【摘要】 目的 了解在围手术期术前30 min应用抗生素的情况。 方法 根据2004年卫生部、国家中医药管理局、总后勤部发布的《抗菌药物临床应用指导原则》中围手术期抗生素的使用原则，对四川大学华西医院2010年4-6月500台手术围手术期抗生素的使用情况进行分析。 结果 抗生素在麻醉前输：0台；抗生素末在术前30 min输2台，占0.4%；抗生素末即用即配：0台；抗生素与麻药及其他禁忌药混合输:0台；手术3 h后末及时追加抗生素：0台；特殊患者使用抗生素的注意事项不清楚2台，占0.4%。 结论 该院99.6%的手术实行在手术室术前30 min输入抗生素，确保抗生素达到有效浓度，有效控制感染，保证手术的成功，保障患者安全。【Abstract】 Objective To investigate the application of perioperative antibiotics half an hour before operation in West China Hospital of Sichuan University. Methods According to Clinical Guidance of Antibiotics published by Ministry of Health, State Administration of Traditional Chinese Medicine and General Logistics Department in 2004, we investigated the application of perioperative antibiotics in 500 operations between April to June 2010 in our hospital. Results There was no operation with infusion of antibiotics before anesthesia, 2 operations without infusion of antibiotics half an hour before operation (0.4%), no operation without immediate infusion after preparation, no operation with mixed infusion of antibiotics and anesthesia and other contraindicated drugs, no operation without infusion of antibiotics 3 hours after operation, and 2 operations in which cautious items about the children, pregnancy and old patients were unclear (0.4%). Conclusion About 99.6% operations in our hospital have the infusion of antibiotics 30 minutes before the operation, which is the guarantee of antibiotics with effective concentration, inhibition of infection, success of the operation and safety of the patients.
Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis of ischemic stroke. Accurate segmentation of the infarct is of great significance for selecting intervention treatment methods and evaluating the prognosis of patients. To address the issue of poor segmentation accuracy of existing methods for multiscale stroke lesions, a novel encoder-decoder architecture network based on depthwise separable convolution is proposed. Firstly, this network replaces the convolutional layer modules of the U-Net with redesigned depthwise separable convolution modules. Secondly, an modified Atrous spatial pyramid pooling (MASPP) is introduced to enlarge the receptive field and enhance the extraction of multiscale features. Thirdly, an attention gate (AG) structure is incorporated at the skip connections of the network to further enhance the segmentation accuracy of multiscale targets. Finally, Experimental evaluations are conducted using the ischemic stroke lesion segmentation 2022 challenge (ISLES2022) dataset. The proposed algorithm in this paper achieves Dice similarity coefficient (DSC), Hausdorff distance (HD), sensitivity (SEN), and precision (PRE) scores of 0.816 5, 3.668 1, 0.889 2, and 0.894 6, respectively, outperforming other mainstream segmentation algorithms. The experimental results demonstrate that the method in this paper effectively improves the segmentation of infarct lesions, and is expected to provide a reliable support for clinical diagnosis and treatment.
Clinically, non-contrastive computed tomography (NCCT) is used to quickly diagnose the type and area of stroke, and the Alberta stroke program early computer tomography score (ASPECTS) is used to guide the next treatment. However, in the early stage of acute ischemic stroke (AIS), it’s difficult to distinguish the mild cerebral infarction on NCCT with the naked eye, and there is no obvious boundary between brain regions, which makes clinical ASPECTS difficult to conduct. The method based on machine learning and deep learning can help physicians quickly and accurately identify cerebral infarction areas, segment brain areas, and operate ASPECTS quantitative scoring, which is of great significance for improving the inconsistency in clinical ASPECTS. This article describes current challenges in the field of AIS ASPECTS, and then summarizes the application of computer-aided technology in ASPECTS from two aspects including machine learning and deep learning. Finally, this article summarizes and prospects the research direction of AIS-assisted assessment, and proposes that the computer-aided system based on multi-modal images is of great value to improve the comprehensiveness and accuracy of AIS assessment, which has the potential to open up a new research field for AIS-assisted assessment.