Immune responses to inactivated COVID-19 vaccine were decreased in Chinese patients with chronic respiratory diseases.

Purpose: The effectiveness of inactivated vaccines against acute respiratory syndrome coronavirus 2 (SARS­CoV­2), the causative agent of coronavirus disease 2019 (COVID-19), has become a global concern. Hence, the aim of this study was to evaluate vaccine safety and to assess immune responses in individuals with chronic respiratory disease (CRD) following a two-dose vaccination. Methods: The study cohort included 191 participants (112 adult CRD patients and 79 healthy controls [HCs]) at least 21 (range, 21-159) days after a second vaccination. Frequencies of memory B cells (MBCs) subsets and titers of SARS-CoV-2 neutralizing antibodies (NAbs) and anti-receptor binding domain (RBD) IgG antibodies (Abs) were analyzed. Results: As compared to the HCs, CRD patients had lower seropositivity rates and titers of both anti-RBD IgG Abs and NAbs, in addition to lower frequencies of RBD-specific MBCs (all, p < 0.05). At 3 months, CRD patients had lower seropositivity rates and titers of anti-RBD IgG Abs than the HCs (p < 0.05). For CoronaVac, the seropositivity rates of both Abs were lower in patients with old pulmonary tuberculosis than HCs. For BBIBP-CorV, the seropositivity rates of CoV-2 NAbs were lower in patients with chronic obstructive pulmonary disease than HCs (all, p < 0.05). Meanwhile, there was no significant difference in overall adverse events between the CRD patients and HCs. Univariate and multivariate analyses identified the time interval following a second vaccination as a risk factor for the production of anti-RBD IgG Abs and CoV-2 NAbs, while the CoronaVac had a positive effect on the titers of both Abs. Female was identified as a protective factor for CoV-2 NAb levels. Conclusion: Inactivated COVID-19 vaccines were safe and well tolerated by CRD patients but resulted in lower Ab responses and the frequencies of RBD-specific MBCs. Therefore, CRD patients should be prioritized for booster vaccinations.

Electrothermal sterilization and self-powered real-time respiratory monitoring of reusable mask based on Ag micro-mesh films.

Since the COVID-19 pandemic outbreaks, the utilization of medical masks plays a critical role in reducing the infected risk. However, constructing multifunctional masks to achieve simultaneously self-sterilization, reusability, and respiratory monitoring capability remains still a huge challenge. Herein, a reusable Ag micro-mesh film-based mask is proposed, which enables the capabilities of electrothermal sterilization and self-powered real-time respiratory monitoring. Highly conductive Ag micro-mesh films prepared by continuous draw spinning method demonstrate excellent electrothermal performances for thermal sterilization and serve as working electrode to fabricate triboelectric nanogenerator (TENG) for real-time respiratory monitoring, respectively. Under a low driving voltage of 3.0 V, the surface temperature of Ag micro-mesh film enables a quick increase to over 60 °C within 30 s, which endows thermal sterilization against S. aureus with antibacterial efficiency of 95.58 % within 20 min to achieve the self-sterilization of medical masks. Furthermore, a self-powered alarm system based on the fabricated TENG as respiratory monitor is developed for real-time respiratory monitoring to render a timely treatment for patients in danger of tachypnea and apnea. Consequently, this work has paved a new and practical avenue to achieve reusable multifunctional masks with capabilities of electrothermal sterilization and real-time respiratory monitoring in clinical medicine.

Estimation of SARS-CoV-2 antibody prevalence through serological uncertainty and daily incidence.

Serology tests for SARS-CoV-2 provide a paradigm for estimating the number of individuals who have had an infection in the past (including cases that are not detected by routine testing, which has varied over the course of the pandemic and between jurisdictions). Such estimation is challenging in cases for which we only have limited serological data and do not take into account the uncertainty of the serology test. In this work, we provide a joint Bayesian model to improve the estimation of the sero-prevalence (the proportion of the population with SARS-CoV-2 antibodies) through integrating multiple sources of data, priors on the sensitivity and specificity of the serological test, and an effective epidemiological dynamics model. We apply our model to the Greater Vancouver area, British Columbia, Canada, with data acquired during the pandemic from the end of January to May 2020. Our estimated sero-prevalence is consistent with previous literature but with a tighter credible interval.

Le dépistage sérologique du SRAS­CoV­2 permet d'estimer le nombre de personnes qui ont déjà été infectées (y compris les cas qui ne sont pas détectés au moyen de tests de dépistage réguliers, qui ont varié au cours de la pandémie et d'une province ou d'un territoire à l'autre). Une telle estimation est difficile lorsqu'il existe peu de données sérologiques et que l'incertitude du test sérologique n'est pas prise en compte. Nous proposons dans ce travail un modèle bayésien conjoint visant à améliorer l'estimation de la séroprévalence (la proportion de la population avec des anticorps SRAS­CoV­2) en intégrant de multiples sources de données, des lois a priori sur la sensibilité et la spécificité du test sérologique, et un modèle efficace des dynamiques épidémiologiques. Nous appliquons ce modèle à des données recueillies dans la région métropolitaine de Vancouver (Colombie­Britannique, Canada) pendant la pandémie de fin janvier à mai 2020. Notre estimation de la séroprévalence est cohérente avec la littérature antérieure tout en ayant un intervalle de crédibilité plus précis.

Long time frames to detect the impact of changing COVID-19 measures, Canada, March to July 2020.

BackgroundMany countries have implemented population-wide interventions to control COVID-19, with varying extent and success. Many jurisdictions have moved to relax measures, while others have intensified efforts to reduce transmission.AimWe aimed to determine the time frame between a population-level change in COVID-19 measures and its impact on the number of cases.MethodsWe examined how long it takes for there to be a substantial difference between the number of cases that occur following a change in COVID-19 physical distancing measures and those that would have occurred at baseline. We then examined how long it takes to observe this difference, given delays and noise in reported cases. We used a susceptible-exposed-infectious-removed (SEIR)-type model and publicly available data from British Columbia, Canada, collected between March and July 2020.ResultsIt takes 10 days or more before we expect a substantial difference in the number of cases following a change in COVID-19 control measures, but 20-26 days to detect the impact of the change in reported data. The time frames are longer for smaller changes in control measures and are impacted by testing and reporting processes, with delays reaching ≥ 30 days.ConclusionThe time until a change in control measures has an observed impact is longer than the mean incubation period of COVID-19 and the commonly used 14-day time period. Policymakers and practitioners should consider this when assessing the impact of policy changes. Rapid, consistent and real-time COVID-19 surveillance is important to minimise these time frames.

Product line transformation, foreign sales, and firm value: Evidence from COVID-19 pandemic governance in urban China.

Using a sample of Chinese firms, we examine stock market reaction to firms that announce a change in their product lines to those related to COVID-19 management (medical masks and ventilators, among others). We find the market reacts positively to the announcements. In addition, when a firm ordinarily has a large share of export sales, the stock market reaction is more salient, indicating that export sales provide a certification effect that positively signals investors. Additional analysis on moderating effects suggest that, conditional on foreign sales, prior experience with medical product lines or less uncertainty about supply availability enhances the cumulative announcement returns (CARs), while the adverse impact of firm size on CAR magnifies.