Impact of inactivated vaccines on decrease of viral RNA levels in individuals with the SARS-CoV-2 Omicron (BA.2) variant: A retrospective cohort study in Shanghai, China.

Background: SARS-CoV-2 Omicron (BA.2) has stronger infectivity and more vaccine breakthrough capability than previous variants. Few studies have examined the impact of inactivated vaccines on the decrease of viral RNA levels in individuals with the Omicron variant, based on individuals' continuous daily cycle threshold (Ct) values and associated medical information from the infection to hospital discharge on a large population. Methods: We extracted 39,811 individuals from 174,371 Omicron-infected individuals according to data inclusion and exclusion criteria. We performed the survival data analysis and Generalized Estimating Equation to calculate the adjusted relative risk (aRR) to assess the effect of inactivated vaccines on the decrease of viral RNA levels. Results: Negative conversion was achieved in 54.7 and 94.3% of all infected individuals after one and 2 weeks, respectively. aRRs were shown weak effects on turning negative associated with vaccinations in asymptomatic infections and a little effect in mild diseases. Vaccinations had a protective effect on persistent positivity over 2 and 3 weeks. aRRs, attributed to full and booster vaccinations, were both around 0.7 and had no statistical significance in asymptomatic infections, but were both around 0.6 with statistical significance in mild diseases, respectively. Trends of viral RNA levels among vaccination groups were not significant in asymptomatic infections, but were significant between unvaccinated group and three vaccination groups in mild diseases. Conclusion: Inactivated vaccines accelerate the decrease of viral RNA levels in asymptomatic and mild Omicron-infected individuals. Vaccinated individuals have lower viral RNA levels, faster negative conversion, and fewer persisting positive proportions than unvaccinated individuals. The effects are more evident and significant in mild diseases than in asymptomatic infections.

Dynamics of disease characteristics and viral RNA decay in patients with asymptomatic and mild infections during the Omicron wave in Shanghai, China: A retrospective cohort study.

OBJECTIVES: Asymptomatic infections and mild diseases were more common during the Omicron outbreak in Shanghai, China in 2022. This study aimed to assess the characteristics and viral RNA decay between patients with asymptomatic and mild infections. METHODS: A total of 55,111 patients infected with SARS-CoV-2 who were quarantined in the National Exhibition & Convention Center (Shanghai) Fangcang shelter hospital within 3 days after diagnosis from April 9 to May 23, 2022 were enrolled. The kinetics of cycle threshold (Ct) values of reverse transcription-polymerase chain reaction were assessed. The influencing factors for disease progression and the risk factors for the viral RNA shedding time (VST) were investigated. RESULTS: On admission, 79.6% (43,852/55,111) of the cases were diagnosed with asymptomatic infections, and 20.4% were mild diseases. However, 78.0% of initially asymptomatic subjects developed mild diseases at the follow-up. The final proportion of asymptomatic infections was 17.5%. The median time of symptom onset, the duration of symptoms, and the VST were 2 days, 5 days, and 7 days, respectively. Female, age 19-40 years, underlying comorbidities with hypertension and diabetes, and vaccination were associated with higher risks of progressing to mildly symptomatic infections. In addition, mildly symptomatic infections were found to be associated with prolonged VST compared with asymptomatic infections. However, the kinetics of viral RNA decay and dynamics of Ct values were similar among asymptomatic subjects, patients with asymptomatic-to-mild infection, and patients with mild infection. CONCLUSION: A large proportion of initially diagnosed asymptomatic Omicron infections is in the presymptomatic stage. The Omicron infection has a much shorter incubation period and VST than previous variants. The infectivity of asymptomatic infections and mildly symptomatic infections with Omicron is similar.

Impacts of Shipping Carbon Tax on Dry Bulk Shipping Costs and Maritime Trades—The Case of China

Greenhouse gas (GHG) emissions in shipping have been receiving growing concerns in the maritime industry. Recently, the International Maritime Organization (IMO) is considering the introduction of a global shipping carbon tax, which has become the most talked-about topic in both industry and academia. To assess the potential impact of the carbon tax on maritime trades, a trade-volume-based model of shipping carbon emissions was developed. Considering that bulk shipping is the second-largest carbon emitter in the maritime industry and the low value-to-weight nature of bulk cargoes, the model was applied to analyze the dry bulk trade in China, one of the leading countries in the global dry bulk trade. The results show that the introduction of the carbon tax could have significant impacts on freight rates and commodity prices. Depending on the trading regions and the carbon charges, shipping freight rates would increase by 10–30%, which is equivalent to 1–4% of the trading prices. Additionally, since shorter shipping distances may have less emission per trading tonnage, the shipping carbon tax may significantly change the dry bulk trade patterns, resulting in China’s increasing reliance on nearby countries, e.g., India and Australia, for the import of key commodities. These findings can help shipping companies and sectors make better carbon reduction responses, such as redeploying their fleets, promoting the development of low-carbon shipping technologies, and increasing investments in Australia, as well as South and Southeast Asia.

Toward an optimized strategy of using various airway mucus clearance techniques to treat critically ill COVID-19 patients

Coronavirus disease 2019 (COVID-19) caused by acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is still threatening the human life and society throughout the world. For those critically ill patients, mechanical ventilation (MV) is essential to provide life support during treatment. However, both the virus infection and MV disrupt the balance between secretion and elimination of airway mucus and lead to mucus accumulation in the lung. Postmortem examination verified that the lungs in patients died of COVID-19 are indeed filled with sticky mucus, suggesting a great need to improve airway mucus clearance in critically ill COVID-19 patients. Therefore, it may be helpful to comprehensively review the current understanding regarding the changes of biochemical and rheological features of airway mucus associated with the disease, as well as the physiological principles and algorithm to decide airway clearance techniques suitable for the critically ill COVID-19 patients. Based on these considerations, optimized strategies may be developed to eliminate the airway mucus accumulated in the airways of critically ill COVID-19 patients.

Treatment strategies for gastric cancer during the COVID-19 pandemic.

The coronavirus disease 2019 pandemic has become a major global public health problem. Governments are taking the necessary steps to reduce the movement of people to contain the spread of the virus. However, these measures have caused considerable distress to patients with gastric cancer who are newly diagnosed or are undergoing treatment. In addition to the cancer, they must deal with longer waiting times for surgery and poor communication with doctors. Furthermore, gastric cancer patients generally have low immunity and a poor nutritional status, so they are a high-risk group for infection with the novel coronavirus. Therefore, it is necessary to formulate reasonable outpatient management strategies to reduce the adverse effects of the pandemic on their treatment. We summarize the management strategies for patients with gastric cancer during the pandemic.

Safety and efficacy of meplazumab in healthy volunteers and COVID-19 patients: a randomized phase 1 and an exploratory phase 2 trial.

Recent evidence suggests that CD147 serves as a novel receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Blocking CD147 via anti-CD147 antibody could suppress the in vitro SARS-CoV-2 replication. Meplazumab is a humanized anti-CD147 IgG2 monoclonal antibody, which may effectively prevent SARS-CoV-2 infection in coronavirus disease 2019 (COVID-19) patients. Here, we conducted a randomized, double-blinded, placebo-controlled phase 1 trial to evaluate the safety, tolerability, and pharmacokinetics of meplazumab in healthy subjects, and an open-labeled, concurrent controlled add-on exploratory phase 2 study to determine the efficacy in COVID-19 patients. In phase 1 study, 59 subjects were enrolled and assigned to eight cohorts, and no serious treatment-emergent adverse event (TEAE) or TEAE grade ≥3 was observed. The serum and peripheral blood Cmax and area under the curve showed non-linear pharmacokinetic characteristics. No obvious relation between the incidence or titer of positive anti-drug antibody and dosage was observed in each cohort. The biodistribution study indicated that meplazumab reached lung tissue and maintained >14 days stable with the lung tissue/cardiac blood-pool ratio ranging from 0.41 to 0.32. In the exploratory phase 2 study, 17 COVID-19 patients were enrolled, and 11 hospitalized patients were involved as concurrent control. The meplazumab treatment significantly improved the discharged (P = 0.005) and case severity (P = 0.021), and reduced the time to virus negative (P = 0.045) in comparison to the control group. These results show a sound safety and tolerance of meplazumab in healthy volunteers and suggest that meplazumab could accelerate the recovery of patients from COVID-19 pneumonia with a favorable safety profile.

Early prediction and identification for severe patients during the pandemic of COVID-19: A severe COVID-19 risk model constructed by multivariate logistic regression analysis.

BACKGROUND: As an emergent and fulminant infectious disease, Corona Virus Disease 2019 (COVID-19) has caused a worldwide pandemic. The early identification and timely treatment of severe patients are crucial to reducing the mortality of COVID-19. This study aimed to investigate the clinical characteristics and early predictors for severe COVID-19, and to establish a prediction model for the identification and triage of severe patients. METHODS: All confirmed patients with COVID-19 admitted by the Second Affiliated Hospital of Air Force Medical University were enrolled in this retrospective non-interventional study. The patients were divided into a mild group and a severe group, and the clinical data were compared between the two groups. Univariate and multivariate analysis were used to identify the independent early predictors for severe COVID-19, and the prediction model was constructed by multivariate logistic regression analysis. Receiver operating characteristic (ROC) curve was used to evaluate the predictive value of the prediction model and each early predictor. RESULTS: A total of 40 patients were enrolled in this study, of whom 19 were mild and 21 were severe. The proportions of patients with venerable age (≥60 years old), comorbidities, and hypertension in severe patients were higher than that of the mild (P < 0.05). The duration of fever and respiratory symptoms, and the interval from illness onset to viral clearance were longer in severe patients (P < 0.05). Most patients received at least one form of oxygen treatments, while severe patients required more mechanical ventilation (P < 0.05). Univariate and multivariate analysis showed that venerable age, hypertension, lymphopenia, hypoalbuminemia and elevated neutrophil lymphocyte ratio (NLR) were the independent high-risk factors for severe COVID-19. ROC curves demonstrated significant predictive value of age, lymphocyte count, albumin and NLR for severe COVID-19. The sensitivity and specificity of the newly constructed prediction model for predicting severe COVID-19 was 90.5% and 84.2%, respectively, and whose positive predictive value, negative predictive value and crude agreement were all over 85%. CONCLUSIONS: The severe COVID-19 risk model might help clinicians quickly identify severe patients at an early stage and timely take optimal therapeutic schedule for them.