Role of La autoantigen and polypyrimidine tract-binding protein in HCV replication

To determine if the cellular factors La autoantigen (La) and polypyrimidine tract-binding protein (PTB) are required for hepatitis C virus (HCV) replication, we used siRNAs to silence these factors and then monitored their effect on HCV replication using quantitative RT-PCR. In addition, we determined the influence of PTB on the activity of the 3' noncoding region (NCR) of HCV and investigated its interaction with the components of the HCV replicase complex. We found that La is essential for efficient HCV replication while PTB appears to partially repress replication. PTB does, however, block the binding of HCV RNA-dependent RNA polymerase (RdRp, NS5B) to the 3'NCR. Indirect immunofluorescence microscopy showed co-localization of cytoplasmic PTB with the HCV RdRp in hepatoma cells (Huh-7) expressing HCV proteins, while in vitro translation of viral proteins from the HCV replicon revealed the interaction of PTB isoforms with NS5B polymerase and NS3.     

2006—2017年贵州省肺结核流行特征分析

目的 分析2006—2017年贵州省肺结核流行特征,为全省肺结核防控提供依据。方法 采用描述性流行病方法分析2006—2017年贵州省肺结核报告发病率,用动态数列法分析9个市/州不同阶段的变化。结果 2006—2017年全省肺结核总发病617 115例,平均发病率为142.15/10万,总体呈下降趋势（χ2趋势= 932.07,P＜0.01）;遵义、铜仁、安顺市,黔南州平均发病率高于全省平均水平,2010年后黔南、黔东南州,六盘水市平均增长速度均＞0;≥61岁人群、农民发病率最高,分别为:270/10万、102.55/10万;男性发病率均高于女性(男:198.00/10万,女:108.34/10万);流动人口肺结核患者构成比在20.3%～24.6%之间,总体呈增长趋势（χ2趋势= 131.47,P＜0.01）;PTB/HIV共感染率在0.11%～0.36%之间。结论 贵州省肺结核发病率仍处较高水平,男性、≥61岁、农民、流动人口为重点人群,建议加强宣传教育,建立有效流动人口管理模式,尤其发病率较高、呈上升趋势区域,完善肺结核中HIV高危人群筛查制度。     

江苏省农村居民结核病危险因素病例对照研究

目的　了解江苏省农村结核发病危险因素 ,为结核病控制提供依据。方法　采用 1∶1配对病例对照方法 ,分层整群抽取病例和对照各 318人进行问卷调查 ,计算OR值和 95 %CI ,并用条件Logistic回归模型进行多因素分析。结果　在单因素分析中 ,人均收入、住房类型、人均住居面积、膳食营养 (鸡蛋、肉类 )、劳动疲乏、经济负担重、有负性事件、结核相关疾病史、吸烟、有结核病 (TB)接触史等因素的OR值分别为 0 72 6 ,0 6 4 9,0 82 3,0 913,0 90 6 ,1 84 9,2 0 2 7,3 86 7,2 35 5 ,1 389,3 5 99;P均 <0 0 5。在多因素分析中 ,人均收入低、家庭经济负担重、劳动疲劳、有结核相关疾病史、有负性事件、有TB接触史是影响农村结核病的主要因素。结论　加强结核病人发现、治疗、管理 ,并针对其他危险因素采取控制措施对降低农村疫情有重要意义。     

Effect of change in coding rules on recording diabetes in hospital administrative datasets

AimDuring 2008–2011 Australian Coding Standards mandated a causal relationship between diabetes and inpatient care as a criterion for recording diabetes as a comorbidity in hospital administrative datasets. We aim to measure the effect of the causality mandate on recorded diabetes and associated inter-hospital variations.MethodFor patients with diabetes, all admissions between 2004 and 2013 to all New South Wales acute public hospitals were investigated. Poisson mixed models were employed to derive adjusted rates and variations.ResultsThe non-recorded diabetes incidence rate was 20.7%. The causality mandate increased the incidence rate four fold during the change period, 2008–2011, compared to the pre- or post-change periods (32.5% vs 8.4% and 6.9%). The inter-hospital variation was also higher, with twice the difference in the non-recorded rate between hospitals with the highest and lowest rates (50% vs 24% and 27% risk gap). The variation decreased during the change period (29%), while the rate continued to rise (53%). Admission characteristics accounted for over 44% of the variation compared with at most two per cent attributable to patient or hospital characteristics. Contributing characteristics explained less of the variation within the change period compared to pre- or post-change (46% vs 58% and 53%). Hospital relative performance was not constant over time.ConclusionThe causality mandate substantially increased the non-recorded diabetes rate and associated inter-hospital variation. Longitudinal accumulation of clinical information at the patient level, and the development of appropriate adoption protocols to achieve comprehensive and timely implementation of coding changes are essential to supporting the integrity of hospital administrative datasets.     

中医药数据元和数据集标准规范化研究

详细阐述借助EXCEL进行中医药数据元、数据集标准规范化的具体流程,指出其应用能够提高工作效率,降低出错率,为相关研究提供参考。     

Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset

ObjectiveThe lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning.Materials and MethodsWe used the publicly available nCov2019 dataset, including patient-level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities.ResultsCases from the 2 countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting.ConclusionsData source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning.     

Characteristics and sputum conversion of tuberculosis (TB) patients in Kalutara,Sri Lanka

Background

Tuberculosis (TB) is an infectious bacterial disease; remains as one of the important public health problem affecting every part of the world. Substantial number of TB cases are reported from Sri Lanka every year irrespective of its strong preventive health system. The aim of this analysis is to describe the characteristics of TB patients and to assess the factors associated with sputum conversion. This analysis was based on the data from the District Chest Clinic of Kalutara district, Sri Lanka.

How many sputum smears are necessary for case finding in pulmonary tuberculosis?

We reviewed the laboratory registers of 42 tuberculosis (TB) diagnostic centres in the southern region of Ethiopia to determine the value of submitting serial sputum samples for the diagnosis of pulmonary TB (PTB) and estimate the proportion of suspects that are smear positive. A total of 15,821 TB suspects submitted three smears each (47,463 smears) in 2000 with a median of 228 per centre. The smear positivity rate (two or more positive smears) was 25%, with a range of 16.8-36.4% per zone. This exceeds the international recommendations of examining 10 suspects to identify one case. A total of 4099 (26%) of the suspects had at least one positive smear with 3753 (91.6%) of the first specimens being positive. A further 303 (7.4%) were negative in the first specimen but had a positive second specimen and 42 (1%) suspects had two negative specimens followed by a positive third smear. The value of the third sputum is negligible as 99% of the cases were identified from the first and second specimens. Reducing the number of specimens to two or even one would have multiple advantages in countries where laboratories are usually over-burdened and are not easily accessible to the population. Submission of two specimens on the same day could improve compliance in submitting samples and collecting results as the number of diagnostic visits would be reduced without significant loss of sensitivity.     

基于非参数相关系数的心肌病自动诊断

针对肥厚型心肌病和扩张型心肌病患者的心电图导联间的相关性,提出心肌病自动诊断的一种方法。该研究从12导联ECG信号中分割出来的单个心跳片段进行识别,以健康人群为对照识别出DCM和HCM的片段。从片段中提取264个非参数相关系数特征并通过变量筛选得到12个特征,输入到支持向量机中进行建模,采用10折交叉验证评价模型。模型的总准确率为99.88%±0.08%。模型使用的特征少,运行速度快,准确率高,有助于临床心肌病的自动化诊断,节约医疗资源。