Comparative transcriptomic analyzes of human lung epithelial cells infected with wild-type SARS-CoV-2 and its variant with a 12-bp missing in the E gene

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that caused a global outbreak of coronavirus disease 2019 (COVID-19) pandemic. To elucidate the mechanism of SARS-CoV-2 replication and immunogenicity, we performed a comparative transcriptome profile of mRNA and long non-coding RNAs (lncRNAs) in human lung epithelial cells infected with the SARS-CoV-2 wild-type strain (8X) and the variant with a 12-bp deletion in the E gene (F8). In total, 3,966 differentially expressed genes (DEGs) and 110 differentially expressed lncRNA (DE-lncRNA) candidates were identified. Of these, 94 DEGs and 32 DE-lncRNAs were found between samples infected with F8 and 8X. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyzes revealed that pathways such as the TNF signaling pathway and viral protein interaction with cytokine and cytokine receptor were involved. Furthermore, we constructed a lncRNA-protein-coding gene co-expression interaction network. The KEGG analysis of the co-expressed genes showed that these differentially expressed lncRNAs were enriched in pathways related to the immune response, which might explain the different replication and immunogenicity properties of the 8X and F8 strains. These results provide a useful resource for studying the pathogenesis of SARS-CoV-2 variants.

Alexithymia and post-traumatic stress disorder symptoms in Chinese undergraduate students during the COVID-19 national lockdown: The mediating role of sleep problems and the moderating role of self-esteem

Objective This study examined whether sleep disturbance was a mediator between alexithymic traits and post-traumatic stress disorder (PTSD) COVID-19 pandemic-related stress symptoms, and explored whether self-esteem moderated the alexithymic contribution to poor sleep and PTSD symptoms. Method A representative sample of young adults (N = 2,485) from six universities in Southwest China completed online self-report surveys on alexithymia, sleep, PTSD, self-esteem, sociodemographic information, and health-related behaviors. Results High alexithymic young adults were found to be more likely to have higher sleep problems and higher PTSD symptoms. The moderated mediation model showed that sleep problems mediated the associations between alexithymia and PTSD symptoms. Alexithymic people with lower self-esteem were more likely to have elevated PTSD symptoms and sleep problems than those with higher self-esteem. Conclusion Targeted psychological interventions for young people who have difficulty expressing and identifying emotions are recommended as these could assist in reducing their post-traumatic psychophysical and psychological problems. Improving self-esteem could also offer some protection for trauma-exposed individuals.

Quantifying the rebound of influenza epidemics after relaxing nonpharmaceutical interventions during the coronavirus disease 2019 pandemic in China (preprint)

Abstract The co-existence of coronavirus disease 2019 (COVID-19) and seasonal influenza epidemics has become a potential threat to human health, particularly in China in the oncoming season. However, with the relaxation of nonpharmaceutical interventions (NPIs) during the COVID-19 pandemic, the rebound extent of the influenza activities is still poorly understood. In this study, we constructed a susceptible-vaccinated-infectious-recovered-susceptible (SVIRS) model to simulate influenza transmission and calibrated it using influenza surveillance data from 2018 to 2022. We projected the influenza transmission over the next 3 years using the SVIRS model. We observed that, in epidemiological year 2021-2022, the reproduction numbers of influenza in southern and northern China were reduced by 38.6% and 30.2%, respectively, compared with those before the pandemic. The percentage of people susceptible to influenza virus increased by 138.6% and 57.3% in southern and northern China by October 1, 2022, respectively. After relaxing NPIs, the potential accumulation of susceptibility to influenza infection may lead to a large-scale and early influenza outbreak in the year 2022-2023, the scale of which may be affected by the intensity of the NPIs. And later relaxation of NPIs in the year 2023 would not lead to much larger rebound of influenza activities in the year 2023-2024. To control the influenza epidemic to the pre-pandemic level after relaxing NPIs, the influenza vaccination rates in southern and northern China should increase to 56.2% and 47.3%, respectively. Vaccination for influenza should be advocated to reduce the potential reemergence of the influenza epidemic in the next few years.

The Effectiveness of Governmental Nonpharmaceutical Interventions Against COVID-19 On The Control of Seasonal Influenza Transmission: An Ecological Study (preprint)

Background: A range of strict nonpharmaceutical interventions (NPIs) had been implemented in many countries to combat the COVID-19 pandemic. These NPIs might also be effective in controlling the seasonal influenza virus, which share the same transmission path with SARS-CoV-2. The aim of this study is to evaluate the effect of different NPIs for control of seasonal influenza. Methods: : Data on 14 NPIs implemented in 33 countries and corresponding data on influenza virologic surveillance were collected. The influenza suppression index was calculated as the difference between the influenza-positive rate during its decline period from 2019 to 2020 and that during influenza epidemic seasons in the previous 9 years. A machine learning model was developed by using extreme gradient boosting tree (XGBoost) regressor to fit NPI data and influenza suppression index. SHapley Additive exPlanations (SHAP) was used to characterize NPIs in suppressing influenza. Results: : Gathering limitation contributed the most (37.60%) among all NPIs in suppressing influenza transmission in the 2019-2020 influenza season. The top three effective NPIs were gathering limitation, international travel restriction, and school closure. Regarding the three NPIs, their intensity threshold to generate effect were restrictions on the size of gatherings less than 1000 people, travel bans on all regions or total border closure, and closing only some categories of schools, respectively. There was a strong positive interaction effect between mask wearing requirement and gathering limitation, whereas merely implementing mask wearing requirement but ignoring other NPIs would dilute mask wearing requirement’s effectiveness in suppressing influenza. Conclusions: : Gathering limitation, travel bans on all regions or total border closure, and closing some levels of schools are the most effective NPIs to suppress influenza transmission. Mask wearing requirement is advised to be combined with gathering limitation and other NPIs. Our findings could facilitate the precise control of future influenza epidemics and potential pandemics.

Treatment Effects of Integrated TCM and Western Medicine Treatment Scheme on COVID-19: A Single-armed Clinical Trial

Backgroud : The outbreak of COVID-19 has brought unprecedented perils to human health and raised public health concerns in more than two hundred countries. Safe and effective treatment scheme is needed urgently. Objective : To evaluate the effects of integrated TCM and western medicine treatment scheme on COVID-19. Methods : A single-armed clinical trial was carried out in Hangzhou Xixi Hospital, an affiliated hospital with Zhejiang Chinese Medical University. 102 confirmed cases were screened out from 725 suspected cases and 93 of them were treated with integrated TCM and western medicine treatment scheme. Results : 83 cases were cured, 5 cases deteriorated, and 5 cases withdrew from the study. No deaths were reported. The mean relief time of fever, cough, diarrhea, and fatigue were (4.78±4.61) days, (7.22±4.99) days, (5.28± 3.39) days, and (5.28± 3.39) days, respectively. It took (14.84±5.50) days for SARS-CoV-2 by nucleic acid amplification-based testing to turn negative. Multivariable cox regression analysis revealed that age, BMI, PISCT, BPC, AST, CK, BS, and UPRO were independent risk factors for COVID-19 treatment. Conclusion : Our study suggested that integrated TCM and western medicine treatment scheme was effective for COVID-19.

DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal (preprint)

ABSTRACT Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb ( https://drugcomb.org/ ) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here we report significant updates of DrugComb, including: 1) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; 2) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; 3) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample; and 4) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.

Effects of Hypertension on the Outcomes of COVID-19: A Multicenter Retrospective Cohort Study (preprint)

Hypertension is thought to be a contributor to mortality in coronavirus disease 2019 (COVID-19) patients; however, that view is controversial, and limited clinical data on the outcomes of COVID-19 in patients with hypertension are available. This study was designed to confirm whether hypertension affects the outcomes of COVID-19. A total of 983 patients with COVID-19 (female, 48%; male, 52%) were enrolled. The COVID-19 patients with hypertension (332, 34%) were older (median age, 72 years versus 58 years; P<0.001); had more comorbidities such as cardio-cerebrovascular diseases, liver and kidney damage; and had a greater inflammatory response than nonhypertensive patients (651, 66%). Significantly higher odds of 60-day mortality (p=0.017) were observed in the hypertensive group, but no significant difference in 28-day mortality (P=0.615) or total 60-day mortality (P=0.791) was observed after adjustment in multivariate analysis. In the hypertensive group, even after adjustment in multivariate analysis, the subgroup of patients 70 years old and older had higher 28-day mortality and total 60-day mortality rates than the other age subgroups (both p<0.05). A total of 297 (89%) COVID-19 patients with hypertension survived, and 35 (11%) died. In addition, compared with hypertensive patients who survived COVID-19, nonsurvivors had more preexisting conditions, including cardiovascular diseases and stroke, higher blood pressure on admission, more severe inflammation, and more liver and kidney damage.In conclusion, hypertension does not directly affect the in-hospital outcome of COVID-19. However, in the hypertensive population aged 70 years and older with COVID-19, the 28- and 60-day mortality rates were significantly elevated.Funding: This work was supported by grants from the PLA Logistics Research Project of China [18CXZ030, 17CXZ008].Declaration of Interests: None.Ethics Approval Statement: This study was approved by the Ethics Committee of the General Hospital of the Southern Theater Command (Number: Hospital Ethics [2020]-8), and the need to obtain informed consent was waived.

Minimal information for Chemosensitivity assays (MICHA): A next-generation pipeline to enable the FAIRification of drug screening experiments (preprint)

ABSTRACT Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of MICHA (Minimal Information for Chemosensitivity Assays), accessed via https://micha-protocol.org . Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents, and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets, and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies, as well as six recently conducted COVID-19 studies. With the MICHA webserver and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.

Drug Repurposing for COVID-19 using Graph Neural Network with Genetic, Mechanistic, and Epidemiological Validation (preprint)

Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but these efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug interactions, deep graph neural networks, and in-vitro/population-based validations. We first collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through CTDbase. We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug’s representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and electronic health records. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment. This paper had been uploaded to arXiv : https://arxiv.org/abs/2009.10931

Drug synergy of combinatory treatment with remdesivir and the repurposed drugs fluoxetine and itraconazole effectively impairs SARS-CoV-2 infection in vitro (preprint)

The SARS-COV-2 pandemic and the global spread of coronavirus disease 2019 (COVID-19) urgently calls for efficient and safe antiviral treatment strategies. A straightforward approach to speed up drug development at lower costs is drug repurposing. Here we investigated the therapeutic potential of targeting the host- SARS-CoV-2 interface via repurposing of clinically licensed drugs and evaluated their use in combinatory treatments with virus- and host-directed drugs. We tested the antiviral potential of repurposing the antifungal itraconazole and the antidepressant fluoxetine on the production of infectious SARS-CoV-2 particles in the polarized Calu-3 cell culture model and evaluated the added benefit of a combinatory use of these host-directed drugs with remdesivir, an inhibitor of viral RNA polymerase. Drug treatments were well-tolerated and potent impaired viral replication was observed with all drug treatments. Importantly, both itraconazole-remdesivir and fluoxetine-remdesivir combinations inhibited the production of infectious SARS-CoV-2 particles > 90% and displayed synergistic effects in commonly used reference models for drug interaction. Itraconazole-Remdesivir and Fluoxetine-Remdesivir combinations are promising therapeutic options to control SARS-CoV-2 infection and severe progression of COVID-19.

Drug Repurposing for COVID-19 using Graph Neural Network with Genetic, Mechanistic, and Epidemiological Validation (preprint)

Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but these efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug interactions, deep graph neural networks, and in-vitro/population-based validations. We first collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through CTDbase. We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and electronic health records. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment. This is a post-peer-review, pre-copyedit version of an article published in Scientific Reports The final authenticated version is available online at: https://www.researchsquare.com/article/rs-114758/v1

The Outcome Impact of Early vs Late HFNC Oxygen Therapy in Elderly Patients with COVID-19 and ARDS (preprint)

Coronavirus disease-2019 (COVID-19) has rapidly spread worldwide. High-flow nasal cannula therapy (HFNC) is a major oxygen supporting therapy for severely ill patients, but information regarding the timing of HFNC application is scarce, especially in elderly patients. We retrospectively analyzed the clinical data of 110 elderly patients ([≥]65 years) who received HFNC from Renmin Hospital of Wuhan University, People's Hospital of Xiantao City and Chinese Medicine Hospital of Shishou City in Hubei Province, China, and from Affiliated Hospital of Guangdong Medical University, People's Hospital of Yangjiang City, People's Hospital of Maoming City in Guangdong Province, China. Of the 110 patients, the median age was 71 years (IQR, 68-78) and 59.1% was male. Thirty-eight patients received HFNC when 200 mmHg < PO2/FiO2 [≤] 300 mmHg (early HFNC group), and 72 patients received HFNC treatment when 100 mmHg < PaO2/FiO2 [≤] 200 mmHg (late HFNC group). Compared with the late HFNC group, patients in the early HFNC group had a lower likelihood of developing severe ARDS, longer time from illness onset to severe ARDS and shorter duration of viral shedding after illness onset, as well as shorter lengths of ICU and hospital stay. Twenty-four patients died during hospitalization, of whom 22 deaths (30.6%) were in the late HFNC group and 2(5.3%) in the early HFNC group. It is concluded that the prognosis was better in severely ill elderly patients with COVID-19 receiving early compared to late HFNC. This suggests HFNC could be considered early in this disease process.