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Background: Patient safety is important in daily anesthesia practices, and providing deep anesthesia is difficult. Current debates on the optimal anesthetic agents highlight the need for safer alternatives. This study was justified by the need for safer and more effective anesthetic protocols for outpatient hysteroscopic procedures, particularly those conducted outside the operating room.

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Background And Aims: Anesthesia involvement has become commonplace in many procedural settings. The goal of this study is to compare sedation modalities utilized by endoscopists and anesthesiologists in the endoscopy suite, particularly with respect to recovery time and adverse events.

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Purpose: To determine whether there is a difference in apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values in white matter pathways in the subacute period after COVID-19 infection and to evaluate the correlation between diffusion tensor imaging (DTI) metrics and laboratory findings.

Material And Methods: The study included 64 healthy controls and 91 patients. Patients were classified as group 1 (all patients, n = 91), group 2 (outpatients, n = 58), or group 3 (inpatients, n = 33).

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Background and objective Retrobulbar block is commonly used for providing effective anesthesia and akinesia for ophthalmic surgery. It can, however, lead to sight-threatening retrobulbar hemorrhage in very rare cases. The aim of this study was to evaluate retrobulbar block quality, to determine the prevalence of retrobulbar hemorrhage associated with these blocks, and to assess whether the use of antithrombotic agents in patients undergoing retrobulbar block for ophthalmic surgery was associated with retrobulbar hemorrhage.

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The clinical application of artificial intelligence in cancer precision treatment.

J Transl Med

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Department of General Surgery & Laboratory of Gastric Cancer, State Key Laboratory of Biotherapy / Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China.

Background: Artificial intelligence has made significant contributions to oncology through the availability of high-dimensional datasets and advances in computing and deep learning. Cancer precision medicine aims to optimize therapeutic outcomes and reduce side effects for individual cancer patients. However, a comprehensive review describing the impact of artificial intelligence on cancer precision medicine is lacking.

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