[Clinical features of severe acute respiratory syndrome coronavirus 2 Omicron variant infection in children: an analysis of 201 cases]. / 201ä¾‹å„¿ç«¥æ–°åž‹å† çŠ¶ç— æ¯’Omicronå˜å¼‚æ ªæ„ŸæŸ“çš„ä¸´åºŠç‰¹å¾åˆ†æž.

OBJECTIVES: To study the clinical features of children with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant infection. METHODS: A retrospective analysis was performed on the medical data of 201 children with coronavirus disease 2019 (COVID-19) who were hospitalized and diagnosed with SARS-CoV-2 Omicron variant infection in Quanzhou First Hospital from March 14 to April 7, 2022. Among the 201 children, there were 34 children with asymptomatic infection and 167 with symptomatic infection. The two groups were compared in terms of clinical features, results of experimental examinations, and outcome. RESULTS: Of all the 201 children, 161 (80.1%) had a history of exposure to COVID-19 patients and 132 (65.7%) had a history of COVID-19 vaccination. Among the 167 children with symptomatic infections, 151 had mild COVID-19 and 16 had common COVID-19, with no severe infection or death. Among the 101 children who underwent chest CT examination, 16 had ground glass changes and 20 had nodular or linear opacities. The mean time to nucleic acid clearance was (14±4) days for the 201 children with Omicron variant infection, and the symptomatic infection group had a significantly longer time than the asymptomatic infection group [(15±4) days vs (11±4) days, P<0.05]. The group vaccinated with one or two doses of COVID-19 vaccine had a significantly higher positive rate of IgG than the group without vaccination (P<0.05). The proportions of children with increased blood lymphocyte count in the symptomatic infection group was significantly lower than that in the asymptomatic infection group (P<0.05). Compared with the asymptomatic infection group, the symptomatic infection group had significantly higher proportions of children with increased interleukin-6, increased fibrinogen, and increased D-dimer (P<0.05). CONCLUSIONS: Most of the children with Omicron variant infection have clinical symptoms, which are generally mild. The children with symptomatic infection are often accompanied by decreased or normal blood lymphocyte count and increased levels of interleukin-6, fibrinogen, and D-dimer, with a relatively long time to nucleic acid clearance. Some of them had ground glass changes on chest CT.

Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures (preprint)

The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells and explore cell heterogeneity. However, the high dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Most of the existing computational methods for single-cell data analysis are either limited to single modality or lack flexibility and interpretability. In this study, we propose an interpretable deep learning method called multi-omic embedded topic model (moETM) to effectively perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder for efficient variational inference and then employs multiple linear decoders to learn the multi-omic signatures of the gene regulatory programs. Through comprehensive experiments on public single-cell transcriptome and chromatin accessibility data (i.e., scRNA+scATAC), as well as scRNA and proteomic data (i.e., CITE-seq), moETM demonstrates superior performance compared with six state-of-the-art single-cell data analysis methods on seven publicly available datasets. By applying moETM to the scRNA+scATAC data in human peripheral blood mononuclear cells (PBMCs), we identified sequence motifs corresponding to the transcription factors that regulate immune gene signatures. Applying moETM analysis to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omic biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.

Influence of Classroom Colour Environment on College Students' Emotions during Campus Lockdown in the COVID-19 Post-Pandemic Era-A Case Study in Harbin, China

Campus lockdown during COVID-19 and the post-pandemic era has had a huge negative effect on college students. As a vital part of interior teaching spaces, colour deeply influences college students' mental health and can be used for healing. Nevertheless, research on this topic has been limited. Based on colour psychology and colour therapy, this paper discusses the relationship between interior teaching space colours (hue and brightness) and emotions among college students. The HAD scale and questionnaire survey method were used. It was concluded that: (1) Anxiety and depression were prominent among the college student population during the quarantine of the university due to the epidemic. (2) Warm colours have an advantage over both cold and neutral colours in creating pleasure, relaxation, and mental attention, with the second in line being the cold and the last being the neutral. Warm colours make it pleasant for individuals while cold colours boost attention. (3) When subjects have higher values of anxiety and depression, they are less satisfied with the colour of the teaching space. (4) In most cases, there is no significant difference in the colour preference of teaching spaces across the gender, grade, and major groups, with females having a higher preference for warm high-brightness classrooms than males. These findings provide crucial ideas for future interior teaching space design and enrich the theories in colour psychology.

Safety of BNT162b2 or CoronaVac COVID-19 vaccines in patients with heart failure: A self-controlled case series study

Summary Background COVID-19 vaccines are important for patients with heart failure (HF) to prevent severe outcomes but the safety concerns could lead to vaccine hesitancy. This study aimed to investigate the safety of two COVID-19 vaccines, BNT162b2 and CoronaVac, in patients with HF. Methods We conducted a self-controlled case series analysis using the data from the Hong Kong Hospital Authority and the Department of Health. The primary outcome was hospitalization for HF and the secondary outcomes were major adverse cardiovascular events (MACE) and all hospitalization. We identified patients with a history of HF before February 23, 2021 and developed the outcome event between February 23, 2021 and March 31, 2022 in Hong Kong. Incidence rate ratios (IRR) were estimated using conditional Poisson regression to evaluate the risks following the first three doses of BNT162b2 or CoronaVac. Findings We identified 32,490 patients with HF, of which 3035 were vaccinated and had a hospitalization for HF during the observation period (BNT162b2 = 755;CoronaVac = 2280). There were no increased risks during the 0–13 days (IRR 0.64 [95% confidence interval 0.33–1.26];0.94 [0.50–1.78];0.82 [0.17–3.98]) and 14–27 days (0.73 [0.35–1.52];0.95 [0.49–1.84];0.60 [0.06–5.76]) after the first, second and third doses of BNT162b2. No increased risks were observed for CoronaVac during the 0–13 days (IRR 0.60 [0.41–0.88];0.71 [0.45–1.12];1.64 [0.40–6.77]) and 14–27 days (0.91 [0.63–1.32];0.79 [0.46–1.35];1.71 [0.44–6.62]) after the first, second and third doses. We also found no increased risk of MACE or all hospitalization after vaccination. Interpretation Our results showed no increased risk of hospitalization for HF, MACE or all hospitalization after receiving BNT162b2 or CoronaVac vaccines in patients with HF. Funding The project was funded by a Research Grant from the Food and Health Bureau, The Government of the Hong Kong Special Administrative Region (Ref. No. COVID19F01). F.T.T.L. (Francisco T.T. Lai) and I.C.K.W. (Ian C.K. Wong)'s posts were partly funded by the D24H;hence this work was partly supported by AIR@InnoHK administered by Innovation and Technology Commission.

Burnout, negative emotions, and wellbeing among social workers in China after community lockdowns during the COVID-19 pandemic: Mediating roles of trait mindfulness

Objective This study aimed to investigate burnout situation of social workers (SWs) who experienced the COVID-19 pandemic-related community lockdown 1 year before, and to assess the protective value of trait mindfulness (TM) in states of burnout. Method We surveyed the burnout, trait mindfulness, negative emotions (NEs) and wellbeing (WB) of 182 social workers provided services to Wuhan lockdowns community by COVID-19 one year before. Burnout were measured using the Maslach Burnout Inventory–Human Services Survey;TM using the Mindful Attention Awareness Scale;NEs using the Depression Anxiety and Stress Scale-21;and WB using the General Wellbeing Schedule. We also performed correlation regression analysis and mediation test for burnout, TM, NEs, and WB. Results Among the 182 respondents, 75 (41.2%) still suffered from severe burnout. TM was negatively correlated with burnout (r = −0.623), negatively correlated with NEs (r = −0.560), and positively correlated with WB (r = 0.617). Burnout had a significantly positive correlation with NEs (r = 0.544) and a significantly negative correlation with WB (r = −0.666). Further, WB had significantly negative correlation with NEs (r = −0.758). After controlling for age, gender, marital status, educational level, and years of employment, burnout had a significantly positive predictive effect on NEs (β = 0.509), whereas TM had a significantly negative predictive effect on NEs (β = −0.334). TM played a partial mediating role in the effect of burnout on NEs, with a mediating effect and effect ratio of 0.088 and 39.7%, respectively. Burnout had a significantly negative predictive effect on WB (β = −0.598), whereas TM had a significantly positive predictive effect on WB (β = 0.299). TM played a partial mediating role in the effect of burnout on NEs, with a mediating effect and effect ratio of −0.164 and 30.3%, respectively. WB had a significantly negative predictive effect on NEs (β = −0.711), and it played a partial mediating role in the effect of burnout on NEs, with a mediating effect and effect ratio of 0.185 and 83.3%, respectively. Conclusion The current levels of burnout among local SWs remained high 1 year after the community lockdowns. TM played a mediating role in the relationship between burnout, NEs, and WB. Concomitantly, WB played a mediating role in the relationship between burnout and NEs. Therefore, in the context of burnout, TM is a protective factor for reducing emotional stress and risks of developing psychiatric disorders through the enhancement of WB.

Characteristics of SARS - CoV - 2 nucleic acids and serum antibodies in different clinical specimens and their correlation with the course of COVID -19

To investigate the characteristics of the nucleic acids of severe acute respiratory syndrome coronavirus (SARS-CoV) -2 and antibodies in different specimens obtained from coronavirus disease 2019 (COVID-19) patients;if a correlation between these parameters and the disease course was present. The throat swabs and stool samples of 39 COVID-19 patients admitted to our hospital were collected in this study. Real-time reverse transcription-quantitative polymerase chain reaction (RT-PCR) was undertaken on throat swabs and stool samples. Peripheral blood was taken and serum levels of immunoglobulin IgM and IgG measured. Results showed That, Throat swabs and stool samples tested positive for the nucleic acid of SARS-CoV-Z, but nucleic acid levels were reduced significantly 15 days after disease onset compared with that upon diagnosis. The Ct value of the nucleic acid test was increased significantly. Serum levels of IgM and IgG were significantly higher than those of healthy people. nucleic acid loads in throat swabs and stool samples as well as serum levels of IgM and IgG were highly correlated with the disease course (r = 0.7387,0.5696, -0.546 and 0.6117,respectively, P < 0.05). In this study nucleic acid loads in throat swabs and stool samples as well as serum levels of IgM and IgG are highly correlated with the course of COVID-19.

Monovalent and trivalent VSV-based COVID-19 vaccines elicit potent neutralizing antibodies and immunodominant CD8+ T cells against diverse SARS-CoV-2 variants (preprint)

Recombinant vesicular stomatitis virus (rVSV) vaccines expressing Spike proteins of Wuhan, Beta and/or Delta variants of SARS-CoV-2 were generated and tested for induction of antibody and T cell immune responses in mice. rVSV-Wuhan and rVSV-Delta vaccines and a rVSV-Trivalent (mixed rVSV-Wuhan, -Beta, -Delta) vaccine elicited potent neutralizing antibodies (nAbs) against live SARS-CoV-2 Wuhan (USAWA1), Beta (B.1.351), Delta (B.1.617.2) and Omicron (B.1.1.529) viruses. Prime-boost vaccination with rVSV-Beta was less effective in this capacity. Heterologous boosting of rVSV-Wuhan with rVSV-Delta induced strong nAb responses against Delta and Omicron viruses, with rVSV-Trivalent vaccine consistently effective in inducing nAbs against all the SARS-CoV-2 variants tested. All vaccines, including rVSV-Beta, elicited a spike-specific immunodominant CD8+ T cell response. Collectively, rVSV vaccines targeting SARS-CoV-2 variants of concern may be considered in the global fight against COVID-19.

Classifying COVID-19 vaccine narratives (preprint)

Vaccine hesitancy is widespread, despite the government's information campaigns and the efforts of the World Health Organisation (WHO). Categorising the topics within vaccine-related narratives is crucial to understand the concerns expressed in discussions and identify the specific issues that contribute to vaccine hesitancy. This paper addresses the need for monitoring and analysing vaccine narratives online by introducing a novel vaccine narrative classification task, which categorises COVID-19 vaccine claims into one of seven categories. Following a data augmentation approach, we first construct a novel dataset for this new classification task, focusing on the minority classes. We also make use of fact-checker annotated data. The paper also presents a neural vaccine narrative classifier that achieves an accuracy of 84% under cross-validation. The classifier is publicly available for researchers and journalists.

Identify the natural and socio-economic influencing factors of the new coronavirus pneumonia（COVID-19） incidence rates in Chinese cities

This study explored the effects of both natural and socio-economic factors, such as city size and healthcare capacity, on the spreading of COVID-19 in China's urban population from January 1 to March 5, 2020. Several statistical models and machine learning methods were used to identify the key determinants of the incidence rate of COVID-19. Based on the interpretable machine learning framework, possible nonlinear relationships between incidences and key impact factors were explored. The results showed that the incidence rate of COVID-19 in cities was influenced by several factors simultaneously. Among the factors, the population inflow rate from Wuhan was the factor that showed the highest correlation coefficient（0.43）, followed by the population growth rate（0.38）. Population migration size, city size and healthcare capacity were the key influencing factors. Nonlinear relationships existed between the key influencing factors and incidence rates. To be specific, the inflow rate from Wuhan had a S-shaped relationship and reaches an asymptote after 2%;the population density had an approximately linear relationship;the per capita GDP showed an evident inverted U curve with the per capita GDP over 100,000 yuan as the inflection point. City development needs to pay more attention to population density control and economic growth in order to bring more health benefits.

Age Difference in Roles of Perceived Social Support and Psychological Capital on Mental Health During COVID-19

Due to the outbreak of the Coronavirus Disease-2019 (COVID-19) pandemic and consequent confinement measures, young people are vulnerable to mental health problems. The current study compared a group of 440 young adolescents (10–12 years) and a group of 330 emerging adults (18–25 years) to investigate the extent to which perceived social support and psychological capital (PsyCap) were differentially associated with mental health problems. Participants were asked to report their current psychosocial adaptation status during the COVID-19 pandemic, and data were collected via online questionnaires during a relatively severe period of COVID-19 in China. Results of the multi-group path analysis indicated that the effect of perceived social support on mental health problems was mediated by PsyCap for young adolescents, but not for emerging adults. These results were discussed with respect to the mechanism of how social support and PsyCap serve as protective mental health factors for youth in the context of the COVID-19 pandemic.

Assessment on Pedestrian-level Wind Around a Pocket Park with and Without Vegetation Inside Street Canyons

Creating a good local microclimate can alleviate urban heat islands and poor urban ventilation. During the COVID-19 epidemic, citizens' cross-city and cross-regional activities were restricted, and most activities were conducted in open/semi-open areas next to residential areas, and local pedestrians were also quantitatively explored. The new characteristics of the microclimate bring difficulties. The RNG k-ε model in the Reynolds time-average method is used to simulate and analyze the wind environment of a typical street valley with a pocket park in a hot summer and a cold winter, and explore whether there are plants in the pocket park. The results show that the results obtained by the used turbulence model, initial edge conditions and numerical method are in good agreement with the selected verification experimental results, which meet the needs of the wind environment simulation of the pocket park. Only under the action of the pocket park, the pedestrian area is dimensionless. The difference in wind speed can reach 0.5 compared to the time when there is no park. When the plants in the park are added, the average wind speed in the pedestrian area of ​​the surrounding street valley is less affected by the plants, and the dimensionless wind speed is only reduced by 0.1 in the core area of ​​the park. Pocket parks can significantly improve the low wind speed in pedestrian areas, and the research results can provide reference for the design of low-carbon livable blocks and microclimate simulation during the epidemic period.

Potential therapeutic effect and methods of traditional Chinese medicine on COVID-19-induced depression: A review.

COVID-19 (coronavirus) has spread all over the world with a high infection rate. Currently, there are no targeted therapeutic drugs for COVID-19 as well as for stress induced by COVID-19. The unpredictable events of COVID-19 can trigger feelings of fear, worry, or unease in people, leading to stress-related disorders such as depression and anxiety. It has been reported that individuals, including COVID-19 patients, medical staff, and ordinary people, are under both physical and psychological pressure, and many of them have developed depression or anxiety during this pandemic. Traditional Chinese medicine (TCM) has been widely used in treating depression with relatively better safety and efficacy and may have an important role in treating stress-related disorders induced by COVID-19. In this review, we collected the common TCM treatment methods including Qigong, Acupuncture, Five Elements Musical Therapy, Five Elements Emotional Therapy, and Chinese herbal medicine from the databases of PubMed and the China National Knowledge Internet to illustrate the effect of TCM on depression. The better knowledge of TCM and implementation of TCM in COVID-19 clinics may help to effectively improve depression induced by COVID-19, may assist people to maintain a healthy physical and mental quality, and may alleviate the current shortage of medical resources.

The effect of Health Literacy on COVID-19 Vaccine Hesitancy: The Moderating Role of Stress (preprint)

Abstract Background The COVID-19 vaccine is an essential means to establish group immunity and prevent the spread of the pandemic. However, the public's hesitation has created major difficulties to the promotion of the vaccine. By investigating the relationship between health literacy and COVID-19 vaccine hesitancy, as well as the potential moderating role of stress, the present study would provide critical insights for tailoring vaccine-promotion strategies. Objective The two-fold research purpose is: i) address the effect of health literacy on people's attitude toward COVID-19 vaccine, ii) clarify the role of stress in this effect. Method With structured questionnaires, an online survey was conducted to evaluate general public's COVID-19 vaccine hesitancy, health literacy, and perceived stress. In total, 560 responses were collected, and moderated regression analysis was conducted to test the effect of health literacy on vaccine hesitancy among people with different levels of stress. Results A total of 560 participants aged over 18 years were included in this study. About 39.8% of the respondents reported vaccine hesitancy, and this rate is higher among those aged 20-30 years old (83%) and female (71.3%). The results showed people with higher level of health literacy are less likely to have vaccine hesitancy . However, this effect was only among those with lower to moderate level of stress , among the people with high stress, no significant effect of health literacy was found . Conclusions By focusing on the effect of health literacy on COVID-19 vaccine hesitancy, the findings showed education program increasing individual's health literacy may also effectively reduce the public's vaccine hesitancy and promote accepting attitude. However, for people with high level of stress, other health programs need to be developed to enhance their positive attitude toward the COVID-19 vaccine. In conclusion, promotion strategies should be tailored for different populations, with considering individual factors such as health literacy and stress. Keywords vaccine hesitancy; health literacy; stress; moderation

Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19 (preprint)

The COVID-19 global pandemic has highlighted the importance of non-pharmacological interventions (NPI) for controlling epidemics of emerging infectious diseases. Despite the importance of NPI, their implementation has been monitored in an ad hoc and uncoordinated manner, mainly through the manual efforts of volunteers. Given the absence of systematic NPI tracking, authorities and researchers are limited in their ability to quantify the effectiveness of NPI and guide decisions regarding their use during the progression of a global pandemic. To address this issue, we propose 3-stage machine learning framework called EpiTopics to facilitate the surveillance of NPI by mining the vast amount of unlabelled news reports about these interventions. Building on topic modeling, our method characterizes online government reports and media articles related to COVID-19 as a mixture of latent topics. Our key contribution is the use of transfer-learning to address the limited number of NPI-labelled documents and topic modelling to support interpretation of the results. At stage 1, we trained a modified version of the unsupervised dynamic embedded topic model (DETM) on 1.2 million international news reports related to COVID-19. At stage 2, we used the trained DETM to infer topic mixture from a small set of 2000 NPI-labelled WHO documents as the input features for predicting NPI labels on each document. At stage 3, we supply the inferred country-level temporal topics from the DETM to the pretrained document-level NPI classifier to predict country-level NPIs. We identified 25 interpretable topics, over 4 distinct and coherent COVID-related themes. These topics contributed to significant improvements in predicting the NPIs labelled in the WHO documents and in predicting country-level NPIs. Together, our work lay the machine learning methodological foundation for future research in global-scale surveillance of public health interventions. The EpiTopics code is available at GitHub: https://github.com/li-lab-mcgill/covid-npi.

Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19 (preprint)

The COVID-19 global pandemic has highlighted the importance of non-pharmacological interventions (NPI) for controlling epidemics of emerging infectious diseases. Despite the importance of NPI, their implementation has been monitored in an ad hoc and uncoordinated manner, mainly through the manual efforts of volunteers. Given the absence of systematic NPI tracking, authorities and researchers are limited in their ability to quantify the effectiveness of NPI and guide decisions regarding their use during the progression of a global pandemic. To address this issue, we propose 3-stage machine learning framework called EpiTopics to facilitate the surveillance of NPI by mining the vast amount of unlabelled news reports about these interventions. Building on topic modeling, our method characterizes online government reports and media articles related to COVID-19 as a mixture of latent topics. Our key contribution is the use of transfer-learning to address the limited number of NPI-labelled documents and topic modelling to support interpretation of the results. At stage 1, we trained a modified version of the unsupervised dynamic embedded topic model (DETM) on 1.2 million international news reports related to COVID-19. At stage 2, we used the trained DETM to infer topic mixture from a small set of 2000 NPI-labelled WHO documents as the input features for predicting NPI labels on each document. At stage 3, we supply the inferred country-level temporal topics from the DETM to the pretrained document-level NPI classifier to predict country-level NPIs. We identified 25 interpretable topics, over 4 distinct and coherent COVID-related themes. These topics contributed to significant improvements in predicting the NPIs labelled in the WHO documents and in predicting country-level NPIs. Together, our work lay the machine learning methodological foundation for future research in global-scale surveillance of public health interventions. The EpiTopics code is available at GitHub: https://github.com/li-lab-mcgill/covid-npi.

Association of state social distancing restrictions with nursing home COVID-19 and non-COVID-19 outcomes (preprint)

The COVID-19 poses a disproportionate threat to nursing home residents. Although recent studies suggested the effectiveness of state social distancing measures in the United States on curbing COVID-19 morbidity and mortality among the general population, there is lack of evidence as to how these state orders may have affected nursing home patients or what potential negative health consequences they may have had. In this longitudinal study, we evaluated changes in state strength of social distancing restrictions from June to August of 2020, and their associations with the weekly numbers of new COVID-19 cases, new COVID-19 deaths, and new non-COVID-19 deaths in nursing homes of the US. We found that stronger state social distancing measures were associated with improved COVID-19 outcomes (case and death rates), reduced across-facility disparities in COVID-19 outcomes, but more deaths due to non-COVID-19 reasons among nursing home residents.