An analysis model of diagnosis and treatment for COVID-19 pandemic based on medical information fusion

Exploring the complicated relationships underlying the clinical information is essential for the diagnosis and treatment of the Coronavirus Disease 2019 (COVID-19). Currently, few approaches are mature enough to show operational impact. Based on electronic medical records (EMRs) of 570 COVID-19 inpatients, we proposed an analysis model of diagnosis and treatment for COVID-19 based on the machine learning algorithms and complex networks. Introducing the medical information fusion, we constructed the heterogeneous information network to discover the complex relationships among the syndromes, symptoms, and medicines. We generated the numerical symptom (medicine) embeddings and divided them into seven communities (syndromes) using the combination of Skip-Gram model and Spectral Clustering (SC) algorithm. After analyzing the symptoms and medicine networks, we identified the key factors using six evaluation metrics of node centrality. The experimental results indicate that the proposed analysis model is capable of discovering the critical symptoms and symptom distribution for diagnosis; the key medicines and medicine combinations for treatment. Based on the latest COVID-19 clinical guidelines, this model could result in the higher accuracy results than the other representative clustering algorithms. Furthermore, the proposed model is able to provide tremendously valuable guidance and help the physicians to combat the COVID-19.     

Knowledge discovery in medicine: Current issue and future trend

Data mining is a powerful method to extract knowledge from data. Raw data faces various challenges that make traditional method improper for knowledge extraction. Data mining is supposed to be able to handle various data types in all formats. Relevance of this paper is emphasized by the fact that data mining is an object of research in different areas. In this paper, we review previous works in the context of knowledge extraction from medical data. The main idea in this paper is to describe key papers and provide some guidelines to help medical practitioners. Medical data mining is a multidisciplinary field with contribution of medicine and data mining. Due to this fact, previous works should be classified to cover all users’ requirements from various fields. Because of this, we have studied papers with the aim of extracting knowledge from structural medical data published between 1999 and 2013. We clarify medical data mining and its main goals. Therefore, each paper is studied based on the six medical tasks: screening, diagnosis, treatment, prognosis, monitoring and management. In each task, five data mining approaches are considered: classification, regression, clustering, association and hybrid. At the end of each task, a brief summarization and discussion are stated. A standard framework according to CRISP-DM is additionally adapted to manage all activities. As a discussion, current issue and future trend are mentioned. The amount of the works published in this scope is substantial and it is impossible to discuss all of them on a single work. We hope this paper will make it possible to explore previous works and identify interesting areas for future research.     

Considerations for infectious disease research and practice

As the 21st century unfolds, strategies to prevent and control infectious diseases remain an area of vital interest and concern. The burden of disease, disability, and death caused by infectious diseases is felt around the world in both developed and developing nations. Moreover, the ability of infectious agents to destabilize populations, economies, and governments is strikingly apparent. To an unprecedented degree, infectious disease-related issues are high on the agendas of world leaders, philanthropists, policymakers, and the public. This enhanced focus, combined with recent scientific and technological advances, creates new opportunities and challenges for infectious disease research and practice. This paper examines these issues in the context of three countries: China, India, and the United States.     

醛化鸡红细胞吸附释放结合NP-40法纯化新城疫病毒蛋白

采用醛化鸡红细胞吸附释放结合NP-40法纯化鸡胚尿囊液中的新城疫病毒蛋白.通过收集纯化的新城疫病毒,并测定新城疫病毒血凝效价和进行9%SDS-PAGE电泳,以测定新城疫病毒蛋白的纯度.用该方法纯化的新城疫病毒血凝效价为1∶256,进行9%SDS-PAGE电泳后,发现得到的新城疫病毒蛋白不含鸡胚尿囊液蛋白,是纯度较高的新城疫病毒蛋白.以上结果表明,醛化鸡红细胞吸附释放法结合NP-40法是一种经济实用、操作简便,并有较高纯化效率的方法.     

低血糖性脑病的临床分析

探讨低血糖性脑病的临床特点、早期诊断和防治。     

卷积神经网络诊断阿尔兹海默症的方法

针对阿尔兹海默症（AD）通常会导致海马体区域萎缩的现象,提出一种使用卷积神经网络（CNN）对脑部磁共振成像（MRI）的海马体区域进行AD识别的方法。测试数据来自ADNI数据库提供的188位患者和229位正常人的脑部MRI图像。首先,将所有脑图像进行颅骨剥离,并配准到标准模板;其次,使用线性回归进行脑部萎缩的年龄矫正;然后,经过预处理后,从每个对象的3D脑图像的海马体区域提取出多幅2.5D的图像;最后,使用CNN对这些图像进行训练和识别,将同一个对象的图像识别结果用于对该对象的联合诊断。通过多次十折交叉验证方式进行实验,实验结果表明所提方法的平均识别准确率达到88.02%。与堆叠自动编码器（SAE）方法进行比较,比较结果表明,所提方法在仅使用MRI进行诊断的情况下效果比SAE方法有较大提高。     

SW-Net: A novel few-shot learning approach for disease subtype prediction

Few-shot learning is becoming more and more popular in many fields, especially in the computer vision field. This inspires us to introduce few-shot learning to the genomic field, which faces a typical few-shot problem because some tasks only have a limited number of samples with high-dimensions. The goal of this study was to investigate the few-shot disease sub-type prediction problem and identify patient subgroups through training on small data. Accurate disease sub-type classification allows clinicians to efficiently deliver investigations and interventions in clinical practice. We propose the SW-Net, which simulates the clinical process of extracting the shared knowledge from a range of interrelated tasks and generalizes it to unseen data. Our model is built upon a simple baseline, and we modified it for genomic data. Support-based initialization for the classifier and transductive fine-tuning techniques were applied in our model to improve prediction accuracy, and an Entropy regularization term on the query set was appended to reduce over-fitting. Moreover, to address the high dimension and high noise issue, we future extended a feature selection module to adaptively select important features and a sample weighting module to prioritize high-confidence samples. Experiments on simulated data and The Cancer Genome Atlas meta-dataset show that our new baseline model gets higher prediction accuracy compared to other competing algorithms.     

磁共振弥散加权成像和动态增强诊断前列腺疾病临床效果观察

目的:探究诊断前列腺疾病应用于磁共振弥散加权成像和动态增强的临床价值。方法:选择2018年6月-2019年5月本院收治的前列腺患者56例作为研究组对象,随机选出56例前列腺正常者作为对照组,两组患者均采用磁共振常规扫描、磁共振弥散加权成像以及动态扫描,并针对感兴趣区记录DWI信号强度与表面扩散系数ADC值。结果:研究组患者ADC值和DWI信号强度均高于对照组,且P<0.05,差异化显著,符合统计学意义。结论:磁共振弥散加权成像和动态增强诊断前列腺疾病,临床意义确切,能够有效提升前列腺癌的分期准确率,便于对病变部位做出诊断。     

A New Role for Psychology: Comment.

Letter comments that, about four years ago, Irving S. Cooper, a neurosurgeon in New York City, developed a new type of brain surgery designed to alleviate the tremor and rigidity associated with Parkinson's Disease. As Cooper's work and research progressed, it soon became evident that a significant factor in the selection of appropriate candidates was the patient's psychological and mental condition. Thus it was decided to add a clinical psychologist to the team of professional and rehabilitation people carrying out preoperative evaluation. So far as the letter's author knows, St, Barnabas Hospital is the only setting in this country in which psychologists work so closely with a Department of Neurologic Surgery in both a clinical and research role. The author feels it is significant to report this development which opens up a broad new area for which psychological services are utilized. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

甘蓝型油菜抗菌核病研究进展

由真菌核盘菌（Sclerotinia sclerotiorum）引起的油菜菌核病是世界范围内最严重的病害之一,也是影响油菜高产稳产的丰要生物逆境.选育抗菌核病油菜品种并在生产上大面积种植是防控菌核病最经济有效和环保的途径.本文从核盘菌的致病机理、油菜抗菌核病的遗传控制、抗病基因表达谱和抗性相关基因的应用等方面综述了油菜抗菌核病研究有关的最新进展.