Chinese university students showed less disordered eating during the COVID-19 campus lockdown.

OBJECTIVE: The rapid spread of the Omicron variant of COVID-19 in China had resulted in campus lockdown in many universities since February 2022, profoundly affecting students' daily lives. Campus lockdown conditions differ considerably from home quarantine, so that the eating patterns of university students may be different. Thus, the current study aimed to: (1) investigate university students' eating patterns during campus lockdown; (2) identify factors associated with their disordered eating. METHOD: An online survey about recent life changes, disordered eating, stress, depression, and anxiety was carried out from April 8th to May 16th, 2022. A total of 2541 responses from 29 provinces/cities of China were received. RESULTS: 2213 participants were included in the main analysis, and other 86 participants were analyzed separately as a subgroup due to their diagnosis of eating disorder. Participants who were undergoing campus lockdown (the lockdown group) showed less disordered eating than those who had never been in campus lockdown (the never-lockdown group), as well as those who had experienced campus lockdown before (the once-lockdown group). However, they perceived more stress and felt more depressed. Being female, higher BMI, gaining weight, increasing exercise, spending more time on social media, higher level of depression and anxiety were all related to disordered eating in the lockdown group. CONCLUSIONS: Disordered eating among Chinese university students was less prevalent during campus lockdown due to the strict and regular diet. However, there is a potential risk of "revenge eating" after campus lockdown ends. Thus, there should be further tracking and related prevention. LEVEL OF EVIDENCE: IV, uncontrolled trials without any interventions.

Etiological identification and genetic source tracking of a cluster of cases with fever during the period of regular prevention and control of COVID-19 epidemic

Objective To identify the pathogen and track the genetic source of a cluster of cases with fever in a kindergarten in Fengtai district during the normalization of COVID-19 prevention and control in Beijing.Methods A descriptive analysis method was used to investigate this cluster of cases with fever in April 2021.Pharyngeal swabs were collected and viral nucleic acid was extracted, real-time PCR was performed to identify SARS-CoV-2 and other common respiratory virus. G gene of human metapneumovirus（hMPV） was amplified by RT-PCR and was then sequenced. BioEdit was used for G gene sequence analysis and the Neighbor-Joining model in MEGA 5. 0 software was used to construct the phylogenic tree of G gene. Results A total of 16 cases were reported in one class with the incidence of 53. 3%（16/30） during 8 days of a cluster outbreak. All pharyngeal swabs collected from 12 cases were tested SARS-CoV-2 negative, six were found to be hMPV positive by multiplex-PCR, and one was positive for both human adenovirus and hMPV. Full-length sequences of G genes were obtained from 2 strains of hMPV. Sequence analysis showed that both strains were hMPV B2 and the nucleic acid homology of G gene was 96. 73%-98. 01% with strains from Japan（LC337940, LC337935, LC1922349） in 2016 and over 98. 40%with strains from Shandong（OL625642, OL625644） in 2019, Henan MN944096 in 2019.Compared with the amino acid sequence of hMPV-B2 reference strain（AY297748）, six amino acid insertions containing EKEKEK were identified between 161-166 amino acid location and N-glycosylation of G protein analysis showed that the two strains had four N-glycosylation sites. Conclusions The leading pathogen for this cluster outbreak is found to be hMPV-B2, which are highly homologous with strains from Japan, Shandong and Henan. Therefore, a non-stop surveillance of hMPV is necessary during the normalization control and prevention period for COVID-19.

Using an influenza surveillance system to estimate the number of SARS-CoV-2 infections in Beijing, China, weeks 2 to 6 2023.

With COVID-19 public health control measures downgraded in China in January 2023, reported COVID-19 case numbers may underestimate the true numbers after the SARS-CoV-2 Omicron wave. Using a multiplier model based on our influenza surveillance system, we estimated that the overall incidence of SARS-CoV-2 infections was 392/100,000 population in Beijing during the 5 weeks following policy adjustment. No notable change occurred after the Spring Festival in early February. The multiplier model provides an opportunity for assessing the actual COVID-19 situation.

Epidemiological features, genomic characteristics, and origin tracing of the COVID-19 outbreaks in Beijing from January to September 2022.

Different variants of severe acute respiratory syndrome coronavirus 2 have been discovered globally. At present, the Omicron variant has been extensively circulated worldwide. There have been several outbreaks of the Omicron variant in China. Here, we investigated the epidemiologic, genetic characteristics, and origin-tracing data of the outbreaks of COVID-19 in Beijing from January to September 2022. During this time, 19 outbreaks occurred in Beijing, with the infected cases ranging from 2 to 2230. Two concern variants were detected, with eight genotypes. Based on origin tracing analysis, two outbreaks were from the cold-chain transmission and three from items contaminated by humans. Imported cases have caused other outbreaks. Our study provided a detailed analysis of Beijing's outbreaks and valuable information to control the outbreak's spread.

Interruption of Influenza Transmission under Public Health Emergency Response for COVID-19: a Study Based on Real-World Data from Beijing, China.

To estimate the effect of the corona virus disease 2019 (COVID-19) control measures taken to mitigate community transmission in many regions, we analyzed data from the influenza surveillance system in Beijing from week 27 of 2014 to week 26 of 2020. We collected weekly numbers of influenza-like illness (ILI) cases, weekly positive proportion of ILI cases, weekly ILI case proportion in outpatients, and the dates of implementation of COVID-19 measures. We compared the influenza activity indicators of the 2019/2020 season with the preceding five seasons and built two ARIMAX models to estimate the effectiveness of COVID-19 measures declared since January 24, 2020 by the emergency response. Based on the observed data, compared to the preceding five influenza seasons, ILIs, positive proportion of ILIs, and duration of the influenza epidemic period in 2019/2020 had increased from 13% to 54%; in particular, the number of weeks from the peak to the end of the influenza epidemic period had decreased from 12 to 1. According to ARIMAX model forecasting, after considering natural decline, weekly ILIs had decreased by 48.6%, weekly positive proportion had dropped by 15% in the second week after the emergency response was declared, and COVID-19 measures had reduced by 83%. We conclude that the public health emergency response can significantly interrupt the transmission of influenza.

Changing Perceptions and Uses of 'Companion Animal';Public and Pseudo-Public Spaces in Cities during COVID-19 Pandemic: The Case of Beijing

This paper examines the debate over the place of 'companion animal';public space in China's cities. With the COVID-19 outbreak, this debate has entered a new phase, where the social response to the outbreak may have fundamentally changed the public's use and perception of 'companion animal';public and pseudo-public space. This paper combines quantitative and qualitative analysis of posts and comments on two of China's largest social media platforms with a big data approach, based on a case study in Beijing, China. There were statistically significant differences in the perception and use of 'companion animal';public spaces and pseudo-public spaces before and after the pandemic. We attribute the impact of the pandemic on 'companion animal';spaces to three pathways: changes in opportunity, changes in ability, and changes in motivation. We found that the pandemic led to an increase in the amount of time available to some people but a decrease in the amount of 'companion animal';public space available due to the pandemic closure. In addition, the use of 'companion animal';public spaces in pseudo-public spaces declined, while those located within the open urban green space on the city's outskirts stood out after the outbreak. With the normalisation of the pandemic, there will be new challenges for the development and operation of companion-animal-related public spaces in cities, which will be the next focus of research. In addition, governments and social media should work together to promote and support sustainable animal ethical practices to better respond to the crisis. These findings will help complement the urban services system and guide future planning, design, and evaluation of related spaces.

Nanobody-based label-free photoelectrochemical immunoassay for highly sensitive detection of SARS-CoV-2 spike protein.

Until now, COVID-19 caused by SARS-CoV-2 is engulfing the worldwide and still ranging to date, continuing to threaten the public health. The main challenge facing COVID-19 epidemic is short of fast-response and high-efficiency methods to determine SARS-CoV-2 viral pathogens. Herein, a nanobody-based label-free photoelectrochemical (PEC) immunosensor has been fabricated for rapidly detecting SARS-CoV-2 spike protein. As a small-size and high-stability antibody, nanobody was directly and well immobilized with Au nanoparticles and TiO2 spheres by the interaction. Au deposited TiO2 nanomaterial possessed 8.5 times photoelectric performance in comparison with TiO2 in the presence of electron donor owing to surface plasma resonance effect of Au. Based on the steric hindrance effect, this immunoassay platform realized the linear detection from 0.015 to 15000 pg mL-1, and a limit of detection was low as 5 fg mL-1. The label-free PEC immunoassay design provides a new idea for convenient, rapid, and efficient test of SARS-CoV-2 spike protein and broadens further application of nanobody as an identification agent to specific biomarkers.

Establishment of Routine Clinical Indicators-Based Nomograms for Predicting the Mortality in Patients With COVID-19.

This study aimed to establish and validate the nomograms to predict the mortality risk of patients with coronavirus disease 2019 (COVID-19) using routine clinical indicators. This retrospective study included a development cohort enrolled 2,119 hospitalized patients with COVID-19 and a validation cohort included 1,504 patients with COVID-19. The demographics, clinical manifestations, vital signs, and laboratory tests of the patients at admission and outcome of in-hospital death were recorded. The independent factors associated with death were identified by a forward stepwise multivariate logistic regression analysis and used to construct the two prognostic nomograms. The nomogram 1 was a full model to include nine factors identified in the multivariate logistic regression and nomogram 2 was built by selecting four factors from nine to perform as a reduced model. The nomogram 1 and nomogram 2 showed better performance in discrimination and calibration than the Multilobular infiltration, hypo-Lymphocytosis, Bacterial coinfection, Smoking history, hyper-Tension and Age (MuLBSTA) score in training. In validation, nomogram 1 performed better than nomogram 2 for calibration. We recommend the application of nomogram 1 in general hospitals which provide robust prognostic performance though more cumbersome; nomogram 2 in the out-patient, emergency department, and mobile cabin hospitals, which depend on less laboratory examinations to make the assessment more convenient. Both the nomograms can help the clinicians to identify the patients at risk of death with routine clinical indicators at admission, which may reduce the overall mortality of COVID-19.

Effects of novel Coronavirus pneumonia pandemic on the spread of multidrug-resistant Acinetobacter in ICU

OBJECTIVE: To investigate the effect of novel coronavirus pneumonia pandemic on multidrug-resistant Acinetobacter baumannii transmission in intensive care units(ICU) and to provide reference for the guidance of nosocomial infection prevention and control. METHODS: Data of multidrug-resistant A. baumannii in ICU1, ICU2 and neurosurgery ICU from Jan. 2019 to Aug. 2020 were collected. Contact times between healthcare workers and environmental surfaces, clearance rate of fluorescent marker in the culture of environment specimens before and after the novel coronavirus pneumonia outbreak, number of free colonies and number of MDR-AB were detected, and the correlation between MDR-AB detection rate of the environment and MDR-AB the infection rate were analyzed by using Pearson correlation analysis. RESULTS: The high-frequency contact surfaces in ICU were bed bars(43.59 times), quilts(39.58 times), treatment vehicles(30.83 times), vein tubes(27.46 times), nursing station table tops(27.20 times) and hand sterilized buttons(26.40 times). The clearance rate of fluorescent marker after novel coronavirus pneumonia outbreak was 81.35%, which was significantly higher than that before the new coronavirus-infected pneumonia epidemic outbreak(P<0.001), with the rate of 56.57%. The detection rate and infection rate of MDR-AB in the environment after new coronavirus-infected pneumonia epidemic were 5.40% and 1.80%, respectively, which were significantly lower than that before the epidemic(P<0.001). Pearson correlation analysis showed that the detection rate of MDR-AB in the ICU environment was positively correlated with the MDR-AB infection rate of patients(r=0.850, P=0.002). CONCLUSION: Methods of novel coronavirus pneumonia epidemic prevention promoted the implementation of environmental sanitation, disinfection and isolation measures, cut off the transmission route of multidrug-resistant A. baumannii, and played a positive role in the prevention and control of infection.

Major Characteristics of Severity and Mortality in Diabetic Patients With COVID-19 and Establishment of Severity Risk Score.

Objectives: Diabetes is a risk factor for poor COVID-19 prognosis. The analysis of related prognostic factors in diabetic patients with COVID-19 would be helpful for further treatment of such patients. Methods: This retrospective study involved 3623 patients with COVID-19 (325 with diabetes). Clinical characteristics and laboratory tests were collected and compared between the diabetic group and the non-diabetic group. Binary logistic regression analysis was applied to explore risk factors associated in diabetic patients with COVID-19. A prediction model was built based on these risk factors. Results: The risk factors for higher mortality in diabetic patients with COVID-19 were dyspnea, lung disease, cardiovascular diseases, neutrophil, PLT count, and CKMB. Similarly, dyspnea, cardiovascular diseases, neutrophil, PLT count, and CKMB were risk factors related to the severity of diabetes with COVID-19. Based on these factors, a risk score was built to predict the severity of disease in diabetic patients with COVID-19. Patients with a score of 7 or higher had an odds ratio of 7.616. Conclusions: Dyspnea is a critical clinical manifestation that is closely related to the severity of disease in diabetic patients with COVID-19. Attention should also be paid to the neutrophil, PLT count and CKMB levels after admission.

Role of presymptomatic transmission of COVID-19: evidence from Beijing, China.

BACKGROUND: The presymptomatic transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been documented in limited clusters, and it is predicted through modelling. However, there is a lack of evidence from observations with a large sample size. METHODS: We used data from meticulous contact tracing of people exposed to cases of SARS-CoV-2 to estimate the proportion of cases that result from the presymptomatic transmission of the virus in Beijing during January 2020 and February 2020. RESULTS: The results showed that presymptomatic transmission occurred in at least 15% of 100 secondary COVID-19 cases. The earliest presymptomatic contact event occurred 5 days prior to the index case's onset of symptoms, and this occurred in two clusters. CONCLUSIONS: The finding suggested that the contact tracing period should be earlier and highlighted the importance of preventing transmission opportunities well before the onset of symptoms.

Unlikely SARS-CoV-2 vertical transmission from mother to child: A case report.

As the 2019 novel coronavirus disease (COVID-19) rapidly spread across China and to more than 70 countries, an increasing number of pregnant women were affected. The vertical transmission potential of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of great concern to the obstetrics, neonatologists, and public health agencies. Though some studies indicated the risk of vertical transmission is low, few cases have been reported with comprehensive serial tests from multiple specimens. In this case, a female preterm infant was born to a mother with confirmed COVID-19. She presented with mild respiratory distress and received general management and a short period of nasal continuous positive airway pressure support. During her stay at the hospital, a series of SARS-CoV-2 nucleic test from her throat and anal swab, serum, bronchoalveolar lavage fluid, and urine were negative. The nucleic acid test from the mother's amniotic fluid, vaginal secretions, cord blood, placenta, serum, anal swab, and breast milk were also negative. The most comprehensively tested case reported to date confirmed that the vertical transmission of COVID is unlikely, but still, more evidence is needed.

Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo

During the ongoing outbreak of coronavirus disease (COVID-19), people use social media to acquire and exchange various types of information at a historic and unprecedented scale. Only the situational information are valuable for the public and authorities to response to the epidemic. Therefore, it is important to identify such situational information and to understand how it is being propagated on social media, so that appropriate information publishing strategies can be informed for the COVID-19 epidemic. This article sought to fill this gap by harnessing Weibo data and natural language processing techniques to classify the COVID-19-related information into seven types of situational information. We found specific features in predicting the reposted amount of each type of information. The results provide data-driven insights into the information need and public attention.