Epidemiological change of influenza virus in hospitalized children with acute respiratory tract infection during 2014-2022 in Hubei Province, China.

PURPOSE: Influenza virus (IFV) causes acute respiratory tract infection (ARTI) and leads to high morbidity and mortality annually. This study explored the epidemiological change of IFV after the implementation of the universal two-child policy and evaluated the impact of coronavirus disease 2019 (COVID-19) pandemic on the detection of IFV. METHODS: Hospitalized children under 18 years with ARTI were recruited from Hubei Maternal and Child Healthcare Hospital of Hubei Province from January 2014 to June 2022. The positive rates of IFV were compared among different periods by the implementation of the universal two-child policy and public health measures against COVID-19 pandemic. RESULTS: Among 75,128 hospitalized children with ARTI, the positive rate of IFV was 1.98% (1486/75128, 95% CI 1.88-2.01). Children aged 6-17 years had the highest positive rate of IFV (166/5504, 3.02%, 95% CI 2.58-3.50). The positive rate of IFV dropped to the lowest in 2015, then increased constantly and peaked in 2019. After the universal two-child policy implementation, the positive rate of IFV among all the hospitalized children increased from 0.40% during 2014-2015 to 2.70% during 2017-2019 (RR 6.72, 95% CI 4.94-9.13, P < 0.001), particularly children under one year shown a violent increasing trend from 0.20 to 2.01% (RR 10.26, 95% CI 5.47-19.23, P < 0.001). During the initial outbreak of COVID-19, the positive rate of IFV decreased sharply compared to that before COVID-19 (0.35% vs. 3.37%, RR 0.10, 95% CI 0.04-0.28, P < 0.001), and then rebounded to 0.91%, lower than the level before COVID-19 (RR 0.26, 95% CI 0.20-0.36, P < 0.001). CONCLUSION: IFV epidemiological pattern has changed after the implementation of the universal two-child policy. More attention should be emphasized to comprehend the health benefits generated by COVID-19 restrictions on IFV transmission in future.

An omicron-based vaccine booster elicits potent neutralizing antibodies against emerging SARS-CoV-2 variants in adults.

SARS-CoV-2 Omicron subvariants have become the predominantly strain in most countries. However, the neutralizing activity of the human serum after Omicron-based vaccine booster against different SARS-CoV-2 variants is poorly understood. Here, we developed an update Omicron vaccine (SCoK-Omicron), based on the RBD-Fc fusion protein vaccine (SCoK) and RBD domain of Omicron BA.1. To assess cross-variant neutralizing activity in adults, 25 volunteers that have received three doses of SCoK and 25 volunteers with two doses of CoronaVac (inactive vaccine) were further boosted with a dose updated vaccine (SCoK-Omicron). The results of pseudovirus neutralization assays demonstrated that the booster potently induced the high-level of neutralizing antibody against SARS-CoV-2 Wild type, Delta and Omicron subvariants in adults. Further assays of single point mutations showed that K444T, L452R, N460K, or F486V was key mutations to cause immune evasion. Together, these data suggest that SCOK-Omicron can be used as a booster vaccine candidate in adults receiving subunit protein or inactivated vaccine in response to the epidemic of COVID-19 Omicron subvariants, and the mutation K444T, L452R, N460K, or F486V needs to be considered in future vaccine design.

A spike-targeting bispecific T cell engager strategy provides dual layer protection against SARS-CoV-2 infection in vivo.

Neutralizing antibodies exert a potent inhibitory effect on viral entry; however, they are less effective in therapeutic models than in prophylactic models, presumably because of their limited efficacy in eliminating virus-producing cells via Fc-mediated cytotoxicity. Herein, we present a SARS-CoV-2 spike-targeting bispecific T-cell engager (S-BiTE) strategy for controlling SARS-CoV-2 infection. This approach blocks the entry of free virus into permissive cells by competing with membrane receptors and eliminates virus-infected cells via powerful T cell-mediated cytotoxicity. S-BiTE is effective against both the original and Delta variant of SARS-CoV2 with similar efficacy, suggesting its potential application against immune-escaping variants. In addition, in humanized mouse model with live SARS-COV-2 infection, S-BiTE treated mice showed significantly less viral load than neutralization only treated group. The S-BiTE strategy may have broad applications in combating other coronavirus infections.

A comprehensive overview on the transmission, pathogenesis, diagnosis, treatment, and prevention of SARS-CoV-2.

Severe acute respiratory syndrome coronavirus (SARS-CoV) is a single positive-strand RNA virus that is responsible for the current pandemic that the world has been facing since 2019. The primary route of transmission of SARS-CoV-2 is through respiratory tract transmission. However, other transmission routes such as fecal-oral, vertical transmission, and aerosol-eye also exist. In addition, it has been found that the pathogenesis of this virus involves the binding of the virus's S protein to its host cell surface receptor angiotensin-converting enzyme 2, which results in the subsequent membrane fusion that is required for SARS-CoV-2 to replicate and complete its entire life. The clinical symptoms of patients infected with SARS-CoV-2 can range from asymptomatic to severe. The most common symptoms seen include fever, dry cough, and fatigue. Once these symptoms are observed, a nucleic acid test is done using reverse transcription-polymerase chain reaction. This currently serves as the main confirmatory tool for COVID-19. Despite the fact that no cure has been found for SARS-CoV-2, prevention methods such as vaccines, specific facial mask, and social distancing have proven to be quite effective. It is imperative to have a complete understanding of the transmission and pathogenesis of this virus. To effectively develop new drugs as well as diagnostic tools, more knowledge about this virus would be needed.

Retrospective analysis of forensic toxicology data from 2021 to identify patterns of various forensic cases.

PURPOSE: Knowing and analyzing the characteristics and trends of forensic toxicology cases in a certain area is particularly important for a local government to establish an effective prevention and control system. The purpose of this work was to summarize data from forensic toxicology cases received by the Academy of Forensic Sciences (AFS) in 2021. METHODS: As requested by the police or according to the details of cases, samples were systematically screened or analyzed by various methods. Liquid chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry were used to identify medicinal drugs, drugs of abuse, pesticides, poison gases, etc. RESULTS: AFS received a total of 17,758 cases in 2021, of which 314 cases underwent autopsy. The main cause of death was sudden death, and the manner of death was mainly accident. Among 13,744 drug abuse cases, the number of positive cases was 1721, with a positive rate of 12.5%, and synthetic cannabinoids were the most frequently detected substances. In 3640 traffic cases, 85.3% of drivers were suspected of drunk driving. In 103 poisoning cases, hydrogen sulfide poisoning and pesticide suicide account for a large proportion. In drug-facilitated crimes, zolpidem was the most frequently tested component. In 55 case of dog poisonings, the main poisons were cyanides and succinylcholine, and the main poisoning tools were poisonous baits and poisonous syringe darts. CONCLUSIONS: This study profiles the toxicological characteristics of forensic toxicological cases conducted at the AFS in 2021 and provides a scientific basis for poisoning cases and drug abuse prevention.

Severity prediction of pulmonary diseases using chest CT scans via cost-sensitive label multi-kernel distribution learning.

BACKGROUND AND OBJECTIVES: The progression of pulmonary diseases is a complex progress. Timely predicting whether the patients will progress to the severe stage or not in its early stage is critical to take appropriate hospital treatment. However, this task suffers from the "insufficient and incomplete" data issue since it is clinically impossible to have adequate training samples for one patient at each day. Besides, the training samples are extremely imbalanced since the patients who will progress to the severe stage is far less than those who will not progress to the non-severe stage. METHOD: We consider the severity prediction of pulmonary diseases as a time estimation problem based on CT scans. To handle the issue of "insufficient and incomplete" training samples, we introduced label distribution learning (LDL). Specifically, we generate a label distribution for each patient, making a CT image contribute to not only the learning of its chronological day, but also the learning of its neighboring days. In addition, a cost-sensitive mechanism is introduced to explore the imbalance data issue. To identify the importance of pulmonary segments in pulmonary disease severity prediction, multi-kernel learning in composite kernel space is further incorporated and particle swarm optimization (PSO) is used to find the optimal kernel weights. RESULTS: We compare the performance of the proposed CS-LD-MKSVR algorithm with several classical machine learning algorithms and deep learning (DL) algorithms. The proposed method has obtained the best classification results on the in-house data, fully indicating its effectiveness in pulmonary disease severity prediction. CONTRIBUTIONS: The severity prediction of pulmonary diseases is considered as a time estimation problem, and label distribution is introduced to describe the conversion time from non-severe stage to severe stage. The cost-sensitive mechanism is also introduced to handle the data imbalance issue to further improve the classification performance.

Evaluating the effect of SARS-CoV-2 spike mutations with a linear doubly robust learner.

Driven by various mutations on the viral Spike protein, diverse variants of SARS-CoV-2 have emerged and prevailed repeatedly, significantly prolonging the pandemic. This phenomenon necessitates the identification of key Spike mutations for fitness enhancement. To address the need, this manuscript formulates a well-defined framework of causal inference methods for evaluating and identifying key Spike mutations to the viral fitness of SARS-CoV-2. In the context of large-scale genomes of SARS-CoV-2, it estimates the statistical contribution of mutations to viral fitness across lineages and therefore identifies important mutations. Further, identified key mutations are validated by computational methods to possess functional effects, including Spike stability, receptor-binding affinity, and potential for immune escape. Based on the effect score of each mutation, individual key fitness-enhancing mutations such as D614G and T478K are identified and studied. From individual mutations to protein domains, this paper recognizes key protein regions on the Spike protein, including the receptor-binding domain and the N-terminal domain. This research even makes further efforts to investigate viral fitness via mutational effect scores, allowing us to compute the fitness score of different SARS-CoV-2 strains and predict their transmission capacity based solely on their viral sequence. This prediction of viral fitness has been validated using BA.2.12.1, which is not used for regression training but well fits the prediction. To the best of our knowledge, this is the first research to apply causal inference models to mutational analysis on large-scale genomes of SARS-CoV-2. Our findings produce innovative and systematic insights into SARS-CoV-2 and promotes functional studies of its key mutations, serving as reliable guidance about mutations of interest.

A Study on Predicting Crisis Information Dissemination in Epidemic-level Public Health Events

Crisis information dissemination plays a key role in the development of emergency responses to epidemic-level public health events. Therefore, clarifying the causes of crisis information dissemination and making accurate predictions to effectively control such situations have attracted extensive attention. Based on media richness theory and persuasion theory, this study constructs an index system of crisis information dissemination impact factors from two aspects: the crisis information publisher and the published crisis information content. A multi-layer perceptron is used to analyze the weight of the index system, and the prediction is transformed into a pattern classification problem to test crisis information dissemination. In this study, COVID-19 is considered a representative event. An experiment is conducted to predict the crisis information dissemination of COVID-19 in two megacities. Data related to COVID-19 from these two megacities are acquired from the well-known Chinese social media platform Weibo. The experimental results show that not only the identity but also the social influence of the information publisher has a significant impact on crisis information dissemination in epidemic-level public health events. Furthermore, the proposed model achieves more than 95% test accuracy, precision rate, recall value and f1-score in the prediction task. The study provides decision-making support for government departments and a guide for correctly disseminating crisis information and public opinion during future epidemic-level public health events.

Does Preoperative Hookwire Localization Influence Postoperative Acute and Chronic Pain After Video-Assisted Thoracoscopic Surgery: A Prospective Cohort Study.

Purpose: This study aimed to investigate whether preoperative computerized tomography-guided hookwire localization-associated pain could affect acute and chronic postsurgical pain (CPSP) in patients undergoing video-assisted thoracoscopic surgery (VATS). Methods: We enrolled 161 adult patients who underwent elective VATS; sixty-nine patients experienced hookwire localization (Group A) and 69 did not (Group B). Group A was further subdivided into the multiple localization group (n=35, Group Amultiple) and the single localization group (n=34, Group Asingle) according to the number of hookwires. The numerical rating scale (NRS) was used preoperatively, during recovery at the post-anesthesia care unit (PACU), and the first two days, 3 and 6 months postoperatively. Furthermore, multivariate regression analysis was used to identify the risk factors associated with CPSP. The postoperative adverse events, length of hospital stay, and satisfaction in pain management were also recorded. Results: The incidence and severity of acute postoperative pain were similar between Group A and Group B (p > 0.05). The incidence (56.5% vs 30.4%, p = 0.002) and the NRS scores (2.0 [2.0-3.0] vs 1.0 [1.0-2.0], p = 0.011) for CPSP were significantly higher in Group A than in Group B at 3 months postoperatively. On subgroup analysis, compared with Group Asingle, the intensity of CPSP (2.0 [2.0-3.0] vs 2.0 [1.0-2.0], p = 0.005) in Group Amultiple was slightly higher at 3 months postoperatively. Conversely, the CPSP incidence (60.0% vs 29.4%, p = 0.011) was significantly higher at 6 months postoperatively in Group Amultiple. The multivariate regression analysis further validated hookwire localization as a risk factor for CPSP (odds ratio: 6.199, 95% confidence interval 2.049-18.749, p = 0.001). Patient satisfaction relating to pain management at 3 months postoperatively was lower in Group A (p = 0.034). Conclusion: The preoperative pain stress of hookwire localization increased the incidence and intensity of CPSP rather than acute pain at 3 months postoperatively, especially in patients with multiple hookwires.

Understanding the risk of transmission of respiratory viral infections in childcare centres: protocol for the DISeases TrANsmission in ChildcarE (DISTANCE) multicentre cohort study.

INTRODUCTION: Childcare centre is considered a high-risk setting for transmission of respiratory viruses. Further evidence is needed to understand the risk of transmission in childcare centres. To this end, we established the DISeases TrANsmission in ChildcarE (DISTANCE) study to understand the interaction among contact patterns, detection of respiratory viruses from environment samples and transmission of viral infections in childcare centres. METHODS AND ANALYSIS: The DISTANCE study is a prospective cohort study in multiple childcare centres of Jiangsu Province, China. Study subjects will be childcare attendees and teaching staff of different grades. A range of information will be collected from the study subjects and participating childcare centres, including attendance, contact behaviours (collected by onsite observers), respiratory viral infection (weekly respiratory throat swabs tested by multiplex PCR), presence of respiratory viruses on touch surfaces of childcare centres and weekly follow-up survey on respiratory symptoms and healthcare seeking among subjects tested positive for any respiratory viruses. Detection patterns of respiratory viruses from study subjects and environment samples, contact patterns, and transmission risk will be analysed by developing statistical and mathematical models as appropriate. The study has been initiated in September 2022 in 1 childcare centre in Wuxi City, with a total of 104 children and 12 teaching staff included in the cohort; data collection and follow-up is ongoing. One more childcare centre in Nanjing City (anticipated to include 100 children and 10 teaching staff) will start recruitment in 2023. ETHICS AND DISSEMINATION: The study has received ethics approval from Nanjing Medical University Ethics Committee (No. 2022-936) and ethics approval from Wuxi Center for Disease Control and Prevention Ethics Committee (No. 2022-011). We plan to disseminate the study findings mainly through publications in peer-reviewed journals and presentations in academic conferences. Aggregated research data will be shared freely to researchers.

Novel Graph Topology Learning for Spatio-Temporal Analysis of COVID-19 Spread.

This article presents a new graph-learning technique to accurately infer the graph structure of COVID-19 data, helping to reveal the correlation of pandemic dynamics among different countries and identify influential countries for pandemic response analysis. The new technique estimates the graph Laplacian of the COVID-19 data by first deriving analytically its precise eigenvectors, also known as graph Fourier transform (GFT) basis. Given the eigenvectors, the eigenvalues of the graph Laplacian are readily estimated using convex optimization. With the graph Laplacian, we analyze the confirmed cases of different COVID-19 variants among European countries based on centrality measures and identify a different set of the most influential and representative countries from the current techniques. The accuracy of the new method is validated by repurposing part of COVID-19 data to be the test data and gauging the capability of the method to recover missing test data, showing 33.3% better in root mean squared error (RMSE) and 11.11% better in correlation of determination than existing techniques. The set of identified influential countries by the method is anticipated to be meaningful and contribute to the study of COVID-19 spread.

Monkeypox virus quadrivalent mRNA vaccine induces immune response and protects against vaccinia virus.

Monkeypox has been declared a public health emergency by the World Health Organization. There is an urgent need for efficient and safe vaccines against the monkeypox virus (MPXV) in response to the rapidly spreading monkeypox epidemic. In the age of COVID-19, mRNA vaccines have been highly successful and emerged as platforms enabling rapid development and large-scale preparation. Here, we develop two MPXV quadrivalent mRNA vaccines, named mRNA-A-LNP and mRNA-B-LNP, based on two intracellular mature virus specific proteins (A29L and M1R) and two extracellular enveloped virus specific proteins (A35R and B6R). By administering mRNA-A-LNP and mRNA-B-LNP intramuscularly twice, mice induce MPXV specific IgG antibodies and potent vaccinia virus (VACV) specific neutralizing antibodies. Further, it elicits efficient MPXV specific Th-1 biased cellular immunity, as well as durable effector memory T and germinal center B cell responses in mice. In addition, two doses of mRNA-A-LNP and mRNA-B-LNP are protective against the VACV challenge in mice. And, the passive transfer of sera from mRNA-A-LNP and mRNA-B-LNP-immunized mice protects nude mice against the VACV challenge. Overall, our results demonstrate that mRNA-A-LNP and mRNA-B-LNP appear to be safe and effective vaccine candidates against monkeypox epidemics, as well as against outbreaks caused by other orthopoxviruses, including the smallpox virus.

Therapeutic drug combinations against COVID-19 obtained by employing a collaborative filtering method

The outbreak of coronavirus disease 2019 (COVID-19) has severely harmed human society and health. Because there is currently no specific drug for the treatment and prevention of COVID-19, we used a collaborative filtering algorithm to predict which traditional Chinese medicines (TCMs) would be effective in combination for the prevention and treatment of COVID-19. First, we performed drug screening based on the receptor structure prediction method, molecular docking using q-vina to measure the binding ability of TCMs, TCM formulas, and neo-coronavirus proteins, and then performed synergistic filtering based on Laplace matrix calculations to predict potentially effective TCM formulas. Combining the results of molecular docking and synergistic filtering, the new recommended formulas were analyzed by reviewing data platforms or tools such as PubMed, Herbnet, the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the Guide to the Dispensing of Medicines for Clinical Evidence, and the Dictionary of Chinese Medicine Formulas, as well as medical experts' treatment consensus in terms of herbal efficacy, modern pharmacological studies, and clinical identification and typing of COVID-19 pneumonia, to determine the recommended solutions. We found that the therapeutic effect of a combination of six TCM formulas on the COVID-19 virus is the result of the overall effect of the formula rather than that of specific components of the formula. Based on this, we recommend a formula similar to that of Jinhua Qinggan Granules for the treatment of COVID-19 pneumonia. This study may provide new ideas and new methods for future clinical research. Classification Biological Science.

Network-based drug repurposing for the treatment of COVID-19 patients in different clinical stages

In the severe acute respiratory coronavirus disease 2019 (COVID-19) pandemic, there is an urgent need to develop effective treatments. Through a network-based drug repurposing approach, several effective drug candidates are identified for treating COVID-19 patients in different clinical stages. The proposed approach takes advantage of computational prediction methods by integrating publicly available clinical transcriptome and experimental data. We identify 51 drugs that regulate proteins interacted with SARS-CoV-2 protein through biological pathways against COVID-19, some of which have been experimented in clinical trials. Among the repurposed drug candidates, lovastatin leads to differential gene expression in clinical transcriptome for mild COVID-19 patients, and estradiol cypionate mainly regulates hormone-related biological functions to treat severe COVID-19 patients. Multi-target mechanisms of drug candidates are also explored. Erlotinib targets the viral protein interacted with cytokine and cytokine receptors to affect SARS-CoV-2 attachment and invasion. Lovastatin and testosterone block the angiotensin system to suppress the SARS-CoV-2 infection. In summary, our study has identified effective drug candidates against COVID-19 for patients in different clinical stages and provides comprehensive understanding of potential drug mechanisms.

A Robust Optimization Problem for Drone-based Equitable Pandemic Vaccine Distribution with Uncertain Supply☆

Widespread vaccination is the only way to overcome the COVID-19 global crisis. However, given the vaccine scarcity during the early outbreak of the pandemic, ensuring efficient and equitable distribution of vaccines, particularly in rural areas, has become a significant challenge. To this end, this study develops a two-stage robust vaccine distribution model that addresses the supply uncertainty incurred by vaccine shortages. The model aims to optimize the social and economic benefits by jointly deciding vaccination facility location, transportation capacity, and reservation plan in the first stage, and rescheduling vaccinations in the second stage after the confirmation of uncertainty. To hedge vaccine storage and transportation difficulties in remote areas, we consider using drones to deliver vaccines in appropriate and small quantities to vaccination points. Two tailored column-and-constraint generation algorithms are proposed to exactly solve the robust model, in which the subproblems are solved via the vertex traversal and the dual methods, respectively. The superiority of the dual method is further verified. Finally, we use real-world data to demonstrate the necessity to account for uncertain supply and equitable distribution, and analyze the impacts of several key parameters. Some managerial insights are also produced for decision-makers.

Epidemiological Characteristics of Respiratory Syncytial Virus Infection Among Hospitalized Children With Acute Respiratory Tract Infections From 2014 to 2022 in a Hospital in Hubei Province, China: Longitudinal Surveillance Study.

BACKGROUND: Longitudinal studies characterizing the epidemic trend of respiratory syncytial virus (RSV) in Hubei Province are scarce. OBJECTIVE: We aimed to depict the dynamics of the RSV epidemic among hospitalized children with acute respiratory tract infections (ARTIs) during 2014 to 2022 in the Maternal and Child Health Hospital of Hubei Province and investigate the influence of the 2-child policy and the COVID-19 pandemic on RSV prevalence. METHODS: The medical records and testing results of hospitalized children with ARTI from January 2014 to June 2022 were extracted. Nasopharyngeal samples were tested with direct immunofluorescence assay. Detection rates of RSV were categorized according to the diagnosis of patients: (1) overall, (2) upper respiratory tract infection (URTI), and (3) lower respiratory tract infection (LRTI). Poisson regression models were used to investigate the association between RSV detection rate and age, gender, or diagnosis. The detection rates of RSV before and after the implementation of the universal 2-child policy were compared using a Poisson regression model. Multiple comparisons of RSV detection rates were conducted among 3 stages of the COVID-19 pandemic using chi-square tests. Seasonal autoregressive integrated moving average was performed to predict RSV behaviors from February 2020 to June 2020 under the assumption of a non-COVID-19 scenario. RESULTS: Among 75,128 hospitalized children with ARTI, 11.1% (8336/75,128) were RSV-positive. Children aged <1 year had higher detection rates than older children (4204/26,498, 15.9% vs 74/5504, 1.3%; P<.001), and children with LRTI had higher detection rates than children with URTI (7733/53,145, 14.6% vs 603/21,983, 2.7%; P<.001). Among all the children, a clear seasonal pattern of the RSV epidemic was observed before 2021. Most of the highest detection rates were concentrated between December and February. The yearly detection rate of RSV remained at a relatively low level (about 8%) from 2014 to 2017, then increased to 12% and above from 2018. The highest monthly detection rate was in December 2018 (539/1493, 36.1%), and the highest yearly rate was in 2021 (1372/9328, 14.7%). There was a moderate increase in the RSV detection rate after the 2-child policy was implemented (before: 860/10,446, 8.2% vs after: 4920/43,916, 11.2%; P<.001). The largest increase, by 5.83%, occurred in children aged <1 year. The RSV epidemic level decreased sharply in the short term after the COVID-19 outbreak (detection rate before: 1600/17,010, 9.4% vs after: 32/1135, 2.8%; P<.001). The largest decrease, by 12.0%, occurred in children aged <1 year, but a rebounding epidemic occurred after 2020 (680/5744, 11.8%; P<.001). CONCLUSIONS: Children have been experiencing increased prevalence of RSV since 2018 based on surveillance from a hospital in Hubei Province with a large sample size. The 2-child policy might have increased the RSV prevalence, and the COVID-19 epidemic had a temporary inhibitory effect on RSV transmission. Vaccines against RSV are urgently needed.

Therapeutic drug combinations against COVID-19 obtained by employing a collaborative filtering method.

The outbreak of coronavirus disease 2019 (COVID-19) has severely harmed human society and health. Because there is currently no specific drug for the treatment and prevention of COVID-19, we used a collaborative filtering algorithm to predict which traditional Chinese medicines (TCMs) would be effective in combination for the prevention and treatment of COVID-19. First, we performed drug screening based on the receptor structure prediction method, molecular docking using q-vina to measure the binding ability of TCMs, TCM formulas, and neo-coronavirus proteins, and then performed synergistic filtering based on Laplace matrix calculations to predict potentially effective TCM formulas. Combining the results of molecular docking and synergistic filtering, the new recommended formulas were analyzed by reviewing data platforms or tools such as PubMed, Herbnet, the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the Guide to the Dispensing of Medicines for Clinical Evidence, and the Dictionary of Chinese Medicine Formulas, as well as medical experts' treatment consensus in terms of herbal efficacy, modern pharmacological studies, and clinical identification and typing of COVID-19 pneumonia, to determine the recommended solutions. We found that the therapeutic effect of a combination of six TCM formulas on the COVID-19 virus is the result of the overall effect of the formula rather than that of specific components of the formula. Based on this, we recommend a formula similar to that of Jinhua Qinggan Granules for the treatment of COVID-19 pneumonia. This study may provide new ideas and new methods for future clinical research. Classification: Biological Science.

CD73: Friend or Foe in Lung Injury.

Ecto-5'-nucleotidase (CD73) plays a strategic role in calibrating the magnitude and chemical nature of purinergic signals that are delivered to immune cells. Its primary function is to convert extracellular ATP to adenosine in concert with ectonucleoside triphosphate diphosphohydrolase-1 (CD39) in normal tissues to limit an excessive immune response in many pathophysiological events, such as lung injury induced by a variety of contributing factors. Multiple lines of evidence suggest that the location of CD73, in proximity to adenosine receptor subtypes, indirectly determines its positive or negative effect in a variety of organs and tissues and that its action is affected by the transfer of nucleoside to subtype-specific adenosine receptors. Nonetheless, the bidirectional nature of CD73 as an emerging immune checkpoint in the pathogenesis of lung injury is still unknown. In this review, we explore the relationship between CD73 and the onset and progression of lung injury, highlighting the potential value of this molecule as a drug target for the treatment of pulmonary disease.

Application of SHEL Model in the Management of Respiratory Tract Exposure in Fangcang Shelter Hospitals for COVID-19.

Objective: To explore the application effect of the (software factors, hardware factors, environmental factors, parties and other factors, SHEL) model in respiratory tract exposure protection of staff in temporary COVID-19 hospitals. Methods: 207 Staff members working in the isolation units of Fangcang shelter hospitals between 20 May 2022 and 5 June 2022 were selected as research subjects. The SHEL model was used to protect and manage the respiratory exposure of the isolation unit staff to the novel coronavirus. The incidence of respiratory exposure among the staff in the isolation units was compared before the SHEL model's implementation (20 May 2022-28 May 2022) and afterwards the SHEL model's implementation (29 May 2022-5 June 2022). Results: Before the implementation of the SHEL model, a total of nine cases (4.35%) from 207 workers had respiratory exposure. Occurrence location: six cases in the isolation room (one-out room, level-one protection zone) and three cases in the drop-off area for patients outside the ward. After implementation, a total of two cases (0.97%) of respiratory tract exposure occurred among the 207 staff members; both occurred in the unprotected zone (two-out room, level-two protection zone), and the difference was statistically significant before and after the implementation (P < 0.05). Conclusion: New coronary pneumonia Fangcang shelter hospitals should use the SHEL model to manage the respiratory exposure of their isolation unit staff to reduce the respiratory exposure risk to staff in isolation units.