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Objective: Many patients with ruptured intracranial aneurysms (RIAs) underrepresented or excluded from previous randomized controlled trials (RCTs) comparing surgery with endovascular treatment (EVT) are still considered for surgical clipping, but the best management of these patients remains unknown.

Methods: The International Subarachnoid Aneurysm Trial-2 was a randomized trial comparing surgical versus EVT of RIAs considered for surgical clipping, despite the results of previous RCTs, and also eligible for EVT. The primary endpoint was death or dependency according to the modified Rankin Scale score (mRS score > 2) at 1 year.

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Survival analysis is critical in many fields, particularly in healthcare where it can guide medical decisions. Conventional survival analysis methods like Kaplan-Meier and Cox proportional hazards models to generate survival curves indicating probability of survival v. time have limitations, especially for long-term prediction, due to assumptions that all instances follow a general population-level survival curve.

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In this work, bridge network model with Rayleigh distribution lifetimes is used. Two main techniques are calculated to upgrade this model: reduction and redundancy techniques. In order to compare the effectiveness of the various approaches, the survival function, the mean time to failure and gamma-fractiles for the original and upgraded model are calculated.

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Background: Metabolic-bariatric surgery (MBS) transcends weight loss and offers wide-ranging health benefits, including positive effects on brain function. However, the mechanisms behind these effects remain unclear, particularly in the context of significant postoperative changes in the inflammatory profile characteristic of MBS. Understanding how inflammation influences postoperative brain function can enhance our decision-making on patient eligibility for MBS and create new opportunities to improve the outcomes of this popular treatment.

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Background: Convolutional neural networks have excellent modeling abilities to complex large-scale datasets and have been applied to genomics. It requires converting genotype data to image format when employing convolutional neural networks to genome-wide association studies. Existing studies converting the data into grayscale images have shown promising.

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