Does the COVID-19 Pandemic Affect Excellence in Academic Research? a Study of Science and Engineering Faculty Members in China.

Research excellence is one of the key missions of universities and an important engine for socio-economic development. However, the outbreak of COVID-19 has affected academic research in many ways. This study examines the impact of the COVID-19 pandemic on the research performance of science and engineering faculty members in China's top research universities. It is found that the pandemic caused a decline in the numbers and quality of published articles, and the effects persisted over time. The negative effect of the pandemic on research excellence was more pronounced in the older faculty groups and departments of science. In addition, the pandemic has harmed international research collaborations among academics, which is likely to obstruct research excellence in the long run. In the end, this paper proposes several policy recommendations to reinvigorate universities' capacity for research innovation in the post-pandemic era.

Effectiveness and safety of Qingfei Dayuan granules for treating influenza and upper respiratory tract infections manifested by the pulmonary heat-toxin syndrome: A multicenter, randomized, double-blind, placebo-controlled trial.

Background: Patients diagnosed with influenza and upper respiratory tract infections (URTIs) have similar clinical manifestations and biochemical indices and a low detection rate of viral pathogens, mixed infection with diverse respiratory viruses, and targeted antiviral treatment difficulty in the early stage. According to the treatment strategy of "homotherapy for heteropathy" in traditional Chinese medicine (TCM), different diseases with the same clinical symptoms can be treated with the same medicines. Qingfei Dayuan granules (QFDY), a type of Chinese herbal preparation included in the TCM Diagnosis and Treatment Protocol for COVID-19 of Hubei Province issued by the Health Commission of Hubei Province in 2021, are recommended for patients suffering from COVID-19 with symptoms of fever, cough, and fatigue, among others. Additionally, recent studies have shown that QFDY effectively alleviates fever, cough, and other clinical symptoms in patients with influenza and URTIs. Materials and methods: The study was designed as a multicenter, randomized, double-blind, placebo-controlled clinical trial for treatment for influenza and URTIs manifested by pulmonary heat-toxin syndrome (PHTS) with QFDY. A total of 220 eligible patients were enrolled from eight first-class hospitals in five cities of Hubei Province in China and randomly assigned to receive either 15 g of QFDY or a placebo three times a day for 5 days. The primary outcome was the complete fever relief time. Secondary outcomes included efficacy evaluation of TCM syndromes, scores of TCM syndromes, cure rate of each single symptom, incidence of comorbidities and progression to severe conditions, combined medications, and laboratory tests. Safety evaluations mainly involved adverse events (AEs) and changes in vital signs during the study. Results: Compared with the placebo group, the complete fever relief time was shorter in the QFDY group, 24 h (12.0, 48.0) in the full analysis set (FAS) and 24 h (12.0, 49.5) in the per-protocol set (PPS) (p ≤ 0.001). After a 3-day treatment, the clinical recovery rate (22.3% in the FAS and 21.6% in the PPS) and cure rate of cough (38.6% in the FAS and 37.9% in the PPS), a stuffy and running nose, and sneezing (60.0% in the FAS and 59.5% in the PPS) in the QFDY group were higher than those in the placebo group (p < 0.05). The number of patients taking antibiotics for more than 24 h in the placebo group (nine cases) was significantly higher than that in the QFDY group (one case) (p < 0.05). There were no significant differences between the two groups in terms of scores of TCM syndromes, incidence of comorbidities or progression to severe conditions, combined use of acetaminophen tablets or phlegm-resolving medicines, and laboratory tests (p > 0.05). Meanwhile, no significant difference was found in the incidence of AEs and vital signs between the two groups (p > 0.05). Conclusion: The trial showed that QFDY was an effective and safe treatment modality for influenza and URTIs manifested by PHTS because it shortened the complete fever relief time, accelerated clinical recovery, and alleviated symptoms such as cough, a stuffy and running nose, and sneezing during the course of treatment. Clinical trial registration: https://www.chictr.org.cn/showproj.aspx?proj=131702, identifier ChiCTR2100049695.

A multiple instance learning approach for detecting COVID-19 in peripheral blood smears.

A wide variety of diseases are commonly diagnosed via the visual examination of cell morphology within a peripheral blood smear. For certain diseases, such as COVID-19, morphological impact across the multitude of blood cell types is still poorly understood. In this paper, we present a multiple instance learning-based approach to aggregate high-resolution morphological information across many blood cells and cell types to automatically diagnose disease at a per-patient level. We integrated image and diagnostic information from across 236 patients to demonstrate not only that there is a significant link between blood and a patient's COVID-19 infection status, but also that novel machine learning approaches offer a powerful and scalable means to analyze peripheral blood smears. Our results both backup and enhance hematological findings relating blood cell morphology to COVID-19, and offer a high diagnostic efficacy; with a 79% accuracy and a ROC-AUC of 0.90.

A comparative bibliometric analysis of Omicron and Delta variants during the COVID-19 pandemic.

BACKGROUND: To compare the research hotspots of infections with the Delta and Omicron variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 2019 (COVID-19) pandemic and to identify future research trends. METHODS: Studies about Delta and Omicron variant infections published over the last 3 years were retrieved from the Web of Science (WoS) database. A comparative bibliometric analysis was conducted through machine learning and visualization tools, including VOSviewer, Bibliographic Item Co-Occurrence Matrix Builder, and Graphical Clustering Toolkit. Research hotspots and trends in the field were analyzed, and the contributions and collaborations of countries, institutions, and authors were documented. A cross-sectional analysis of the relevant studies registered at ClinicalTrials.gov was also performed to clarify the direction of future research. RESULTS: A total of 1,787 articles distributed in 107 countries and 374 publications from 77 countries focused on the Delta and Omicron variants were included in our bibliometric analysis. The top five productive countries in both variants were the USA, China, the UK, India, and Germany. In 5,999 and 1,107 keywords identified from articles on the Delta and Omicron, the top two frequent keywords were the same: "COVID-19" (occurrence: 713, total link strength: 1,525 in Delta; occurrence: 137, total link strength: 354 in Omicron), followed by "SARS-CoV-2" (occurrence: 553, total link strength: 1,478 in Delta; occurrences 132, total link strength: 395 in Omicron). Five theme clusters from articles on Delta variant were identified: transmission, molecular structure, activation mode, epidemiology, and co-infection. While other three theme clusters were recognized for the Omicron variant: vaccine, human immune response, and infection control. Meanwhile, 21 interventional studies had been registered up to April 2022, most of which aimed to evaluate the immunogenicity and safety of different kinds of vaccines in various populations. CONCLUSIONS: Publications and clinical trials related to COVID-19 increased annually. As the first comparative bibliometric analysis for Delta and Omicron variants, we noticed that the relevant research trends have shifted from vaccine development to infection control and management of complications. The ongoing clinical studies will verify the safety and efficacy of promising drugs.

A novel quantification method of lung fibrosis based on Micro-CT images developed with the optimized pulmonary fibrosis mice model induced by bleomycin.

Background and aims: Idiopathic pulmonary fibrosis (IPF) is a fibrosing lung disease with unknown etiology, leading to cough and dyspnoea, which is also one of the most common sequelae affecting the quality of life of COVID-19 survivors. There is no cure for IPF patients. We aim to develop a reliable IPF animal model with quantification of fibrosis based on Micro-Computer Tomography (micro-CT) images for the new drug discovery, because different bleomycin administration routes, doses, and intervals are reported in the literature, and there is no quantitative assessment of pulmonary fibrosis based on micro-CT images in animal studies. Methods: We compared three dosages (1.25 mg/kg, 2.5 mg/kg, and 5 mg/kg) of intratracheal bleomycin administration and experiment intervals (14 and 21 days) in C57BL/6 mice by investigating survival rates, pulmonary histopathology, micro-CT, peripheral CD4+ & CD8+ cells, and cytokines. Moreover, a simple and reliable new method was developed for scoring fibrosis in live mice based on Micro-CT images by using Image J software, which transfers the dark sections in pulmonary Micro-CT images to light colors on a black background. Results: The levels of hydroxyproline, inflammation cytokine, fibrotic pathological changes, and collagen deposition in the lungs of mice were bleomycin dose-dependent and time-dependent as well as the body weight loss. Based on the above results, the mice model at 21 days after being given bleomycin at 1.25 mg/kg has optimal pulmonary fibrosis with a high survival rate and low toxicity. There is a significant decrease in the light area (gray value at 9.86 ± 0.72) in the BLM mice, indicating that a significant decrease in the alveolar air area was observed in BLM injured mice compared to normal groups (###p < 0.001), while the Pirfenidone administration increased the light area (gray value) to 21.71 ± 2.95 which is close to the value observed in the normal mice (gray value at 23.23 ± 1.66), which is consistent with the protein levels of Col1A1, and α-SMA. Notably, the standard deviations for the consecutive six images of each group indicate the precision of this developed quantitation method for the micro-CT image taken at the fifth rib of each mouse. Conclusion: Provided a quantifying method for Micro-CT images in an optimal and repeatable pulmonary fibrosis mice model for exploring novel therapeutic interventions.

Inspiration for COVID-19 Treatment: Network Analysis and Experimental Validation of Baicalin for Cytokine Storm.

Background: Cytokine storm (CS) is a systemic inflammatory syndrome and a major cause of multi-organ failure and even death in COVID-19 patients. With the increasing number of COVID-19 patients, there is an urgent need to develop effective therapeutic strategies for CS. Baicalin is an anti-inflammatory and antiviral traditional Chinese medicine. In the present study, we aimed to evaluate the therapeutic mechanism of baicalin against CS through network analysis and experimental validation, and to detect key targets of CS that may bind closely to baicalin through molecular docking. Method: Access to potential targets of baicalin and CS in public databases. We constructed the protein-protein interaction (PPI) network of baicalin and CS by Cytoscape 9.0 software and performed network topology analysis of the potential targets. Then, the hub target was identified by molecular docking technique and validated in the CS model. Finally, GO and KEGG pathway functional enrichment analysis of common targets were confirmed using R language, and the location of overlapping targets in key pathways was queried via KEGG Mapper. Result: A total of 86 overlapping targets of baicalin and CS were identified, among which MAPK14, IL2, FGF2, CASP3, PTGS2, PIK3CA, EGFR, and TNF were the core targets. Moreover, it was found that baicalin bound most closely to TNF through molecular docking, and demonstrated that baicalin can effectively inhibit the elevation of TNF-α in vitro and in vivo. Furthermore, bioenrichment analysis revealed that the TNF signaling pathway and IL-17 signaling pathway may be potential key pathways for baicalin to treat CS. Conclusion: Based on this study, baicalin was identified as a potential drug for the alleviation of CS, and the possible key targets and pathways of baicalin for the treatment of CS were elucidated to reveal the main pharmacological mechanisms.

Risk factors for air leakage during invasive mechanical ventilation in pediatric intensive care units.

PURPOSE: This study aimed to investigate air leakage during invasive mechanical ventilation (IMV) in a pediatric intensive care unit (PICU) and explore potential risk factors. METHODS: We conducted a retrospective cohort study of children who underwent IMV in a single-center PICU in a tertiary referral hospital. Air leakage risk factors and factors associated with an improved outcome were assessed. RESULTS: A total of 548 children who underwent IMV were enrolled in this study. Air leakage occurred in 7.5% (41/548) of the cases in the PICU. Air leakage increased the duration of IMV and hospitalization time. Multivariate logistic regression analysis showed a higher risk of air leakage during IMV for PICU patients with acute respiratory dyspnea syndrome (ARDS) (OR = 4.38), a higher pediatric critical illness score (PCIS) (OR = 1.08), or a higher peak inspiratory pressure (PIP) (OR = 1.08), whereas the risk was lower for patients with central respiratory failure (OR = 0.14). The logistic model had excellent predictive power for air leakage, with an area under the curve of 0.883 and tenfold cross-validation. Patients aged between 1 and 6 years who were diagnosed with measles or pneumonia and had a low positive end-expiratory pressure (PEEP) or high PaO2/FiO2 ratio were associated with improved outcomes. Patients diagnosed with central respiratory failure or congenital heart diseases were associated with less desirable outcomes. CONCLUSIONS: Patients with ARDS, a higher PCIS at admission or a higher PIP were at higher risk of air leakage.

The relations between different components of intolerance of uncertainty and symptoms of depression during the COVID-19 pandemic: A network analysis.

Background: The relations between depression and intolerance of uncertainty (IU) have been extensively investigated during the COVID-19 pandemic. However, there is a lack of understanding on how each component of IU may differentially affect depression symptoms and vice versa. The current study used a network approach to reveal the component-to-symptom interplay between IU and depression and identify intervention targets for depression during the COVID-19 pandemic. Methods: A total of 624 college students participated in the current study. An IU-Depression network was estimated using items from the 12-item Intolerance of Uncertainty Scale and the Patient Health Questionnaire-9. We examined the network structure, node centrality, and node bridge centrality to identify component-to-symptom pathways, central nodes, and bridge nodes within the IU-Depression network. Results: Several distinct pathways (e.g., "Frustration when facing uncertainty" and "Feelings of worthlessness") emerged between IU and Depression. "Fatigue" and "Frustration when facing uncertainty" were identified as the central nodes in the estimated network. "Frustration when facing uncertainty," "Psychomotor agitation/retardation," and "Depressed or sad mood" were identified as bridging nodes between the IU and Depression communities. Conclusion: By delineating specific pathways between IU and depression and highlighting the influential role of "Frustration when facing uncertainty" in maintaining the IU-Depression co-occurrence, current findings may inform targeted prevention and interventions for depression during the COVID-19 pandemic.

Effect of SARS-CoV-2 infection on asthma patients.

Background: SARS-CoV-2 causes coronavirus disease 2019 (COVID-19), a new coronavirus pneumonia, and containing such an international pandemic catastrophe remains exceedingly difficult. Asthma is a severe chronic inflammatory airway disease that is becoming more common around the world. However, the link between asthma and COVID-19 remains unknown. Through bioinformatics analysis, this study attempted to understand the molecular pathways and discover potential medicines for treating COVID-19 and asthma. Methods: To investigate the relationship between SARS-CoV-2 and asthma patients, a transcriptome analysis was used to discover shared pathways and molecular signatures in asthma and COVID-19. Here, two RNA-seq data (GSE147507 and GSE74986) from the Gene Expression Omnibus were used to detect differentially expressed genes (DEGs) in asthma and COVID-19 patients to find the shared pathways and the potential drug candidates. Results: There were 66 DEGs in all that were classified as common DEGs. Using a protein-protein interaction (PPI) network created using various bioinformatics techniques, five hub genes were found. We found that asthma has some shared links with the progression of COVID-19. Additionally, protein-drug interactions with common DEGs were also identified in the datasets. Conclusion: We investigated possible links between COVID-19 and asthma using bioinformatics databases, which might be useful in treating COVID-19 patients. More studies on populations affected by these diseases are needed to elucidate the molecular mechanism behind their association.

A Closed-Loop Approach to Fight Coronavirus: Early Detection and Subsequent Treatment.

The recent COVID-19 pandemic has caused tremendous damage to the social economy and people's health. Some major issues fighting COVID-19 include early and accurate diagnosis and the shortage of ventilator machines for critical patients. In this manuscript, we describe a novel solution to deal with COVID-19: portable biosensing and wearable photoacoustic imaging for early and accurate diagnosis of infection and magnetic neuromodulation or minimally invasive electrical stimulation to replace traditional ventilation. The solution is a closed-loop system in that the three modules are integrated together and form a loop to cover all-phase strategies for fighting COVID-19. The proposed technique can guarantee ubiquitous and onsite detection, and an electrical hypoglossal stimulator can be more effective in helping severe patients and reducing complications caused by ventilators.

Discovery and characterization of SARS-CoV-2 reactive and neutralizing antibodies from humanized CAMouseHG mice through rapid hybridoma screening and high-throughput single-cell V(D)J sequencing.

The coronavirus disease 2019 pandemic has caused more than 532 million infections and 6.3 million deaths to date. The reactive and neutralizing fully human antibodies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are effective detection tools and therapeutic measures. During SARS-CoV-2 infection, a large number of SARS-CoV-2 reactive and neutralizing antibodies will be produced. Most SARS-CoV-2 reactive and neutralizing fully human antibodies are isolated from human and frequently encoded by convergent heavy-chain variable genes. However, SARS-CoV-2 viruses can mutate rapidly during replication and the resistant variants of neutralizing antibodies easily survive and evade the immune response, especially in the face of such focused antibody responses in humans. Therefore, additional tools are needed to develop different kinds of fully human antibodies to compensate for current deficiency. In this study, we utilized antibody humanized CAMouseHG mice to develop a rapid antibody discovery method and examine the antibody repertoire of SARS-CoV-2 RBD-reactive hybridoma cells derived from CAMouseHG mice by using high-throughput single-cell V(D)J sequencing analysis. CAMouseHG mice were immunized by 28-day rapid immunization method. After electrofusion and semi-solid medium screening on day 12 post-electrofusion, 171 hybridoma clones were generated based on the results of SARS-CoV-2 RBD binding activity assay. A rather obvious preferential usage of IGHV6-1 family was found in these hybridoma clones derived from CAMouseHG mice, which was significantly different from the antibodies found in patients with COVID-19. After further virus neutralization screening and antibody competition assays, we generated a noncompeting two-antibody cocktail, which showed a potent prophylactic protective efficacy against SARS-CoV-2 in cynomolgus macaques. These results indicate that humanized CAMouseHG mice not only provide a valuable platform to obtain fully human reactive and neutralizing antibodies but also have a different antibody repertoire from humans. Thus, humanized CAMouseHG mice can be used as a good complementary tool in discovery of fully human therapeutic and diagnostic antibodies.

Study of anxiety and job burnout, and awareness among young anesthetists during COVID‐19 pandemic

To describe the psychological impact of coronavirus disease 2019 (COVID‐19) on young doctors and their job burnout in the Department of Anesthesiology during the initial days of the pandemic and examine their awareness and familiarity with this pneumonia. We conducted a cross‐sectional study in West China Hospital in February 2020. A self‐designed questionnaire was sent to all young doctors working in the department of anesthesiology. Impact of Event Scale‐Revised and Maslach Burnout Inventory General Survey were used to evaluate the psychological impact and degree of job burnout. Another questionnaire was conducted to explore the awareness and familiarity of COVID‐19. All participants were divided into five groups according to the time of clinical practice: Postgraduate year (PGY) 0.5 (less than 0.5 year), 0.6–1 (0.6–1 year), 1–2 (1–2 years), 2–3 (2–3 years), 3 (more than 3 years) groups. The results were collected and analyzed subsequently. A total of 188 questionnaires were collected. There were significant differences in distress level between PGY 0.5 and PGY 0.6–1 (17.60 ± 12.53 vs. 12.05 ± 10.65;p = 0.029), and PGY 3 and PGY 0.6–1 (19.92 ± 11.88 vs. 12.05 ± 10.65;p = 0.031). As for job burnout, there were no differences among the five subgroups. Most participants (86.70%) were kept in good working condition, and 25 participants showed a mild level of job burnout. Although all of the respondents had high awareness of the basic elements of COVID‐19, they had little knowledge about the details, such as lab tests, release criteria, and recommended therapy, and this result had no significant difference among the five groups. COVID‐19 had caused a mild level of distress and work burnout in young anesthetists. Most of the participants were not clear about the diagnostic, release criteria, and therapeutic method, which will become key teaching points in the future. In this study, the psychological impact of coronavirus disease 2019 (COVID‐19) on young doctors and their job burnout in the Department of Anesthesiology during the initial days of the pandemic are described, using the Impact of Event Scale‐Revised and Maslach Burnout Inventory General Survey and their awareness and familiarity of this pneumonia is also investigated. We found that most participants were as expected maintaining a positive mood during this hard period, yet COVID‐19 has caused a mild level of distress among young doctors in anesthesia. Besides, some of them were undergoing mild work burnout. Teaching hospitals should provide timely multichannel information about the diagnostic criteria and therapeutic methods due to the lack of detailed knowledge about the COVID‐19 in young doctors, which will become key teaching points in the future. Our survey can provide references for the professional training and psychological counseling of young anesthesiology doctors during the pandemic.

Lung ultrasonography findings of coronavirus disease 2019 patients: Comparison between primary and secondary regions of China.

BACKGROUND: An unexplained pneumonia occurred in Wuhan, China in December 2019, later identified and named coronavirus disease 2019 (COVID-19). This study aimed to compare the ultrasonographic features of the lung between patients with COVID-19 in Wuhan (the primary region) and those in Beijing (the secondary region) and to find the value of applying ultrasound in COVID-19. METHODS: A total of 248 COVID-19 cases were collected, including long-term residents in Wuhan (138), those who had a short-term stay in Wuhan (72), and those who had never visited Wuhan (38). Ultrasound examination was performed daily; the highest lung ultrasound score (LUS) was the first comparison point, while the LUS of the fifth day thereafter was the second comparison point. The differences between overall treatment and ultrasonography of left and right lungs among groups were compared. RESULTS: The severity decreased significantly after treatment. The scores of the groups with long-term residence and short-term stay in Wuhan were higher than those of the group that had never been to Wuhan. CONCLUSION: Ultrasonography is effective for dynamic monitoring of COVID-19. The ultrasonographic features of patients in the Wuhan area indicated relatively severe disease. Thus, Wuhan was the main affected area of china.

Effect of surgical mask on fMRI signals during task and rest.

Wearing a face mask has become essential to contain the spread of COVID-19 and has become mandatory when collecting fMRI data at most research institutions. Here, we investigate the effects of wearing a surgical mask on fMRI data in n = 37 healthy participants. Activations during finger tapping, emotional face matching, working memory tasks, and rest were examined. Preliminary fMRI analyses show that despite the different mask states, resting-state signals and task activations were relatively similar. Resting-state functional connectivity showed negligible attenuation patterns in mask-on compared with mask-off. Task-based ROI analysis also demonstrated no significant difference between the two mask states under each contrast investigated. Notwithstanding the overall insignificant effects, these results indicate that wearing a face mask during fMRI has little to no significant effect on resting-state and task activations.

A bibliometric analysis of the application of artificial intelligence to advance individualized diagnosis and treatment of critical illness.

Background: Artificial intelligence (AI) has been extensively applied in the individualized diagnosis and treatment of critical illness, and numerous studies have been published on this topic. Therefore, a bibliometric analysis of these publications should be performed to provide a direction of hot topics and future research trends. Methods: A bibliometric analysis was performed on the research articles to identify the hot topics and any unsolved issues regarding the use of AI in individualized diagnosis and treatment of critical illness. Articles published from January 2011 to December 2021 were retrieved from the Web of Science (WOS) core collection database for bibliometric analysis, and a cross-sectional analysis of the relevant studies that had been registered at ClinicalTrials.gov was also conducted. Results: The number of articles published showed an annually increasing trend, with a worldwide geographic distribution over the past decade. Ultimately, 427 research articles were included in the bibliometric analysis. The relevant articles were divided into four separate clusters that focused on AI application aspects, prediction model establishment, coronavirus disease 2019 (COVID-19) treatment and outcome assessments, respectively. "Machine learning" was the most frequent keyword (147 occurrences, 165 links, and 395 total link strengths) followed by "risk", "models", and "mortality". With 205 articles, the United States of America (USA) had interacted the most with other countries (20 links, and 94 total link strength), while the domestic research institutes in China had infrequently collaborated with others. Approximately 130 trials focusing on the application of AI in the intensive care unit (ICU) and emergency department (ED) had been registered at ClinicalTrial.gov, and most of them (n=71, 54.6%) were interventional. The main research objectives of these trials were to provide decision making assistance and establish prediction models. However, only 3.8% (5 trials) of them had reached exact conclusions which favored the application of AI. Conclusions: The application of AI has raised great interest in critical illness and has mainly been focused on decision making assistance and prediction model establishment. Cooperation between agencies engaged in AI research needs to be strengthened. An increasing number of trials have been registered at ClinicalTrial.gov, and the results of them are promising. Keywords: Bibliometric analysis; artificial intelligence (AI); individualized diagnosis; critical care medicine; emergency department (ED).

Effect of SARS-CoV-2 infection on asthma patients

Background SARS-CoV-2 causes coronavirus disease 2019 (COVID-19), a new coronavirus pneumonia, and containing such an international pandemic catastrophe remains exceedingly difficult. Asthma is a severe chronic inflammatory airway disease that is becoming more common around the world. However, the link between asthma and COVID-19 remains unknown. Through bioinformatics analysis, this study attempted to understand the molecular pathways and discover potential medicines for treating COVID-19 and asthma. Methods To investigate the relationship between SARS-CoV-2 and asthma patients, a transcriptome analysis was used to discover shared pathways and molecular signatures in asthma and COVID-19. Here, two RNA-seq data (GSE147507 and GSE74986) from the Gene Expression Omnibus were used to detect differentially expressed genes (DEGs) in asthma and COVID-19 patients to find the shared pathways and the potential drug candidates. Results There were 66 DEGs in all that were classified as common DEGs. Using a protein-protein interaction (PPI) network created using various bioinformatics techniques, five hub genes were found. We found that asthma has some shared links with the progression of COVID-19. Additionally, protein-drug interactions with common DEGs were also identified in the datasets. Conclusion We investigated possible links between COVID-19 and asthma using bioinformatics databases, which might be useful in treating COVID-19 patients. More studies on populations affected by these diseases are needed to elucidate the molecular mechanism behind their association.

Glycyrrhetinic acid: A potential drug for the treatment of COVID-19 cytokine storm.

BACKGROUND: The cytokine storm (CS) triggered by coronavirus disease 2019 (COVID-19) has caused serious harm to health of humanity and huge economic burden to the world, and there is a lack of effective methods to treat this complication. PURPOSE: In this research, we used network pharmacology and molecular docking to reveal the interaction mechanism in the glycyrrhetinic acid (GA) for the treatment of CS, and validated the effect of GA intervention CS by experiments. STUDY DESIGN: First, we screened corresponding target of GA and CS from online databases, and obtained the action target genes through the Venn diagram. Then, protein-protein interaction (PPI) network, Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of the action target genes were acquired by R language to predict its mechanism. Next, molecular docking was performed on core targets. Finally, experiments in which GA intervened in lipopolysaccharide (LPS)-induced CS were implemented. RESULTS: 84 action target genes were obtained from online database. The PPI network of target genes showed that TNF, IL6, MAPK3, PTGS2, ESR1 and PPARG were considered as the core genes. The results of GO and KEGG showed that action target genes were closely related to inflammatory and immune related signaling pathways, such as TNF signaling pathway, IL-17 signaling pathway, Human cytomegalovirus infection, PPAR signaling pathway and so on. Molecule docking results prompted that GA had fine affinity with IL6 and TNF proteins. Finally, in vivo and in vitro experimental results showed that GA could significantly inhibit LPS-induced CS. CONCLUSION: GA has a potential inhibitory effect on CS, which is worthy of further exploration.

[Mahuang (herbaceous stem of Ephedra spp.): chemistry, pharmacodynamics, and pharmacokinetics].

The Chinese medicinal herb Mahuang is herbaceous stem of Ephedra sinica, E. intermedia, or E. equisetina(Family, Ephedraceae). In China, Mahuang has been used, all the way over a millennium, as a key component herb of many herbal medicines for management of epidemics of acute respiratory illness and is also used in officially recommended herbal medicines for COVID-19. Mahuang is the first-line medicinal herb for cold and wheezing and also an effective diuretic herb for edema. However, Mahuang can also exert significant adverse effects. The key to safety and effectiveness is rational and precise use of the herb. In this review article, we comprehensively summarize chemical composition of Mahuang and associated differences in pharmacognosy, pharmacodynamics and pharmacokinetics of Mahuang compounds, along with the adverse effects of Mahuang compounds and products. Based on full understanding of how Mahuang is used in Chinese traditional medicine, systematic research on Mahuang in line with contemporary standards of pharmaceutical sciences will facilitate promoting Chinese herbal medicines to become more efficient in management of epidemic illnesses, such as COVID-19. To this end, we recommend research on Mahuang of two aspects, i.e., pharmacological investigation for its multicompound-involved therapeutic effects and toxicological investigation for clinical manifestation of the adverse effects, chemicals responsible for the adverse effects, and conditions for safe use of the herb and the herb-containing medicines.