抑瘤素M受体对慢性自身免疫性荨麻疹发病机制的影响

本研究旨在探讨抑瘤素M受体(OSMR)在慢性自身免疫性荨麻疹(CAU)发病机制中的作用。本研究分别检测30例CAU患者及30名健康受试者的皮肤组织中OSMR、JAK和STAT3的表达,研究显示OSMR、JAK和STAT3在CAU患者皮肤组织中高表达(p<0.05)。转染OSMR-siRNA可显著降低CAU模型小鼠血清炎症因子IL-1、IL-6和IFN-γ水平,而转染JAK/STAT3信号通路激动剂Tyr705则可显著升高炎症因子水平(p<0.05)。转染OSMR-siRNA可显著降低CAU小鼠瘙痒次数、瘙痒时间和嗜酸性粒细胞计数,而转染Tyr705则可显著升高CAU小鼠瘙痒次数、瘙痒时间和嗜酸性粒细胞计数(p<0.05)。转染OSMR-siRNA促进了CAU小鼠上皮细胞的增殖能力,并抑制了细胞凋亡(p<0.05)。而转染Tyr705则抑制了CAU小鼠上皮细胞的增殖能力,并促进了细胞凋亡(p<0.05)。转染OSMR-siRNA下调了上皮细胞中OSMR、JAK和STAT3的表达,而转染Tyr705则上调了OSMR、JAK和STAT3的表达(p<0.05)。总之,本研究表明OSMR基因在CAU患者皮肤组织中高表达。OSMR基因沉默可通过抑制JAK/STAT3信号通路来抑制炎症因子表达及嗜酸性粒细胞数量,促进上皮细胞增殖并抑制细胞凋亡。     

On the Use of Marker Strategy Design to Detect Predictive Marker Effect in Cancer Immunotherapy and Targeted Therapy

The marker strategy design (MSGD) has been proposed to assess and validate predictive markers for targeted therapies and immunotherapies. Under this design     

Apelin-13对LPS诱导人脐静脉内皮细胞屏障损伤的干预作用*

目的: 探讨Apelin-13对LPS诱导人脐静脉内皮细胞(HUVECs)屏障损伤的影响。方法: 将体外培养的HUVECs分为4组:正常对照组、LPS组、Apelin-13+LPS组、Apelin-13组。用5 μg/ml LPS作用细胞24 h, 复制屏障功能受损模型。1 μmol/L Apelin-13提前30 min给予,再给予LPS作用24 h,确定Apelin-13的影响。通过CCK8法检测Apelin-13对细胞活力的影响;Western blot检测血管内皮细胞钙粘蛋白(VE-cadherin)、纤维状肌动蛋白(F-actin)表达变化;免疫荧光检测VE-cadherin、F-actin的表达变化及核转录因子Kappa B(NF-κB p65)入核情况。结果: 与正常对照组相比,单独给予Apelin-13对细胞活力无明显影响。与正常对照组相比,LPS组细胞活力明显下降(P＜0.01),VE-cadherin蛋白表达下降(P＜0.01)、F-actin蛋白表达升高(P＜0.05),NF-κB p65入核明显增加。与LPS组相比,Apelin-13明显增加细胞活力(P＜0.01),VE-cadherin蛋白表达增加(P＜0.05)、F-actin蛋白表达下降(P＜0.01),蛋白NF-κB p65入核下降明显。结论: Apelin-13可减轻LPS诱导的人脐静脉内皮细胞损伤及屏障受损,其机制可能与抑制炎症相关。     

14种中国典型极小种群野生植物繁育特性和人工繁殖研究进展

繁殖是植物种群更新与维持的重要环节。包括极小种群野生植物在内的受威胁物种, 其濒危原因是在长期演化过程中自身繁育力的衰退、生活力的下降等内在因素和人类的过度采挖和生境的破坏等外在因素共同作用的结果。对极小种群野生植物进行高效的人工繁殖, 能扩大种群数量并应用于迁地保护、自然回归和满足商品市场的需求, 有利于其种质资源的保护和可持续利用。为了保持物种的遗传多样性, 采用种子繁殖育苗是有效的方法, 扦插、嫁接和组织培养技术等无性繁殖方法则可用于对难以用种子繁殖的种类进行快速繁殖。本文对14种中国典型极小种群野生植物的繁殖特性和已有的人工繁殖方法进行了综述, 并简要介绍在其种苗繁殖研究方面取得的进展。其中利用播种繁殖成功的物种有12种, 共繁殖230,000株种苗; 利用扦插繁殖成功的物种有5种, 共繁殖33,100株种苗; 华盖木(Manglietiastrum sinicum)、河北梨(Pyrus hopeiensis)和黄梅秤锤树(Sinojackia huangmeiensis)采用嫁接繁殖出了2,415株种苗; 9个物种的组织培养技术获得成功, 共繁殖了24,850株种苗。这些种苗有些已应用于迁地保护和自然回归。上述研究结果为这14种极小种群野生植物的保护和利用提供了理论和技术基础, 也能为其他极小种群野生植物的保护和利用提供参考。     

Effects of growth-promoting rhizobacteria on maize growth and rhizosphere microbial community under conservation tillage in Northeast China

Conservation tillage in conjunction with straw mulching is a sustainable agricultural approach. However, straw mulching reduces the soil temperature, inhibits early maize growth and reduces grain yield in cold regions. To address this problem, we investigated the effects of inoculation of plant growth-promoting rhizobacteria (PGPR) on maize growth and rhizosphere microbial communities under conservation tillage in Northeast China. The PGPR strains Sinorhizobium sp. A15, Bacillus sp. A28, Sphingomonas sp. A55 and Enterobacter sp. P24 were isolated from the maize rhizosphere in the same area and inoculated separately. Inoculation of these strains significantly enhanced maize growth, and the strains A15, A28 and A55 significantly increased grain yield by as much as 22%–29%. Real-time quantitative PCR and high-throughput sequencing showed that separate inoculation with the four strains increased the abundance and species richness of bacteria in the maize rhizosphere. Notably, the relative abundance of Acidobacteria_Subgroup_6, Chloroflexi_KD4-96, and Verrucomicrobiae at the class level and Mucilaginibacter at the genus level were positively correlated with maize biomass and yield. Inoculation with PGPR shows potential for improvement of maize production under conservation tillage in cold regions by regulating the rhizosphere bacterial community structure and by direct stimulation of plant growth.     

Novel artificial intelligence machine learning approaches to precisely predict survival and site-specific recurrence in cervical cancer: A multi-institutional study

BackgroundMachine learning (ML) has been gradually integrated into oncologic research but seldom applied to predict cervical cancer (CC), and no model has been reported to predict survival and site-specific recurrence simultaneously. Thus, we aimed to develop ML models to predict survival and site-specific recurrence in CC and to guide individual surveillance.MethodsWe retrospectively collected data on CC patients from 2006 to 2017 in four hospitals. The survival or recurrence predictive value of the variables was analyzed using multivariate Cox, principal component, and K-means clustering analyses. The predictive performances of eight ML models were compared with logistic or Cox models. A novel web-based predictive calculator was developed based on the ML algorithms.ResultsThis study included 5112 women for analysis (268 deaths, 343 recurrences): (1) For site-specific recurrence, larger tumor size was associated with local recurrence, while positive lymph nodes were associated with distant recurrence. (2) The ML models exhibited better prognostic predictive performance than traditional models. (3) The ML models were superior to traditional models when multiple variables were used. (4) A novel predictive web-based calculator was developed and externally validated to predict survival and site-specific recurrence.ConclusionML models might be a better analytic approach in CC prognostic prediction than traditional models as they can predict survival and site-specific recurrence simultaneously, especially when using multiple variables. Moreover, our novel web-based calculator may provide clinicians with useful information and help them make individual postoperative follow-up plans and further treatment strategies.     

Prevalence and prediction of Lyme disease in Hainan province

Lyme disease (LD) is one of the most important vector-borne diseases worldwide. However, there is limited information on the prevalence and risk analysis using correlated factors in the tropical areas. A total of 1583 serum samples, collected from five hospitals of Hainan Province, were tested by immunofluorescence assay (IFA) and western blot (WB) analyses using anti-Borrelia burgdorferi antibodies. Then, we mapped the distribution of positive rate (by IFA) and the spread of confirmed Lyme patients (by WB). Using ArcGIS, we compiled host-vector-human interactions and correlated data as risk factor layers to predict LD risk in Hainan Province. There are three LD hotspots, designated hotspot I, which is located in central Hainan, hotspot II, which contains Sanya district, and hotspot III, which lies in the Haikou-Qiongshan area. The positive rate (16.67% by IFA) of LD in Qiongzhong, located in hotspot I, was higher than that in four other areas. Of confirmed cases of LD, 80.77% of patients (42/52) whose results had been confirmed by WB were in hotspots I and III. Hotspot II, with unknowed prevalence of LD, need to be paid more attention considering human-vector interaction. Wuzhi and Limu mountains might be the most important areas for the prevalence of LD, as the severe host-vector and human-vector interactions lead to a potential origin site for LD. Qiongzhong is the riskiest area and is located to the east of Wuzhi Mountain. In the Sanya and Haikou-Qiongshan area, intervening in the human-vector interaction would help control the prevalence of LD.