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Background: The apolipoprotein A1 level is recognized as a better indicator of cardiovascular disease than other cholesterol measures. Objectives: To assess the serum level of ApoA1 in acute stroke patients and correlate it with the degree of vessel stenosis, stroke severity, prognosis, and functional outcome. Patients and methods: We prospectively included 60 patients with first-ever cerebrovascular ischemic stroke, and they were matched with 30 healthy individuals matched in age and sex. Patients' neurological status was assessed via National Institute of Health and Stroke Scale (NIHSS). A venous blood sample was taken within the first 24 h of stroke onset and assayed for ApoA1 level by Human ApoA1 ELISA kit. Results: ApoA1 level could be used to discriminate between cases and controls at a level of 6.2 μg/ml, with 94.9% sensitivity and 86.6% specificity. Furthermore, there is an inverse relationship between the level of ApoA1 and the clinical outcome expressed by NIHSS score and their prognosis after 3 months. Finally, there is an inverse relationship between ApoA1 level and the degree of stenosis whether intracranial or extracranial. Conclusion: ApoA1 level can be used as a predictor of ischemic stroke and as a prognostic tool for those patients with ischemic stroke.

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Ketanserin is a selective 5-hydroxytryptamine (5-HT2) antagonist with vasodilator properties in the systemic and pulmonary circulation. Ketanserin also can inhibit serotonin-induced coronary artery vasoconstriction during percutaneous transluminal coronary angioplasty (PTCA). The in vivo effect of ketanserin on the coronary arteries of patients with stable angina has not previously been reported. The effects of intravenous ketanserin on cardiac haemodynamics and coronary artery diameter were measured in 10 patients with stable angina undergoing diagnostic cardiac catheterisation. Ketanserin (10 mg, i.v.) was associated with significant reductions in systemic and pulmonary arterial pressure (p < 0.05) and total systemic (SVR) and pulmonary (PVR) vascular resistance (p < 0.05). No significant change in mean coronary artery diameter or coronary artery stenotic index was evident after ketanserin. Vasodepressor responses in the systemic and pulmonary arterial circulation were observed after ketanserin injection. We assume these responses to be a direct effect of ketanserin, although non-drug-induced changes over time cannot be excluded. No significant effect on coronary artery diameter was observed, presumably because circulating serotonin levels are low in patients with stable anginal symptoms.

Machine Learning algorithms have a variety of important applications, and among them, Recommender systems are crucial. The internet hosts an extensive volume of information, making it challenging for users to navigate and find relevant content. Recommender systems have therefore emerged as valuable tools to bridge this gap. They facilitate the connection between users and relevant content by offering personalized recommendations. In recent years personalized recommendation service has become a hotspot of web technology, and is widely used in information, shopping, film and television, etc. [1] . Recommender systems have been proved to be an important response to the information overload problem [17] . In this research paper, we describe our approach for a Movie Recommender System Utilizing Mean Reversion via the Bollinger Bands formulae. Collaborative filtering is a popular technique used in Recommender systems. However, it poses a challenge in the form of the cold start issue, where new users are added to the system without any ratings, and the filter is unable to offer useful recommendations due to a lack of understanding of their preferences. Similarly, newly released movies without any ratings also suffer from the same issue, leading to recommendations reinforcing themselves. To address this challenge, we incorporated the concept of Mean Reversion, which is a fundamental component of Natural Mathematics. Mean Reversion helps in mitigating the cold start issue by bringing new users and newly released movies into the fold of the Recommender system. Mean reversion is a statistical concept that refers to the tendency of a series of values to return to its longterm average after experiencing temporary fluctuations. In the context of Recommender systems, Mean Reversion can be used to address the cold start issue by estimating the average rating for a movie and adjusting it based on a new user's preference. This technique can help improve the accuracy of recommendations, particularly for new users and newly released movies that lack sufficient data.