近端胃切除人工三角瓣成形抗反流术的力学机理仿真分析

目的:对食管胃结合部腺癌近端胃切除术（PG）人工三角瓣成形后残胃食糜进行流体动力学数值模拟，并计算不同性质胃内食糜的流动特征。方法:构建常规PG和人工三角瓣成形术术后胃仿真模型，运用Fluent软件对不同粘度胃内食糜反流问题进行数值模拟。结果:站立位姿态时，相对常规PG方案，人工三角瓣成形手术方案表现出较好的抗反流作用；卧位姿态时，当胃内食糜粘度大于0.145 2 Pa[?s，且胃内食糜不超过人工三角瓣情况下，人工三角瓣成形抗反流手术表现出较好的抗反流效果；人工三角瓣抗反流成形手术方案数值模拟结果与临床上患者表现一致。结论:本研究仿真分析为人工三角瓣成形抗反流手术方案的有效性机理分析、临床患者术后饮食及手术方案的进一步改进提供理论及数值依据。 【关键词】食管胃结合部腺癌；抗反流；人工三角瓣；计算流体力学     

Demise of the stethoscope

Medical practice has undergone a massive drift over the past several years. With the advent of modernization and technical advancements in both diagnostic and therapeutic fields, bedside clinical skills have suffered a setback. Increasing patient load in the hospitals, administrative commitments of the physicians, and need to get superspecialty fellowships are some of the factors which preclude resident teaching, which, in-turn hamper their long-term clinical skills and influence the patient–doctor relationship. In this perspective, I narrate my own experience regarding changing attitude of the younger generation of doctors toward patient care and also describe the role of a mentor in shaping the believes and practices of his medical students. Using real-world examples, I further discuss the obstacles which hamper good medical practice and suggest possible ways to overcome some of them.     

北京市精神卫生医务人员对严重精神障碍发病报告制度的认知研究

背景 严重精神障碍发病报告制度作为精神卫生监测的重要一环已运行实施多年，实施效果需要进一步探知。 目的 分析北京市三类精神卫生医务人员对严重精神障碍发病报告制度重要性、主要作用、实施效果、存在问题及完善措施的认知，为促进该制度的完善提供建议。 方法 2019年3—6月，采用方便抽样方法选择234例市级精神专科医院医务人员，采用分层抽样方法选择183例区级精神专科医院医务人员，采用分层抽样方法选择214例社区精神卫生防治医务人员作为研究对象，采用"北京市精神卫生法治状况调查问卷"进行问卷调查，问卷主要内容包括医务人员基本信息及对严重精神障碍发病报告制度的认知。 结果 对于制度重要性，56.3%（103/183）的区级精神专科医院和54.7%（117/214）的社区精神卫生防治医务人员认为其"非常重要"，66.7%（156/234）的市级精神专科医院医务人员认为其"比较重要"。对于主要作用，76.9%（180/234）的市级精神专科医院医务人员和82.7%（177/214）的社区精神卫生防治医务人员认为该制度主要作用为"风险预警"，80.9%（148/183）的区级精神专科医院医务人员认为该制度主要作用为"社区管理"。对于实施效果，44.4%（104/234）的市级精神专科医院医务人员、50.3%（92/183）的区级精神专科医院医务人员和50.9%（109/214）的社区精神卫生防治医务人员认为严重精神障碍发病报告制度实施效果比较好。对于存在问题，77.8%（182/234）的市级精神专科医院医务人员、78.1%（143/183）的区级精神专科医院医务人员和83.2%（178/214）的社区精神卫生防治医务人员认为该制度主要存在的问题为"报告涉及患者隐私信息的收集"。对于完善措施，73.5%（172/234）的市级精神专科医院医务人员和76.6%（164/214）的社区精神卫生防治医务人员认为严重精神障碍发病报告制度完善措施为"规范信息录入、登记、更正、汇总制度"，68.9%（126/183）的区级精神专科医院医务人员认为制度完善措施为"规范信息共享及对患者隐私保护的制度"。 结论 三类医务人员对严重精神障碍发病报告制度的作用及实施效果表示肯定，为实现风险防控与隐私保护并重，兼顾患者管理与服务，应当对该制度予以完善，包括：在法律层面细化相关规定；对患者坚持服务保障与管理并重，完善精神卫生服务体系，落实基本与重大公共卫生服务项目；增加政策宣传力度，提高患者及家属对该制度的认可度。     

左乙拉西坦治疗小儿癫痫的影响分析

目的研究左乙拉西坦治疗小儿癫痫的疗效以及对智力的影响。方法在本院2017年4月-2019年5月收治的小儿癫痫患儿中选取74例开展研究,按照随机数表法分两组观察组和对照组,观察组37例,对照组37例,对照组采用奥卡西平进行治疗,观察组采用左乙拉西坦进行治疗,对比观察组与对照组组的治疗总有效率和智力评分变化。结果观察组与对照组比较,观察组的治疗总有效率较高,智力评分明显较高,两项对比差异有统计学意义(P <0.05)。结论左乙拉西坦治疗小儿癫痫有较好的治疗效果,治疗总有效率较高,且对患儿的智力有明显的改善作用,在实际临床小儿癫痫的治疗中具有较高的运用价值。     

浅谈人工智能对中医诊断的影响＊

中医药是中华民族的瑰宝，随着人工智能（Artificial Intelligence，AI）的迅猛发展，中医药与AI的结合将可以为中医药的传承和现代化提供助力。本研究将从搭建AI中医专家系统入手，阐述AI对提高中医诊断的作用，物联网搭建AI中医药服务，存在的问题和对策，以及中医药人工智能化的前景，以图为中医药的发展提供新的思路和方法。     

口语化疾病名称向国际规范疾病术语集的映射研究

目的：研究患者口语化疾病名称到术语疾病名称的映射，解决在线口语化疾病名称难以有效利用的问题。方法：抓取“微医网”上患者口语中的疾病名，将PubMed摘要、百度搜索页摘要与百科内容作为语料，训练词向量，用余弦距离计算相似度，由2名具有临床知识和从业经验的编码人员对结果的准确性进行检验。结果：将最相似词条的参数TopN设置为>= 30的时候效果比较好，映射到大类和细类的准确性分别稳定在60%和70%左右。结论：利用词向量将口语化疾病名称映射到国际疾病术语集，具有较强的通用性和可行性，对于在线问诊、智能分诊甚至自动化编码具有明显的参考借鉴价值。     

癌性肠梗阻内科治疗的“6字方针”

癌性肠梗阻（MBO）是指原发性或转移性恶性肿瘤本身及其抗肿瘤治疗引起的肠梗阻，是晚期肿瘤患者的常见并发症，总体发生率3%~15%。目前，MBO的治疗仍缺乏统一、有效的治疗规范，大多数医院都采用对症处理的。本文在总结数百例治疗经验基础上，提炼出“减（压）、加（营养）、抑（制消化液分泌）、激（素）、利（尿）、动（运动及促进肠蠕动）”六字方针，简单、实用、有效，通过临床观察，该方案能改善患者症状，恢复经口进食率，提高生活质量和手术治疗的机会，缩短住院时间及降低治疗费用，尽管尚缺乏前瞻性的随机对照研究结果，仍值得推广。     

Prospective Analysis Using a Novel CNN Algorithm to Distinguish Atypical Ductal Hyperplasia From Ductal Carcinoma in Situ in Breast

IntroductionWe previously developed a convolutional neural networks (CNN)-based algorithm to distinguish atypical ductal hyperplasia (ADH) from ductal carcinoma in situ (DCIS) using a mammographic dataset. The purpose of this study is to further validate our CNN algorithm by prospectively analyzing an unseen new dataset to evaluate the diagnostic performance of our algorithm.Materials and MethodsIn this institutional review board-approved study, a new dataset composed of 280 unique mammographic images from 140 patients was used to test our CNN algorithm. All patients underwent stereotactic-guided biopsy of calcifications and underwent surgical excision with available final pathology. The ADH group consisted of 122 images from 61 patients with the highest pathology diagnosis of ADH. The DCIS group consisted of 158 images from 79 patients with the highest pathology diagnosis of DCIS. Two standard mammographic magnification views (craniocaudal and mediolateral/lateromedial) of the calcifications were used for analysis. Calcifications were segmented using an open source software platform 3D slicer and resized to fit a 128 × 128 pixel bounding box. Our previously developed CNN algorithm was used. Briefly, a 15 hidden layer topology was used. The network architecture contained 5 residual layers and dropout of 0.25 after each convolution. Diagnostic performance metrics were analyzed including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. The “positive class” was defined as the pure ADH group in this study and thus specificity represents minimizing the amount of falsely labeled pure ADH cases.ResultsArea under the receiver operating characteristic curve was 0.90 (95% confidence interval, ± 0.04). Diagnostic accuracy, sensitivity, and specificity was 80.7%, 63.9%, and 93.7%, respectively.ConclusionProspectively tested on new unseen data, our CNN algorithm distinguished pure ADH from DCIS using mammographic images with high specificity.     

Evaluation of a delineation software for cardiac atlas-based autosegmentation: An example of the use of artificial intelligence in modern radiotherapy

PurposeThe primary objective of this work was to implement and evaluate a cardiac atlas-based autosegmentation technique based on the “Workflow Box” software (Mirada Medical, Oxford UK), in order to delineate cardiac substructures according to European Society of Therapeutic Radiation Oncology (ESTRO) guidelines; review and comparison with other cardiac atlas-based autosegmentation algorithms published to date.Materials and MethodsOf an atlas of data set from 20 breast cancer patients’ CT scans with recontoured cardiac substructures creation according to the ESTRO guidelines. Performance evaluation on a validation data set consisting of 20 others CT scans acquired in the same treatment position: cardiac substructure were automatically contoured by the Mirada system, using the implemented cardiac atlas, and simultaneously manually contoured by a radiation oncologist. The Dice similarity coefficient was used to evaluate the concordance level between the manual and the automatic segmentations.ResultsDice similarity coefficient value was 0.95 for the whole heart and 0.80 for the four cardiac chambers. Average Dice similarity coefficient value for the left ventricle walls was 0.50, ranging between 0.34 for the apical wall and 0.70 for the lateral wall. Compared to manual contours, autosegmented substructure volumes were significantly smaller, with the exception of the left ventricle. Coronary artery segmentation was unsuccessful. Performances were overall similar to other published cardiac atlas-based autosegmentation algorithms.ConclusionThe evaluated cardiac atlas-based autosegmentation technique, using the Mirada software, demonstrated acceptable performance for cardiac cavities delineation. However, algorithm improvement is still needed in order to develop efficient and trusted cardiac autosegmentation working tools for daily practice.