Epstein‐Barr virus‐encoded latent membrane protein 1 promotes extracellular vesicle secretion through syndecan‐2 and synaptotagmin‐like‐4 in nasopharyngeal carcinoma cells

Increasing evidence indicates that extracellular vesicles (EVs) play an important role in cancer cell‐to‐cell communication. The Epstein‐Barr virus (EBV)‐encoded latent membrane protein 1 (LMP1), which is closely associated with nasopharyngeal carcinoma (NPC) pathogenesis, can trigger multiple cell signaling pathways that affect cell progression. Several reports have shown that LMP1 promotes EV secretion, and LMP1 trafficking by EVs can enhances cancer progression and metastasis. However, the molecular mechanism by which LMP1 promotes EV secretion is not well understood. In the present study, we found that LMP1 promotes EV secretion by upregulated syndecan‐2 (SDC2) and synaptotagmin‐like‐4 (SYTL4) through nuclear factor (NF)‐κB signaling in NPC cells. Further study indicated that SDC2 interacted with syntenin, which promoted the formation of the EVs, and SYTL4 is associated with the release of EVs. Moreover, we found that stimulation of EV secretion by LMP1 can enhance the proliferation and invasion ability of recipient NPC cells and tumor growth in vivo. In summary, we found a new mechanism by which LMP1 upregulates SDC2 and SYTL4 through NF‐κB signaling to promote EV secretion, and further enhance cancer progression of NPC.     

CIP2A在子宫内膜样腺癌中的表达及临床意义

目的:探究蛋白磷酸酶2A(CIP2A)在子宫内膜样腺癌中的表达情况及临床意义。方法:选取2011年1月至2014年3月行手术切除并经病理证实的50例子宫内膜样腺癌(EAC)及同期行门诊刮宫术获取的40例正常增生期子宫内膜(NE)组织标本。应用RT-PCR、Western blot法检测EAC和NE组织中CIP2A mRNA及蛋白水平,免疫组化法检测CIP2A阳性表达情况。分析CIP2A在EAC和NE组织中的表达差异及与子宫内膜样腺癌临床病理特征的关系。采用Kaplan-Meier法分析CIP2A不同表达水平对患者预后生存的影响,通过COX分析预后独立危险因素。结果:免疫组化染色显示:CIP2A在子宫内膜样腺癌中呈高表达,阳性着色定位于细胞浆和细胞核中,EAC组织中CIP2A阳性表达率显著高于NE组织(P＜0.01)。RT-PCR和Western blot检测显示EAC组织中CIP2A mRNA及蛋白表达水平高于NE组织(P＜0.01)。CIP2A表达与EAC的组织学分级、FIGO分期、宫颈管受累情况、p53表达及Ki-67增殖指数有关（P＜0.05）。Kaplan-Meier法生存分析显示EAC患者5年无病生存率为92.0%、总生存率为88.0%;CIP2A表达、组织学分级、FIGO分期、肌层浸润深度、附件转移、脉管内癌栓及Ki-67增殖指数与患者预后不良相关（P＜0.05）。多因素分析显示,组织学分级、FIGO分期及脉管内癌栓是影响子宫内膜样腺癌患者预后生存的独立危险因素（P＜0.05）。结论:CIP2A在子宫内膜样腺癌中呈高表达,与患者总生存率下降相关,并非影响患者预后的独立危险因素。     

极年轻乳腺癌患者生育问题关注度的影响因素及其预后分析

目的研究极年轻乳腺癌患者生育相关问题关注度的影响因素,并分析其预后。 方法收集2009年12月至2019年1月经河北医科大学第四医院乳腺中心诊治、年龄≤25岁且有完整临床病理资料的50例极年轻乳腺癌女性患者进行回顾性研究。所有患者均完成了生育问题和结果量表(FIS)。采用单因素和多因素Logistic回归模型评估社会人口统计学因素、肿瘤因素与生育相关问题关注度之间的关系;采用Kaplan-Meier方法进行患者生存分析,用log-rank检验进行组间比较,采用Cox比例风险回归模型探讨影响极年轻乳腺癌患者预后的因素。 结果50例极年轻乳腺癌患者中,36例患者与其主管医师在确诊后/治疗前未沟通生育相关问题,仅有14例患者在确诊后/治疗前沟通过;28例患者表示乳腺癌治疗后仍有生育愿望;11例患者在治疗结束后妊娠,占全部患者的22%(11/50),其中,有6例患者在未咨询医师的情况下,自行选择人工流产,其余5例患者均足月妊娠,新生儿健康。单因素和多因素Logistic回归分析显示确诊前生育状态是极年轻乳腺癌患者生育相关问题关注度的独立影响因素(单因素分析：OR＝0.250, 95%CI: 0.070～0.897, P＝0.033;多因素分析：OR＝0.270,95%CI：0.048～0.901,P＝0.035)。50例患者中共有9例(18%)患者复发或转移,其中,7例(14%)患者死亡,原因与乳腺癌直接相关。单因素分析显示：诊断延迟时间是极年轻乳腺癌患者DFS和OS的影响因素(χ²＝8.857、6.928,P＝0.003、0.008),病理类型是患者DFS的影响因素(χ²＝4.824,P＝0.028),但不是OS的影响因素(χ²＝3.339,P＝0.069)。多因素分析结果显示：诊断延迟时间是患者DFS的独立预后因素(HR＝13.121,95%CI：1.385～124.348,P＝0.025)。生存分析结果显示：诊断延迟时间>3个月组与诊断延迟时间≤3个月组比较,患者的DFS差异有统计学意义(χ²＝4.834,P＝0.025),而OS差异无统计学意义(χ²＝1.035,P＝0.311)。 结论治疗前未生育的患者对生育相关问题关注度高。诊断延迟可能导致极年轻乳腺癌患者的预后变差,值得临床医师关注。     

消化道恶性肿瘤住院患者的能量消耗研究

目的评估消化道恶性肿瘤患者的能量消耗,探讨最佳计算公式及能量消耗的影响因素。方法采用连续入组法,纳入2016年3月至2016年12月在陆军军医大学第一附属医院肿瘤科住院治疗患者,运用代谢车测定其静息代谢能量（REE）,使用Harris-Benedict公式和30kcal/（kg·d） 公式预测患者的一日总能量消耗（TEE）。收集研究对象的相关指标如年龄、身高、体重、病程、原位癌部位、是否荷瘤等。结果共纳入26例患者,其中包括食管癌11例,胃癌8例,结直肠癌7例,73%的患者处于高代谢状态,约69%的患者处于肿瘤Ⅳ期;其中不同病程和原位癌位置与静息能量消耗有差异,差异具有统计学意义;用30kcal/（kg·d）×体重估算TEE可能并不适用于消瘦的消化道肿瘤患者。结论消化道恶性肿瘤患者大多存在营养不良且处于高代谢状态,在给消化道恶性肿瘤患者提供能量时应适当考虑病程长短、肿瘤分期以及肿瘤部位等因素。尽量使用代谢车估算恶性肿瘤患者的TEE,若没有代谢车条件时,对于能下床活动的消化道恶性肿瘤患者,体质指数（BMI）≥18.5kg/m2者推荐使用30kcal/（kg·d）×实际体重的方法估算TEE,BMI<18.5kg/m2者推荐使用30kcal/（kg·d）×标准体重的方法估算TEE。     

Spot双目视力筛查仪和自动电脑验光仪在近视筛查中结果的比较

目的：:比较Spot双目视力筛查仪和自动电脑验光仪在近视筛查中结果的差异、相关性和一致性。方法：:横断面研究。采用分层随机方法抽取徐州某学校6～19岁的学生共500人,分别用Spot双目视力筛查仪（VS100,美国伟伦公司）和自动电脑验光仪（KR800,日本拓普康公司）进行验光检查。记录睫状肌麻痹（1%复方托吡卡胺眼药...     

Modeling for COVID-19 college reopening decisions: Cornell,a case study

We consider epidemiological modeling for the design of COVID-19 interventions in university populations, which have seen significant outbreaks during the pandemic. A central challenge is sensitivity of predictions to input parameters coupled with uncertainty about these parameters. Nearly 2 y into the pandemic, parameter uncertainty remains because of changes in vaccination efficacy, viral variants, and mask mandates, and because universities’ unique characteristics hinder translation from the general population: a high fraction of young people, who have higher rates of asymptomatic infection and social contact, as well as an enhanced ability to implement behavioral and testing interventions. We describe an epidemiological model that formed the basis for Cornell University’s decision to reopen for in-person instruction in fall 2020 and supported the design of an asymptomatic screening program instituted concurrently to prevent viral spread. We demonstrate how the structure of these decisions allowed risk to be minimized despite parameter uncertainty leading to an inability to make accurate point estimates and how this generalizes to other university settings. We find that once-per-week asymptomatic screening of vaccinated undergraduate students provides substantial value against the Delta variant, even if all students are vaccinated, and that more targeted testing of the most social vaccinated students provides further value.

When is it safe to offer in-person university instruction during the COVID-19 pandemic? What interventions, if any, provide the level of safety required? Colleges and universities across the globe faced this question in summer 2020 as they considered whether to offer in-person instruction. They continue to face this question today as they contemplate partially vaccinated student populations, waning immunity, booster shots, and the potential for new variants to emerge.These questions are significant because outbreaks in university student populations have occurred regularly (1) and may harm the health of students and more-vulnerable employees and community members that interact with them (2). Even when vaccination protects the bulk of the population against the most severe health outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, widespread breakthrough infections would threaten the health of unvaccinated and immunocompromised individuals in their midst. At the same time, social distancing, masking, asymptomatic screening, the migration of in-person instruction to a virtual format, vaccine mandates, and other interventions that can be brought to bear against university outbreaks all incur social and financial costs (3, 4). Better understanding the protection offered by these interventions would support providing safety while minimizing these costs.These questions remain difficult to answer because vaccination levels, SARS-CoV-2 variants, and other conditions continue to change and because experiences at the city, state, and national level do not easily generalize to university populations. Indeed, university populations are younger than the general population and thus have increased rates of contact (5) that may elevate virus transmission (2, 6). In addition, universities can implement interventions that would be substantially more difficult for the general population, such as mandatory vaccination and mandatory asymptomatic screening (7, 8).Universities have responded to this central question in dramatically different ways. In the 2020–2021 academic year, many schools went fully online, while many others opened for in-person instruction with a modest set of interventions centered around symptomatic testing, contact tracing, and social distancing (9). Moreover, those schools that opened for in-person instruction pursued dramatically different testing strategies (10). Some tested only symptomatic students, others tested all students once on arrival, and others tested all students at least once per week. In the fall 2021 semester, schools differ in whether they mandate vaccines, their testing strategies, and masking policies (11).This diversity in approach reflects, in part, a diversity of circumstance, such as proximity to, and interaction with, population centers, prevalence in those population centers, availability of housing to quarantine students, and the desires of the surrounding community (12). However, it also reflects substantial continued uncertainty about how policy translates into outcomes. Such uncertainty and diversity in approach among universities reflects the larger response to the pandemic, in which US states and national governments adopted dramatically different responses to the pandemic despite apparently similar circumstances.Simulation-based epidemic models would seem to offer the power to resolve this uncertainty in support of high-quality decisions. They allow prediction, customized to the circumstances of a university, city, state, or nation. By varying the interventions in silico and observing predicted outcomes, one can hope to choose the best course of action. Unfortunately, epidemic models only approximate reality (13). Ever-present uncertainty in model input parameters coupled with the potential for exponential growth significantly limit accuracy. Small differences in behavioral and biological parameters can cause huge differences in predicted case counts. As a consequence, epidemic models have been maligned for producing inaccurate point estimates (13, 14).This article demonstrates that simulation models can support effective selection of COVID-19 interventions even when they are unable to provide accurate point estimates of epidemic outcomes. We demonstrate this through a case study of how simulation models supported the design of COVID-19 interventions that were subsequently implemented at Cornell University. We also present a modeling framework that can support decisions at other universities. (The use of epidemic models in the presence of significant parameter uncertainty is also discussed in, for example, ref. 15. In such settings, clear communication of uncertainties is key; see, for example, ref. 16).)In close communication with Cornell University’s administration, we conducted a simulation-based analysis in summer 2020 using a compartmental Susceptible, Exposed, Infectious, and Recovered model with multiple subpopulations; see refs. 17–19 for closely related models. Our work was the basis for the decision to reopen Cornell’s Ithaca campus for residential instruction in fall 2020 (20) and was used to design an asymptomatic screening program that was and remains a critical part of Cornell’s strategy.Based on these modeling recommendations, all students were invited to return to Cornell’s Ithaca campus for residential instruction during the 2020–2021 school year under an asymptomatic screening program, and 75% of students returned (21). The surveillance program used pooled PCR testing with the testing frequencies obtained through our modeling. The surveillance program used less-sensitive but more-comfortable anterior nares (AN) sampling over nasopharyngeal (NP) sampling, because modeling suggested that the benefits of comfort to test compliance outweighed a potential loss in sensitivity. Asymptomatic surveillance was enabled at Cornell through a major effort to support large-scale sample collection and develop a new COVID-19 testing laboratory based on diagnostic expertise in Cornell’s College of Veterinary Medicine, and through a unique partnership with a local health care provider. Based on recommendations from our simulation modeling approach, this strategy was updated for the spring 2021 semester to test varsity athletes and students in Greek-life organizations more frequently (contact tracing data showed them to have more social contact than other individuals) and again in fall 2021 to adjust for the Delta variant, changes in social distancing policies, and the protection offered by vaccination. Over the course of the 2020–2021 academic year, there were fewer than 1,044 infections among students and employees, fewer than many schools with similar student populations offering only virtual instruction (1, 22).Our modeling approach hinges on delineating those simulation model input parameters yielding epidemics that can be successfully controlled versus those that cannot. If the set of plausible input parameters are contained within the set of safe parameters, then we can be highly confident, although never certain, that the epidemic can be controlled. At Cornell in summer 2020, we demonstrated this to the university administration for a suite of interventions available with in-person instruction: frequent asymptomatic screening, testing students on arrival to campus, contact tracing, social distancing on campus, limits on student and employee travel, masking requirements, and a behavioral compact curtailing student social gatherings. It was also possible that we would have found that plausible ranges of the input parameters overlapped the portion of parameter space where epidemics would grow out of control, in which case we would not have been able to recommend reopening.We found that access to regular asymptomatic screening (7, 23), with an ability to increase testing frequency if needed, was critical. Indeed, those few universities employing a similar asymptomatic screening approach succeeded, by and large, in controlling campus outbreaks (24–27). See also refs. 28–33 for explorations of the interaction of pooled testing and asymptomatic surveillance for controlling epidemics.We also found it was critical to analyze epidemic growth if in-person instruction were not offered, to quantify the relative merits of the alternative to in-person instruction. Survey results (20, 34) suggested that a significant number of students would return to the Ithaca area even if in-person instruction were not offered. Without the benefits of the legal framework offered by in-person instruction, frequent asymptomatic screening would have been difficult to mandate for this population. Moreover, our analysis suggested that many of those parameter settings in which asymptomatic screening would not ensure safe in-person instruction would also be ones in which a significant outbreak would occur in the local student population under virtual instruction. This resulted in the decision to reopen Cornell’s Ithaca campus with a fully residential semester in fall 2020 (20).We additionally measure key parameters of a university population needed for understanding the dynamics of epidemic spread, including university subpopulations’ intergroup and intragroup rates of viral transmission and how it has changed over time with vaccination, the Delta variant, and relaxation in social distancing. We find that a small group of students has significantly more intergroup viral transmission than other groups and plays an important role in determining the risk of an outbreak. We find that targeting interventions to this group provides substantial protection against outbreaks. Unlike students, we find that employees have very little transmission at work and are well separated from students, with extremely little transmission across the two groups. This has implications for understanding the risk to older and more vulnerable individuals from student infections.When considering a range of interventions against the Delta variant, we find that achieving high levels of vaccination provides significant protection, but that, even in a 100% vaccinated student population, there is significant potential for breakthrough outbreaks in the absence of asymptomatic screening and social distancing. This is consistent with findings from other modeling studies (19). While once per week asymptomatic screening of vaccinated students might be sufficient in many situations, we find that testing vaccinated student groups with high rates of social contact twice per week substantially reduces risk even when the entire population is vaccinated. We also find that moving from 75% vaccination to full vaccination provides substantial additional protection.To summarize, the key contributions of this paper are 1) providing a simulation framework for supporting the design of COVID-19 interventions despite parameter uncertainty; 2) demonstrating this framework through its implementation at Cornell University; 3) measuring key parameters of the dynamics of the spread of SARS-CoV-2 in university populations and the effectiveness of interventions; and 4) providing a framework for making decisions moving forward, including the design of asymptomatic screening strategies in the presence of partial vaccination and the Delta variant.Our work adds to the broader literature using epidemic modeling in the context of universities. See, for example, ref. 35 for a perspective on the challenges of reopening as informed by a variety of epidemic models, refs. 36 and 37 for the use of agent-based modeling to evaluate mitigation strategies to enable safe in-person instruction, ref. 38 for probabilistic modeling of strategies to suppress virus spread in dorms and classrooms, and ref. 39 for a study of interventions for generic small residential campuses.     

浅议药物警戒制度中药物有害反应的控制

药物警戒制度贯穿于药品全生命周期,是对药品安全风险进行管理的最基本制度,它包含对药物有害反应的监测、识别、评估与控制,其中"控制"与其他三个过程一脉相承、相互交叉,是药物警戒制度相对药品不良反应监测制度最为先进性的体现,应当加强研究.本文通过梳理我国药物警戒制度的概念、分析我国对药物有害反应"控制"的实践以及国外药物有...     

LAS样本前处理流程监控的实现

目的 建立临床实验室信息系统(clinical laboratory information system CLIS)对实验室自动化系统(laboratory automation system LAS)的样本检测前处理流程进行实时监控,实现"高品质、高效率、高自动化"管理.方法 在临床实验室信息系统的信息流管理支撑下,根据医嘱条码信息,实现CLIS实时监控LAS上的样本按检测项目分类及随机编号、离心与开盖及根据实验要求完成样本分杯和将离线检测样本传送到特定区域待检的前处理流程.结果 建立完善实验室内检测的信息化监管系统,利用LAS强大的样本处理功能,改变以往样本检测前处理流程中无法有效节点实时监控,规范实验室检测管理.结论 CLIS对LAS的样本检测前处理流程的实时监控,实现CLIS有效的实验室业务流监控机制,对于规范实验室管理具有重要意义.