Nursing workforce allocation in the intensive care units of COVID-19-designated hospitals: A nationwide cross-sectional survey in China.

AIM: To explore the nursing workforce allocation in intensive care units (ICUs) of COVID-19-designated hospitals during the epidemic peak in China. DESIGN: A nationwide cross-sectional online survey. METHODS: A total of 37 head nurses and 262 frontline nurses in 37 ICUs of COVID-19-designated tertiary hospitals located in 22 cities of China were surveyed. The self-reported human resource allocation questionnaire was used to assess the nursing workforce allocation. RESULTS: The average patient-to-nurse ratio was 1.89 ± 1.14, and the median working hours per shift was 5 h. The top four majors of front-line nurses in ICUs were respiratory (31.30%), lemology (27.86%), intensive care (21.76%) and emergency (17.18%). We also found that a smaller average patient-to-nurse ratio (odds ratio [OR]: 0.328, 95% CI: 0.108, 1.000), longer average weekly rest time per person (OR: 0.193, 95% CI: 0.051, 0.729) and larger proportion of 6-9 working years (OR: 0.002, 95% CI: 0.001, 1.121) decreased the occurrence of nursing adverse events.

Management strategies for pediatric wards of designated hospitals for omicron variant BA.5.1.3 infection in south area of Hainan

OBJECTIVE: To describe the experience of comprehensive management of pediatric COVID-19 cases in designated hospitals under 'mixed management mode' during the '0801' epidemic in Sanya. METHODS: A pediatric comprehensive ward of children and parents has been specially opened in the designated hospitals to carry out comprehensive treatment and management of pediatric children. RESULTS: From Aug 1, 2022 to Oct 1, 2022, Sanya Central Hospital(Hainan Third People's Hospital), as the designated hospital for the treatment of COVID-19 cases in South area of Hainan, a total of 251 children aged between 0 and 14 year old and 195 aged more than 14 years old were treated for COVID-19 in the pediatric comprehensive wards. Among children under 14 years old, including 10(4.0%) neonates aged between 0 and 28 days, 32(12.7%) cases aged between 29 days and 1 year old, 87(34.7%) cases aged between 1 and 3 years old and 122(48.6%) cases aged between 3 and 14 years old. There were 16(6.4%) cases of common type, 223(88.8%) cases of mild type, 12(4.8%)asymptomatic cases and 7(2.8%) re-positive cases. There were 246(98.0%) children with accompanying care and 5(2.0%) children without accompanying care and 23(9.2%) children complicated with underlying diseases. The youngest age was 11 days, and the oldest age was 77 years old. The large age span and poor compliance with treatment brought unprecedented challenges to the diagnosis and treatment of children in the designated hospitals. The mixed model of comprehensive management and multidisciplinary diagnosis and treatment helped to achieve the ultimate goals of 'zero death of patients and 'zero infection' of health care workers. CONCLUSION: The comprehensive management means such as optimization of flow, stress on prevention and control of nosocomial infection, multidisciplinary collaboration and support of information platform offer strong guarantee for the mixed mode of management as well as scientific experience for centralized diagnosis and treatment of respiratory infectious diseases in children.

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis.

BACKGROUND: Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy. METHODS: We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning. RESULTS: To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF. CONCLUSIONS: All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

The HDAC inhibitor zabadinostat is a systemic regulator of adaptive immunity.

Protein acetylation plays a key role in regulating cellular processes and is subject to aberrant control in diverse pathologies. Although histone deacetylase (HDAC) inhibitors are approved drugs for certain cancers, it is not known whether they can be deployed in other therapeutic contexts. We have explored the clinical HDAC inhibitor, zabadinostat/CXD101, and found that it is a stand-alone regulator of the adaptive immune response. Zabadinostat treatment increased expression of MHC class I and II genes in a variety of cells, including dendritic cells (DCs) and healthy tissue. Remarkably, zabadinostat enhanced the activity of DCs, and CD4 and CD8 T lymphocytes. Using an antigenic peptide presented to the immune system by MHC class I, zabadinostat caused an increase in antigen-specific CD8 T lymphocytes. Further, mice immunised with covid19 spike protein and treated with zabadinostat exhibit enhanced covid19 neutralising antibodies and an increased level of T lymphocytes. The enhanced humoral response reflected increased activity of T follicular helper (Tfh) cells and germinal centre (GC) B cells. Our results argue strongly that zabadinostat has potential to augment diverse therapeutic agents that act through the immune system.

Development of Clinical Risk Scores for Detection of COVID-19 in Suspected Patients During a Local Outbreak in China: A Retrospective Cohort Study

Objectives: To develop and internally validate two clinical risk scores to detect coronavirus disease 2019 (COVID-19) during local outbreaks. Methods: Medical records were extracted for a retrospective cohort of 336 suspected patients admitted to Baodi hospital between 27 January to 20 February 2020. Multivariate logistic regression was applied to develop the risk-scoring models, which were internally validated using a 5-fold cross-validation method and Hosmer-Lemeshow (H-L) tests. Results: Fifty-six cases were diagnosed from the cohort. The first model was developed based on seven significant predictors, including age, close contact with confirmed/suspected cases, same location of exposure, temperature, leukocyte counts, radiological findings of pneumonia and bilateral involvement (the mean area under the receiver operating characteristic curve [AUC]:0.88, 95% CI: 0.84–0.93). The second model had the same predictors except leukocyte and radiological findings (AUC: 0.84, 95% CI: 0.78–0.89, Z = 2.56, p = 0.01). Both were internally validated using H-L tests and showed good calibration (both p > 0.10). Conclusion: Two clinical risk scores to detect COVID-19 in local outbreaks were developed with excellent predictive performances, using commonly measured clinical variables. Further external validations in new outbreaks are warranted.

A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.

Evaluation of inactivated COVID-19 vaccine on semen parameters in reproductive-age males: a retrospective cohort study.

During the coronavirus disease (COVID-19) epidemic, there have been concerns about the impact of vaccines on people's fertility, including the fertility of those who are currently preparing for pregnancy and those who might become pregnant in future. However, there is still a lack of research on the effect of the COVID-19 vaccine on male fertility, and it is not surprising that couples and donors have concerns regarding vaccination. In this study, a retrospective cohort study was conducted to examine semen quality before and after receipt of the inactivated COVID-19 vaccine. There were no statistically significant changes in semen parameters (volume, sperm concentration, progressive motility, and total progressive motile count) after two doses of vaccine (all P > 0.05). In summary, our study updates the most recent studies on the effects of the COVID-19 vaccine on male fertility, and the information from this study could be used to guide fertility recommendations for assisted reproductive technology (ART) patients and donors.

Willingness to Pay for Healthy Housing During the COVID-19 Pandemic in China: Evidence From Eye-Tracking Experiment.

Healthy housing can set its occupants completely in good physical, mental and social conditions, but there is a lack of research in China on the public's willingness to pay (WTP) for healthy housing. From the perspective of cognitive psychology, this study constructs an analytical framework based on the model of "theory of planned behavior" (TPB), the theory of selective information exposure, and the model of "emotions as social information," while exploring the effect mechanism of the online reviews on the public's WTP for healthy housing during COVID-19 pandemic. In combination with eye-tracking experiments and subjective reports, physiological, psychological and behavioral multimodal data on WTP of 65 participants for healthy housing are collected. Partial least squares structural equation modeling (PLS-SEM) is adopted to analyze the formation effect mechanism of the public's WTP for healthy housing. This study acquires the following results: (i) Information attentiveness to online reviews on different valence information of healthy housing as obtained in eye tracking experiments delivers significant effect on attitude, subjective norm (SN) and perceived behavioral control (PBC), but has no direct effect on the public's WTP for healthy housing; (ii) Hypotheses from TPB model are verified. attitude, PBC and SN can all make significant effect on WTP for healthy housing, with attitude showcasing the most prominent effect; and (iii) In terms of the mediating effect, information attentiveness can deliver significant indirect effect on WTP through attitude.

Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS.

Purpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services. Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed. Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.

Spatiotemporal Variations of Aerosols in China during the COVID-19 Pandemic Lockdown

The widespread nature of the coronavirus disease 2019 (COVID-19) pandemic is gradually changing people’s lives and impacting economic development worldwide. Owing to the curtailment of daily activities during the lockdown period, anthropogenic emissions of air pollutants have greatly reduced, and this influence is expected to continue in the foreseeable future. Spatiotemporal variations in aerosol optical depth (AOD) can be used to analyze this influence. In this study, we comprehensively analyzed AOD and NO2 data obtained from satellite remote sensing data inversion. First, data were corrected using Eidetic three-dimensional‒long short-term memory to eliminate errors related to sensors and algorithms. Second, taking Hubei Province in China as the experimental area, spatiotemporal variations in AOD and NO2 concentration during the pandemic were analyzed. Finally, based on the results obtained, the impact of the COVID-19 pandemic on human life has been summarized. This work will be of great significance to the formulation of regional epidemic prevention and control policies and the analysis of spatiotemporal changes in aerosols.

[Proposal of Living Evidence-based Guideline for Combination of Traditional Chinese and Western Medicine for Treatment of COVID-19].

In order to standardize the clinical diagnosis and treatment decision-making with traditional Chinese medicine for pa-tients of coronavirus disease 2019(COVID-19) and put the latest clinical study evidence into clinical practice, the international trust-worthy traditional Chinese medicine recommendations( TCM Recs) working group started the compilation of Living Evidence-based Guideline for Combination of Traditional Chinese and Western Medicine for Treatment of COVID-19 on the basis of the standards and re-quirements of WHO handbook, GRADE and RIGHT. This proposal mainly introduces the formulation methods and processes of the living guidelines in details, such as the composition of the working group, the collection and identification of clinical issues and out-comes, the production of the living systematic review and the consensus of recommendations. The guidelines will continue to monitor the clinical study evidences of TCM in the prevention and treatment of COVID-19, and conduct regular evidence updating, retrieval and screening. When there is new study evidence, the steering committee will evaluate the possibility of the evidence to change clinical practice or previous recommendations, so as to decide whether the recommendations for the guidelines shall be implemented or upda-ted. The main criteria considered in the guideline updating are as follows:(1) There are new high-quality randomized controlled trial(RCT) evidences for TCM uninvolved in the previous edition of the guidelines;(2) as for the TCM involved in the guidelines, living sys-tematic review shows that new evidence may change the direction or strength of the existing recommendations. The specific implementation of the living evidence-based guidelines will take this proposal as the study basis and framework, in order to ensure the standardization of the formulation process and methods. This will be the first exploration of the methodology for living guidelines in the field of TCM.

Author Correction: Seroprevalence of immunoglobulin M and G antibodies against SARS-CoV-2 in China.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Glycyrrhizic acid: a potential drug against COVID-19

Pneumonia caused by SARS-CoV-2 has seriously threatened human life and health worldwide and caused a large number of deaths. Viral infection and acute inflammation are important causes of death, so it is particularly important to combine antiviral therapy with anti-inflammatory therapy. Glycyrrhizic acid, the main component of the glycyrrhizic root extract, has a wide range of pharmacological effects as well as high efficiency and low toxicity, its preparation has been widely used in the treatment of chronic hepatitis and other diseases. Glycyrrhizic acid can regulate the expression and release of a variety of cytokines and play a significant anti-inflammatory effect. At the same time, glycyrrhizic acid also showed significant inhibition towards a variety types of viruses. Therefore, the potential application of glycyrrhizic acid as COVID-19 treatment should be explored.

Differences in game consumption patterns between North and South China. (Special Issue: Socio-economic assessment of diet on health and quality of life.)

Purpose: The 2019 novel coronavirus (COVID-19) pandemic has affected many countries around the world. Due to the debate on the source of the outbreak, wildlife meat consumption has gained international attention and become an area that requires further exploration. The purpose of this research is to explore the differences in game consumption motivations and behaviours among populations in northern and southern China. Design/methodology/approach: An online survey on wild meat awareness with (potential) game consumers across the country as target groups was conducted. Findings: Results from this study showed that consumers in the southern region have a preference for wildlife. This preference was determined by factors such as natural environment, history and culture and the level of economic development. However, there was no significant difference in the frequency of game consumption between the north and south since game meat is not a major component of the daily diet. Practical implications: The government should consider banning this trade permanently to prevent future disease outbreaks caused by wildlife-to-human transmissions. Originality/value: The consumption of game meat is regional, but the animals in game trades live across a wider area, and thus, game trades have a global influence on ecological systems and human health. Therefore, this epidemic is also a global concern, which might lead people to display a highly negative attitude towards consuming game meat.

Motivation-based segmentation of game meat consumers: A look at the beliefs of food consumers during the COVID-19 crisis in China.

OBJECTIVES: This research aims to explore the factors motivate consumers to eat game meat during a multi-state disease outbreak. METHODS: It proposes a segmentation of consumers based on their attitudes toward and reveals the consumers' food beliefs that motivate their actions. Three segments of game meat consumers were identified: identity seekers, health seekers, and taste seekers. RESULTS: A survey of the potential impact that the COVID-19 crisis has on these three clusters' future food choices showed that the identity and health seekers are more open to a change in food choices. However, the taste seekers are less likely to be influenced by external factors. CONCLUSIONS: This research indicates that for the policymakers, the key is to take game meat consumers as an effective intervention entry point. It is crucial to facilitate healthy food choices and to promote socially- and culturally-appropriate food beliefs by improving public awareness of the risks of game meat, and invest in organic food. RESEARCH IMPLICATIONS: This research provides new insights into the food beliefs of game meat consumers via motivation-based segmentation.

Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom.

BACKGROUND: A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity. METHODS: We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status. RESULTS: We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8-40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0-33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity. CONCLUSION: We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies.

Anesthetic Complications Associated With Severe Acute Respiratory Syndrome Coronavirus 2 in Pediatric Patients.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with high perioperative morbidity and mortality among adults. The incidence and severity of anesthetic complications in children with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unknown. We hypothesized that there would be an increased incidence of intra- and postoperative complications in children with SARS-CoV-2 infection as compared to those with negative testing. METHODS: We conducted a retrospective cohort study analyzing complications for children <18 years of age who underwent anesthesia between April 28 and September 30, 2020 at a large, academic pediatric hospital. Each child with a positive SARS-CoV-2 test within the prior 10 days was matched to a patient with a negative SARS-CoV-2 test based on American Society of Anesthesiologists (ASA) physical status, age, gender, and procedure. Children who were intubated before the procedure, underwent organ transplant surgery, or had severe COVID-19 were excluded. The primary outcome was the risk difference of a composite of intra- or postoperative respiratory complications in children positive for SARS-CoV-2 compared to those with negative testing. Secondarily, we used logistic regression to determine the odds ratio for respiratory complications before and after adjustment using propensity scores weighting to adjust for possible confounders. Other secondary outcomes included neurologic, cardiovascular, hematologic, and renal complications, unanticipated postoperative admission to the intensive care unit, length of hospital stay, and mortality. RESULTS: During the study period, 9812 general anesthetics that had a preoperative SARS-CoV-2 test were identified. Sixty encounters occurred in patients who had positive SARS-CoV-2 testing preoperatively and 51 were included for analysis. The matched controls cohort included 99 encounters. A positive SARS-CoV-2 test was associated with a higher incidence of respiratory complications (11.8% vs 1.0%; risk difference 10.8%, 95% confidence interval [CI], 1.6-19.8; P = .003). After adjustment, the odds ratio for respiratory complications was 14.37 (95% CI, 1.59-130.39; P = .02) for SARS-CoV-2-positive children as compared to controls. There was no occurrence of acute respiratory distress syndrome, postoperative pneumonia, or perioperative mortality in either group. CONCLUSIONS: Pediatric patients with nonsevere SARS-CoV-2 infection had higher rates of perianesthetic respiratory complications than matched controls with negative testing. However, severe morbidity was rare and there were no mortalities. The incidence of complications was similar to previously published rates of perianesthetic complications in the setting of an upper respiratory tract infection. This risk persisted after adjustment for preoperative upper respiratory symptoms, suggesting an increased risk in symptomatic or asymptomatic SARS-CoV-2 infection.

Contribution of traditional Chinese medicine combined with conventional western medicine treatment for the novel coronavirus disease (COVID-19), current evidence with systematic review and meta-analysis.

This study provides current evidence for efficacy and safety of treating COVID-19 with combined traditional Chinese medicine (TCM) and conventional western medicine (CWM). Six databases were searched from January 1 to December 31, 2020. Randomized controlled trials (RCTs), case-control studies (CCTs), and cohort studies on TCM or TCM combined with CWM treatment for COVID-19 were included. The quality of included RCTs was assessed by Cochrane risk of bias tool, and the Newcastle-Ottawa Scale (NOS) was used to assess the quality of cohort studies and CCTs. Review Manager 5.4 software was used to perform meta-analysis. The quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. A total of 35 studies (3,808 patients) composing 19 RCTs and 16 observational studies were included. The results of meta-analysis revealed that comparing with CWM alone, integrated TCM and CWM had significant improvement in total effective rate, improvement rate of chest CT, the rate of disease progression, as well as improvement of fever, fatigue and cough. The overall quality of evidence was very low to moderate. In conclusion, TCM combined with CWM was a potential treatment option for increasing clinical effective rate, improving the clinical symptoms, and preventing disease progression in COVID-19 patients. High-quality clinical trials are required in the further.