Immunogenicity of COVID-19 vaccines in chronic liver disease patients and liver transplant recipients: A systematic review and meta-analysis.

BACKGROUND AND AIMS: Chronic liver disease (CLD) patients and liver transplant (LT) recipients have an increased risk of morbidity and mortality from coronavirus disease 2019 (COVID-19). The immunogenicity of COVID-19 vaccines in CLD patients and LT recipients is poorly understood. The present study aimed to evaluate the immunogenicity of COVID-19 vaccines in CLD patients and LT recipients. METHODS: We searched electronic databases for eligible studies. Two reviewers independently conducted the literature search, extracted the data and assessed the risk of bias of included studies. The rates of detectable immune response were pooled from single-arm studies. For comparative studies, we compared the rates of detectable immune response between patients and healthy controls. The meta-analysis was conducted using the Stata software with a random-effects model. RESULTS: In total, 19 observational studies involving 4191 participants met the inclusion criteria. The pooled rates of detectable humoral immune response after two doses of COVID-19 vaccination in CLD patients and LT recipients were 95% (95% confidence interval [CI] = 88%-99%) and 66% (95% CI = 57%-74%) respectively. After two doses of vaccination, the humoral immune response rate was similar in CLD patients and healthy controls (risk ratio [RR] = 0.96; 95% CI = 0.90-1.02; p = .14). In contrast, LT recipients had a lower humoral immune response rate after two doses of vaccination than healthy controls (RR = 0.68; 95% CI = 0.59-0.77; p < .01). CONCLUSIONS: Our meta-analysis demonstrated that COVID-19 vaccination induced strong humoral immune responses in CLD patients but poor humoral immune responses in LT recipients.

PregTox: A Resource of Knowledge about Drug Fetal Toxicity.

Background: It is of vital importance to determine the safety of drugs. Pregnant women, as a special group, need to evaluate the effects of drugs on pregnant women as well as the fetus. The use of drugs during pregnancy may be subject to fetal toxicity, thus affecting the development of the fetus or even leading to stillbirth. The U.S. Food and Drug Administration (FDA) issued a toxicity rating for drugs used during pregnancy in 1979. These toxicity ratings are denoted by the letters A, B, C, D, and X. However, the query of drug pregnancy category has yet to be well established as electronic service. Results: Here, we presented PregTox, a publicly accessible resource for pregnancy category information of 1114 drugs. The PregTox database also included chemical structures, important physico-chemical properties, protein targets, and relevant signaling pathways. An advantage of the database is multiple search options which allow systematic analyses. In a case study, we demonstrated that a set of chemical descriptors could effectively discriminate high-risk drugs from others (area under ROC curve reached 0.81). Conclusions: PregTox can serve as a unique drug safety data source for drug development and pharmacological research.

Nanoparticle-mediated surfactant therapy in patients with severe COVID-19: a perspective.

Coronavirus disease 2019 (COVID-19) is an RNA virus-based disease that can be deadly. For critically ill patients, mechanical ventilation is an important life-saving treatment. However, mechanical ventilation shows a trade-off between supporting respiratory function and ventilator-induced lung injury (VILI). Surfactant therapy is a medical administration of exogenous surfactant to supplement or replace deficient or dysfunctional endogenous surfactant. Surfactant therapy can be used to postpone or shorten the use of mechanical ventilation to minimize or avoid VILI, because surfactants can reduce surface tension, improve lung compliance, and enhance oxygenation. In addition, nanotechnology can be applied to improve the therapeutic effect and reduce the adverse effects of surfactants. In this perspective, we discussed how nanoparticles deliver surfactants through intravenous injection and inhalation to the expected lung disease regions where surfactants are mostly needed, and discussed the prospects of nanoparticle-mediated surfactant therapy in the treatment of patients with severe COVID-19.

Erratum: Author Correction: Machine learning model to project the impact of COVID-19 on US motor gasoline demand.

[This corrects the article DOI: 10.1038/s41560-020-0662-1.].

Machine learning model to project the impact of COVID-19 on US motor gasoline demand

Owing to the global lockdowns that resulted from the COVID-19 pandemic, fuel demand plummeted and the price of oil futures went negative in April 2020. Robust fuel demand projections are crucial to economic and energy planning and policy discussions. Here we incorporate pandemic projections and people's resulting travel and trip activities and fuel usage in a machine-learning-based model to project the US medium-term gasoline demand and study the impact of government intervention. We found that under the reference infection scenario, the US gasoline demand grows slowly after a quick rebound in May, and is unlikely to fully recover prior to October 2020. Under the reference and pessimistic scenario, continual lockdown (no reopening) could worsen the motor gasoline demand temporarily, but it helps the demand recover to a normal level quicker. Under the optimistic infection scenario, gasoline demand will recover close to the non-pandemic level by October 2020. The COVID 19 pandemic and consequent lockdown has had a substantial impact on mobility and therefore fuel demand and it is not clear when demand will recover. Ou et al. use a machine learning model that integrates health recovery scenarios to project the near-term future of gasoline demand.