Post-COVID chronic neuropsychiatric symptoms persisting beyond one year from infection- a case-control study and network analysis (preprint)

Background Limited data about chronic post-COVID neuropsychiatric complaints exist in the literature. Aim: Our study aims to delineate the phenotypes of chronic neuropsychiatric symptoms among adult subjects recovering from their first COVID that occurred more than one year ago. We also aim to explore the clinical and socioeconomic risk factors of having a high loading of chronic neuropsychiatric symptoms. Methods We recruited a post-COVID group who suffered from their first pre-Omicron COVID more than a year ago, and a control group who had never had COVID. The subjects completed app-based questionnaires on demographic, socioeconomic and health status, a COVID symptoms checklist, mental and sleep health measures, and neurocognitive tests. Results The post-COVID group has a statistically significantly higher level of fatigue compared to the control group (p<0.001). Among the post-COVID group, the lack of any COVID vaccination before the first COVID and a higher level of material deprivation before the COVID pandemic predicts a higher load of chronic post-COVID neuropsychiatric symptoms. Partial correlation network analysis suggests that the chronic post-COVID neuropsychiatric symptoms can be clustered into two major (cognitive complaints -fatigue and anxiety-depression) and one minor (headache-dizziness) cluster. A higher level of material deprivation predicts a higher number of symptoms in both major clusters, but the lack of any COVID vaccination before the first COVID only predicts a higher number of symptoms in the cognitive complaints-fatigue cluster. Conclusions Our result suggests heterogeneity among chronic post-COVID neuropsychiatric symptoms, which are associated with the complex interplay of biological and socioeconomic factors.

The influence of changes in the Chinese COVID-19 prevention and control policies on mental health of medical staff: A network analysis.

BACKGROUND: Shortly after the first outbreak of COVID-19 in Wuhan, the disease spread rapidly around the world. Previous findings reported an increase in mental health problems among Chinese medical staff, but there was a lack of research following changes in COVID-19 prevention and control policies. METHODS: Medical staff were recruited separately in China from 15 to 16 December 2022 (N = 765, wave 1) and from 5 to 8 January 2023 (N = 690, wave 2). All participants completed the assessments of Generalized Anxiety Disorder-7, the Patient Health Questionnaire-9 and the Euthymia Scale. Network analysis was used to explore the relationships between symptoms both within and across depression, anxiety and euthymia. RESULTS: Medical staff showed worse anxiety, depression and euthymia at wave 2 than at wave 1. Depression, motor, restlessness and uncontrollable worrying showed high centrality (i.e., strength, expected influence, closeness) at wave 1, but higher at peak. Meanwhile, motor symptoms and restlessness showed the strongest connection between different mental disorders at both wave 1 and wave 2. The network structure was stable over time after the relaxation of the infection policy. LIMITATIONS: Our participants were not a random sample and the assessments were based on self-reports. CONCLUSIONS: This study indicated the changes in central and bridging symptoms in medical staff at different stages after lifting of restrictions and the withdrawal of testing requirements, which provided management suggestions for the Chinese government and hospitals, as well as clinical guidance for psychological interventions.

A Wide-bandwidth Nanocomposite-Sensor Integrated Smart Mask for Tracking Multi-phase Respiratory Activities for COVID-19 Endemic (preprint)

A global sentiment in early 2022 is that the COVID-19 virus could become endemic just like common cold flu viruses soon. The most optimistic view is that, with minimal precautions, such as vaccination, boosters and optional masking, life for most people will proceed as normal soon. However, as warned by A. Katzourakis of Oxford University recently [1], we must set aside lazy optimism, and must be realistic about the likely levels of death, disability and sickness that will be brought on by a COVID-19 endemic. Moreover, the world must also consider that continual circulation of the virus could give rise to new variants such as the new BA.2 variant (a subvariant of Omicron) continues to spread across the US and parts of Europe. Data from the CDC is already showing that BA.2 has been tripling in prevalence every two weeks [2]. Hence, globally, we must use available and proven weapons to continue to fight the COVID-19 viruses, i.e., effective vaccines, antiviral medications, diagnostic tests and stop an airborne virus transmission through social distancing, and mask wearing. For this work, we have demonstrated a smart mask with an optimally-coupled ultra-thin flexible soundwave sensors for tracking, classifying, and recognizing different respiratory activities, including breathing, speaking, and two-/tri-phase coughing; the masks functionality can also be augmented in the future to monitor other human physiological signals. Although researchers have integrated sensors into masks to detect respiratory activities in the past, they only based on measuring temperature and air flow during coughing, i.e., counting only the number of coughs. However, coughing is a process consisting of several phases, including an explosion of the air with glottal opening producing some noise-like waveform, a decrease of airflow to decrease sound amplitude, and a voiced stage which is the interruption of the air flow due to the closure of glottal and periodical vibration of partly glottis, which is not always present. Therefore, sensors used for cough detection should not be only sensitive to subtle air pressure but also the high-frequency vibrations, i.e., a pressure sensor that needs to be responsive to a wide input amplitude and bandwidth range, in order to detect air flows between hundreds of hertz from breath, and acoustic signals from voice that could reach [~] 8000 Hz. Respiratory activities data from thirty-one (31) human subjects were collected. Machine learning methods such as Support Vector Machines and Convolutional Neural Networks were used to classify the collected sensor data from the smart mask, which show an overall macro-recall of about 93.88% for the three respiratory sounds among all 31 subjects. For individual subjects, the 31 human subjects have the average macro-recall of 95.23% (ranging from 90% to 100%) for these 3 respiratory activities. Our work bridges the technological gap between ultra-lightweight but high-frequency response sensor material fabrication, signal transduction and conditioning, and applying machining learning algorithms to demonstrate a reliable wearable device for potential applications in continual healthy monitoring of subjects with cough symptoms during the eventual COVID-19 endemic. The monitoring and analysis of cough sound should be highly beneficial for human health management. These health monitoring data could then be shared with doctors via cloud storage and transmission technique to help disease diagnosis more effectively. Also, communication barriers caused by wearing masks can be alleviated by combining with the speech recognition techniques. In general, this research helps to advance the wearable device technology for tracking respiratory activities, similar to an Apple Watch or a Fitbit smartwatch in tracking physical and physiological activities.

Nasal prevention of SARS-CoV-2 infection by intranasal influenza-based boost vaccination (preprint)

Background Vaccines in emergency use are efficacious against COVID-19, yet vaccine-induced prevention against nasal SARS-CoV-2 infection remains suboptimal. Methods Since mucosal immunity is critical for nasal prevention, we investigated an intramuscular PD1-based receptor-binding domain (RBD) DNA vaccine (PD1-RBD-DNA) and intranasal live attenuated influenza-based vaccines (LAIV-CA4-RBD and LAIV-HK68-RBD) against SARS-CoV-2. Findings Substantially higher systemic and mucosal immune responses, including bronchoalveolar lavage IgA/IgG and lung polyfunctional memory CD8 T cells, were induced by the heterologous PD1-RBD-DNA/LAIV-HK68-RBD as compared with other regimens. When vaccinated animals were challenged at the memory phase, prevention of robust SARS-CoV-2 infection in nasal turbinate was achieved primarily by the heterologous regimen besides consistent protection in lungs. The regimen-induced antibodies cross-neutralized variants of concerns. Furthermore, LAIV-CA4-RBD could boost the BioNTech vaccine for improved mucosal immunity. Interpretation Our results demonstrated that intranasal influenza-based boost vaccination is required for inducing mucosal and systemic immunity for effective SARS-CoV-2 prevention in both upper and lower respiratory systems. Funding This study was supported by the Research Grants Council Collaborative Research Fund (C7156-20G, C1134-20G and C5110-20G), General Research Fund (17107019) and Health and Medical Research Fund (19181052 and 19181012) in Hong Kong; Outbreak Response to Novel Coronavirus (COVID-19) by the Coalition for Epidemic Preparedness Innovations; Shenzhen Science and Technology Program (JSGG20200225151410198); the Health@InnoHK, Innovation and Technology Commission of Hong Kong; and National Program on Key Research Project of China (2020YFC0860600, 2020YFA0707500 and 2020YFA0707504); and donations from the Friends of Hope Education Fund. Z.C.’s team was also partly supported by the Theme-Based Research Scheme (T11-706/18-N).

SARS-CoV-2 hijacks neutralizing dimeric IgA for enhanced nasal infection and injury (preprint)

Robust severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in nasal turbinate (NT) accounts for high viral transmissibility, yet whether neutralizing IgA antibodies can control it remains unknown. Here, we evaluated receptor binding domain (RBD)-specific monomeric B8-mIgA1 and B8-mIgA2, and dimeric B8-dIgA1 and B8-dIgA2 against intranasal SARS-CoV-2 challenge in Syrian hamsters. These antibodies exhibited comparably potent neutralization against authentic virus by competing with human angiotensin converting enzyme-2 (ACE2) receptor for RBD binding. While reducing viruses in lungs, pre-exposure intranasal B8-dIgA1 or B8-dIgA2 led to 81-fold more infectious viruses and severer damage in NT than placebo. Virus-bound B8-dIgA1 and B8-dIgA2 could engage CD209 as an alternative receptor for entry into ACE2-negative cells and allowed viral cell-to-cell transmission. Cryo-EM revealed B8 as a class II neutralizing antibody binding trimeric RBDs in 3-up or 2-up/1-down conformation. Therefore, RBD-specific neutralizing dIgA engages an unexpected action for enhanced SARS-CoV-2 nasal infection and injury in Syrian hamsters.

SARS-CoV-2 hijacks neutralizing dimeric IgA for enhanced nasal infection and injury (preprint)

Robust severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in nasal turbinate (NT) accounts for high viral transmissibility, yet whether neutralizing IgA antibodies can control it remains unknown. Here, we evaluated receptor binding domain (RBD)-specific monomeric B8-mIgA1 and B8-mIgA2, and dimeric B8-dIgA1 and B8-dIgA2 against intranasal SARS-CoV-2 challenge in Syrian hamsters. These antibodies exhibited comparably potent neutralization against authentic virus by competing with human angiotensin converting enzyme-2 (ACE2) receptor for RBD binding. While reducing viruses in lungs, pre-exposure intranasal B8-dIgA1 or B8-dIgA2 led to 81-fold more infectious viruses and severer damage in NT than placebo. Virus-bound B8-dIgA1 and B8-dIgA2 could engage CD209 as an alternative receptor for entry into ACE2-negative cells and allowed viral cell-to-cell transmission. Cryo-EM revealed B8 as a class II neutralizing antibody binding trimeric RBDs in 3-up or 2-up/1-down conformation. Therefore, RBD-specific neutralizing dIgA engages an unexpected action for enhanced SARS-CoV-2 nasal infection and injury in Syrian hamsters.

How Can We Support Online Learning for Elementary Students? Perceptions and Experiences of Award-Winning K-6 Teachers.

K-12 online learning can be advantageous in a variety of circumstances, including inclement weather days and emergency remote teaching. With the lessons learned from the COVID-19 pandemic, many K-12 districts may consider ways to incorporate online learning into their regular school plans after they resume face-to-face instruction. However, the most challenges to online learning seemed to take place at the elementary level. This brings up an important question: What should elementary online teaching and learning look like? We examined six award-winning K-6 teachers' perspectives on and experiences with online instruction and practices for elementary students. The teachers suggested that online instruction to support elementary students' learning should be (a) organized, (b) engaging, and (c) interactive. Teachers also suggested that developmentally appropriate use of technology and parental involvement may foster elementary students' online learning experiences.

Multimorbidity and adverse events of special interest associated with CoronaVac (Sinovac) and Comirnaty (Pfizer-BioNTech) (preprint)

We examined the potential additional risk of adverse events of special interest (AESI) within 28 days post-Covid-19 vaccination with CoronaVac or Comirnaty (Pfizer-BioNTech) imposed by multimorbidity (2 + chronic conditions). Using a territory-wide public healthcare database with linkage to population-based vaccination records in Hong Kong, we conducted a retrospective cohort study of patients with chronic diseases. Thirty AESI according to World Health Organization’s Global Advisory Committee on Vaccine Safety were examined. In total, 883,416 patients were included. During follow-up, 2,807 (0.3%) patients had AESI. Weighted Cox models suggested that vaccinated patients had lower risks of any AESI than those unvaccinated, that multimorbidity was associated with an increased risk regardless of vaccination status, and there was no significant effect modification of the association of vaccination with AESI by multimorbidity status. To conclude, we found no evidence that multimorbidity imposes extra risks of AESI within 28 days following Covid-19 vaccination.