Therapeutic drug combinations against COVID-19 obtained by employing a collaborative filtering method

The outbreak of coronavirus disease 2019 (COVID-19) has severely harmed human society and health. Because there is currently no specific drug for the treatment and prevention of COVID-19, we used a collaborative filtering algorithm to predict which traditional Chinese medicines (TCMs) would be effective in combination for the prevention and treatment of COVID-19. First, we performed drug screening based on the receptor structure prediction method, molecular docking using q-vina to measure the binding ability of TCMs, TCM formulas, and neo-coronavirus proteins, and then performed synergistic filtering based on Laplace matrix calculations to predict potentially effective TCM formulas. Combining the results of molecular docking and synergistic filtering, the new recommended formulas were analyzed by reviewing data platforms or tools such as PubMed, Herbnet, the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the Guide to the Dispensing of Medicines for Clinical Evidence, and the Dictionary of Chinese Medicine Formulas, as well as medical experts' treatment consensus in terms of herbal efficacy, modern pharmacological studies, and clinical identification and typing of COVID-19 pneumonia, to determine the recommended solutions. We found that the therapeutic effect of a combination of six TCM formulas on the COVID-19 virus is the result of the overall effect of the formula rather than that of specific components of the formula. Based on this, we recommend a formula similar to that of Jinhua Qinggan Granules for the treatment of COVID-19 pneumonia. This study may provide new ideas and new methods for future clinical research. Classification Biological Science.

Therapeutic drug combinations against COVID-19 obtained by employing a collaborative filtering method.

The outbreak of coronavirus disease 2019 (COVID-19) has severely harmed human society and health. Because there is currently no specific drug for the treatment and prevention of COVID-19, we used a collaborative filtering algorithm to predict which traditional Chinese medicines (TCMs) would be effective in combination for the prevention and treatment of COVID-19. First, we performed drug screening based on the receptor structure prediction method, molecular docking using q-vina to measure the binding ability of TCMs, TCM formulas, and neo-coronavirus proteins, and then performed synergistic filtering based on Laplace matrix calculations to predict potentially effective TCM formulas. Combining the results of molecular docking and synergistic filtering, the new recommended formulas were analyzed by reviewing data platforms or tools such as PubMed, Herbnet, the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the Guide to the Dispensing of Medicines for Clinical Evidence, and the Dictionary of Chinese Medicine Formulas, as well as medical experts' treatment consensus in terms of herbal efficacy, modern pharmacological studies, and clinical identification and typing of COVID-19 pneumonia, to determine the recommended solutions. We found that the therapeutic effect of a combination of six TCM formulas on the COVID-19 virus is the result of the overall effect of the formula rather than that of specific components of the formula. Based on this, we recommend a formula similar to that of Jinhua Qinggan Granules for the treatment of COVID-19 pneumonia. This study may provide new ideas and new methods for future clinical research. Classification: Biological Science.

Signaling repurposable drug combinations against COVID-19 by developing the heterogeneous deep herb-graph method.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spurred a boom in uncovering repurposable existing drugs. Drug repurposing is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication. MOTIVATION: Current works of drug repurposing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are mostly limited to only focusing on chemical medicines, analysis of single drug targeting single SARS-CoV-2 protein, one-size-fits-all strategy using the same treatment (same drug) for different infected stages of SARS-CoV-2. To dilute these issues, we initially set the research focusing on herbal medicines. We then proposed a heterogeneous graph embedding method to signaled candidate repurposing herbs for each SARS-CoV-2 protein, and employed the variational graph convolutional network approach to recommend the precision herb combinations as the potential candidate treatments against the specific infected stage. METHOD: We initially employed the virtual screening method to construct the 'Herb-Compound' and 'Compound-Protein' docking graph based on 480 herbal medicines, 12,735 associated chemical compounds and 24 SARS-CoV-2 proteins. Sequentially, the 'Herb-Compound-Protein' heterogeneous network was constructed by means of the metapath-based embedding approach. We then proposed the heterogeneous-information-network-based graph embedding method to generate the candidate ranking lists of herbs that target structural, nonstructural and accessory SARS-CoV-2 proteins, individually. To obtain precision synthetic effective treatments forvarious COVID-19 infected stages, we employed the variational graph convolutional network method to generate candidate herb combinations as the recommended therapeutic therapies. RESULTS: There were 24 ranking lists, each containing top-10 herbs, targeting 24 SARS-CoV-2 proteins correspondingly, and 20 herb combinations were generated as the candidate-specific treatment to target the four infected stages. The code and supplementary materials are freely available at https://github.com/fanyang-AI/TCM-COVID19.

Machine Learning Applications in Drug Repurposing.

The coronavirus disease (COVID-19) has led to an rush to repurpose existing drugs, although the underlying evidence base is of variable quality. Drug repurposing is a technique by taking advantage of existing known drugs or drug combinations to be explored in an unexpected medical scenario. Drug repurposing, hence, plays a vital role in accelerating the pre-clinical process of designing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repurposing depends on massive observed data from existing drugs and diseases, the tremendous growth of publicly available large-scale machine learning methods supplies the state-of-the-art application of data science to signaling disease, medicine, therapeutics, and identifying targets with the least error. In this article, we introduce guidelines on strategies and options of utilizing machine learning approaches for accelerating drug repurposing. We discuss how to employ machine learning methods in studying precision medicine, and as an instance, how machine learning approaches can accelerate COVID-19 drug repurposing by developing Chinese traditional medicine therapy. This article provides a strong reasonableness for employing machine learning methods for drug repurposing, including during fighting for COVID-19 pandemic.

Signaling Potential Therapeutic Herbal Medicine Prescription for Treating COVID-19 by Collaborative Filtering

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has aggressed in more than 200 countries and territories since Dec 2019, and 30 million cases of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 have been reported, including 950,000 deaths. Supportive treatment remains the mainstay of therapy for COVID-19. There are no small-molecule–specific antiviral drugs available to prevent and treat COVID-19 until recently. Herbal medicine can facilitate syndrome differentiation and treatment according to the clinical manifestations of patients and has demonstrated effectiveness in epidemic prevention and control. The National Health Commission (NHC) of China has recommended “three TCM prescriptions and three medicines,” as a group of six effective herbal formulas against COVID-19 in the released official file “Diagnosis and Treatment Protocol for COVID-19 Patients: Herbal Medicine for the Priority Treatment of COVID-19.” This study aimed to develop a collaborative filtering approach to signaling drug combinations that are similar to the six herbal formulas as potential therapeutic treatments for treating COVID-19. The results have been evaluated by herbal medicine experts’ domain knowledge.

Predictors of Shift Work Sleep Disorder Among Nurses During the COVID-19 Pandemic: A Multicenter Cross-Sectional Study.

Background: Nurses have a high incidence of shift work sleep disorder, which places their health and patient safety in danger. Thus, exploring the factors associated with shift work sleep disorder in nurses is of great significance in improving their sleep health, nursing personnel staffing, and scheduling during the COVID-19 pandemic. Objectives: The purpose of this study was to investigate the incidence of shift work sleep disorder during the COVID-19 pandemic and explore the factors associated with shift work sleep disorder in Chinese nurses. Methods: This was a multicenter cross-sectional study using an online survey. Stratified cluster sampling was used to include 4,275 nurses from 14 hospitals in Shandong, China from December 2020 to June 2021. Stepwise multivariate logistic regression analysis and random forest were used to identify the factors associated with shift work sleep disorder. Results: The prevalence of shift work sleep disorder in the sampled shift nurses was 48.5% during the COVID-19 pandemic. Physical fatigue, psychological stress, shift work more than 6 months per year, busyness during night shift, working more than 40 h per week, working more than four night shifts per month, sleeping more than 8 h before night shift, using sleep medication, irregular meals, and high-intensity physical activity were associated with increased odds of shift work sleep disorder. Good social support, good work-family balance, napping two or three times per week, resting more than one day after shifts, intervals of 8 days or more between shifts, and taking turns to rest during the night shift were associated with decreased odds of shift work sleep disorder. Conclusions: Shift work sleep disorder may be associated with scheduling strategies and personal behavior during the COVID-19 pandemic. To reduce the incidence of shift work sleep disorders in nurses, nursing managers should increase night shift staffing, extend rest days after shift, increase night shift spacing, and reduce overtime, and nurses need to seek more family and social support and control their sleep schedules and diet.

SeRenDIP-CE: Sequence-based Interface Prediction for Conformational Epitopes.

MOTIVATION: Antibodies play an important role in clinical research and biotechnology, with their specificity determined by the interaction with the antigen's epitope region, as a special type of protein-protein interaction (PPI) interface. The ubiquitous availability of sequence data, allows us to predict epitopes from sequence in order to focus time-consuming wet-lab experiments towards the most promising epitope regions. Here, we extend our previously developed sequence-based predictors for homodimer and heterodimer PPI interfaces to predict epitope residues that have the potential to bind an antibody. RESULTS: We collected and curated a high quality epitope dataset from the SAbDab database. Our generic PPI heterodimer predictor obtained an AUC-ROC of 0.666 when evaluated on the epitope test set. We then trained a random forest model specifically on the epitope dataset, reaching AUC 0.694. Further training on the combined heterodimer and epitope datasets, improves our final predictor to AUC 0.703 on the epitope test set. This is better than the best state-of-the-art sequence-based epitope predictor BepiPred-2.0. On one solved antibody-antigen structure of the COVID19 virus spike RNA binding domain, our predictor reaches AUC 0.778. We added the SeRenDIP-CE Conformational Epitope predictors to our webserver, which is simple to use and only requires a single antigen sequence as input, which will help make the method immediately applicable in a wide range of biomedical and biomolecular research. AVAILABILITY: Webserver, source code and datasets at www.ibi.vu.nl/programs/serendipwww/.

Epidemic and control of COVID-19 in Niger: quantitative analyses in a least developed country.

BACKGROUND: The COVID-19 pandemic is challenging the public health response system worldwide, especially in poverty-stricken, war-torn, and least developed countries (LDCs). METHODS: We characterized the epidemiological features and spread dynamics of COVID-19 in Niger, quantified the effective reproduction number (Rt ), evaluated the impact of public health control measures, and estimated the disease burden. RESULTS: As of 4 July 2020, COVID-19 has affected 29 communes of Niger with 1093 confirmed cases, among whom 741 (67.8%) were males. Of them 89 cases died, resulting in a case fatality rate (CFR) of 8.1%. Both attack rates and CFRs were increased with age (P < 0.0001). Health care workers accounted for 12.8% cases. Among the reported cases, 39.3% were isolated and treated at home, and 42.3% were asymptomatic. 74.6% cases were clustered in Niamey, the capital of Niger. The Rt fluctuated in correlation to control measures at different outbreak stages. After the authorities initiated the national response and implemented the strictest control measures, Rt quickly dropped to below the epidemic threshold (<1), and maintained low level afterward. The national disability-adjusted life years attributable to COVID-19 was 1267.38 years in total, of which years of life lost accounted for over 99.1%. CONCLUSIONS: Classic public health control measures such as prohibition of public gatherings, travelling ban, contact tracing, and isolation and quarantine at home, are proved to be effective to contain the outbreak in Niger, and provide guidance for controlling the ongoing COVID-19 pandemic in LDCs.

The Effects of Diabetes and Hypertension on the Severity of COVID-19 - Yichang, Hubei Province, 2020.

What is already known on this topic? COVID-19 has become a serious public health issue. A higher proportion of severe patients were senior patients with underlying diseases such as diabetes and hypertension and had a lack of statistical evidence so far. What is added by this report? When severe illness was compared with non-severe illness, senior patients were at a greater risk (4.71) than young and middle-aged patients, as well as the odds ratio was about 2.99 patients with diabetes compared to patients without diabetes and hypertension. COVID-19-infectious senior patients with diabetes were inclined to suffer severe illness. What are the implications for public health practice? Much more attention should be provided for the elderly and individuals with diabetes, for which a community-based education and surveillance program could be considered.

Analysis of the global situation of COVID-19 research based on bibliometrics.

With the rapid global spread of the COVID-19 pandemic, researchers have contributed several important advances. The WHO and countries with severe outbreaks have developed diagnosis and treatment guidelines. Here, we analyze the current transformation and application of scientific research to global epidemic prevention and control. We described and analyzed current COVID-19 research from the perspectives of international cooperation, interdisciplinary cooperation, and research hotspots using a bibliometric clustering algorithm. Using the diagnosis and treatment guidelines of the WHO and the United States and China as examples, we evaluate the transformation of scientific results from basic research to applications. Scientific research results that have not yet been incorporated into these guidelines are summarized to encourage updates and improvements by applying scientific research to prevention and control. COVID-19 has fostered interdisciplinary cooperative research, and the current results are mainly focused on the origin, epidemiological characteristics, clinical research, and diagnosis and treatment methods for the virus. Due to the ongoing publication of new research, diagnosis and treatment guidelines are constantly improving. However, some research gaps still exist, and some results have not yet been incorporated into the guidelines. The current research is still in the preliminary exploratory stage, and some problems, such as weak international cooperation, unbalanced interdisciplinary cooperation, and the lack of coordination between research and applications, exist. Therefore, countries around the world must improve the International Public Health Emergency Management System and prepare for major public health emergencies in the future.

Impacts of transportation and meteorological factors on the transmission of COVID-19.

The ongoing pandemic of 2019 novel coronavirus disease (COVID-19) is challenging global public health response system. We aim to identify the risk factors for the transmission of COVID-19 using data on mainland China. We estimated attack rate (AR) at county level. Logistic regression was used to explore the role of transportation in the nationwide spread. Generalized additive model and stratified linear mixed-effects model were developed to identify the effects of multiple meteorological factors on local transmission. The ARs in affected counties ranged from 0.6 to 9750.4 per million persons, with a median of 8.8. The counties being intersected by railways, freeways, national highways or having airports had significantly higher risk for COVID-19 with adjusted odds ratios (ORs) of 1.40 (p = 0.001), 2.07 (p < 0.001), 1.31 (p = 0.04), and 1.70 (p < 0.001), respectively. The higher AR of COVID-19 was significantly associated with lower average temperature, moderate cumulative precipitation and higher wind speed. Significant pairwise interactions were found among above three meteorological factors with higher risk of COVID-19 under low temperature and moderate precipitation. Warm areas can also be in higher risk of the disease with the increasing wind speed. In conclusion, transportation and meteorological factors may play important roles in the transmission of COVID-19 in mainland China, and could be integrated in consideration by public health alarm systems to better prevent the disease.