高糖饮食对小鼠真菌性角膜炎的影响

目的　探究高糖饮食对小鼠真菌性角膜炎的影响。方法　选取健康无眼疾的雄性C57BL/6J小鼠78只，随机分为高糖饮食组和模型对照组，每组36只，模型对照组给予正常饮用水，高糖饮食组给予含体积分数10%果糖溶液，每2 d测量两组小鼠体质量及血糖，10 d后建立真菌性角膜炎模型。造模后24 h、36 h、48 h、72 h、96 h、120 h、168 h裂隙灯显微镜下对角膜进行临床评分并拍照。处死小鼠后，取角膜组织进行HE染色和PAS染色；测定角膜内中性粒细胞和巨噬细胞浸润体积。利用酶联免疫吸附实验对小鼠角膜内的白细胞介素-1β含量进行测定。结果　造模后 0~14 d，两组小鼠体质量与血糖差异均无统计学意义（均为P>0.05）。造模后24 h、36 h、48 h、120 h、168 h，高糖饮食组小鼠角膜临床评分均明显高于模型对照组，差异均有统计学意义（均为P<0.05）。高糖饮食组小鼠角膜穿孔率79.5%，高于模型对照组的40.9%，差异有统计学意义（P=0.000）。造模后各时间点，高糖饮食组中性粒细胞浸润体积均高于模型对照组，差异均有统计学意义（均为P=0.000）。造模后72 h、96 h、120 h、168 h，高糖饮食组巨噬细胞浸润体积均高于模型对照组（均为P=0.000）。角膜组织病理学检查结果示，高糖饮食组炎症反应更重，角膜组织破坏更早且更为严重。造模后24 h、48 h高糖饮食组白细胞介素-1β含量均明显高于模型对照组（均为P<0.05）。结论　高糖饮食加重了真菌性角膜炎感染的严重程度，增强了中性粒细胞、巨噬细胞的趋化，促进了IL-1β的分泌。     

The circRNA–miRNA–mRNA regulatory network in systemic lupus erythematosus

Clinical Rheumatology - Systemic lupus erythematosus (SLE) was an autoimmune disease with a large variety of clinical manifestations and involving many organs. Its exact etiology was unclear, and...     

The resilience status of empty-nest elderly in a community: A latent class analysis

ObjectiveThe aim of this study was to examine the status and characteristics of resilience among empty-nest elderly in a community in China using exploratory latent class analysis (LCA).MethodsThis study enrolled 250 empty-nest elderly as the study respondents. General information regarding the resilience of empty-nest elderly was investigated using the General Information Questionnaire and Connor-Davidson Resilience Scale, Chinese version, and we then used LCA and multivariate logistic regression to discuss the characteristics of resilience among empty-nest elderly individuals.ResultsThrough the analysis, we found that the resilience of empty-nest elderly had obvious group characteristics and that statistical indicators can support the three categories of potential model. On the basis of the conditional probability on the various items of the questionnaire in each category, they were named “high resilience group,” “low pressure resilience group,” and “low resilience group,” and the proportion was 26.6%, 40.4%, and 32.9%, respectively. Further study showed that age, marital status, education level, relationship with children, and physical exercise had a significant effect on the high resilience group compared to the low resilience group. Gender, education level, relationship with children, and physical exercise had a significant effect on the low pressure resilience group compared to the low resilience group.ConclusionsThe resilience status of empty-nest elderly in communities can be divided into three categories. Each category had different characteristics of demographic information.     

经颌下置管全麻在复杂全面部骨折中的应用

目的：介绍特殊全面部骨折手术中一种经颌下置管全麻的方法并探讨其适应症及注意事项。方法：7例全面部骨折、无法实施鼻插全麻患者,口插全麻并稳定后制备口底与颌下间隧道并将气管导管导出颌下、固定。手术结束后将导管导回口腔。结果：所有患者口腔插管和口底导出均顺利,导管位置无改变,双肺呼吸音对称。术中麻醉平稳,管理方便,不影响手术操作。术后局部无气肿、感染、气道阻塞、神经损伤。结论：在无法鼻插全麻前提下,经颌下置管全麻避免了气管切开,同时满足了颌骨骨折术中咬合对位的要求,是特殊全面部骨折麻醉手段的有效方法。     

Nucleus classification in histology images using message passing network

Identification of nuclear components in the histology landscape is an important step towards developing computational pathology tools for the profiling of tumor micro-environment. Most existing methods for the identification of such components are limited in scope due to heterogeneous nature of the nuclei. Graph-based methods offer a natural way to formulate the nucleus classification problem to incorporate both appearance and geometric locations of the nuclei. The main challenge is to define models that can handle such an unstructured domain. Current approaches focus on learning better features and then employ well-known classifiers for identifying distinct nuclear phenotypes. In contrast, we propose a message passing network that is a fully learnable framework build on classical network flow formulation. Based on physical interaction of the nuclei, a nearest neighbor graph is constructed such that the nodes represent the nuclei centroids. For each edge and node, appearance and geometric features are computed which are then used for the construction of messages utilized for diffusing contextual information to the neighboring nodes. Such an algorithm can infer global information over an entire network and predict biologically meaningful nuclear communities. We show that learning such communities improves the performance of nucleus classification task in histology images. The proposed algorithm can be used as a component in existing state-of-the-art methods resulting in improved nucleus classification performance across four different publicly available datasets.