ABSTRACT
Coronavirus has brought about three massive outbreaks in the past two decades. Each step of its life cycle invariably depends on the interactions among virus and host molecules. The interaction between virus RNA and host protein (IVRHP) is unique compared to other virus-host molecular interactions and represents not only an attempt by viruses to promote their translation/replication, but also the host's endeavor to combat viral pathogenicity. In other words, there is an urgent need to develop a database for providing such IVRHP data. In this study, a new database was therefore constructed to describe the interactions between coronavirus RNAs and host proteins (CovInter). This database is unique in (a) unambiguously characterizing the interactions between virus RNA and host protein, (b) comprehensively providing experimentally validated biological function for hundreds of host proteins key in viral infection and (c) systematically quantifying the differential expression patterns (before and after infection) of these key proteins. Given the devastating and persistent threat of coronaviruses, CovInter is highly expected to fill the gap in the whole process of the 'molecular arms race' between viruses and their hosts, which will then aid in the discovery of new antiviral therapies. It's now free and publicly accessible at: https://idrblab.org/covinter/.
ABSTRACT
BACKGROUND: Given the context of rapid technological change and COIVD-19 pandemics, E-learning may provide a unique opportunity for addressing the challenges in traditional face-to-face continuing medical education (CME). However, the effectiveness of E-learning in CME interventions remains unclear. This study aims to evaluate whether E-learning training program can improve TB health personnel's knowledge and behaviour in China. METHODS: This study used a convergent mixed method research design to evaluate the impact of E-learning programs for tuberculosis (TB) health workers in terms of knowledge improvement and behaviour change during the China-Gates TB Project (add the time span). Quantitative data was collected by staff surveys (baseline n = 555; final n = 757) and management information systems to measure the demographic characteristics, training participation, and TB knowledge. Difference-in-difference (DID) and multiple linear regression models were employed to capture the effectiveness of knowledge improvement. Qualitative data was collected by interviews (n = 30) and focus group discussions (n = 44) with managers, teachers, and learners to explore their learning experience. RESULTS: Synchronous E-learning improved the knowledge of TB clinicians (average treatment effect, ATE: 7.3 scores/100, P = 0.026). Asynchronous E-learning has a significant impact on knowledge among primary care workers (ATE: 10.9/100, P < 0.001), but not in clinicians or public health physicians. Traditional face-to-face training has no significant impact on all medical staff. Most of the learners (57.3%) agreed that they could apply what they learned to their practice. Qualitative data revealed that high quality content is the key facilitator of the behaviour change, while of learning content difficulty, relevancy, and hardware constraints are key barriers. CONCLUSIONS: The effectiveness of E-learning in CME varies across different types of training formats, organizational environment, and target audience. Although clinicians and primary care workers improved their knowledge by E-learning activities, public health physicians didn't benefit from the interventions.