiple Sclerosis. Multiple sclerosis (MS) affects around 2.5 million individuals worldwide, is the leading cause of neurological impairment in young adults, and can have a variety of progressions and consequences. Despite significant discoveries in immunology and molecular biology, the root cause of MS is still not fully understood, as do the immunological triggers and causative pathways. Recent research into vascular anomalies associated with MS suggests that a vascular component may be pivotal to the etiology of MS, and there can be actually a completely new entity in the already available classification of MS, which can be called 'vascular multiple sclerosis'. Unlike the usual other causes of MS, vascular MS is not dependent on autoimmune pathophysiologic mechanisms, instead, it is caused due to the blood vessels pathology. This review aims to thoroughly analyze existing information and updates about the scattered available findings of genetics, pro-angiogenetic factors, and vascular abnormalities in this important spectrum, the vascular facets of MS.