Exploring lemology teaching with "internet plus" flipped classroom pedagogy.

BACKGROUND: To investigate the use of flipped classroom pedagogy based on "Internet plus" in teaching viral hepatitis in the lemology course during the COVID-19 epidemic. METHODS: This study included students from the clinical medicine general practitioner class at Nanjing Medical University's Kangda College, with the observation group consisting of 67 students from the 2020-2021 school year and the control group consisting of 70 students from the 2019-2020 school year. The observation group used "Internet plus" flipped classroom pedagogy, while the control group used conventional offline instruction. The theory course and case analysis ability scores from the two groups were compared and analyzed, and questionnaire surveys were administered to the observation group. RESULT: After the flipped classroom, the observation group had significantly higher theoretical test scores (38.62 ± 4.52) and case analysis ability scores (21.08 ± 3.58) than the control group (37.37 ± 2.43) (t = 2.024, P = 0.045) and (19.16 ± 1.15) (t = 4.254, P < 0.001), respectively. The questionnaire survey in the observation group revealed that the "Internet plus" flipped classroom pedagogy approach can help enhance students' enthusiasm to learn, clinical thinking ability, practical application ability, and learning efficiency, with satisfaction rates of 81.7%, 85.0%, 83.3%, and 78.8%, respectively; 89.4% of students expressed hope that whenever physical classes resumed, the offline courses could be combined with this pedagogy approach. CONCLUSION: The use of the "Internet plus" flipped classroom pedagogy technique for teaching viral hepatitis in a lemology course boosted students' theory learning ability as well as their case analysis ability. The majority of students were pleased with this type of instruction and hoped that whenever physical classes resumed, the offline courses may be integrated with the "Internet plus" flipped classroom pedagogical approach.

Association of Glucose-Lowering Drugs With Outcomes in Patients With Diabetes Before Hospitalization for COVID-19: A Systematic Review and Network Meta-analysis.

Importance: Patients with COVID-19 have a high prevalence of diabetes, and diabetes and blood glucose control are determinants of intensive care unit admission and mortality. Objective: To evaluate the association between COVID-19-related adverse outcomes and 8 antihyperglycemic drugs in patients with diabetes who were subsequently diagnosed and hospitalized with COVID-19. Data Sources: Data were retrieved and collected in PubMed, Embase, Cochrane Central Register, Web of Science, and ClinicalTrials.gov from database inception to September 5, 2022. Study Selection: For this systematic review and network meta-analysis, randomized clinical trials and observational studies conducted among patients with diabetes while receiving glucose-lowering therapies for at least 14 days before the confirmation of COVID-19 infection were included after blinded review by 2 independent reviewers and consultations of disagreement by a third independent reviewer. Of 1802 studies initially identified, 31 observational studies met the criteria for further analysis. Data Extraction and Synthesis: This study follows the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline. Bayesian network meta-analyses were performed with random effects. Main Outcomes and Measures: A composite adverse outcome, including the need for intensive care unit admission, invasive and noninvasive mechanical ventilation, or in-hospital death. Results: Thirty-one distinct observational studies (3 689 010 patients with diabetes hospitalized for COVID-19) were included. The sodium-glucose cotransporter-2 inhibitors (SGLT-2is) were associated with relatively lower risks of adverse outcomes compared with insulin (log of odds ratio [logOR], 0.91; 95% credible interval [CrI], 0.57-1.26), dipeptidyl peptidase-4 inhibitors (logOR, 0.61; 95% CrI, 0.28-0.93), secretagogues (logOR, 0.37; 95% CrI, 0.02-0.72), and glucosidase inhibitors (logOR, 0.50; 95% CrI, 0.00-1.01). Based on the surface under the cumulative ranking curves value, SGLT-2is were associated with the lowest probability for adverse outcomes (6%), followed by glucagon-like peptide-1 receptor agonists (25%) and metformin (28%). A sensitivity analysis revealed that the study was reliable. Conclusions and Relevance: These findings suggest that the use of an SGLT-2i before COVID-19 infection is associated with lower COVID-19-related adverse outcomes. In addition to SGLT-2is, glucagon-like peptide-1 receptor agonists and metformin were also associated with relatively low risk of adverse outcomes.

WebFace260M: A Benchmark for Million-Scale Deep Face Recognition.

In this paper, we contribute a new million-scale recognition benchmark, containing uncurated 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training data, as well as an elaborately designed time-constrained evaluation protocol. Firstly, we collect 4M name lists and download 260M faces from the Internet. Then, a Cleaning Automatically utilizing Self-Training pipeline is devised to purify the tremendous WebFace260M, which is efficient and scalable. To our best knowledge, the cleaned WebFace42M is the largest public face recognition training set in the community. Referring to practical deployments, Face Recognition under Inference Time conStraint (FRUITS) protocol and a new test set with rich attributes are constructed. Moreover, we gather a large-scale masked face sub-set for biometrics assessment under COVID-19. For a comprehensive evaluation of face matchers, three recognition tasks are performed under standard, masked and unbiased settings, respectively. Equipped with this benchmark, we delve into million-scale face recognition problems. Enabled by WebFace42M, we reduce 40% failure rate on the challenging IJB-C set and rank the 3rd among 430 entries on NIST-FRVT. Even 10% data (WebFace4M) shows superior performance compared with the public training set. The proposed benchmark shows enormous potential on standard, masked and unbiased face recognition scenarios.

Influences of obstacle factors on the transmission trends of respiratory infectious diseases in indoor public places

Public facilities are important transmission places for respiratory infectious diseases (e.g., COVID-19), due to the frequent crowd interactions inside. Usually, changes of obstacle factors can affect the movements of human crowds and result in different epidemic transmissions among individuals. However, most related studies only focus on the specific scenarios, but the common rules are usually ignored for the impacts of obstacles' spatial elements on epidemic transmission. To tackle these problems, this study aims to evaluate the impacts of three spatial factors of obstacles (i.e., size, quantity, and placement) on infection spreading trends in two-dimension, which can provide scientific and concise spatial design guidelines for indoor public places. Firstly, we used the obstacle area proportion as the indicator of the size factor, gave the mathematical expression of the quantity factor, and proposed the walkable-space distribution indicator to represent the placement factor by introducing the Space Syntax. Secondly, two spreading epidemic indicators (i.e., daily new cases and people's average exposure risk) were estimated based on the fundamental model named exposure risk with the virion-laden particles, which accurately forecasted the disease spreading between individuals. Thirdly, 120 indoor scenarios were built and simulated, based on which the value of independent and dependent variables can be measured. Besides, structural equation modeling was employed to examine the effects of obstacle factors on epidemic transmissions. Finally, three obstacle-related guidelines were provided for policymakers to mitigate the disease spreading: minimizing the size of obstacles, dividing the obstacle into more sub-ones, and placing obstacles evenly distributed in space.

An innovative two-wing model for balancing the demands of inpatients with COVID-19 and general medical service in a designated hospital for COVID-19 in Shenzhen, China.

Since COVID-19 was first reported in 2019, the pandemic has posed a great threat to human health. Due to its multiple transmission pathways and virus mutation, this epidemic may be protracted further, and it has already placed a heavy burden on healthcare systems. A strategy needs to be devised to address both needs for COVID-19 treatment and demands for general medical service. A two-wing model of hospital operation, which provides a safe treatment environment for patients, an On duty/On Standby work approach for medical staff, and a reliable surveillance system for hospital operation, is an effective management template to help achieve a balance between multiple demands for medical service in this new era of a long-term war against COVID-19.

Predictive value of HbA1c for in-hospital adverse prognosis in COVID-19: A systematic review and meta-analysis.

BACKGROUND AND AIMS: Clinical and laboratory predictors of adverse clinical course and death in COVID-19 patients urgently need to be identified. So far, the association between HbA1c and in-hospital mortality of COVID-19 remains a controversial issue. The aim of this study is to analyze predictive value of HbA1c for adverse prognosis in COVID-19. METHODS: Both Chinese and English databases were systematically searched using specific keywords associated with the aims until November 21th, 2020. The Newcastle-Ottawa Scale (NOS) was used for quality assessment. A Statistical analysis was carried out using Review Manager 5.3 and STATA 15.1. RESULTS: Nine clinical trials were included in this study involving 2577 subjects. The results indicate that the association between elevated HbA1c referred as a continuous variable and adverse prognosis of COVID-19 was not significant (OR, 1.02; 95%CI, 0.95-1.09). However, higher HbA1c levels regarded as a dichotomous variable contributed to an increase mortality of COVID-19 (OR, 2.300; 95%CI, 1.679-3.150). Results were stable in a sensitivity analysis. More studies are needed to demonstrate the effect of HbA1c on hospital mortality. CONCLUSION: Prolonged uncontrolled hyperglycemia increases the risk of adverse prognosis in COVID-19. Patients with higher HbA1c should be monitored strictly to minimize the risk of adverse prognosis in COVID-19.

Is there a difference in the efficacy of COVID-19 vaccine in males and females? - A systematic review and meta-analysis.

INTRODUCTION: Females generally have higher antibody responses to viral vaccines. Our objectives were to compare gender differences in the efficacy of COVID-19 vaccination. METHODS: Data sources: Studies from PubMed, Embase, Cochrane Central Register, Web of Science, ClinicalTrials.gov, and the World Health Organization's International Clinical Trials Registry Platform. RESULTS: We included four eligible trials; all were categorized as having a low risk of bias. COVID-19 vaccine was significantly effective in both males and females. Slightly more SARS-CoV-2 infections were recorded in females than in males, but the difference was not significant (RR 1.064 [0.888-1.274]; p = .502, I2 = 5.7%; p = .367, 643,127 participants). CONCLUSION: Despite significant biological and behavioral differences between males and females, we found no significant gender differences in the efficacy of the COVID-19 vaccines, especially in younger populations. Further pragmatic trials are needed to confirm the gender differences in protective response of different types of vaccines to different age groups.

Quantifying the impact of COVID-19 on e-bike safety in China via multi-output and clustering-based regression models.

The impacts of COVID-19 on travel demand, traffic congestion, and traffic safety are attracting heated attention. However, the influence of the pandemic on electric bike (e-bike) safety has not been investigated. This paper fills the research gap by analyzing how COVID-19 affects China's e-bike safety based on a province-level dataset containing e-bike safety metrics, socioeconomic information, and COVID-19 cases from 2017 to 2020. Multi-output regression models are adopted to investigate the overall impact of COVID-19 on e-bike safety in China. Clustering-based regression models are used to examine the heterogeneous effects of COVID-19 and the other explanatory variables in different provinces/municipalities. This paper confirms the high relevance between COVID-19 and the e-bike safety condition in China. The number of COVID-19 cases has a significant negative effect on the number of e-bike fatalities/injuries at the country level. Moreover, two clusters of provinces/municipalities are identified: one (cluster 1) with lower and the other (cluster 2 that includes Hubei province) higher number of e-bike fatalities/injuries. In the clustering-based regressions, the absolute coefficients of the COVID-19 feature for cluster 2 are much larger than those for cluster 1, indicating that the pandemic could significantly reduce e-bike safety issues in provinces with more e-bike fatalities/injuries.

Efficacy of Huoxiang Zhengqi dropping pills and Lianhua Qingwen granules in treatment of COVID-19: A randomized controlled trial.

BACKGROUND: With the global epidemic of coronavirus disease (COVID-19), China has made progress in the prevention and control of the epidemic, and traditional Chinese medicine (TCM) has played a key role in dealing with the disease's effects on the respiratory system. This randomized controlled clinical trial evaluated the clinical efficacy and prognosis of Huoxiang Zhengqi dropping pills and Lianhua Qingwen granules in patients with COVID-19. METHODS: A total of 283 patients participated in this clinical trial, and participants were randomly assigned to receive either 1) Huoxiang Zhengqi dropping pills and Lianhua Qingwen granules or 2) Linahua granules, both combined with western medicine, or 3) western medicine alone for 14 days. At the end of the trial, the improvement and resolution rates of clinical symptoms and the rate of patients who progressed to severe disease status were evaluated. RESULTS: After 14 days of treatment, there was no significant difference in the improvement rate of clinical symptoms among the three groups (P > 0.05). Huoxiang Zhengqi dropping pills combined with Lianhua Qingwen granules has advantages in the treatment of nausea, vomiting and limb soreness. During treatment, all participants were treated with western medicine, and there was a significant difference in the use of macrolides among the three groups (P < 0.05). Specifically, the utilization rate of antibiotics in the western medicine group was significantly greater than that of the other two groups. Among the 182 diagnosed patients who completed this clinical trial, 13 patients progressed to severe disease, including one case in the Huoxiang + Lianhua group (1.6 %), five cases in the Lianhua group (8.6 %), and seven cases in the western medicine group (11.1 %). There was no statistical differences in this rate among the three groups (P > 0.05). However, the proportion of patients who progressed to severe disease in the Huoxiang + Lianhua group was the lowest, suggesting that the combination of TCM with western medicine has a potential advantage in improving the prognosis of patients with COVID-19. CONCLUSION: The use of Huoxiang Zhengqi dropping pills and Lianhua Qingwen granules combined with western medicine may have clinical advantages for COVID-19 patients in improving clinical symptoms, reducing utilization rate of anti-infective drugs, and improving patient prognosis, which could pave the way for the use of complementary medicine in treating this infection.

Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: A pedestrian dynamics-based microscopic simulation approach.

Mathematical modeling of epidemic spreading has been widely adopted to estimate the threats of epidemic diseases (i.e., the COVID-19 pandemic) as well as to evaluate epidemic control interventions. The indoor place is considered to be a significant epidemic spreading risk origin, but existing widely-used epidemic spreading models are usually limited for indoor places since the dynamic physical distance changes between people are ignored, and the empirical features of the essential and non-essential travel are not differentiated. In this paper, we introduce a pedestrian-based epidemic spreading model that is capable of modeling indoor transmission risks of diseases during people's social activities. Taking advantage of the before-and-after mobility data from the University of Maryland COVID-19 Impact Analysis Platform, it's found that people tend to spend more time in grocery stores once their travel frequencies are restricted to a low level. In other words, an increase in dwell time could balance the decrease in travel frequencies and satisfy people's demands. Based on the pedestrian-based model and the empirical evidence, combined non-pharmaceutical interventions from different operational levels are evaluated. Numerical simulations show that restrictions on people's travel frequency and open hours of indoor places may not be universally effective in reducing average infection risks for each pedestrian who visit the place. Entry limitations can be a widely effective alternative, whereas the decision-maker needs to balance the decrease in risky contacts and the increase in queue length outside the place that may impede people from fulfilling their travel needs. The results show that a good coordination among the decision-makers can contribute to the improvement of the performance of combined non-pharmaceutical interventions, and it also benefits the short-term and long-term interventions in the future.

Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan.

A mysterious outbreak of atypical pneumonia in late 2019 was traced to a seafood wholesale market in Wuhan of China. Within a few weeks, a novel coronavirus tentatively named as 2019 novel coronavirus (2019-nCoV) was announced by the World Health Organization. We performed bioinformatics analysis on a virus genome from a patient with 2019-nCoV infection and compared it with other related coronavirus genomes. Overall, the genome of 2019-nCoV has 89% nucleotide identity with bat SARS-like-CoVZXC21 and 82% with that of human SARS-CoV. The phylogenetic trees of their orf1a/b, Spike, Envelope, Membrane and Nucleoprotein also clustered closely with those of the bat, civet and human SARS coronaviruses. However, the external subdomain of Spike's receptor binding domain of 2019-nCoV shares only 40% amino acid identity with other SARS-related coronaviruses. Remarkably, its orf3b encodes a completely novel short protein. Furthermore, its new orf8 likely encodes a secreted protein with an alpha-helix, following with a beta-sheet(s) containing six strands. Learning from the roles of civet in SARS and camel in MERS, hunting for the animal source of 2019-nCoV and its more ancestral virus would be important for understanding the origin and evolution of this novel lineage B betacoronavirus. These findings provide the basis for starting further studies on the pathogenesis, and optimizing the design of diagnostic, antiviral and vaccination strategies for this emerging infection.

Competing endogenous RNA network profiling reveals novel host dependency factors required for MERS-CoV propagation.

Circular RNAs (circRNAs) are an integral component of the host competitive endogenous RNA (ceRNA) network. These noncoding RNAs are characterized by their unique splicing reactions to form covalently closed loop structures and play important RNA regulatory roles in cells. Recent studies showed that circRNA expressions were perturbed in viral infections and circRNAs might serve as potential antiviral targets. We investigated the host ceRNA network changes and biological relevance of circRNAs in human lung adenocarcinoma epithelial (Calu-3) cells infected with the highly pathogenic Middle East respiratory syndrome coronavirus (MERS-CoV). A total of ≥49337 putative circRNAs were predicted. Among the 7845 genes which generated putative circRNAs, 147 (1.9%) of them each generated ≥30 putative circRNAs and were involved in various biological, cellular, and metabolic processes, including viral infections. Differential expression (DE) analysis showed that the proportion of DE circRNAs significantly (P < 0.001) increased at 24 h-post infection. These DE circRNAs were clustered into 4 groups according to their time-course expression patterns and demonstrated inter-cluster and intra-cluster variations in the predicted functions of their host genes. Our comprehensive circRNA-miRNA-mRNA network identified 7 key DE circRNAs involved in various biological processes upon MERS-CoV infection. Specific siRNA knockdown of two selected DE circRNAs (circFNDC3B and circCNOT1) significantly reduced MERS-CoV load and their target mRNA expression which modulates various biological pathways, including the mitogen-activated protein kinase (MAPK) and ubiquitination pathways. These results provided novel insights into the ceRNA network perturbations, biological relevance of circRNAs, and potential host-targeting antiviral strategies for MERS-CoV infection.