Maternal Secondhand Smoke Exposure Enhances Macrosomia Risk Among Pregnant Women Exposed to PM2.5: A New Interaction of Two Air Pollutants in a Nationwide Cohort.

Background: Fine particulate matter (PM2.5) is one of the most common outdoor air pollutants, and secondhand smoking (SHS) is an important source of inhalable indoor air pollution. Previous studies were controversial and inconsistent about PM2.5 and SHS air pollutants on neonatal birth weight outcomes, and no studies assessed the potential interactive effects between PM2.5 and SHS on birth weight outcomes. Purpose: To investigate the interaction between gestational PM2.5 and SHS air pollution exposure on the risk of macrosomia among pregnant women and examine the modifying effect of SHS exposure on the association of PM2.5 air pollution and birth weight outcomes during pregnancy. Methods: Research data were derived from the National Free Preconception Health Examination Project (NFPHEP), which lasted 3 years from January 1, 2010, to December 31, 2012. At least 240,000 Chinese women from 220 counties were enrolled in this project. PM2.5 exposure concentration was obtained using a hindcast model specific for historical PM2.5 estimation from satellite-retrieved aerosol optic depth. Different interaction models about air pollution exposure on birth weight outcomes were established, according to the adjustment of different confounding factors and different pregnancy stages. The establishment of interaction models was based on multivariable logistic regression, and the main confounding factors were maternal age at delivery and pre-pregnancy body mass index (BMI) of participants. SHS subgroups analysis was conducted to further confirm the results of interaction models. Results: In total, 197,877 participants were included in our study. In the full-adjusted interaction model, maternal exposure to PM2.5 was associated with an increased risk of macrosomia in whole, the first-, second-, and third trimesters of pregnancy (p < 0.001). The interactive effect was statistically significant between maternal exposure to PM2.5 and SHS on the risk of macrosomia in the whole (interaction p < 0.050) and the first-trimester pregnancy (interaction p < 0.050), not in the second (interaction p > 0.050) or third trimester (interaction p > 0.050) of pregnancy. The higher frequency of SHS exposure prompted the stronger interaction between the two air pollutants in the whole pregnancy and the first-trimester pregnancy. Conclusions: In the whole and first-trimester pregnancy, maternal exposure to SHS during pregnancy enhanced the risk of macrosomia among pregnant women exposed to PM2.5 air pollutants, and the interaction became stronger with the higher frequency of SHS exposure.

Re-shaping Post-COVID-19 Teaching and Learning: A Blueprint of Virtual-Physical Blended Classrooms in the Metaverse Era (preprint)

During the COVID-19 pandemic, most countries have experienced some form of remote education through video conferencing software platforms. However, these software platforms fail to reduce immersion and replicate the classroom experience. The currently emerging Metaverse addresses many of such limitations by offering blended physical-digital environments. This paper aims to assess how the Metaverse can support and improve e-learning. We first survey the latest applications of blended environments in education and highlight the primary challenges and opportunities. Accordingly, we derive our proposal for a virtual-physical blended classroom configuration that brings students and teachers into a shared educational Metaverse. We focus on the system architecture of the Metaverse classroom to achieve real-time synchronization of a large number of participants and activities across physical (mixed reality classrooms) and virtual (remote VR platform) learning spaces. Our proposal attempts to transform the traditional physical classroom into virtual-physical cyberspace as a new social network of learners and educators connected at an unprecedented scale.

Strategic COVID-19 vaccine distribution can simultaneously elevate social utility and equity (preprint)

Balancing social utility and equity in distributing limited vaccines represents a critical policy concern for protecting against the prolonged COVID-19 pandemic. What is the nature of the trade-off between maximizing collective welfare and minimizing disparities between more and less privileged communities? To evaluate vaccination strategies, we propose a novel epidemic model that explicitly accounts for both demographic and mobility differences among communities and their association with heterogeneous COVID-19 risks, then calibrate it with large-scale data. Using this model, we find that social utility and equity can be simultaneously improved when vaccine access is prioritized for the most disadvantaged communities, which holds even when such communities manifest considerable vaccine reluctance. Nevertheless, equity among distinct demographic features are in tension due to their complex correlation in society. We design two behavior-and-demography-aware indices, community risk and societal harm, which capture the risks communities face and those they impose on society from not being vaccinated, to inform the design of comprehensive vaccine distribution strategies. Our study provides a framework for uniting utility and equity-based considerations in vaccine distribution, and sheds light on how to balance multiple ethical values in complex settings for epidemic control.

Inflammation-related adverse reactions following vaccination potentially indicate a stronger immune response.

Concerns about vaccine safety are an important reason for vaccine hesitancy, however, limited information is available on whether common adverse reactions following vaccination affect the immune response. Data from three clinical trials of recombinant vaccines were used in this post hoc analysis to assess the correlation between inflammation-related solicited adverse reactions (ISARs, including local pain, redness, swelling or induration and systematic fever) and immune responses after vaccination. In the phase III trial of the bivalent HPV-16/18 vaccine (Cecolin®), the geometric mean concentrations (GMCs) for IgG anti-HPV-16 and -18 (P<0.001) were significantly higher in participants with any ISAR following vaccination than in those without an ISAR. Local pain, induration, swelling and systemic fever were significantly correlated with higher GMCs for IgG anti-HPV-16 and/or anti-HPV-18, respectively. Furthermore, the analyses of the immunogenicity bridging study of Cecolin® and the phase III trial of a hepatitis E vaccine yielded similar results. Based on these results, we built a scoring model to quantify the inflammation reactions and found that the high score of ISAR indicates the strong vaccine-induced antibody level. In conclusion, this study suggests inflammation-related adverse reactions following vaccination potentially indicate a stronger immune response.

Patterns of mental health problems before and after easing COVID-19 restrictions: Evidence from a 105248-subject survey in general population in China.

BACKGROUND: The COVID-19 pandemic has alarming implications for individual and population level mental health. Although the future of COVID-19 is unknown at present, more countries or regions start to ease restrictions. The findings from this study have provided the empirical evidence of prevalence and patterns of mental disorders in Chinese general population before and after easing most COVID-19 restrictions, and information of the factors associated with these patterns. METHODS: A cross-sectional population-based online survey was carried out from February to March 2020 in the general population across all provinces in China. The 12-item General Health Questionnaire (GHQ-12) was incorporated in the survey. Latent class analyses were performed to investigate the patterns of mental disorders and multinomial logistic regressions were used to examine how individual and regional risk factors can predict mental disorder patterns. RESULTS: Four distinctive patterns of mental health were revealed in the general population. After the ease of most COVID-19 restrictions, the prevalence of high risk of mental disorders decreased from 25.8% to 20.9% and prevalence of being high risk of unhappiness and loss of confidence decreased from 10.1% to 8.1%. However, the prevalence of stressed, social dysfunction and unhappy were consistently high before and after easing restrictions. Several regional factors, such as case mortality rate and healthcare resources, were associated with mental health status. Of note, healthcare workers were less likely to have mental disorders, compared to other professionals and students. CONCLUSIONS: The dynamic management of mental health and psychosocial well-being is as important as that of physical health both before and after the ease of COVID-19 restrictions. Our findings may help in mental health interventions in other countries and regions while easing COVID-19 restrictions.

Intraoperative care of elderly patients with COVID-19 undergoing double lung transplantation: Two case reports.

BACKGROUND: Lung transplantation is considered an effective method for treating patients with end-stage coronavirus disease 2019 (COVID-19). The nurses play a pivotal role in lung transplantation for COVID-19 patients. CASE SUMMARY: Two elderly patients were admitted to the emergency department due to cough, and the nucleic acid test was positive. The patients were preliminarily diagnosed with COVID-19. The chest computed tomography scan revealed massive lung consolidation, and the patients did not improve after treatment with antiviral drugs, glucocorticoids, antibiotics, and other drugs. Under the support of pure oxygen in extracorporeal membrane oxygenation, the patients' oxygen levels were still poor, indicating that they were in the terminal stage of viral pneumonia. The patients underwent double-lung transplantation and received intraoperative care. The extracorporeal membrane oxygenation device was removed within a week of the surgical procedure, and the respiratory circulation of the patients were stabilized. CONCLUSION: High-quality care for COVID-19 patients undergoing lung transplantation ensures patient safety during operation while protecting the safety of the medical staff.

Gastrointestinal endoscopy infection control strategy during COVID-19 pandemic: Experience from a tertiary medical center in China.

OBJECTIVES: Coronavirus disease 2019 (COVID-19) has spread globally and become a pandemic. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) not only infects the gastrointestinal (GI) tract and causes GI symptoms, but also increases nosocomial transmission risk during endoscopic procedures for aerosol generation. We hereby share our infection control strategies aiming to minimize COVID-19 transmission in the endoscopy center. METHODS: We established our infection control strategies based on the guidance of Chinese Society of Digestive Endoscopy and inputs from hospital infection control experts: admission control through the procedure and patient triage, environmental control to reduce possible virus exposure, proper usage of personal protective equipment (PPE), and scope disinfection and room decontamination. All endoscopic procedures accomplished during COVID-19 outbreak and progress of stepwise resumption of elective endoscopy procedures were retrospectively reviewed. RESULTS: Only urgent or semi-urgent procedures were performed during COVID-19 outbreak. After no local new-onset COVID-19 case in Beijing for four weeks, we reopened the endoscopy center for elective procedures and monitored the outbreak continuously while maintaining a sustainable endoscopy service. CONCLUSIONS: It is imperative that all endoscopy centers should establish standard infection control strategies in order to fight COVID-19 pandemic based on national guidance and academic society guidelines and tailor them to individual resources. These measures and setup can also be reserved for future pandemics.

Modelling global public health strategies in COVID-19 pandemic using deep reinforcement learning (preprint)

Rationale: Unprecedented public health measures have been used during this coronavirus 2019 (COVID-19) pandemic but with a cost to economic and social disruption. It is a challenge to implement timely and appropriate public health interventions.Objectives: This study evaluates the timing and intensity of public health policies in each country and territory in the COVID-19 pandemic, and whether machine learning can help them to find better global health strategies.Methods: Population and COVID-19 epidemiological data between 21st January 2020 to 7th April 2020 from 183 countries and 78 territories were included with the implemented public health interventions. We used deep reinforcement learning, and the model was trained to try to find the optimal public health strategies with maximizing total reward on controlling spread of COVID-19. The results proposed by the model were analyzed against the actual timing and intensity of lockdown and travel restrictions.Measurements and Main Results: Early implementation of the actual lockdown and travel restriction policies were associated with gradually groups of less severe crisis severity, relative to local index case date in each country or territory, not to 31st December 2019. However, our model suggested to initiate at least minimal intensity of lockdown or travel restriction even before index cases in each country and territory. In addition, the model mostly recommended a combination of lockdown and travel restrictions and higher intensity policies than the implemented policies by government, but did not always encourage rapid full lockdown and full border closures.Conclusion: Compared to actual government implementation, our model mostly recommended earlier and higher intensity of lockdown and travel restrictions. Machine learning may be used as a decision support tool for implementation of public health interventions during COVID-19 and future pandemics.