Inhibitory effects of GT0918 on acute lung injury and the molecular mechanisms of anti-inflammatory response (preprint)

Coronavirus disease 2019 (COVID-19) has caused the public health crisis in the whole world. Anti-androgens block severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entry and protect against severe clinical COVID-19 outcomes. GT0918, a second-generation androgen receptor antagonist, accelerated viral clearance and increased recovery rate in outpatients, reduced mortality rate and shortened hospital stay in hospitalized COVID-19 patients. GT0918 also had an effective trend for severe COVID-19 patient treatment. But the mechanism of GT0918 treatment for severe COVID-19 patient is unknown. Here, we found GT0918 decreased the expression and secretion of proinflammatory cytokines through NF-{kappa}B signaling. The acute lung injury induced by LPS or Poly(I:C) was also attenuated in GT0918-treated mice. Moreover, GT0918 increased the NRF2 protein level. GT0918 induced proinflammatory cytokines downregulation was partially dependent on NRF2. In conclusion, our data showed GT0918 reduced cytokine release and suppressed inflammatory responses through inhibiting NF-{kappa}B signaling and activating NRF2. GT0918 is not only effective for treatment of mild to moderate COVID-19 patients, but also a potential therapeutic drug for severe COVID-19 patients.

Invariant in variants (preprint)

The coronavirus Covid-19 mutates quickly in the pandemic, leaves people struggling to verify and improve the effectiveness of the vaccine based on biochemistry. Is there any physical invariant in the variants of such kind of pathogen that could be taken advantage to ease the tensions? To this point, extensive numerical experiments based on continuity mechanics were carried out to discover the vibration modes and the range of natural frequency of coronavirus Covid-19. Such invariant could help us in developing some flexible technique to deactivate the coronavirus, like as resonantly breaking the viral spike by ultrasound wave. The fundamental mechanisms governing such process are demonstrated via solving the coupled equations of acoustics and dynamics and thereafter the technique strategies proposed to efficiently realize the concept.

A Retrospective Analysis of COVID-19 mRNA Vaccine Breakthrough Infections - Risk Factors and Vaccine Effectiveness (preprint)

Importance: Little is known about COVID vaccine breakthrough infections and their risk factors. Objective: To identify risk factors associated with COVID19 breakthrough infections among vaccinated individuals and to reassess the effectiveness of COVID19 vaccination against severe outcomes using realworld data. Design, Setting, and Participants: We conducted a series of observational retrospective analyses using the electronic health records (EHRs) of Columbia University Irving Medical Center/New York Presbyterian (CUIMC/NYP) up to September 21, 2021. New York adult residence with PCR test records were included in this analysis. Main Outcomes and Measures: Poisson regression was used to assess the association between breakthrough infection rate in vaccinated individuals and multiple risk factors, including vaccine brand, demographics, and underlying conditions while adjusting for calendar month, prior number of visits and observational days. Logistic regression was used to assess the association between vaccine administration and infection rate by comparing a vaccinated cohort to a historically matched cohort in the pre-vaccinated period. Infection incident rate was also compared between vaccinated individuals and longitudinally matched unvaccinated individuals. Cox regression was used to estimate the association of the vaccine and COVID19 associated severe outcomes by comparing breakthrough cohort and two matched unvaccinated infection cohorts. Results: Individuals vaccinated with Pfizer/BNT162b2 (IRR against Moderna/mRNA1273 [95% CI]: 1.66 [1.17 - 2.35]); were male (1.47 [1.11 - 1.94%]); and had compromised immune systems (1.48 [1.09 - 2.00]) were at the highest risk for breakthrough infections. Vaccinated individuals had a significant lower infection rate among all subgroups. An increased incidence rate was found in both vaccines over the time. Among individuals infected with COVID19, vaccination significantly reduced the risk of death (adj. HR: 0.20 [0.08 - 0.49]). Conclusion and Relevance: While we found both mRNA vaccines were effective, Moderna/mRNA1273 had a lower incidence rate of breakthrough infections. Both vaccines had increased incidence rates over the time. Immunocompromised individuals were among the highest risk groups experiencing breakthrough infections. Given the rapidly changing nature of the SARSCoV2, continued monitoring and a generalizable analysis pipeline are warranted to inform quick updates on vaccine effectiveness in real time.

COHD-COVID: Columbia Open Health Data for COVID-19 Research (preprint)

Massive research efforts have been made in response to the COVID-19 (coronavirus disease-2019) pandemic. Utilization of clinical data can accelerate these research efforts to fight against the pandemic since important characteristics of the patients are often found by examining the clinical data. To provide shareable clinical data to catalyze COVID-19 research, we present Columbia Open Health Data for COVID-19 Research (COHD-COVID), a publicly accessible database providing clinical concept prevalence, clinical concept co-occurrence, and clinical symptom prevalence for hospitalized COVID-19 patients. COHD-COVID also provides data on hospitalized influenza patients and general hospitalized patients as comparator cohorts. The data used in COHD-COVID were obtained from Columbia University Irving Medical Centers electronic health records. We expect COHD-COVID will provide researchers and clinicians quantitative measures of COVID-19 related clinical features to better understand and fight against the pandemic.

Severity Prediction for COVID-19 Patients via Recurrent Neural Networks (preprint)

The novel coronavirus disease-2019 (COVID-19) pandemic has threatened the health of tens of millions of people worldwide and posed enormous burden on global healthcare systems. In this paper, we propose a model to predict whether a patient infected with COVID-19 will develop severe outcomes based only on the patient's historical electronic health records (EHR) using recurrent neural networks (RNN). The predicted severity risk score represents the probability for a person to progress into severe status (mechanical ventilation, tracheostomy, or death) after being infected with COVID-19. While many of the existing models use features obtained after diagnosis of COVID-19, our proposed model only utilizes a patient's historical EHR to enable proactive risk management at the time of hospital admission

Psychological distress surveillance and related impact analysis of hospital staff during the COVID-19 epidemic in Chongqing, China.

BACKGROUND: Hospital staff are vulnerable and at high risk of novel coronavirus disease (COVID-19) infection. The aim of this study was to monitor the psychological distress in hospital staff and examine the relationship between the psychological distress and possible causes during the COVID-19 epidemic. METHODS: An online survey was conducted from February 1 to February 14, 2020. Hospital staff from five national COVID-19 designated hospitals in Chongqing participated. Data collected included demographics and stress responses to COVID-19: 1) the impact of event scale to measure psychological stress reactions; 2) generalizedanxietydisorder 7 to measure anxiety symptoms; 3) Patient Health Questionnaire 9 to measure depression symptoms; 4) Yale-Brown Obsessive-Compulsive Scale to measure obsessive-compulsive symptoms (OCS); and 5) Patient Health Questionnaire 15 to measure somatization symptoms. Multiple logistic regression analysis was used to identify factors that were correlated with psychological distress. RESULTS: Hospital staff that participated in this study were identified as either doctors or nurses. A total of 456 respondents completed the questionnaires with a response rate of 91.2%. The mean age was 30.67 ± 7.48 years (range, 17 to 64 years). Of all respondents, 29.4% were men. Of the staff surveyed, 43.2% had stress reaction syndrome. The highest prevalence of psychological distress was OCS (37.5%), followed by somatization symptoms (33.3%), anxiety symptoms (31.6%), and depression symptoms (29.6%). Univariate analyses indicated that female subjects, middle aged subjects, subjects in the low income group, and subjects working in isolation wards were prone to experience psychological distress. Multiple logistic regression analysis showed "Reluctant to work or considered resignation" (odds ratio [OR], 5.192; 95%CI, 2.396-11.250; P < .001), "Afraid to go home because of fear of infecting family" (OR, 2.099; 95%CI, 1.299-3.391; P = .002) "Uncertainty about frequent modification of infection and control procedures" (OR, 1.583; 95%CI, 1.061-2.363; P = .025), and"Social support" (OR, 1.754; 95%CI, 1.041-2.956; P = .035) were correlated with psychological reactions. "Reluctant to work or considered resignation" and "Afraid to go home because of fear of infecting family" were associated with a higher risk of symptoms of Anxiety (OR, 3.622; 95% CI, 1.882-6.973; P < .001; OR, 1.803; 95% CI, 1.069-3.039; P = .027), OCS (OR, 5.241; 95% CI, 2.545-10.793; P < .001; OR, 1.999; 95% CI, 1.217-3.282; P = .006) and somatization (OR, 5.177; 95% CI, 2.595-10.329; P < .001; OR, 1.749; 95% CI, 1.051-2.91; P = .031). "Stigmatization and rejection in neighborhood because of hospital work", "Reluctant to work or considered resignation" and "Uncertainty about frequent modification of infection and control procedures" were associated with a higher risk of symptoms of Depression(OR, 2.297; 95% CI, 1.138-4.637; P = .020; OR, 3.134; 95% CI, 1.635-6.006; P = .001; OR, 1.645; 95% CI, 1.075-2.517; P = .022). CONCLUSIONS: Hospital staff showed different prevalence of psychological distress during the COVID-19 epidemic. Our study confirmed the severity of negative psychological distress on hospital staff and identified factors associated with negative psychological distress that can be used to provide valuable information for psychological interventions to improve the mental health of vulnerable groups during the COVID-19 epidemic.