Screening and identification of biomarkers associated with the immune infiltration of intracerebral hemorrhage

BackgroundRecent studies showed that inflammation and immunity might play essential roles in the progression of intracerebral hemorrhage (ICH). However, the underlying mechanisms for changes at the cellular and molecular levels after ICH remain unclear.MethodsWe downloaded the microarray dataset of ICH from the Gene Expression Omnibus (GEO) database. The differential expression gene analysis was obtained by weighted gene co‐expression network analysis (WGCNA). We got the hub genes and performed the biological functions and signaling pathways of these genes by Metascape. GSVA algorithm was used to evaluate the potential physical function of time‐varying ICH samples. We used single‐sample gene set enrichment analysis (ssGSEA) to assess the immune signatures infiltration and analyzed the correlation between hub genes and immune signatures.ResultsThe data sets of all 22 ICH samples in {"type":"entrez-geo","attrs":{"text":"GSE125512","term_id":"125512"}}GSE125512 were examined by the WGCNA R package. We finally screened five hub genes (GAPDH, PF4, SELP, APP, and PPBP) in the royal blue module. Metascape analysis displayed the biological processes related to inflammation and immunology. Cell adhesion molecule binding, myeloid leukocyte activation, CXCR chemokine receptor binding, and regulation of cytokine production were the most enriched pathophysiological process. The immune signatures infiltration analyses showed that ICH patients’ early and late samples had different activity and abundance of immune‐related cells and types.ConclusionsGAPDH, PF4, SELP, APP, and PPBP are identified as potential biomarkers for predicting the progression of ICH. This study may help us better understand the immunologic mechanism and shed new light on the promising approaches of immunotherapy for ICH patients.     

应急救援专业技术网络教学平台应用研究

通过电子化学习技术(E-learning),将现有的网络资源和应急救援数字化教学培训相结合,充分利用现代信息技术所提供的全新沟通机制与丰富资源,为应急救援培训提供一个受众面更为广阔的平台,进一步为培训基地开展应急救援培训提供科学的、互动的、个性化的和不受时空约束的教学管理平台.该平台有利于各级人员根据自我需求制定各自的...     

网络环境下教师指导性活动的设计

网络教学为学生的学习提供了丰富的资源，拓延了教学时空的维度，对现有的教学内容、教学手段和教学方法提出了挑战。本文探讨了在此环境下教师指导活动的内容，设计原则及其设计方法等问题。     

Divergent Histopathological Networks of Frontotemporal Degeneration Proteinopathy Subytpes

Network analyses inform complex systems such as human brain connectivity, but this approach is seldom applied to gold-standard histopathology. Here, we use two complimentary computational approaches to model microscopic progression of the main subtypes of tauopathy versus TDP-43 proteinopathy in the human brain. Digital histopathology measures were obtained in up to 13 gray matter (GM) and adjacent white matter (WM) cortical brain regions sampled from 53 tauopathy and 66 TDP-43 proteinopathy autopsy patients. First, we constructed a weighted non-directed graph for each group, where nodes are defined as GM and WM regions sampled and edges in the graph are weighted using the group-level Pearson''s correlation coefficient for each pairwise node comparison. Additionally, we performed mediation analyses to test mediation effects of WM pathology between anterior frontotemporal and posterior parietal GM nodes. We find greater correlation (i.e., edges) between GM and WM node pairs in tauopathies compared with TDP-43 proteinopathies. Moreover, WM pathology strongly correlated with a graph metric of pathology spread (i.e., node-strength) in tauopathies (r = 0.60, p < 0.03) but not in TDP-43 proteinopathies (r = 0.03, p = 0.9). Finally, we found mediation effects for WM pathology on the association between anterior and posterior GM pathology in FTLD-Tau but not in FTLD-TDP. These data suggest distinct tau and TDP-43 proteinopathies may have divergent patterns of cellular propagation in GM and WM. More specifically, axonal spread may be more influential in FTLD-Tau progression. Network analyses of digital histopathological measurements can inform models of disease progression of cellular degeneration in the human brain.SIGNIFICANCE STATEMENT In this study, we uniquely perform two complimentary computational approaches to model and contrast microscopic disease progression between common frontotemporal lobar degeneration (FTLD) proteinopathy subtypes with similar clinical syndromes during life. Our models suggest white matter (WM) pathology influences cortical spread of disease in tauopathies that is less evident in TDP-43 proteinopathies. These data support the hypothesis that there are neuropathologic signatures of cellular degeneration within neurocognitive networks for specific protienopathies. These distinctive patterns of cellular pathology can guide future efforts to develop tissue-sensitive imaging and biological markers with diagnostic and prognostic utility for FTLD. Moreover, our novel computational approach can be used in future work to model various neurodegenerative disorders with mixed proteinopathy within the human brain connectome.     

Classification of hepatic cavernous hemangioma or hepatocellular carcinoma using a convolutional neural network model

BackgroundComputed tomography (CT) is a common imaging technique for diagnosis of liver tumors. However, the intensity similarity on non-contrast CT images is small, making it difficult for radiologists to visually identify hepatic cavernous hemangioma (HCH) and hepatocellular carcinoma (HCC). Recently, convolutional neural networks (CNN) have been widely used in the study of medical image classification because more discriminative image features can be extracted than the human eye. Therefore, this study focused on developing a CNN model for identifying HCH and HCC.MethodsThis study is a retrospective study. A dataset consisting of 774 non-contrast CT images was collected from 50 patients with HCC or HCH, and the ground truth was given by three radiologists based on contrast-enhanced CT. Firstly, the non-contrast CT images dataset were randomly divided into a training set (n=559) and a test set (n=215). Then, we performed preprocessing of the non-contrast CT images using pseudo-color conversion, and the proposed CNN model developed using training set. Finally, the following indicators (accuracy, precision, recall) were used to quantitatively analyze the results.ResultsIn the test set, the proposed CNN model achieved a high classification accuracy of 84.25%, precision of 81.36%, and recall of 82.18%.ConclusionsThe CNN model for identifying HCH and HCC improves the accuracy of diagnosis on non-contrast CT images.     

Identification of potential prognostic biomarkers for hepatocellular carcinoma

BackgroundThe incidence of liver cancer is increasing every year. Hepatocellular carcinoma (HCC) accounts for nearly 90% of liver cancer, and the overall 5-year survival rate of become of Hepatocellular carcinoma patients less than 20%. However, the molecular mechanism of HCC progression and prognosis still requires further exploration.MethodsIn this study, we downloaded the gene expression data from the Cancer Genome Atlas (TCGA) Genomic Data and the official website of GEO database. Weighted gene co-expression network analysis (WGCNA) and Pearson’s correlation coefficient were utilized to detect the gene modules. The shared differentially-expressed genes (DEGs) were screened out by a Venn diagram, and the hub genes were identified through protein-protein interaction (PPI) network analyses. GO and KEGG enrichment analyses were constructed for these hub genes. Overall survival (OS) and correlation analysis were conducted to investigate the relationship between the hub genes and clinical features.ResultsWe screened out 27 shared DEGs, and the mainly enriched GO terms were mitotic nuclear division, chromosomal region, and tubulin binding. Furthermore, the top three enriched KEGG pathways were “cell cycle”, “oocyte meiosis”, and “p53 signaling pathway”. According to the Maximal Clique Centrality (MCC) algorithm, the top 10 candidate hub genes were MYC, MCM3, CDC20, CCNB1, BIRC5, UBE2C, TOP2A, RRM2, TK1, and PTTG1, among which BIRC5, CDC20, and UBE2C showed a strong correlation with the OS.ConclusionsThree hub genes (BIRC5, CDC20, and UBE2C) were identified and found to be correlated to the progression and prognosis of HCC. These may become potential targets for HCC therapy.     

国家网络直报系统属地化数据交换与共享的设计与实现

为充分利用中国疾病预防控制中心建设的网络直报信息系统资源,避免重复建设,利用现有成熟的数据库数据交换和数据复制的解决方案,并辅助开发实现系统功能,国家与地方之间遵循统一规范的数据交换标准,建立数据交换接口,实现了属地化数据交换与共享。     

腹部带蒂含真皮下血管网超薄皮瓣在手部损伤修复中的应用价值

目的：探讨腹部带蒂含真皮下血管网超薄皮瓣在手部损伤修复中的临床效果。方法：对我院接受手部损伤修复的60例患者随机分为实验组和对照组。对照组采用常规腹部带蒂皮瓣方法修复,实验组采用腹部带蒂含真皮下血管网超薄皮瓣修复,比较两组修复效果。结果：实验组12例感觉功能恢复评分为5分,患者感觉功能恢复良好,6例患者评分为4分,修复总有效率为90％（27／30）高于对照组（83．3％）;实验组修复时间为（71．9±12．6）min多于对照组（41．3±10．3）min;实验组修复过程中的出血量为（20．6±5．2）mL、患者修复后住院时间为（2．6±0．4）d,对照组住院时间为（3．5±4）周,实验组住院时间明显优于对照组（P＜0．05）;修复后实验组有1例患者出现并发症（2．5％）低于对照组（P＜0．05）。结论：采用腹部带蒂含真皮下血管网超薄皮瓣在手部损伤修复中效果较好,值得推广应用。     

The analysis of social network data: an exciting frontier for statisticians

The catalyst for this paper is the recent interest in the relationship between social networks and an individual's health, which has arisen following a series of papers by Nicholas Christakis and James Fowler on person‐ to‐person spread of health behaviors. In this issue, they provide a detailed explanation of their methods that offers insights, justifications, and responses to criticisms [1]. In this paper, we introduce some of the key statistical methods used in social network analysis and indicate where those used by Christakis and Fowler (CF) fit into the general framework. The intent is to provide the background necessary for readers to be able to make their own evaluation of the work by CF and understand the challenges of research involving social networks. We entertain possible solutions to some of the difficulties encountered in accounting for confounding effects in analyses of peer effects and provide comments on the contributions of CF. Copyright © 2012 John Wiley & Sons, Ltd.