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 ).

The predicting roles of carcinoembryonic antigen and its underlying mechanism in the progression of coronavirus disease 2019 (preprint)

Background The coronavirus disease 2019 (COVID-19) has induced a worldwide pneumonia with a high infectivity and mortality. However, the predicting biomarkers and their potential mechanism in the progression of COVID-19 are not well known.Objective The aim of this study is to identify the candidate predictors of COVID-19 and investigate their underlying mechanism.Methods The retrospective study was conducted to identify the potential laboratory indicators with prognostic values of COVID-19 disease. Then, the prognostic nomogram was constructed to predict the overall survival of COVID-19 patients. Additionally, the scRNA-seq data of BALF and PBMCs from COVID-19 patients were downloaded to investigate the underlying mechanism of the most important prognostic indicators in lungs and peripherals, respectively.Results 304 hospitalized adult COVID-19 patients in Wuhan Jinyintan Hospital were included in the retrospective study. CEA was the only laboratory indicator with significant difference in the univariate (P < 0.001) and multivariate analysis (P = 0.021). The scRNA-seq data of BALF and PBMCs from COVID-19 patients were downloaded to investigate the underlying mechanism of CEA in lungs and peripherals, respectively. The results revealed the potential roles of CEA were significantly distributed in Type II pneumocytes of BALF and developing neutrophils of PBMCs, participating in the progression of COVID-19 by regulating the cell-cell communication.Conclusion This study identifies the prognostic roles of CEA in COVID-19 patients and implies the potential roles of CEACAM8-CEACAM6 in the progression of COVID-19 by regulating the cell-cell communication of developing neutrophils and Type II pneumocyte.

The Prediction for Development of COVID-19 in Global Major Epidemic Areas Through Empirical Trends in China by Utilizing State Transition Matrix Model (preprint)

Background: Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to its high transmissibility. We aimed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from China. Methods: : Data from reports released by the National Health Commission of the People’s Republic of China (Dec 31, 2019 to Mar 5, 2020) and the World Health Organization (Jan 20, 2020 to Mar 5, 2020) were extracted as the training set and the data from Mar 6 to 9 as the validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death were collected and analyzed. We analyzed the data above through the State Transition Matrix model. Results: : The optimistic scenario (non-Hubei model, daily increment rate of -3.87%), the cautiously optimistic scenario (Hubei model, daily increment rate of -2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of -1.50%) were inferred and modeling from data in China. The IFP of time in South Korea would be Mar 6 to 12, Italy Mar 10 to 24, and Iran Mar 10 to 24. The numbers of cumulative confirmed patients will reach approximately 20k in South Korea, 209k in Italy, and 226k in Iran under fitting scenarios, respectively. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be earlier than predicted above. Conclusion: The end of the pandemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to curb the development of COVID-19.

Corticosteroid therapy for corona virus disease 2019-related acute respiratory distress syndrome: a cohort study with propensity score analysis (preprint)

Background: The impact of corticosteroid therapy on outcomes of patients with Coronavirus disease-2019 (COVID-19) is highly controversial. We aimed to compare the risk of death between COVID-19-related ARDS patients with corticosteroid treatment and those without. Methods In this single-centre retrospective observational study, patients with ARDS caused by COVID-19 between 24 December 2019 and 24 February 2020 were enrolled. The primary outcome was 60-day in-hospital death. The exposure was prescribed systemic corticosteroids or not. Time-dependent Cox regression models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for 60-day in-hospital mortality. Results A total of 382 patients including 226 (59.2%) patients who received systemic corticosteroids and 156 (40.8%) patients with standard treatment were analyzed. The maximum dose of corticosteroids was 80.0 (IQR 40.0–80.0) mg equivalent methylprednisolone per day, and duration of corticosteroid treatment was 7.0 (4.0–12.0) days in total. In Cox regression analysis using corticosteroid treatment as a time-varying variable, corticosteroid treatment was associated with a significant reduction in risk of in-hospital death within 60 days (HR, 0.48; 95% CI, 0.25, 0.93; p  = 0.0285). The association remained significantly after adjusting for age, sex, Sequential Organ Failure Assessment score at hospital admission, propensity score of corticosteroid treatment, and comorbidities (HR: 0.51; CI: 0.27, 0.99; p  = 0.0471). Corticosteroids were not associated with delayed viral RNA clearance in our cohort. Conclusion In this clinical practice setting, low-to-moderate dose corticosteroid treatment was associated with reduced risk of death in COVID-19 patients who developed ARDS.

Corticosteroid Therapy of Patients in ICU with Coronavirus Disease 2019(COVID-19): A Single-center, Retrospective, Observational study (preprint)

The authors have withdrawn this preprint due to author disagreement.

Supporting Real-Time COVID-19 Medical Management Decisions: The Transition Matrix Model Approach (preprint)

Since the onset of the COVID-19 outbreak in Wuhan, China, numerous forecasting models have been proposed to project the trajectory of coronavirus infection cases. We propose a new discrete-time Markov chain transition matrix model that directly incorporates stochastic behavior and for which parameter estimation is straightforward from available data. Using such data from China's Hubei province (for which Wuhan is the provincial capital city), the model is shown to be flexible, robust, and accurate. As a result, it has been adopted by the first Shanghai assistance medical team in Wuhan's Jinyintan Hospital, which was the first designated hospital to take COVID-19 patients in the world. The forecast has been used for preparing medical staff, intensive care unit (ICU) beds, ventilators, and other critical care medical resources and for supporting real-time medical management decisions. Empirical data from China's first two months (January/February) of fighting COVID-19 was collected and used to enhance the model by embedding NPI efficiency into the model. We applied the model to forecast Italy, South Korea, and Iran on March 9. Later we made forecasts for Spain, Germany, France, US on March 24. Again, the model has performed very well, proven to be flexible, robust, and accurate for most of these countries/regions outside China.

Construction and Validation of a Machine Learning-based Nomogram: A TOOL to Predict the Risk of Getting Severe Corona Virus Disease 2019 (COVID-19) (preprint)

Background Identifying patients who may develop severe coronavirus disease 2019 (COVID-19) will facilitate personalized treatment and optimize the distribution of medical resources.Methods In this study, 590 COVID-19 patients during hospitalization were enrolled (Training set: n = 285; Internal validation set: n = 127; Prospective set: n = 178). After filtered by 2 machine learning methods in the training set, 5 out of 31 clinical features were selected into model building to predict the risk of developing severe COVID-19 disease. Multivariate logistic regression was applied to build the prediction nomogram and validated in 2 different sets. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used to evaluate its performance.Results From 31 potential predictors in the training set, 5 independent predictive factors were identified and included in the risk score: C-reactive protein (CRP), Lactate dehydrogenase (LDH), Age, Charlson/Deyo comorbidity score (CDCS) and Erythrocyte sedimentation rate (ESR). Subsequently, we generated the nomogram based on the above features for predicting severe COVID-19. In the training cohort, the Area under curves (AUCs) were 0.822 (95% CI 0.765–0.875) and the internal validation cohort was 0.762 (95% CI 0.768–0.844). Further, we validated it in a prospective cohort with the AUCs of 0.705 (95% CI 0.627–0.778). The internally bootstrapped calibration curve showed favorable consistency between prediction by nomogram and actual situation. And DCA analysis also conferred high clinical net benefit.Conclusion In this study, our predicting model based on 5 clinical characteristics of COVID-19 patients will enable clinicians to predict the potential risk of developing critical illness and thus optimize medical management.

Immune environment modulation in pneumonia patients caused by coronavirus: SARS-CoV, MERS-CoV and SARS-CoV-2.

Currently, we are on a global pandemic of Coronavirus disease-2019 (COVID-19) which causes fever, dry cough, fatigue and acute respiratory distress syndrome (ARDS) that may ultimately lead to the death of the infected. Current researches on COVID-19 continue to highlight the necessity for further understanding the virus-host synergies. In this study, we have highlighted the key cytokines induced by coronavirus infections. We have demonstrated that genes coding interleukins (Il-1α, Il-1ß, Il-6, Il-10), chemokine (Ccl2, Ccl3, Ccl5, Ccl10), and interferon (Ifn-α2, Ifn-ß1, Ifn2) upsurge significantly which in line with the elevated infiltration of T cells, NK cells and monocytes in SARS-Cov treated group at 24 hours. Also, interleukins (IL-6, IL-23α, IL-10, IL-7, IL-1α, IL-1ß) and interferon (IFN-α2, IFN2, IFN-γ) have increased dramatically in MERS-Cov at 24 hours. A similar cytokine profile showed the cytokine storm served a critical role in the infection process. Subsequent investigation of 463 patients with COVID-19 disease revealed the decreased amount of total lymphocytes, CD3+, CD4+, and CD8+ T lymphocytes in the severe type patients which indicated COVID-19 can impose hard blows on human lymphocyte resulting in lethal pneumonia. Thus, taking control of changes in immune factors could be critical in the treatment of COVID-19.

The Prediction for Development of COVID-19 in Global Major Epidemic Areas Through Empirical Trends in China by Utilizing State Transition Matrix Model (preprint)

Background: Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to high transmissibility. We managed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from Hubei and non-Hubei in China. Methods: We extracted data from reports released by the National Health Commission of the People's Republic of China (Dec 31, 2019 to Mar 5, 2020) and World Health Organization (Jan 20, 2020 to Mar 5, 2020) as the training set to deduce the arrival of the IFP of new cases in Hubei and non-Hubei on subsequent days and the data from Mar 6 to Mar 9 as validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death data were collected and analyzed. Using this state transition matrix model, the horizon of the IFP of time (the rate of new increment reaches zero) could be predicted in South Korean, Italy, and Iran. Also, through this model, the global trend of the epidemic will be decoded to allocate international medical resources better and instruct the strategy for quarantine. Results: the optimistic scenario (non-Hubei model, daily increment rate of -3.87%), the relative pessimistic scenario (Hubei model, daily increment rate of -2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of -1.50%) were inferred and modeling from data in China. Matching and fitting with these scenarios, the IFP of time in South Korea would be Mar 6-Mar 12, Italy Mar 10-Mar 24, and Iran is Mar 10-Mar 24. The numbers of cumulative confirmed patients will reach approximately 20k in South Korea, 209k in Italy, and 226k in Iran under fitting scenarios, respectively. There should be room for improvement if these metrics continue to improve. In that case, the IFP will arrive earlier than our estimation. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be higher than predicted above. Conclusion: We can affirm that the end of the burst of the epidemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to manipulate the development of COVID-19.

Heart injury signs are associated with higher and earlier mortality in coronavirus disease 2019 (COVID-19) (preprint)

Importance: Heart injury can be easily induced by viral infection such as adenovirus and enterovirus. However, whether coronavirus disease 2019 (COVID-19) causes heart injury and hereby impacts mortality has not yet been fully evaluated. Objective: To explore whether heart injury occurs in COVID-19 on admission and hereby aggravates mortality later. Design, Setting, and Participants A single-center retrospective cohort study including 188 COVID-19 patients admitted from December 25, 2019 to January 27, 2020 in Wuhan Jinyintan Hospital, China; follow up was completed on February 11, 2020. Exposures: High levels of heart injury indicators on admission (hs-TNI; CK; CK-MB; LDH; -HBDH). Main Outcomes and Measures: Mortality in hospital and days from admission to mortality (survival days). Results: Of 188 patients with COVID-19, the mean age was 51.9 years (standard deviation: 14.26; range: 21~83 years) and 119 (63.3%) were male. Increased hs-TnI levels on admission tended to occur in older patients and patients with comorbidity (especially hypertension). High hs-TnI on admission ([≥] 6.126 pg/mL), even within the clinical normal range (0~28 pg/mL), already can be associated with higher mortality. High hs-TnI was associated with increased inflammatory levels (neutrophils, IL-6, CRP, and PCT) and decreased immune levels (lymphocytes, monocytes, and CD4+ and CD8+ T cells). CK was not associated with mortality. Increased CK-MB levels tended to occur in male patients and patients with current smoking. High CK-MB on admission was associated with higher mortality. High CK-MB was associated with increased inflammatory levels and decreased lymphocytes. Increased LDH and -HBDH levels tended to occur in older patients and patients with hypertension. Both high LDH and -HBDH on admission were associated with higher mortality. Both high LDH and -HBDH were associated with increased inflammatory levels and decreased immune levels. hs-TNI level on admission was negatively correlated with survival days (r= -0.42, 95% CI= -0.64~-0.12, P=0.005). LDH level on admission was negatively correlated with survival days (r= -0.35, 95% CI= -0.59~-0.05, P=0.022). Conclusions and Relevance: Heart injury signs arise in COVID-19, especially in older patients, patients with hypertension and male patients with current smoking. COVID-19 virus might attack heart via inducing inflammatory storm. High levels of heart injury indicators on admission are associated with higher mortality and shorter survival days. COVID-19 patients with signs of heart injury on admission must be early identified and carefully managed by cardiologists, because COVID-19 is never just confined to respiratory injury.

Utilize State Transition Matrix Model to Predict the Novel Corona Virus Infection Peak and Patient Distribution (preprint)

Background: Since December 2019, a pneumonia caused by the 2019 novel coronavirus (2019-nCoV) has broken out in Wuhan, Hubei province, China. The continuous rising of infected cases has imposed overwhelming pressure on public health decision and medical resource allocation in China. We managed to forecast the infection peak time in Hubei province and the severe and critical case distribution. Methods: We used data resource according to cases reported by the National Health Commission of the People's Republic of China (Jan 25, 2019, to Feb 28, 2020) as the training set to deduce the arrival of the peak infection time and the number of severe and critical cases in Wuhan on subsequent days. Medical observation, discharge, infected, non-Severe, infected and severe, cure and death data were collected and analyzed. Using this state transition matrix model, we will be able predict when the inflection peak time (the maximum open infection cases) in Hubei Province will occur. Also, we can use this model to predict the patient distribution (severe, non-severe) to better allocate medical resource. Under relative pessimistic scenario, the inflection peak time is April 6-April 14. The numbers of critically ill and critically ill patients will lie between 8300-9800 and 2200-2700, respectively. Results: In very optimistic scenarios (daily NCC decay rate of -10%), the peak time of open inflection cases will arrive around February 23-February 26. At the same time, there will be a peak in the numbers of severely ill and critically ill patients, between 6800-7200 and 1800-2000, respectively. In a relative optimistic scenario (daily NCC decay rate of -5%), the inflection case peak time will arrive around February 28-March 2. The numbers of critically ill and critically ill patients will lie between 7100-7800 and 1900-2200, respectively. In a relatively pessimistic scenario (daily NCC decay rate of -1%), the inflection peak time does not arrive around the end of March. Estimated time is April 6-April 14. The numbers of critically ill and critically ill patients will lie between 8300-9800 and 2200-2700, respectively. We are using the diagnosis rate, mortality rate, cure rate as the 2/8 data. There should be room for improvement, if these metrics continue to improve. In that case, the peak time will arrive earlier than our estimation. Also, the severe and critical case ratios are likely to decline as the virus becomes less toxic and medical conditions improve. If that happens, the peak numbers will be lower than predicted above. Conclusion: We can infer that we are still not close to the end of this outbreak and the number of critically ill patients is still climbing. Assisting critical care resources in Hubei province requires the government to consider further tilt, and it is vital to make reasonable management of doctors and medical assistance systems to curb the transmission trend.