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.     

Prevalence of, and risk factors for, hepatitis C virus infection among recent initiates to injecting in London and Glasgow: cross sectional analysis

Our aim was to compare the prevalence of antibody to hepatitis C virus (anti-HCV) among recently initiated injecting drug users (IDUs) in London and Glasgow, and to identify risk factors which could explain differences in prevalence between the cities. Complementary studies of community recruited IDUs who had initiated injection drug use since 1996 were conducted during 2001-2002. Data on HCV risk behaviours were gathered using structured questionnaires with identical core questions and respondents were asked to provide an oral fluid specimen which was tested anonymously for anti-HCV but was linked to the questionnaire. Sensitivities of the anti-HCV assays for oral fluid were 92-96%. Prevalence of anti-HCV was 35% (122/354) in London and 57% (207/366) in Glasgow (P < 0.001). Multifactorially, factors significantly associated with raised odds of anti-HCV positivity were increasing length of injecting career, daily injection, polydrug use, having had a needlestick injury, and having served a prison sentence. In addition lower odds of anti-HCV positivity were associated with non-injection use of crack cocaine and recruitment from drug agencies. After adjustment for these factors, the increased odds of anti-HCV associated with being a Glasgow IDU were diminished but remained significant. HCV continues to be transmitted among the IDU population of both cities at high rates despite the availability of syringe exchange and methadone maintenance. Effectiveness of harm reduction interventions may be compromised by inadequate coverage and failure to reduce sufficiently the frequency of sharing different types of injecting equipment, as well as the high background prevalence of HCV, and its high infectivity. Comprehensive action is urgently required to reduce the incidence of HCV among injectors.     

Predicting alcohol dependence from multi-site brain structural measures

To identify neuroimaging biomarkers of alcohol dependence (AD) from structural magnetic resonance imaging, it may be useful to develop classification models that are explicitly generalizable to unseen sites and populations. This problem was explored in a mega-analysis of previously published datasets from 2,034 AD and comparison participants spanning 27 sites curated by the ENIGMA Addiction Working Group. Data were grouped into a training set used for internal validation including 1,652 participants (692 AD, 24 sites), and a test set used for external validation with 382 participants (146 AD, 3 sites). An exploratory data analysis was first conducted, followed by an evolutionary search based feature selection to site generalizable and high performing subsets of brain measurements. Exploratory data analysis revealed that inclusion of case- and control-only sites led to the inadvertent learning of site-effects. Cross validation methods that do not properly account for site can drastically overestimate results. Evolutionary-based feature selection leveraging leave-one-site-out cross-validation, to combat unintentional learning, identified cortical thickness in the left superior frontal gyrus and right lateral orbitofrontal cortex, cortical surface area in the right transverse temporal gyrus, and left putamen volume as final features. Ridge regression restricted to these features yielded a test-set area under the receiver operating characteristic curve of 0.768. These findings evaluate strategies for handling multi-site data with varied underlying class distributions and identify potential biomarkers for individuals with current AD.     

Estimating Intervention Effectiveness: Synthetic Projection of Field Evaluation Results

In a 46-site, 5-year high-risk youth substance abuse prevention evaluation, effect sizes were adjusted using a meta-analytic regression technique to project potential effectiveness under more optimal research and implementation conditions. Adjusting effect size estimates to control for the impact of comparison group prevention exposure, service intensity, and coherent program implementation raised the mean effectiveness estimate from near zero (.02, SD = .21) to .24 (SD = .18). This finding suggests that adolescent prevention programs can have significant positive effects under optimal, yet obtainable conditions.Editors’ Strategic Implications: The authors present a meta-analytic technique that promises to be an important tool for understanding what works in multi-site community-based prevention settings. Researchers will find this to be a creative approach to model the “noise’’ in implementation that may often overshadow the potential impact of prevention programs.     

多院区网真视频网络的QoS设计

介绍网真技术的特点,医院网真系统的设计架构和QoS的相关技术,讨论了在多院区环境下如何设计园区网络QoS,给出了配置参考方案,保障网真视频系统端到端的IP网络服务质量,充分发挥网真系统的先进性。     

An i2b2-based,generalizable, open source,self-scaling chronic disease registry

Objective

Registries are a well-established mechanism for obtaining high quality, disease-specific data, but are often highly project-specific in their design, implementation, and policies for data use. In contrast to the conventional model of centralized data contribution, warehousing, and control, we design a self-scaling registry technology for collaborative data sharing, based upon the widely adopted Integrating Biology & the Bedside (i2b2) data warehousing framework and the Shared Health Research Information Network (SHRINE) peer-to-peer networking software.

Materials and methods

Focusing our design around creation of a scalable solution for collaboration within multi-site disease registries, we leverage the i2b2 and SHRINE open source software to create a modular, ontology-based, federated infrastructure that provides research investigators full ownership and access to their contributed data while supporting permissioned yet robust data sharing. We accomplish these objectives via web services supporting peer-group overlays, group-aware data aggregation, and administrative functions.

Results

The 56-site Childhood Arthritis & Rheumatology Research Alliance (CARRA) Registry and 3-site Harvard Inflammatory Bowel Diseases Longitudinal Data Repository now utilize i2b2 self-scaling registry technology (i2b2-SSR). This platform, extensible to federation of multiple projects within and between research networks, encompasses >6000 subjects at sites throughout the USA.

Discussion

We utilize the i2b2-SSR platform to minimize technical barriers to collaboration while enabling fine-grained control over data sharing.

Conclusions

The implementation of i2b2-SSR for the multi-site, multi-stakeholder CARRA Registry has established a digital infrastructure for community-driven research data sharing in pediatric rheumatology in the USA. We envision i2b2-SSR as a scalable, reusable solution facilitating interdisciplinary research across diseases.     

由执业医师多点执业到多科目执业的思考

医师多点执业在发达国家是比较成熟的制度之一,我国自2011年开始试点实施,逐渐被医务工作者接受,被广大群众认可。循着整合医疗资源、减少资源浪费,使有限的医务人员满足更多医疗需求的线索,笔者思考医师多科目执业的可行性,或对目前中国的医疗现状有一定贡献。     

Implementation fidelity of Multidimensional Family Therapy in an international trial

Implementation fidelity, a critical aspect of clinical trials research that establishes adequate delivery of the treatment as prescribed in treatment manuals and protocols, is also essential to the successful implementation of effective programs into new practice settings. Although infrequently studied in the drug abuse field, stronger implementation fidelity has been linked to better outcomes in practice but appears to be more difficult to achieve with greater distance from model developers. In the INternational CAnnabis Need for Treatment (INCANT) multi-national randomized clinical trial, investigators tested the effectiveness of Multidimensional Family Therapy (MDFT) in comparison to individual psychotherapy (IP) in Brussels, Berlin, Paris, The Hague, and Geneva with 450 adolescents with a cannabis use disorder and their parents. This study reports on the implementation fidelity of MDFT across these five Western European sites in terms of treatment adherence, dose and program differentiation, and discusses possible implications for international implementation efforts.     

肿瘤坏死因子相关凋亡诱导配体修饰的人羊水间充质干细胞

背景：以间充质干细胞为载体的基因治疗新方法具有广阔的应用价值。目的：利用慢病毒感染方法将肿瘤坏死因子相关凋亡诱导配体导入人羊水来源的间充质干细胞,以期获得稳定表达肿瘤坏死因子相关凋亡诱导配体的人羊水间充质干细胞。方法：首先利用多位点Gateway技术构建慢病毒表达载体pLVpuro／EFlet．肿瘤坏死因子相关凋亡诱导配体,将该表达载体与慢病毒包装质粒同时转染293FT细胞,从而获得携带肿瘤坏死因子相关凋亡诱导配体基因的慢病毒颗粒。利用重组慢病毒颗粒感染人羊水间充质干细胞,并通过抗生素筛选的方法获得稳定表达目的基因一肿瘤坏死因子相关凋亡诱导配体的羊水间充质干细胞,并对其稳定性进行鉴定。结果与结论：酶联免疫吸附法和Westernblot检测结果表明,肿瘤坏死因子相关凋亡诱导配体蛋白在感染的人羊水间充质干细胞内呈高表达,其表达量可达到72μg／L。说明实验成功制备了肿瘤坏死因子相关凋亡诱导配体修饰的人羊水间充质干细胞。     

虚拟化技术基础上的医师多点执业信息系统

针对新医疗改革对医疗信息化提出的要求,分析虚拟化技术的内涵,提出建设基于虚拟化技术的医师多点职业信息系统,从医院内网实现、虚拟桌面分配、安全措施等方面介绍系统实现过程,阐述应用效果。