The term “big data analytics (BDA)” defines the computational techniques to study complex datasets that are too large for common data processing software, encompassing techniques such as data mining (DM), machine learning (ML), and predictive analytics (PA) to find patterns, correlations, and insights in massive datasets. Cardiovascular diseases (CVDs) are attributed to a combination of various risk factors, including sedentary lifestyle, obesity, diabetes, dyslipidaemia, and hypertension. We searched PubMed and published research using the Google and Cochrane search engines to evaluate existing models of BDA that have been used for CVD prediction models. We critically analyse the pitfalls and advantages of various BDA models using artificial intelligence (AI), machine learning (ML), and artificial neural networks (ANN). BDA with the integration of wide-ranging data sources, such as genomic, proteomic, and lifestyle data, could help understand the complex biological mechanisms behind CVD, including risk stratification in risk-exposed individuals. Predictive modelling is proposed to help in the development of personalized medicines, particularly in pharmacogenomics; understanding genetic variation might help to guide drug selection and dosing, with the consequent improvement in patient outcomes. To summarize, incorporating BDA into cardiovascular research and treatment represents a paradigm shift in our approach to CVD prevention, diagnosis, and management. By leveraging the power of big data, researchers and clinicians can gain deeper insights into disease mechanisms, improve patient care, and ultimately reduce the burden of cardiovascular disease on individuals and healthcare systems.

Background and Purpose: Contrast-enhanced MRI findings of leptomeningeal enhancement (LME) indicate dysfunction of the blood–brain barrier and can be seen in various neoplastic, infectious, and inflammatory conditions. To the best of our knowledge, systematic evaluation of different patterns of LME remains limited. This case series aims to describe and compare MRI patterns of leptomeningeal enhancement across diverse etiologies. Materials and Methods: We retrospectively evaluated six patients with known or suspected leptomeningeal disease on contrast-enhanced MRI studies of the brain and/or spine with regard to pattern, distribution, localization, and thickness of LME. Results: The patterns of neoplastic causes (glioblastoma, lymphoma, and adenocarcinoma) were characterized by diffuse, thick, and nodular LME, as well as frequently affected cranial nerve and nerve-root involvement. The infections caused various patterns: in cases of tuberculous meningitis, LME was focal and basal in nature, accompanied by parenchymal lesions; whereas in cases of HIV-related meningitis, LME was diffuse and exhibited a pseudocisternogram pattern. The inflammation (suspected neurosarcoidosis) revealed smooth-to-nodular LME with spinal and suprasellar involvement. Conclusions: Although LME patterns are not specific, systematic evaluation of enhancement characteristics may provide useful diagnostic clues and assist in narrowing the differential diagnosis.

Background: Multiple sclerosis (MS), an autoimmune disease, involves peripheral immune activation followed by CNS inflammation in a compartmentalized manner. Although high-efficacy disease-modifying therapies (HE-DMTs) have been effective in suppressing relapses in MS patients, they fail to effectively target chronic microglial activation and smoldering lesions in MS patients. Bruton’s tyrosine kinase inhibitors (BTKis), which are orally active and capable of crossing the blood–brain barrier, have been found to be effective in modulating B cells and CNS-resident myeloid cells. Objective: The objective was to assess the efficacy and safety of Bruton’s tyrosine kinase inhibitors in patients with relapsing, secondary, and primary progressive MS. Methods: We performed a systematic review and meta-analysis according to the Cochrane and PRISMA guidelines (PROSPERO registration number: 1323474). We included randomized controlled trials (RCTs) that assessed fenebrutinib, evobrutinib, or tolebrutinib in adult MS patient populations. The main outcome measures were annualized relapse rate, MRI lesion activity, disability progression (EDSS), and hepatotoxicity. The quality of the included trials was assessed for bias by the RoB2 tool. Results: Six RCTs with 3616 participants were included. BTK inhibitors significantly reduced ARR compared with control therapy (pooled RR 0.24; 95% CI 0.15–0.39). MRI activity was reduced (mean difference −1.45 new/enlarging T2 lesions; 95% CI −2.08 to −0.82). Disability progression was unchanged in short-term relapsing MS trials. Serious hepatotoxicity was reported in 11.0% of BTKi-treated patients compared with 13.7% of control patients (pooled RR 0.80; 95% CI 0.66–0.96). However, increased transaminase elevations were reported in placebo-controlled trials, which indicates that hepatotoxicity remains a clinically relevant safety concern for the class. Conclusions: BTK inhibitors reduce inflammatory disease activity in relapsing MS and have emerging efficacy in progressive MS phenotypes; however, continued monitoring for hepatotoxicity is warranted. Optimization of CNS penetrance and pharmacologic selectivity may influence long-term clinical positioning.