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1.
  目的  了解2010—2019年中国经同性传播感染HIV的18~24岁男性青年学生HIV感染者时空分布特征及变化趋势,为开展青年学生艾滋病防控措施提供参考。  方法  以18~24岁经同性传播感染HIV的男性青年学生为研究对象,通过ArcGIS 10.3软件进行分布描述、局域热点分析,并通过SaTScan 9.6软件进行时空扫描分析,探索聚集区及聚集时段。  结果  2010—2019年共报告经同性传播感染的18~24岁男性青年学生HIV感染者16 918例。全国18~24岁青年学生报告病例数由2010年的488例增长至2019年的2 481例,其中重庆市、四川省和北京市等地区报告数较高。经同性传播感染HIV的18~24岁男性青年学生在同期同年龄段男性人口中占比超过万分之一的地市个数由2010年的1个增加至2019年的17个。2010—2019年共有16个聚集区;第一类聚集区涵盖重庆市、成都市和西安市等地区,聚集时段为2015—2019年(RR=2.66, P < 0.001)。  结论  中国经同性传播感染的18~24岁男性青年学生HIV感染者具有一定时空聚集性,应在热点区域、聚集区开展有针对性的防控工作,降低HIV在青年学生中的流行。  相似文献   
2.
We carried out a longitudinal study on the associations between residential greenness and depression risk in urban areas in Finland. Residential greenness indicators were estimated within various buffer sizes around individuals' home locations (selected n = 14424) using time-series of normalized differential vegetation index (NDVI) and CORINE land cover data (CLC). We estimated individuals’ cumulative exposure to residential greenness over a 5-years and 14-years follow-up. We used doctor-diagnosed depression and Beck Depression Inventory for depression assessment. Our multi-logistic model showed an inverse association between residential greenness and depression, implying lowered depression risk for individuals with higher residential greenness. The association was particularly evident when using NDVI-based residential greenness (within a buffer of 100 m radius) and doctor-diagnosis depression data, adjusted with individual-level covariates. The odds ratio was 0.56 (95% CI 0.33 to 0.96) for the 5-years follow-up, and 0.54 (95% CI 0.30 to 0.98) for the 14-years follow-up. The associations between CLC-based total residential green space and depression varied across the different buffer sizes. In general, all the associations depended on the type of depression assessment, quality of greenness indicators, and the spatial scale of analysis. The associations also varied across the socio-demographic groups and neighborhood socioeconomic disadvantage level.  相似文献   
3.
目的 以2017年某省食品安全监测大米中砷含量数据为例,探讨空间统计学方法在食品污染物分析中的应用价值。方法 采用空间点模式估计、核密度分析,全局以及局部自相关性分析等空间统计学方法,在县级空间尺度下,对某省大米中砷含量进行探索性空间数据分析。结果 空间点模式分布图显示,该省大米砷污染的空间分布比较分散,核密度分析结果显示污染热点区域主要在该省中东部地区。全局自相关Moran''s I指数值为0.11,有统计学意义,大米样品中砷污染呈现出低度空间聚集性。有1个"高-高"聚集区,2个典型的"低-低"聚集区。结论 空间统计学运用于食物污染物分布研究上,可以很好地可视化展示、识别污染分布规律、热点地区和聚集区,为基于问题的监测工作的开展提供技术支持。  相似文献   
4.
目的 探讨山东省新型冠状病毒肺炎(COVID-19)的空间分布与相关影响因素,进一步了解山东省疫情的区域分布特征,为指导防控策略提供科学依据。 方法 收集2020年1月21日至3月1日山东省COVID-19确诊病例数及相关影响因素数据,采用地理加权广义线性模型(GWGLM)分析COVID-19确诊病例数及各影响因素间的空间异质性及其相关关系。 结果 对558例确诊病例的空间分布进行分析,广义线性模型(GLM)分析结果显示,人口密度、人均可支配收入、公共预算支出、湖北迁入规模占比和距武汉的空间距离均有统计学意义。人口越密集、人均可支配收入越高、公共预算支出越多,则确诊病例数越多;绝大多数县区的湖北迁入人口规模和距武汉的空间距离与确诊病例数呈负相关。GWGLM的R2为0.363,模型可解释COVID-19确诊病例数总变异的36.3%。 结论 GWGLM能够揭示COVID-19及其影响因素的空间异质性,有助于局域精准施策,应根据各因素的空间分布特征及其与确诊病例数的局域关系制定不同区域的分级防控措施。  相似文献   
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6.
目的 分析2011—2020年安徽省钉螺分布时空特征,为全省钉螺精准防控提供参考。方法 收集2011—2020年安徽省实有钉螺面积、新发钉螺面积和感染性钉螺面积等钉螺分布指标并进行描述性分析,对实有和新发钉螺面积进行空间自相关、热点分析、标准差椭圆分析及时空扫描等分析,探索安徽省钉螺聚集与扩散高风险区。结果 2011—2020年,安徽省实有钉螺面积逐渐降低。2020年全省实有钉螺面积26 238.85 hm2,主要分布在湖沼型血吸虫病流行区。各年份间新发钉螺面积波动较大,2016年最高(1 287.65 hm2);2020年在池州市贵池区新发现1.96 hm2感染性钉螺面积。空间自相关和热点分析显示,2011—2020年安徽省实有钉螺面积分布具有空间聚集性(Z = 3.00 ~ 3.43,P均 < 0.01),热点主要集中在湖沼型流行区并沿长江南岸分布;冷点主要集中在皖南山区。2011—2020年安徽省新发钉螺面积分布在整体上不存在空间聚集性(Z = -2.20 ~ 1.71,P均> 0.05),局部呈散点分布。标准差椭圆分析显示,2011—2020年安徽省实有钉螺面积分布相对稳定,与长江流向一致;新发钉螺面积分布重心逐渐从长江安徽段下游向上游移动。时空扫描分析显示,2011—2020年安徽省实有钉螺面积两个高值聚集区以从长江安徽段下游到中游的顺序出现;新发钉螺面积两个高值聚集区聚集时间和范围类似,均分布在山区。结论 2011—2020年安徽省钉螺分布呈空间聚集性,存在向长江南岸、上游聚集的趋势,但山区钉螺扩散问题亦不容忽视,需加强对山区和长江沿岸新发钉螺孳生地的监测力度。  相似文献   
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BackgroundIn Ontario, Canada, little is currently known about the extent to which un-immunized children may cluster geographically. Our objectives were to: describe the geographic distribution of fully un-immunized children; identify geographic clusters (hotspots) of un-immunized children; and to characterize the contribution of spatial effects and covariates on hotspots, where found.MethodsOur analytic cohort consisted of Ontario students aged 7–17 years in the 2016–2017 school year. We defined students as un-immunized if they had zero doses of any vaccine and a non-medical exemption recorded in Ontario’s registry. We calculated unadjusted proportions of un-immunized students by Census Subdivision (CSD) and then used a sequential approach to identify hotspots starting first with hotspot identification at the CSD level and then probed identified hotspots further by Dissemination Area (DA) and including covariates. Hotspots were identified using the Besag-York-Mollie Bayesian spatial model and were defined as areas with >95% probability of having two times the proportion of un-immunized students, relative to the province overall.ResultsWe identified 15,208 (0.94%) un-immunized children within our cohort consisting of more than 1.61 million students. Unadjusted proportions of un-immunized students varied greatly by geography, ranging from 0% to 21.5% by CSD. We identified 16 hotspot CSDs which clustered in five distinct areas, all of which were located in southern Ontario. The contribution of covariates and spatial effects on the risk of having un-immunized students varied greatly across hotspot areas.ConclusionsAlthough the provincial proportion (0.94%) of un-immunized students is small, geographical clustering of such students is evident in Ontario and in some areas presents an important risk for future outbreaks. Further qualitative work within these hotspot areas would be a helpful next step to better characterize the factors associated with vaccine refusal in these communities.  相似文献   
9.
空间定位能力是一种高级认知功能,个体之间存在明显差异。大脑内部的空间导航神经 网络系统是空间定位能力的结构基础。海马区、内嗅区、海马旁回、压后皮质和丘脑是与空间导航功 能相关的大脑皮质。位置细胞、头向细胞、网格细胞、中间神经元、边界细胞、整合细胞和运动敏感 性细胞是参加空间导航功能的主要细胞。与空间导航相关的大脑皮质及细胞相互作用形成空间导 航神经网络通路。本文对与空间导航功能相关的大脑区域、空间细胞及神经网络通路进行综述,以 期为今后深入研究空间定位能力提供切入点。  相似文献   
10.
Galectin-3 is a glycan-binding protein (GBP) that binds β-galactoside glycan structures to orchestrate a variety of important biological events, including the activation of hepatic stellate cells and regulation of immune responses. While the requisite glycan epitopes needed to bind galectin-3 have long been elucidated, the cellular glycoproteins that bear these glycan signatures remain unknown. Given the importance of the three-dimensional (3D) arrangement of glycans in dictating GBP interactions, strategies that allow the identification of GBP receptors in live cells, where the native glycan presentation and glycoprotein expression are preserved, have significant advantages over static and artificial systems. Here we describe the integration of a proximity labeling method and quantitative mass spectrometry to map the glycan and glycoprotein interactors for galectin-3 in live human hepatic stellate cells and peripheral blood mononuclear cells. Understanding the identity of the glycoproteins and defining the structures of the glycans will empower efforts to design and develop selective therapeutics to mitigate galectin-3–mediated biological events.

The noncovalent interactions between glycan-binding proteins (GBPs) and glycans dictate many important biological events. Among such GBPs is galectin-3, a 26-kDa β-galactoside GBP that plays key roles in many physiological and pathological events (1). In hepatic fibrosis, a disease that manifests as the excessive buildup of scar tissue, liver-resident macrophages secrete galectin-3 (2, 3), which then binds cell surface glycans on quiescent hepatic stellate cells (HSCs), activating them to transdifferentiate into a muscle-like phenotype. Galectin-3–null mice exhibit attenuated liver fibrosis even after induced injury, highlighting its critical role (3). Galectin-3 is also known to interact with cells of the innate immune system (4, 5) to regulate apoptosis (6) or control dendritic cell differentiation (7). In these cases, as well as in other cases in which galectin-3 is involved, the full complement of interacting glycoprotein receptors remains unknown.Despite significant advances in glycoscience, the study of GBP–glycan interactions and the identification of glycan-mediated counter-receptors remains a recurring challenge. Capturing these binding events often requires some form of artificial reconstitution to amplify individually weak interactions into high-avidity binding. Indeed, glycan microarrays with defined mixtures of homogenous glycans or recombinant GBPs have significantly propelled our understanding of glycan-mediated function (8). Conventional immunoprecipitation and lectin affinity techniques using cell lysates have similarly been used to reveal an initial catalog of 100 to 185 galectin-3–associated proteins (914). However, these manipulations alter the cell’s native and three-dimensional (3D) configuration and multivalent arrangement, both of which are critically important in the study of GBP–glycan interactions (15, 16).Another key issue involves the underlying glycoprotein ligand. Although many glycoproteins carry the glycan epitope for binding a GBP, only a limited set should be recognized as physiologically relevant receptors, owing to the physical constraints imposed by the living cell (17). While often overlooked, the glycoprotein carrying the glycan can impart specific biological functions to a GBP–glycan binding event (17). Recent work has put forth the concept of “professional glycoprotein ligands,” in which a specific set of glycoproteins (instead of a broadly defined glycome) can exhibit exquisite binding and functional roles (18). Thus, determining the identity of the underlying core protein that anchors the glycan can be greatly empowering. Not only can it provide an understanding of the 3D arrangement of the glycan (if the 3D structure of the core protein is known), but it can also provide additional insight into its expression levels in different cell types and tissues, further informing strategies for selective drug development.Thus, comprehensive approaches that permit the study of GBP–glycan interactions in live cells while simultaneously facilitating identification of the physiological glycoprotein receptors have great potential to impact glycoscience. We hypothesize that proximity labeling strategies (19) using an engineered ascorbate peroxidase, APEX2 (20), could be compatible for elucidating glycan-mediated GBP–glycoprotein interactions. In this approach (Fig. 1), APEX2 is fused to a protein of interest, followed by the treatment of cells with biotin-phenol and subsequently with hydrogen peroxide (H2O2). Under these conditions, APEX2 catalyzes the formation of highly reactive, short-lived (<1 ms), and proximally restricted (<20 nm) biotin-phenoxyl radicals that covalently tag nearby electron-rich residues. The biotinylated proteins can then be enriched and identified using quantitative mass spectrometry (MS)-based proteomics. Because the (glyco)proteins adjacent to the APEX2 fusion protein are preferentially biotinylated, the resulting MS data provide a readout of its immediate environment.Open in a separate windowFig. 1.Schematic illustration of the identification of galectin-3 (Gal-3) interacting proteins by in situ proximity labeling. Recombinant APEX2 and galectin-3 fusion proteins are applied to living cells where galectin-3 can freely diffuse to bind its cognate ligands. On addition of biotin phenol (yellow circle with “B”; 30 min) and hydrogen peroxide (H2O2; 1 min), APEX2 catalyzes the formation of highly-reactive biotin-phenoxyl radicals that react within a short range (<20 nm) of the galectin-3 complex within a short time frame (<1 ms). The biotin-tagged protein interactors can then be identified using MS-based proteomics.We reasoned that proximity labeling could offer significant advantages over other approaches to determining GBP–glycan interactions, including the opportunity to perform the labeling in live cells and the ability to tag weakly bound glycan-mediated interactors, as the covalent biotinylation reaction ensures that the enrichment step no longer relies on weak GBP–glycan interactions alone. When coupled with inhibitors, the proximity labeling strategy can also distinguish between glycan-mediated and non–glycan-mediated interactors. Integration of this approach with quantitative MS-based proteomics would also expedite the assignment of the interacting proteins and provide calculable measures to distinguish interactors from nonspecific binders.Here we report that the use of an APEX2 and galectin-3 fusion protein (PX-Gal3) provides a sensitive and comprehensive approach to mapping the proteome-wide glycan-mediated galectin-3 interactome in live human HSCs and peripheral blood mononuclear cells (PBMCs). We found that the exogenous incubation of cells with PX-Gal3 in HSCs leads to glycan-dependent interactions, whereas its cellular overexpression does not. We further validated the interactions between galectin-3 and candidate proteins in vitro and discovered that some proteins are direct glycan-mediated receptors. Using MS-based glycomics, we also examined the glycomes of HSC surfaces, PX-Gal3 tagged glycoproteins, and an individual glycoprotein receptor for galectin-3. Our results highlight the utility of the in situ proximity labeling approach in discovering physiologically relevant GBP interactors in living cells.  相似文献   
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