Qi-Dong-Huo-Xue-Yin balances the immune microenvironment to protect against LPS induced acute lung injury.

COVID-19 induces acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) and leads to severe immunological changes that threatens the lives of COVID-19 victims. Studies have shown that both the regulatory T cells and macrophages were deranged in COVID-19-induced ALI. Herbal drugs have long been utilized to adjust the immune microenvironment in ALI. However, the underlying mechanisms of herbal drug mediated ALI protection are largely unknown. This study aims to understand the cellular mechanism of a traditional Chinese medicine, Qi-Dong-Huo-Xue-Yin (QD), in protecting against LPS induced acute lung injury in mouse models. Our data showed that QD intrinsically promotes Foxp3 transcription via promoting acetylation of the Foxp3 promoter in CD4+ T cells and consequently facilitates CD4+CD25+Foxp3+ Tregs development. Extrinsically, QD stabilized ß-catenin in macrophages to expedite CD4+CD25+Foxp3+ Tregs development and modulated peripheral blood cytokines. Taken together, our results illustrate that QD promotes CD4+CD25+Foxp3+ Tregs development via intrinsic and extrinsic pathways and balanced cytokines within the lungs to protect against LPS induced ALI. This study suggests a potential application of QD in ALI related diseases.

Causal relationships between COVID-19 and osteoporosis: a two-sample Mendelian randomization study in European population.

Introduction: The causal relationship between Coronavirus disease 2019 (COVID-19) and osteoporosis (OP) remains uncertain. We aimed to assess the effect of COVID-19 severity (severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, COVID-19 hospitalization, and severe COVID-19) on OP by a two-sample Mendelian randomization (MR) study. Methods: We conducted a two-sample MR analysis using publicly available genome-wide association study (GWAS) data. Inverse variance weighting (IVW) was used as the main analysis method. Four complementary methods were used for our MR analysis, which included the MR-Egger regression method, the weighted median method, the simple mode method, and the weighted mode method. We utilized the MR-Egger intercept test and MR pleiotropy residual sum and outlier (MR-PRESSO) global test to identify the presence of horizontal pleiotropy. Cochran's Q statistics were employed to assess the existence of instrument heterogeneity. We conducted a sensitivity analysis using the leave-one-out method. Results: The primary results of IVW showed that COVID-19 severity was not statistically related to OP (SARS-CoV-2 infection: OR (95% CI) = 0.998 (0.995 ~ 1.001), p = 0.201403; COVID-19 hospitalization: OR (95% CI) =1.001 (0.999 ~ 1.003), p = 0.504735; severe COVID-19: OR (95% CI) = 1.000 (0.998 ~ 1.001), p = 0.965383). In addition, the MR-Egger regression, weighted median, simple mode and weighted mode methods showed consistent results. The results were robust under all sensitivity analyses. Conclusion: The results of the MR analysis provide preliminary evidence that a genetic causal link between the severity of COVID-19 and OP may be absent.

Development of a nomogram to assess the impact of the myocardial injury on the prognosis of COVID-19 patients.

Coronavirus disease 2019 (COVID-19) has been spreading worldwide. Many COVID-19 patients were accompanied by myocardial injury during the course of the disease. To evaluate the association of cardiac injury with clinical outcomes in COVID-19 patients, we recruited 261 COVID-19 cases admitted to Tongji Hospital of Huazhong University of Science and Technology in this study. Compared with patients without myocardial injury, those with myocardial injury were older, with shorter hospital stays and lower survival rates. They also had higher levels of inflammatory biomarkers (Interleukin-6,8,10 and C-reactive protein), coagulation biomarkers, liver and kidney function markers. Kaplan-Meier analysis demonstrated that patients with myocardial injury had a higher mortality rate. The multivariate Cox regression model and the nomogram revealed that myocardial injury, co-morbidity, and abnormal procalcitonin (PCT) levels were independent risk factors of the mortality of COVID-19 patients. The linear correlation analysis and the ROC curve suggested a predictive value of the neutrophil-lymphocyte ratio (NLR) in cardiac injury. Summarily, myocardial injury in COVID-19 patients is associated with a higher mortality risk. Attention should be paid to monitoring myocardial injury in patients with significantly elevated myocardial markers and NLR at admission.

Use of public data to describe COVID-19 contact tracing in Hubei Province and non-Hubei provinces in China between 20 January and 29 February 2020.

OBJECTIVE: Contact tracing has been used in China and several other countries in the WHO Western Pacific Region as part of the COVID-19 response. We describe COVID-19 cases and the number of contacts traced and quarantined per case as part of COVID-19 emergency public health response activities in China. METHODS: We abstracted publicly available, online aggregated data published in daily COVID-19 situational reports by China's National Health Commission and provincial health commissions between 20 January and 29 February 2020. The number of new contacts traced by report date was computed as the difference between total contacts traced in consecutive reports. A proxy for the number of contacts traced per case was computed as the number of new contacts traced divided by the number of new cases. RESULTS: During the study period, China reported 80 968 new COVID-19 cases and 659 899 contacts. In Hubei Province, there were 67 608 cases and 264 878 contacts, representing 83% and 40% of the total, respectively. Non-Hubei provinces reported tracing 1.5 times more contacts than Hubei Province; the weekly number of contacts traced per case was also higher in non-Hubei provinces than in Hubei Province and increased from 17.2 in epidemiological week 4 to 115.7 in epidemiological week 9. DISCUSSION: More contacts per case were reported from areas and periods with lower COVID-19 case counts. With other non-pharmaceutical interventions used in China, contact tracing and quarantining large numbers of potentially infected contacts probably contributed to reducing SARS-CoV-2 transmission.

Two novel nomograms based on inflammatory cytokines or lymphocyte subsets to differentially diagnose severe or critical and Non-Severe COVID-19.

We intend to evaluate the differences of the clinical characteristics, cytokine profiles and immunological features in patients with different severity of COVID-19, and to develop novel nomograms based on inflammatory cytokines or lymphocyte subsets for the differential diagnostics for severe or critical and non-severe COVID-19 patients. We retrospectively studied 254 COVID-19 patients, 90 of whom were severe or critical patients and 164 were non-severe patients. Severe or critical patients had significantly higher levels of inflammatory cytokines than non-severe patients as well as lower levels of lymphocyte subsets. Significantly positive correlations between cytokine profiles were observed, while they were all significantly negatively correlated with lymphocyte subsets. Two effective nomograms were developed according to two multivariable logistic regression cox models based on inflammatory cytokine profiles and lymphocyte subsets separately. The areas under the receiver operating characteristics of two nomograms were 0.834 (95% CI: 0.779-0.888) and 0.841 (95% CI: 0.756-0.925). The bootstrapped-concordance indexes of two nomograms were 0.834 and 0.841 in training set, and 0.860 and 0.852 in validation set. Calibration curves and decision curve analyses demonstrated that the nomograms were well calibrated and had significantly more clinical net benefits. Our novel nomograms can accurately predict disease severity of COVID-19, which may facilitate the identification of severe or critical patients and assist physicians in making optimized treatment suggestions.

A Retrospective Study of 268 Patients with SARS-CoV-2 Infection to Evaluate the Association Between Blood Glucose and Severity of COVID-19 Pneumonia and Patient Mortality.

BACKGROUND Diabetes is one of the most commonly reported comorbidities among patients infected with SARS-CoV-2. This retrospective study of patients with SARS-CoV-2 infection was conducted to evaluate the association between blood glucose levels and the severity of COVID-19 pneumonia and patient mortality. MATERIAL AND METHODS A total of 268 patients with confirmed SARS-CoV-2 infection were included in this retrospective study. We obtained demographic characteristics, clinical symptoms, laboratory data, and survival information from patients' electronic medical records. Blood glucose was measured on admission to the hospital. Comorbidities, including hypertension, diabetes, chronic kidney disease, chronic liver disease, chronic obstructive pulmonary disease, and cardiovascular disease, were collected by self-reported medical history. RESULTS Significantly higher risks of severe COVID-19 were found in patients with blood glucose levels ranging from 5.53 to 7.27 mmol/L (odds ratio [OR], 3.98; 95% confidence interval [CI], 1.81-8.75) and in patients with blood glucose ≥7.27 mmol/L (OR, 12.10; 95% CI, 5.53-26.48) than in those with blood glucose <5.53 mmol/L. There was a trend toward better survival in patients with blood glucose <5.53 mmol/L than in patients with blood glucose from 5.53 to 7.27 mmol/L (hazard ratio [HR], 6.34; 95% CI, 1.45-27.71) and ≥7.27 mmol/L (HR, 19.37; 95% CI, 4.68-80.17). Estimated 10-day overall survival rates were 96.8%, 90.6%, and 69.3% in patients with blood glucose <5.53 mmol/L, 5.53 to 7.27 mmol/L, and ³7.27 mmol/L, respectively. CONCLUSIONS Hyperglycemia was association with severity of COVID-19 pneumonia and with increased patient mortality. These findings support the need for blood glucose monitoring and control of hyperglycemia in patients with COVID-19 pneumonia.

Toward a Country-Based Prediction Model of COVID-19 Infections and Deaths Between Disease Apex and End: Evidence From Countries With Contained Numbers of COVID-19.

The complexity of COVID-19 and variations in control measures and containment efforts in different countries have caused difficulties in the prediction and modeling of the COVID-19 pandemic. We attempted to predict the scale of the latter half of the pandemic based on real data using the ratio between the early and latter halves from countries where the pandemic is largely over. We collected daily pandemic data from China, South Korea, and Switzerland and subtracted the ratio of pandemic days before and after the disease apex day of COVID-19. We obtained the ratio of pandemic data and created multiple regression models for the relationship between before and after the apex day. We then tested our models using data from the first wave of the disease from 14 countries in Europe and the US. We then tested the models using data from these countries from the entire pandemic up to March 30, 2021. Results indicate that the actual number of cases from these countries during the first wave mostly fall in the predicted ranges of liniar regression, excepting Spain and Russia. Similarly, the actual deaths in these countries mostly fall into the range of predicted data. Using the accumulated data up to the day of apex and total accumulated data up to March 30, 2021, the data of case numbers in these countries are falling into the range of predicted data, except for data from Brazil. The actual number of deaths in all the countries are at or below the predicted data. In conclusion, a linear regression model built with real data from countries or regions from early pandemics can predict pandemic scales of the countries where the pandemics occur late. Such a prediction with a high degree of accuracy provides valuable information for governments and the public.

Comparison of Clinical Features and Outcomes of Medically Attended COVID-19 and Influenza Patients in a Defined Population in the 2020 Respiratory Virus Season.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), which is causing the coronavirus disease-2019 (COVID-19) pandemic, poses a global health threat. However, it is easy to confuse COVID-19 with seasonal influenza in preliminary clinical diagnosis. In this study, the differences between influenza and COVID-19 in epidemiological features, clinical manifestations, comorbidities and pathogen biology were comprehensively compared and analyzed. SARS-CoV-2 causes a higher proportion of pneumonia (90.67 vs. 17.07%) and acute respiratory distress syndrome (12.00 vs. 0%) than influenza A virus. The proportion of leukopenia for influenza patients was 31.71% compared with 12.00% for COVID-19 patients (P = 0.0096). The creatinine and creatine kinase were significantly elevated when there were COVID-19 patients. The basic reproductive number (R0) for SARS-CoV-2 is 2.38 compared with 1.28 for seasonal influenza A virus. The mutation rate of SARS-CoV-2 ranges from 1.12 × 10-3 to 6.25 × 10-3, while seasonal influenza virus has a lower evolutionary rate (0.60-2.00 × 10-6). Overall, this study compared the clinical features and outcomes of medically attended COVID-19 and influenza patients. In addition, the S477N and N439K mutations on spike may affect the affinity with receptor ACE2. This study will contribute to COVID-19 control and epidemic surveillance in the future.

Peripheral Inflammatory Cytokines and Lymphocyte Subset Features of Deceased COVID-19 Patients.

OBJECTIVE: To compare the difference of inflammatory cytokines and lymphocyte subsets between deceased patients and survivors with COVID-19. METHODS: This retrospective study included 254 confirmed patients from 10 January to 11 March, 2020, at Tongji Hospital of Wuhan, China. Laboratory and immunologic features were collected and analyzed, and the main outcomes focused on inflammatory cytokines and lymphocyte subsets. RESULTS: A trend of markedly higher levels of inflammatory cytokines as well as lower lymphocyte subset levels in deceased patients was observed compared with survivors. ROC curve analyses indicated that inflammatory cytokines and the decrease levels of T cell, Th (helper T cells) cell, Ts (suppressor T cells) cell, B cell, and NK cell along with Th/Ts ratio increase could be used to predict the death of COVID-19. Multivariate analyses showed that higher levels of IL-6, IL-8, and IL-10 remained significantly correlated with shorter survival time and that the amount of Ts cells was negatively associated with the possibility of death in COVID-19 patients. In conclusion, SARS-CoV-2 would cause lymphopenia and result in decreased lymphocyte subset cells, particularly in Ts cell counts, which further induces hyperinflammatory response and cytokine storm. IL-6, IL-8, IL-10, and Ts cell might be independent predictors for the poor outcome of COVID-19.