Development of quantitative wastewater surveillance models facilitated the precise epidemic management of COVID-19.

Wastewater surveillance serves as a promising approach to elucidate the silent transmission of SARS-CoV-2 in communities. To understand the decay of the coronavirus in sewage pipes, the decay of the coronavirus traveling over 20 km distance of pipeline was analyzed. Based on the decay model, a WWTP and a community model were then proposed for predicting COVID-19 cases in Xi'an and Nanchang city during the COVID-19 outbreak in 2021 and 2022. The results suggested that Monte Carol simulations estimated 23.3, 50.1, 127.3 and 524.2 infected persons in the Yanta district of Xi'an city on December 14th, 18th, 22nd and 26th of 2021, respectively, which is largely consistent with the clinical reports. Next, we further conducted wastewater surveillance in two WWTPs that covered the whole metropolitan region in Nanchang to validate the robustness of the WWTP model from December 2021 to April 2022. SARS-CoV-2 signals were detected in two WWTPs from March 15th to April 5th. Predicted infection numbers were in agreement with the actual infection cases, which promoted precise epidemic control. Finally, community wastewater surveillance was conducted for 40 communities that were not 100 % covered by massive nucleic acid testing in Nanchang city, which accurately identified the SARS-CoV-2 carriers not detected by massive nucleic acid testing. In conclusion, accurate prediction of COVID-19 cases based on WWTP and community models promoted precise epidemic control. This work highlights the viability of wastewater surveillance for outbreak evaluation and identification of hidden cases, which provides an extraordinary example for implementing precise epidemic control of COVID-19.

Development of quantitative wastewater surveillance models facilitated the precise epidemic management of COVID-19

Wastewater surveillance serves as a promising approach to elucidate the silent transmission of SARS-CoV-2 in communities. To understand the decay of the coronavirus in sewage pipes, the decay of the coronavirus traveling over 20 km distance of pipeline was analyzed. Based on the decay model, a WWTP and a community model were then proposed for predicting COVID-19 cases in Xi'an and Nanchang city during the COVID-19 outbreak in 2021 and 2022. The results suggested that Monte Carol simulations estimated 23.3, 50.1, 127.3 and 524.2 infected persons in the Yanta district of Xi'an city on December 14th, 18th, 22nd and 26th of 2021, respectively, which is largely consistent with the clinical reports. Next, we further conducted wastewater surveillance in two WWTPs that covered the whole metropolitan region in Nanchang to validate the robustness of the WWTP model from December 2021 to April 2022. SARS-CoV-2 signals were detected in two WWTPs from March 15th to April 5th. Predicted infection numbers were in agreement with the actual infection cases, which promoted precise epidemic control. Finally, community wastewater surveillance was conducted for 40 communities that were not 100 % covered by massive nucleic acid testing in Nanchang city, which accurately identified the SARS-CoV-2 carriers not detected by massive nucleic acid testing. In conclusion, accurate prediction of COVID-19 cases based on WWTP and community models promoted precise epidemic control. This work highlights the viability of wastewater surveillance for outbreak evaluation and identification of hidden cases, which provides an extraordinary example for implementing precise epidemic control of COVID-19. Graphical Unlabelled Image

[Clinical observation of Xuanfei Baidu Decoction in treatment of severe coronavirus disease 2019 (COVID-19)].

The present study evaluated the effectiveness and safety of Xuanfei Baidu Decoction(XFBD) for severe cases with coronavirus disease 2019(COVID-19).Forty-one patients(diagnosed as severe or critical type) admitted to Hubei Provincial Hospital of Integrated Chinese and Western Medicine and Wuhan Hospital of Traditional Chinese Medicine from February 1 to March 1, 2020, were included.All patients were treated with XFBD based on conventional therapies.Clinical outcomes, length of hospital stay, and lung CT images of patients were observed.Laboratory indicators were compared between admission and the 14 th day of treatment.Traditional Chinese medicine(TCM) symptoms and signs on the 7 th and 14 th days of treatment were also compared with baseline.The differences in clinical characteristics and clinical outcomes between XFBD and western medicine or conventional therapies were analyzed with the published trials on severe COVID-19 cases during the same period as external controls.According to the results, among the 41 cases, 40 were cured and discharged, and 1 died; the median length of hospital stay was 22 days, and the improvement rate of lung CT was 87.2%(34/39).Compared with the conditions on admission, the levels of white blood cells(WBC), C-reactive protein(CRP), fibrinogen(FIB), and lactate dehydrogenase(LDH) were reduced(P<0.05, P<0.01), and levels of procalcitonin(PCT), prothrombin time(PT), creatine kinase(CK), alanine aminotransferase(AST), total bilirubin(TBiL), and other indicators showed a downward trend.Moreover, symptoms like fever, cough, chest tightness/shortness of breath, dyspnea, head and body pain, anorexia, and greasy tongue coating were significantly improved on the 7 th and 14 th days of treatment(P<0.05, P<0.01), and fatigue was improved on the 14 th day of treatment(P<0.01).The mortality, adverse reactions, and major events of the XFBD group were significantly lower than those of the western medicine and conventional treatment groups in the same period, and the usage of antibiotics, hormones, vasopressin, and invasive mechanical ventilation during treatment were generally less than other groups.In conclusion, XFBD has good efficacy and safety in the treatment of severe COVID-19 cases by improving inflammation and clinical symptoms, promoting the absorption of lung inflammation, and reducing mortality.

The Impact of the Closure of the Live Poultry Market due to COVID-19 on the Avian Influenza Virus in Nanchang, Jiangxi Province, China.

This article aims to understand the changes in the detection rates of H5, H7, and H9 subtypes of avian influenza viruses (AIVs) in the live poultry markets (LPMs) in Nanchang City, Jiangxi Province, before and after the outbreak of the COVID-19. From 2019 to 2020, we monitored the LPM and collected specimens, using real-time reverse transcription polymerase chain reaction technology to detect the nucleic acid of type A AIV in the samples. The H5, H7, and H9 subtypes of influenza viruses were further classified for positive results. We analyzed 1,959 samples before and after the outbreak and found that the positive rates of avian influenza A virus (39.69%) and H9 subtype (30.66%) after the outbreak were significantly higher than before the outbreak (26.84% and 20.90%, respectively; P < 0.001). In various LPMs, the positive rate of H9 subtypes has increased significantly (P ≤ 0.001). Positive rates of the H9 subtype in duck, fecal, daub, and sewage samples, but not chicken samples, have increased to varying degrees. This study shows that additional measures are needed to strengthen the control of AIVs now that LPMs have reopened after the relaxing of COVID-19-related restrictions.

Effect of Bacillus licheniformis capsule on the cytokines level and clinical prognosis of COVID-19 patients

Objective: To investigate the drug of Bacillus licheniformis Capsule (BLC) to improve the high levels of cytokines and clinical prognosis in COVID-19 patients, and to provide the clinical evidence for gut microflora therapy in COVID-19.

Suppressed T cell-mediated immunity in patients with COVID-19: A clinical retrospective study in Wuhan, China.

IMPORTANCE: An ongoing outbreak of COVID-19 has exhibited significant threats around the world. We found a significant decrease of T lymphocyte subsets and an increase of inflammatory cytokines of hospitalized patients with COVID-19 in clinical practice. METHODS: We conducted a retrospective, single-center observational study of in-hospital adult patients with confirmed COVID-19 in Hubei Provincial Hospital of traditional Chinese and Western medicine (Wuhan, China) by Mar 1, 2020. Demographic, clinical, laboratory information, especially T lymphocyte subsets and inflammatory cytokines were reported. For patients who died or discharge from hospital, the associations of T lymphocyte subsets on admission were evaluated by univariate logistic regression with odds ratios (ORs) and 95% confidence intervals (CIs), warning values to predict in-hospital death were assessed by Receiver Operator Characteristic (ROC) curves. RESULTS: A total of 187 patients were enrolled in our study from Dec 26, 2019 to Mar 1, 2020, of whom 145 were survivors (discharge = 117) or non-survivors (in-hospital death ==28). All patients exhibited a significant drop of T lymphocyte subsets counts with remarkably increasing concentrations of SAA, CRP, IL-6, and IL-10 compared to normal values. The median concentrations of SAA and CRP in critically-ill patients were nearly 4- and 10-fold than those of mild-ill patients, respectively. As the severity of COVID-19 getting worse, the counts of T lymphocyte drop lower.28 patients died in hospital, the median lymphocyte, CD3+ T-cell, CD4+ T-cell, CD8+ T-cell and B-cell were significantly lower than other patients. Lower counts (/uL) of T lymphocyte subsets lymphocyte (<500), CD3+T-cell (<200), CD4+ T-cell (<100), CD8+ T-cell (<100) and B-cell (<50) were associated with higher risks of in-hospital death of CIVID-19. The warning values to predict in-hospital death of lymphocyte, CD3+ T-cell, CD4+ T-cell, CD8+ T-cell, and B-cell were 559, 235, 104, 85 and 82, respectively. CONCLUSION: We find a significant decrease of T lymphocyte subset is positively correlated with in-hospital death and severity of illness. The decreased levels of T lymphocyte subsets reported in our study were similar with SARS but not common among other virus infection, which may be possible biomarkers for early diagnosis of COVID-19. Our findings may shed light on early warning of high risks of mortality and help early intervention and treatment of COVID-19.