Safety and Efficacy of Oral administrated Cepharathine in Non-hospitalized, asymptomatic or mild COVID-19 patients: A Double-blind, Randomized, Placebo-controlled Trial (preprint)

Cepharanthine (CEP) is a natural remedy that potently inhibits SARS-CoV-2 activity both in vitro and in vivo. We conducted a proof-of-concept, double-blind, randomized, placebo-controlled trial among adults with asymptomatic or mild coronavirus disease 2019 (COVID-19). Patients were stratified randomly to de novo infection or viral rebound, and assigned in a 1:1:1 ratio to receive 60 mg/day or 120 mg/day of CEP or placebo. Primary outcome the time from randomization to negative nasopharyngeal swab, and safety were evaluated. A total of 262 de novo infected and 124 viral rebound patients underwent randomization. In the 188 de novo patients included in modified intention-to-treat (mITT) population, when compared with placebo, 60 mg/day CEP slightly shortened the time to negative (difference=-0.77 days, hazard ratio (HR)=1.40, 95% CI 0.97 to 2.01, p=0.072), and 120 mg/day CEP did not show the trend. Among de novo patients in the per-protocol set (PPS), 60 mg/day CEP significantly shortened the time to negative (difference=-0.87 days, HR=1.56, 95% CI 1.03 to 2.37, p=0.035). Among viral rebound patients in the mITT population, neither 120 mg/day nor 60 mg/day CEP significantly shortened the time to negative compared to placebo. Adverse events were not different among the three groups, and no serious adverse events occurred. Treatment of asymptomatic or mild Covid-19 with 120 mg/day or 60 mg/day CEP did not shorten the time to negative compared with placebo, without evident safety concerns. Among de novo infected patients with good compliance, 60 mg/day CEP significantly shortened the time to negative compared with placebo ( NCT05398705 ).

Self-Adaptive Telemedicine Specialist Recommendation Considering Specialist Activity and Patient Feedback

Purpose: With the rapid development of medical informatization, information overload and asymmetry have become major obstacles that limit patients’ ability to find appropriate telemedicine specialists. Although doctor recommendation methods have been proposed, they fail to address data sparsity and cold-start issues, and electronic medical records (EMRs), patient preferences, potential interest of service providers and the changes over time are largely under-explored. Therefore, this study develops a self-adaptive telemedicine specialist recommendation method that incorporates specialist activity and patient utility feedback from the perspective of privacy protection to fill the research gaps. Methods: First, text vectorization, view similarity and probabilistic topic model are used to construct the patient and specialist feature models based on patients’ EMRs and specialists’ long- and short-term knowledge backgrounds, respectively. Second, the recommended specialist candidate set and recommendation index are obtained based on the similarity between patient features. Then, the specialist long-term knowledge feature model is used to update the newly registered specialist recommendation index and the recommended specialist candidate set to overcome the data sparsity and cold-start issues, and the specialist short-term knowledge feature model is adopted to extend the recommended specialist candidate set at the semantic level. Finally, we introduce the specialists’ activity and patients’ perceived utility feedback mechanism to construct a closed-loop adjusted and optimized specialist recommendation method. Results: An empirical study was conducted integrating EMRs of telemedicine patients from the National Telemedicine Center of China and specialists’ profiles and ratings from an online healthcare platform. The proposed method successfully recommended relevant and active telemedicine specialists to the target patient, and increased the recommended opportunities for newly registered specialists to some extent. Conclusions: The proposed method emphasizes the adaptability and acceptability of the recommended results while ensuring their accuracy and relevance. Specialists’ activity and patients’ perceived utility jointly contribute to the acceptability of recommended results, and the recommendation strategy achieves the organic fusion of the two. Several comparative experiments demonstrate the effectiveness and operability of the hybrid recommendation strategy under the premise of data sparsity and privacy protection, enabling effective matching of patients’ demand and service providers’ capabilities, and providing beneficial insights for data-driven telemedicine services.

A systematic literature review of individual-level psychological and behavioral responses to the health information of COVID-19 from social media and legacy media (preprint)

Covid-19 has been recognized as a terrifying global health threat since its detection, with far-reaching consequences that are unprecedented in the modern era. Since the outbreak of the pandemic, social media and legacy media have collectively delivered health information related to COVID-19 to the public as a catalyst to community perception of risk. However, the existing literature exhibits different viewpoints toward the role of social media and legacy media in disseminating health information of COVID-19. In this regard, this article conducted a systematic literature review to provide an overview of the current state of research concerning individuals-level psychological and behavioral response to COVID-19 related information from different sources, as well as presents the challenges and future research directions.

Detection of Respiratory Infections Using RGB-Infrared Sensors on Portable Device

Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronaviruses 2 (SARS-CoV-2) has become a serious global pandemic in the past few months and caused huge loss to human society worldwide. For such a large-scale pandemic, early detection and isolation of potential virus carriers is essential to curb the spread of the pandemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the pandemic, many people tend to wear masks to reduce the risk of getting sick. Therefore, in this paper, we propose a portable non-contact method to screen the health conditions of people wearing masks through analysis of the respiratory characteristics from RGB-infrared sensors. We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with an attention mechanism is applied to the respiratory data to obtain the health screening result. The results of validation experiments show that our model can identify the health status of respiratory with 83.69% accuracy, 90.23% sensitivity and 76.31% specificity on the real-world dataset. This work demonstrates that the proposed RGB-infrared sensors on portable device can be used as a pre-scan method for respiratory infections, which provides a theoretical basis to encourage controlled clinical trials and thus helps fight the current COVID-19 pandemic. The demo videos of the proposed system are available at: https://doi.org/10.6084/m9.figshare.12028032.