Primary and booster vaccination in reducing severe clinical outcomes associated with Omicron Naïve infection.

BACKGROUND: Little is known about long-term effectiveness of COVID-19 vaccine in reducing severity and deaths associated with Omicron VOC not perturbed by prior infection and independent of oral anti-viral therapy and non-pharmaceutical (NPI). METHODS: A retrospective observational cohort study was applied to Taiwan community during the unprecedent large-scale outbreaks of Omicron BA.2 between April and August, 2022. Primary vaccination since March, 2021 and booster vaccination since January, 2022 were offered on population level. Oral Anti-viral therapy was also offered as of mid-May 2022. The population-based effectiveness of vaccination in reducing the risk of moderate and severe cases of and death from Omicron BA.2 with the consideration of NPI and oral anti-viral therapy were assessed by using Bayesian hierarchical models. RESULTS: The risks of three clinical outcomes associated with Omicron VOC infection were lowest for booster vaccination, followed by primary vaccination, and highest for incomplete vaccination with the consistent trends of being at increased risk for three outcomes from the young people aged 12 years or below until the elderly people aged 75 years or older with 7 age groups. Before the period using oral anti-viral therapy, complete primary vaccination with the duration more than 9 months before outbreaks conferred the statistically significant 47 % (23-64 %) reduction of death, 48 % (30-61 %) of severe disease, and 46 % (95 % CI: 37-54 %) of moderate disease after adjusting for 10-20 % independent effect of NPI. The benefits of booster vaccination within three months were further enhanced to 76 % (95 % CI: 67-86 %), 74 % (95 % CI: 67-80 %), and 61 % (95 % CI: 56-65 %) for three corresponding outcomes. The additional effectiveness of oral anti-viral therapy in reducing moderate disease was 13 % for the booster group and 5.8 % for primary vaccination. CONCLUSIONS: We corroborated population effectiveness of primary vaccination and its booster vaccination, independent of oral anti-viral therapy and NPI, in reducing severe clinical outcomes associated with Omicron BA.2 naïve infection population.

Primary and Booster Vaccination in Reducing Severe Clinical Outcomes Associated with Omicron Naïve Infection

Background Little is known about long-term effectiveness of COVID-19 vaccine in reducing severity and deaths associated with Omicron VOC not perturbed by prior infection and independent of oral anti-viral therapy and non-pharmaceutical (NPI). Methods A retrospective observational cohort study was applied to Taiwan community during the unprecedent large-scale outbreaks of Omicron BA.2 between April and August, 2022. Primary vaccination since March, 2021 and booster vaccination since January, 2022 were offered on population level. Oral Anti-viral therapy was also offered as of mid-May 2022. The population-based effectiveness of vaccination in reducing the risk of moderate and severe cases of and death from Omicron BA.2 with the consideration of NPI and oral anti-viral therapy were assessed by using Bayesian hierarchical models. Results The risks of three clinical outcomes associated with Omicron VOC infection were lowest for booster vaccination, followed by primary vaccination, and highest for incomplete vaccination with the consistent trends of being at increased risk for three outcomes from the young people aged 12 years or below until the elderly people aged 75 years or older with 7 age groups. Before the period using oral anti-viral therapy, complete primary vaccination with the duration more than 9 months before outbreaks conferred the statistically significant 47% (23-64%) reduction of death, 48% (30-61%) of severe disease, and 46% (95% CI: 37-54%) of moderate disease after adjusting for 10-20% independent effect of NPI. The benefits of booster vaccination within three months were further enhanced to 76% (95% CI: 67-86%), 74% (95% CI: 67-80%), and 61% (95% CI: 56-65%) for three corresponding outcomes. The additional effectiveness of oral anti-viral therapy in reducing moderate disease was 13% for the booster group and 5.8% for primary vaccination. Conclusions We corroborated population effectiveness of primary vaccination and its booster vaccination, independent of oral anti-viral therapy and NPI, in reducing severe clinical outcomes associated with Omicron BA.2 naïve infection population.

Hippo signaling pathway activation during SARS-CoV-2 infection contributes to host antiviral response.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), responsible for the Coronavirus Disease 2019 (COVID-19) pandemic, causes respiratory failure and damage to multiple organ systems. The emergence of viral variants poses a risk of vaccine failures and prolongation of the pandemic. However, our understanding of the molecular basis of SARS-CoV-2 infection and subsequent COVID-19 pathophysiology is limited. In this study, we have uncovered a critical role for the evolutionarily conserved Hippo signaling pathway in COVID-19 pathogenesis. Given the complexity of COVID-19-associated cell injury and immunopathogenesis processes, we investigated Hippo pathway dynamics in SARS-CoV-2 infection by utilizing COVID-19 lung samples and human cell models based on pluripotent stem cell-derived cardiomyocytes (PSC-CMs) and human primary lung air-liquid interface (ALI) cultures. SARS-CoV-2 infection caused activation of the Hippo signaling pathway in COVID-19 lung and in vitro cultures. Both parental and Delta variant of concern (VOC) strains induced Hippo pathway. The chemical inhibition and gene knockdown of upstream kinases MST1/2 and LATS1 resulted in significantly enhanced SARS-CoV-2 replication, indicating antiviral roles. Verteporfin, a pharmacological inhibitor of the Hippo pathway downstream transactivator, YAP, significantly reduced virus replication. These results delineate a direct antiviral role for Hippo signaling in SARS-CoV-2 infection and the potential for this pathway to be pharmacologically targeted to treat COVID-19.

Molecular and cellular similarities in the brain of SARS-CoV-2 and Alzheimer's disease individuals (preprint)

Infection with the etiological agent of COVID-19, SARS-CoV-2, appears capable of impacting cognition, which some patients with Post-acute Sequelae of SARS-CoV-2 (PASC). To evaluate neuro-pathophysiological consequences of SARS-CoV-2 infection, we examine transcriptional and cellular signatures in the Broadman area 9 (BA9) of the frontal cortex and the hippocampal formation (HF) in SARS-CoV-2, Alzheimers disease (AD) and SARS-CoV-2 infected AD individuals, compared to age- and gender-matched neurological cases. Here we show similar alterations of neuroinflammation and blood-brain barrier integrity in SARS-CoV-2, AD, and SARS-CoV-2 infected AD individuals. Distribution of microglial changes reflected by the increase of Iba-1 reveal nodular morphological alterations in SARS-CoV-2 infected AD individuals. Similarly, HIF-1 is significantly upregulated in the context of SARS-CoV-2 infection in the same brain regions regardless of AD status. The finding may help to inform decision-making regarding therapeutic treatments in patients with neuro-PASC, especially those at increased risk of developing AD.

Impactful publications of critical care medicine research in China: A bibliometric analysis

Background Although publications have been increasing rapidly, the research quality has yet to improve in the field of critical care medicine (CCM) in China. This study aimed at investigating the current status of and the influential factors for impactful publications in CCM research by Chinese authors. Methods Publications by authors with the affiliation of critical care medicine department or intensive care unit (CCM/ICU) in Chinese as well as American hospitals from 2001 to 2020 were retrieved from the Web of Science Core Collection (WoSCC) database for this bibliometric analysis. Moreover, statistical analyses to test factors affecting impactful publications by Chinese authors were performed. Results Of 13,487 articles retrieved by this search strategy, 6,622 were published by Chinese authors as first or corresponding authors. The annual publications by Chinese authors have been rapidly increasing from 2001 to 2020, and so did the citations to these articles. However, the proportion in the world of publications by Chinese authors was much less than that by American authors each year [M (IQR): 1.85 (9.592) vs. 27.77 (7.3), p < 0.001]. In addition, impactful articles were significantly less published by Chinese than by American authors, including articles either in journals with a high impact factor (p < 0.001) or in the top 10 journals in the field of CCM (5.4 vs 13.4%, p < 0.001), and articles with high citation frequency as well (p < 0.001). Moreover, the percentage of impactful publications by Chinese authors was likely associated with academic background and regions of the author's affiliations, funds support, public health events of COVID-19, and collaboration between authors. Conclusion Our results demonstrated that CCM research in China grew rapidly in the recent 20 years. However, the impactful publications remained limited, largely owing to the shortage of comprehensive research training, inactive collaboration, and underfunded CCM research.

CSES Satellite Observations of 50 Hz Power Line Radiation Over Mainland China and Its Response to COVID‐19

We present a low‐altitude satellite survey of power line harmonic radiation (PLHR) at 50 Hz over Mainland China. We analyzed the month‐to‐month variation pattern in PLHR occurrence rate and further analyzed its correlation with some influencing factors (i.e., solar radiation, lightning flashes, and electricity consumption) using CSES satellite electric field data from 2019 to 2021. We also investigate the response of PLHR occurrence rate to COVID‐19. The statistical results show the dayside PLHR occurrence rate decreasing from winter to summer solstice and increasing from summer to winter solstice, which indicates it is controlled by the solar radiation. The nightside variation is more complex, which may be due to many sources that could influence the nightside lower ionosphere. The PLHR occurrence rate significantly decreased over Mainland China in February 2020, which is because of the significant decrease in electricity consumption due to the suspension of industrial production caused by COVID‐19.Alternate :Plain Language SummaryPower line harmonic radiation (PLHR) is the electromagnetic waves radiated by electric power systems at harmonic frequencies of 50 or 60 Hz, depending on the frequency of the system on the ground. Previous research mainly focuses on identification of individual PLHR events and their subsequent analysis. However, the number of base‐frequency PLHR signal events is the most abundant, which is suitable for the statistical study of PLHR occurrence rate and its variation pattern, and further study of the factors affecting its variation pattern. In this paper, we use 3 years of electric field data from the China Seismo‐Electromagnetic Satellite (CSES) which is an LEO satellite launched into orbit in February 2018 to investigate the month‐to‐month variation pattern of PLHR occurrence rate over Mainland China and its correlation with the influencing factors. The response of PLHR occurrence rate to COVID‐19 are also investigated.

Fair Assignment for Reserved Nucleic Acid Testing

Corona Virus Disease 2019 (COVID-19) is now treating the health of millions of people worldwide. The Chinese government now applies nucleic acid testing as a tool to detect patients from healthy people to control the spread of COVID-19. However, people may come to the nucleic acid testing stations simultaneously, leading to long queues and wasting time. In this paper, we proposed the reserved nucleic acid testing method, which could be easily implemented via Web applications associated with nucleic acid testing. Its key idea is to assign people to different pre-scheduled time slots so that the number of people arriving at a certain time slot can be controlled under the capacity, and thus congestion can be relieved. The key question is how to assign people in a fair manner. We propose a concise model to formalize and analyze the minimum total envy and pairwise fairness assignment problem for a variety of reservation-based applications, including nuclear acid testing. Its objective is to maximize the sum of each person's utility under the capacity constraints of time slots. The decision variables are the time slot assignment of each person. We show that the envy-freeness solution is usually unavailable. However, we can minimize the total envy through appropriate arrangements and realize pairwise fairness with equal-chance shuffling.

The interaction effects of congruence type, assessment perspective and social distance on visit intention

Purpose: The purpose of this study is to explore the three-way interaction effects among congruence type (proximal vs distal) of nonverbal ad messages, assessment perspective (internal vs external) of verbal ad messages and social distance (close vs faraway) on consumers' visit intention. Design/methodology/approach: After developing the four categories of restaurant advertisements and scenarios for each type of social distance, the authors used 780 observations collected from Chinese consumers via the online survey platform Sojump and WeChat. The authors conducted ANOVA to test the hypotheses. Findings: The results reveal that in proximal congruence situations, consumers who feel a close social distance between themselves and their companions report higher visit intentions when exposed to internal versus external perspective-oriented ad messages;in distal congruence situations, external perspective-oriented ad messages elicit higher intention to visit advertised restaurant when consumers feel a far social distance between themselves and their companions. Research limitations/implications: Future research can focus on the different categories of messages, such as functional and experiential messages, to find whether similar interaction effects are explored or not. Practical implications: This paper suggests some practical implications for advertisers to maximize the impact of advertisements on consumers' behavioral outcomes via combining the different characteristics of nonverbal and verbal messages effectively, especially according to their target consumers' characteristics. Originality/value: In the view of the three-way interaction effects, this paper offers a new lens on understanding how advertisements influence consumers' behavioral outcomes, which could contribute to the advancement of advertisement theories.

Simultaneous measurement of the antibody responses against SARS-CoV-2 and its multiple variants by a phage display mediated immuno-multiplex quantitative PCR-based assay.

To combat the continued pandemic of COVID-19, multiplex serological assays have been developed to comprehensively monitor the humoral immune response and help to design new vaccination protocols to different SARS-CoV-2 variants. However, multiplex beads and stably transfected cell lines require stringent production and storage conditions, and assays based on flow cytometry is time-consuming and its application is therefore restricted. Here, we describe a phage display system to distinguish the differences of immune response to antigenic domains of multiple SARS-CoV-2 variants simultaneously. Compared with linear peptides, the recombinant antigens displayed on the phage surface have shown some function that requires the correct folding to form a stable structure, and the binding efficiency between the recombinant phage and existing antibodies is reduced by mutations on antigens known to be important for antigen-antibody interaction. By using Phage display mediated immuno-multiplex quantitative PCR (Pi-mqPCR), the binding efficiency between the antibody and antigens of different SARS-CoV-2 variants can be measured in one amplification reaction. Overall, these data show that this assay is a valuable tool to evaluate the humoral response to the same antigen of different SARS-CoV-2 variants or antigens of different pathogens. Combined with high-throughput DNA sequencing technology, this phage display system can be further applied in monitoring humoral immune response in a large population before and after vaccination.

The prevalence and correlates of peripartum depression in different stages of pregnancy during COVID-19 pandemic in China.

BACKGROUND: Peripartum depression in and after pregnancy are common, reported by 11.9% of women worldwide, and the proportion was even higher during the outbreak of coronavirus disease 2019 (COVID-19). We aimed to investigate the prevalence and risk factors of peripartum depression under the influence of COVID-19 in China. METHODS: Using a cross-sectional design, 2026 pregnant and postpartum women residing in Beijing, Wuhan, and Lanzhou of China were recruited from February 28 to April 9, 2020. The Patient Health Questionnaire-9 was used to assess their depressive symptoms. The women were divided into four subgroups based on pregnancy stage, and a binary logistic regression analysis was conducted on each subgroup. RESULTS: Under the influence of COVID-19, the prevalence rate of peripartum depression among Chinese women was 9.7%. It was 13.6, 10.8, 7.9 and 7.3% in the first, second, third trimester and puerperium, respectively. Regression analysis showed that the influence of current pregnancy status on movement (Mild vs. No, aORs were 3.89, P < 0.001, 2.92, P = 0.003, 1.58, P = 0.150 in the three trimesters, respectively; Severe vs. No, aORs were 13.00, 20.45, 5.38 in the three trimesters, respectively, all P < 0.05), and worries and fears about childbirth (aORs were 2.46, 2.96, 2.50 in the three trimesters, respectively, all P < 0.05) were associated with depression throughout pregnancy. CONCLUSIONS: The prevalence rate of peripartum depression during the COVID-19 outbreak in China was not higher than usual. The influence of current pregnancy status on movement, as well as worries and fears about childbirth were independent risk factors for peripartum depression throughout pregnancy during COVID-19. The stage of pregnancy should be considered when implementing interventions.

Prediction of Online Psychological Help-Seeking Behavior During the COVID-19 Pandemic: An Interpretable Machine Learning Method.

Online mental health service (OMHS) has been named as the best psychological assistance measure during the COVID-19 pandemic. An interpretable, accurate, and early prediction for the demand of OMHS is crucial to local governments and organizations which need to allocate and make the decision in mental health resources. The present study aimed to investigate the influence of the COVID-19 pandemic on the online psychological help-seeking (OPHS) behavior in the OMHS, then propose a machine learning model to predict and interpret the OPHS number in advance. The data was crawled from two Chinese OMHS platforms. Linguistic inquiry and word count (LIWC), neural embedding-based topic modeling, and time series analysis were utilized to build time series feature sets with lagging one, three, seven, and 14 days. Correlation analysis was used to examine the impact of COVID-19 on OPHS behaviors across different OMHS platforms. Machine learning algorithms and Shapley additive explanation (SHAP) were used to build the prediction. The result showed that the massive growth of OPHS behavior during the COVID-19 pandemic was a common phenomenon. The predictive model based on random forest (RF) and feature sets containing temporal features of the OPHS number, mental health topics, LIWC, and COVID-19 cases achieved the best performance. Temporal features of the OPHS number showed the biggest positive and negative predictive power. The topic features had incremental effects on performance of the prediction across different lag days and were more suitable for OPHS prediction compared to the LIWC features. The interpretable model showed that the increase in the OPHS behaviors was impacted by the cumulative confirmed cases and cumulative deaths, while it was not sensitive in the new confirmed cases or new deaths. The present study was the first to predict the demand for OMHS using machine learning during the COVID-19 pandemic. This study suggests an interpretable machine learning method that can facilitate quick, early, and interpretable prediction of the OPHS behavior and to support the operational decision-making; it also demonstrated the power of utilizing the OMHS platforms as an always-on data source to obtain a high-resolution timeline and real-time prediction of the psychological response of the online public.

The Psychological and Behavioral Patterns of Online Psychological Help-Seekers before and during COVID-19 Pandemic: A Text Mining-Based Longitudinal Ecological Study.

Online mental health service (OMHS) platforms have contributed significantly to the public's mental health during the COVID-19 pandemic in China. However, it remains unclear why the public used OMHS platforms for psychological help-seeking (PHS) behavior and how PHS behavior varied across different stages of the COVID-19 pandemic. Based on the ecological PHS behavior data from two OMHS platforms, we extracted population, psychological problems, and influential factors of PHS behavior by text mining and time series analysis methods. Seven top-ranked psychological problems (i.e., depression and anxiety, lack of interest, suicidal tendencies, social phobia, feelings of being worried and afraid, suffering, anger) and seven influential factors (i.e., interpersonal relationships, love, family, work, psychotherapy, personal characteristics, marriage) were found. The online PHS behaviors related to different psychological problems and influential factors remained a growing trend before 2020 and have been increasing significantly due to the COVID-19 outbreak. Four main stages were found during the pandemic according to the changes in the online PHS population: sharp growth, significant decline, slight rebound, and slow decline. This study identified large-scale, spontaneous PHS behaviors among the online public during the COVID-19 pandemic and the various psychological problems and influential factors that varied across different stages of the pandemic, suggesting that the government and health practitioners should adopt effective policies and strategies to prevent and intervene in mental health problems for the online public.

"Help! I Need Somebody": Music as a Global Resource for Obtaining Wellbeing Goals in Times of Crisis.

Music can reduce stress and anxiety, enhance positive mood, and facilitate social bonding. However, little is known about the role of music and related personal or cultural (individualistic vs. collectivistic) variables in maintaining wellbeing during times of stress and social isolation as imposed by the COVID-19 crisis. In an online questionnaire, administered in 11 countries (Argentina, Brazil, China, Colombia, Italy, Mexico, the Netherlands, Norway, Spain, the UK, and USA, N = 5,619), participants rated the relevance of wellbeing goals during the pandemic, and the effectiveness of different activities in obtaining these goals. Music was found to be the most effective activity for three out of five wellbeing goals: enjoyment, venting negative emotions, and self-connection. For diversion, music was equally good as entertainment, while it was second best to create a sense of togetherness, after socialization. This result was evident across different countries and gender, with minor effects of age on specific goals, and a clear effect of the importance of music in people's lives. Cultural effects were generally small and surfaced mainly in the use of music to obtain a sense of togetherness. Interestingly, culture moderated the use of negatively valenced and nostalgic music for those higher in distress.

Burnout and Its Relationship With Depressive Symptoms in Medical Staff During the COVID-19 Epidemic in China.

OBJECTIVE: The large-scale epidemic of Coronavirus Disease 2019 (COVID-19) has triggered unprecedented physical and psychological stress on health professionals. This study aimed to investigate the prevalence and risk factors of burnout syndrome, and the relationship between burnout and depressive symptoms among frontline medical staff during the COVID-19 epidemic in China. METHODS: A total of 606 frontline medical staff were recruited from 133 cities in China using a cross-sectional survey. The Maslach Burnout Inventory (MBI) was used to assess the level of burnout. Depressive symptoms were assessed by the Patient Health Questionnaire Depression (PHQ-9). RESULTS: During the COVID-19 pandemic, 36.5% of the medical staff experienced burnout. Personal and work-related factors were independently associated with burnout, including age (OR = 0.68, 95% CI: 0.52-0.89, p = 0.004), family income (OR = 0.72, 95% CI: 0.53-0.99, p = 0.045), having physical diseases (OR = 2.16, 95% CI: 1.42-3.28, p < 0.001), daily working hours (OR = 1.35, 95% CI: 1.03-1.77, p = 0.033), and profession of nurse (OR = 2.14, 95% CI: 1.12-4.10, p = 0.022). The correlation coefficients between the scores of each burnout subscale and the scores of depressive symptoms were 0.57 for emotional exhaustion, 0.37 for cynicism, and -0.41 for professional efficacy (all p < 0.001). CONCLUSIONS: Our findings suggest that the prevalence rate of burnout is extremely high among medical staff during the COVID-19 pandemic, which is associated with other psychological disorders, such as depression. Psychological intervention for medical staff is urgently needed. Young and less experienced medical staff, especially nurses, should receive more attention when providing psychological assistance.

Development and validation of a prognostic nomogram for predicting in-hospital mortality of COVID-19: a multicenter retrospective cohort study of 4086 cases in China.

To establish an effective nomogram for predicting in-hospital mortality of COVID-19, a retrospective cohort study was conducted in two hospitals in Wuhan, China, with a total of 4,086 hospitalized COVID-19 cases. All patients have reached therapeutic endpoint (death or discharge). First, a total of 3,022 COVID-19 cases in Wuhan Huoshenshan hospital were divided chronologically into two sets, one (1,780 cases, including 47 died) for nomogram modeling and the other (1,242 cases, including 22 died) for internal validation. We then enrolled 1,064 COVID-19 cases (29 died) in Wuhan Taikang-Tongji hospital for external validation. Independent factors included age (HR for per year increment: 1.05), severity at admission (HR for per rank increment: 2.91), dyspnea (HR: 2.18), cardiovascular disease (HR: 3.25), and levels of lactate dehydrogenase (HR: 4.53), total bilirubin (HR: 2.56), blood glucose (HR: 2.56), and urea (HR: 2.14), which were finally selected into the nomogram. The C-index for the internal resampling (0.97, 95% CI: 0.95-0.98), the internal validation (0.96, 95% CI: 0.94-0.98), and the external validation (0.92, 95% CI: 0.86-0.98) demonstrated the fair discrimination ability. The calibration plots showed optimal agreement between nomogram prediction and actual observation. We established and validated a novel prognostic nomogram that could predict in-hospital mortality of COVID-19 patients.

Metabolic reprogramming and epigenetic changes of vital organs in SARS-CoV-2-induced systemic toxicity.

Extrapulmonary manifestations of COVID-19 are associated with a much higher mortality rate than pulmonary manifestations. However, little is known about the pathogenesis of systemic complications of COVID-19. Here, we create a murine model of SARS-CoV-2-induced severe systemic toxicity and multiorgan involvement by expressing the human ACE2 transgene in multiple tissues via viral delivery, followed by systemic administration of SARS-CoV-2. The animals develop a profound phenotype within 7 days with severe weight loss, morbidity, and failure to thrive. We demonstrate that there is metabolic suppression of oxidative phosphorylation and the tricarboxylic acid (TCA) cycle in multiple organs with neutrophilia, lymphopenia, and splenic atrophy, mirroring human COVID-19 phenotypes. Animals had a significantly lower heart rate, and electron microscopy demonstrated myofibrillar disarray and myocardial edema, a common pathogenic cardiac phenotype in human COVID-19. We performed metabolomic profiling of peripheral blood and identified a panel of TCA cycle metabolites that served as biomarkers of depressed oxidative phosphorylation. Finally, we observed that SARS-CoV-2 induces epigenetic changes of DNA methylation, which affects expression of immune response genes and could, in part, contribute to COVID-19 pathogenesis. Our model suggests that SARS-CoV-2-induced metabolic reprogramming and epigenetic changes in internal organs could contribute to systemic toxicity and lethality in COVID-19.

Mental healthcare for psychiatric inpatients during the COVID-19 epidemic.

During this 2019 coronavirus disease (COVID-19) pneumonia epidemic, some experts have expressed concern for the mental healthcare of different types of population groups. However, hospitalised patients with severe mental illness are seemingly overlooked. Psychiatric patients are still a vulnerable group who need to obtain more attention and respect, particularly during the COVID-19 outbreak in China. In this commentary, we briefly introduce the situation of hospitalized patients with severe mental illness and suggest some effective measures that should be rapidly undertaken to reverse current challenges.

Network reinforcement driven drug repurposing for COVID-19 by exploiting disease-gene-drug associations (preprint)

Currently, the number of patients with COVID-19 has significantly increased. Thus, there is an urgent need for developing treatments for COVID-19. Drug repurposing, which is the process of reusing already-approved drugs for new medical conditions, can be a good way to solve this problem quickly and broadly. Many clinical trials for COVID-19 patients using treatments for other diseases have already been in place or will be performed at clinical sites in the near future. Additionally, patients with comorbidities such as diabetes mellitus, obesity, liver cirrhosis, kidney diseases, hypertension, and asthma are at higher risk for severe illness from COVID-19. Thus, the relationship of comorbidity disease with COVID-19 may help to find repurposable drugs. To reduce trial and error in finding treatments for COVID-19, we propose building a network-based drug repurposing framework to prioritize repurposable drugs. First, we utilized knowledge of COVID-19 to construct a disease-gene-drug network (DGDr-Net) representing a COVID-19-centric interactome with components for diseases, genes, and drugs. DGDr-Net consisted of 592 diseases, 26,681 human genes and 2,173 drugs, and medical information for 18 common comorbidities. The DGDr-Net recommended candidate repurposable drugs for COVID-19 through network reinforcement driven scoring algorithms. The scoring algorithms determined the priority of recommendations by utilizing graph-based semi-supervised learning. From the predicted scores, we recommended 30 drugs, including dexamethasone, resveratrol, methotrexate, indomethacin, quercetin, etc., as repurposable drugs for COVID-19, and the results were verified with drugs that have been under clinical trials. The list of drugs via a data-driven computational approach could help reduce trial-and-error in finding treatment for COVID-19.

Network reinforcement driven drug repurposing for COVID-19 by exploiting disease-gene-drug associations (preprint)

Currently, the number of patients with COVID-19 has significantly increased. Thus, there is an urgent need for developing treatments for COVID-19. Drug repurposing, which is the process of reusing already-approved drugs for new medical conditions, can be a good way to solve this problem quickly and broadly. Many clinical trials for COVID-19 patients using treatments for other diseases have already been in place or will be performed at clinical sites in the near future. Additionally, patients with comorbidities such as diabetes mellitus, obesity, liver cirrhosis, kidney diseases, hypertension, and asthma are at higher risk for severe illness from COVID-19. Thus, the relationship of comorbidity disease with COVID-19 may help to find repurposable drugs. To reduce trial and error in finding treatments for COVID-19, we propose building a network-based drug repurposing framework to prioritize repurposable drugs. First, we utilized knowledge of COVID-19 to construct a disease-gene-drug network (DGDr-Net) representing a COVID-19-centric interactome with components for diseases, genes, and drugs. DGDr-Net consisted of 592 diseases, 26,681 human genes and 2,173 drugs, and medical information for 18 common comorbidities. The DGDr-Net recommended candidate repurposable drugs for COVID-19 through network reinforcement driven scoring algorithms. The scoring algorithms determined the priority of recommendations by utilizing graph-based semi-supervised learning. From the predicted scores, we recommended 30 drugs, including dexamethasone, resveratrol, methotrexate, indomethacin, quercetin, etc., as repurposable drugs for COVID-19, and the results were verified with drugs that have been under clinical trials. The list of drugs via a data-driven computational approach could help reduce trial-and-error in finding treatment for COVID-19.