Yadollahi, Sepideh

Background: Optimal Blood pressure management after thrombectomy for acute ischemic stroke and its association with clinical outcomes remains unclear. We performed this study to compare clinical outcomes between intensive systolic blood pressure (SBP) control (<120-140mmHg) and conventional SBP control (< 180mmHg). Methods: In September 2023, this registered systematic review (CRD42023464176) was performed using multiple databases for the following keywords: stroke, thrombectomy, and Blood Pressure from 2010 to present. Randomized control studies were included if they had the reported the outcome for adults aged >18 who underwent successful thrombectomy (defined as mTICI 2b or higher) for acute ischemic stroke. The primary outcome of interest was the rate of functional independence, defined as a modified Rankin scale of 0-2 at 90 days. The secondary outcomes of interest included rates of mortality, and symptomatic intracranial hemorrhage (sICH). For all outcome events, corresponding odds ratios (OR) and 95% confidence intervals (CI) were calculated using a random-effects meta-analysis model. Results: Four studies comprising 1497 patients were included. Compared to conventional SBP control, intensive SBP control was associated with lower rates of functional independence (OR 0.67, 95% CI 0.51- 0.88 p=0.0). There were comparable rates of sICH (OR 1.12, 95% CI 0.75-1.67; p = 0.56) and mortality (OR 1.21, 95% CI 0.90-1.64; p = 0.19) between intensive and conventional SBP control groups. Conclusions: Our findings suggest that intensive blood pressure control does not improve clinical outcomes. More robust data is needed to draw definite conclusions.

A BSTRACT In the present study, a particular technique of artificial intelligence (AI) is applied for diagnosis and classifying medical images of patients with coronavirus disease (COVID-19). Chest radiography and laboratory-based tests are two of the most important diagnostic approaches for the detection of people with the coronavirus. Recently, a lot of studies have been carried out on using AI techniques for achieving appropriate diagnosis of COVID-19 patients using computed tomography (CT) of the chest. The present study is reviewing all available literature that have investigated the role of chest CT toward AI in the detection of COVID-19. As a novel field of computer science, AI focuses on teaching computers to be capable of learning complex tasks and decide about their solution methods. In this study, we used Matlab, Payton, and Fortran software as well as other software which are suitable for this research. In this regard, the present review study is aimed to collect the information from all the studies conducted on the role of AI as a decisive and comprehensive technology for the detection of coronavirus in patients to have a more accurate diagnosis and investigate its epidemiology.