Personality traits predict the effects of Internet and academic self-efficacy on practical performance anxiety in online learning under the COVID-19 lockdown

Due to COVID-19, the primary teaching method has changed from traditional face-to-face teaching to online teaching. The present study explored the correlates between two personality traits, Neuroticism and Extraversion, and two types of self-efficacy, Internet self-efficacy and academic self-efficacy, on practical performance anxiety. Data from 273 technical college students were collected. Structural equation modeling analysis was performed. Results show that Neuroticism and Extraversion can predict students' practical performance anxiety through Internet and academic self-efficacy. Moreover, Neuroticism can negatively predict Internet and academic self-efficacy. Extraversion can positively predict Internet and academic self-efficacy. The two types of self-efficacy can positively predict practical performance anxiety. According to the results, it seems necessary to reduce students' practical performance anxiety by paying attention to their personality features and their self-efficacy. [ FROM AUTHOR] Copyright of Journal of Research on Technology in Education is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Global Disease Burden and Attributable Risk Factor Analysis of Asthma in 204 Countries and Territories From 1990 to 2019.

PURPOSE: Asthma is a common chronic inflammatory respiratory tract disease with high morbidity and mortality. The global trends in asthma burden remain poorly understood, and asthma incidence has increased during the worldwide coronavirus disease 2019 (COVID-19) pandemic. This study aimed to provide a comprehensive view of the global distribution of asthma burden and its attributable risk factors from 1990 to 2019. METHODS: Based on the Global Burden of Disease Study 2019 Database, asthma incidence, deaths, disability-adjusted life years (DALYs), the corresponding age-standardized incidence rate (ASIR), age-standardized death rate (ASDR), age-standardized DALY rate, and estimated annual percentage change were analyzed according to age, sex, sociodemographic index (SDI) quintiles, and locations. Risk factors contributing to asthma deaths and DALYs were also investigated. RESULTS: Globally, the asthma incidence increased by 15%, but deaths and DALYs decreased. The corresponding ASIR, ASDR, and age-standardized DALY rate also decreased. The high SDI region had the highest ASIR, and the low SDI region had the highest ASDR. The ASDR and age-standardized DALY rate were negatively correlated with the SDI. The low-middle SDI region, particularly South Asia, showed the highest asthma-related deaths and DALYs. The incidence peak was under 9 years old, and more than 70% of all deaths occurred in the population over 60 years old. Smoking, occupational asthmagens, and a high body mass index were the main risk factors for asthma-related mortality and DALYs, and their distributions varied between sexes. CONCLUSIONS: Globally, the asthma incidence has increased since 1990. The greatest asthma burden is borne by the low-middle SDI region. The 2 groups that need special attention are those under 9 years old and those over 60 years old. Targeted strategies are needed to reduce the asthma burden based on geographic and sex-age characteristics. Our findings also provide a platform for further investigation into the asthma burden in the era of COVID-19.

COVID-19 infection in patients with haematological malignancies: A single-centre survey in the latest Omicron wave in China.

As the COVID-19 variant Omicron surge in Beijing, China, a better understanding of risk factors for adverse outcomes may improve clinical management in patients with haematological malignancies (HM) diagnosed with COVID-19. The study sample includes 412 cases, mainly represented by acute leukaemia, chronic myeloid leukaemia (CML), plasma cell disorders and lymphoma and chronic lymphocytic leukaemia. COVID-19 pneumonia was observed in 10.4% (43/412) of patients, and severe/critical illness was observed in 5.3% (22/412). Among the 86 cases with advanced malignancies, 17.6% (12/86) of patients developed severe/critical COVID-19, which was significantly higher than reported in patients with stable malignancies (9/326, 2.70%, p < 0.001). Similarly, the advanced malignancy cohort had a higher mortality rate (9/86, 10.5% vs. 0/326, 0%, p < 0.001) and a poor 30-day overall survival (OS) compared with the stable malignancy cohort (74.2% vs. 100.0%, p < 0.0001). Overall, nine patients (2.2%) died. The primary cause of death was progressive HM in four patients and a combination of both COVID-19 and HM in five patients. In the multivariable analysis, over 65 years of age, comorbidities and advanced malignancy were correlated with severe/critical COVID-19 in HM patients. This study sheds light on the poor outcomes among COVID-19 HM patients with the leading cause of advanced malignancy.

LightR-YOLOv5: A compact rotating detector for SARS-CoV-2 antigen-detection rapid diagnostic test results☆

Nucleic acid testing is currently the golden reference for coronaviruses (SARS-CoV-2) detection, while the SARS-CoV-2 antigen-detection rapid diagnostic tests (RDT) is an important adjunct. RDT can be widely used in the community or regional screening management as self-test tools and may need to be verified by healthcare authorities. However, manual verification of RDT results is a time-consuming task, and existing object detection algorithms usually suffer from high model complexity and computational effort, making them difficult to deploy. We propose LightR-YOLOv5, a compact rotating SARS-CoV-2 antigen-detection RDT results detector. Firstly, we employ an extremely light-weight L-ShuffleNetV2 network as a feature extraction network with a slight reduction in recognition accuracy. Secondly, we combine semantic and texture features in different layers by judiciously combining and employing GSConv, depth-wise convolution, and other modules, and further employ the NAM attention to locate the RDT result detection region. Furthermore, we propose a new data augmentation approach, Single-Copy-Paste, for increasing data samples for the specific task of RDT result detection while achieving a small improvement in model accuracy. Compared with some mainstream rotating object detection networks, the model size of our LightR-YOLOv5 is only 2.03MB, and it is 12.6%, 6.4%, and 7.3% higher in mAP@.5:.95 metrics compared to RetianNet, FCOS, and R3Det, respectively.

Identification of natural compounds as SARS-CoV-2 inhibitors via molecular docking and molecular dynamic simulation.

Background: Base mutations increase the contagiousness and transmissibility of the Delta and Lambda strains and lead to the severity of the COVID-19 pandemic. Molecular docking and molecular dynamics (MD) simulations are frequently used for drug discovery and relocation. Small molecular compounds from Chinese herbs have an inhibitory effect on the virus. Therefore, this study used computational simulations to investigate the effects of small molecular compounds on the spike (S) protein and the binding between them and angiotensin-converting enzyme 2 (ACE2) receptors. Methods: In this study, molecular docking, MD simulation, and protein-protein analysis were used to explore the medicinal target inhibition of Chinese herbal medicinal plant chemicals on SARS-CoV-2. 12,978 phytochemicals were screened against S proteins of SARS-CoV-2 Lambda and Delta mutants. Results: Molecular docking showed that 65.61% and 65.28% of the compounds had the relatively stable binding ability to the S protein of Lambda and Delta mutants (docking score ≤ -6). The top five compounds with binding energy with Lambda and Delta mutants were clematichinenoside AR2 (-9.7), atratoglaucoside,b (-9.5), physalin b (-9.5), atratoglaucoside, a (-9.4), Ochnaflavone (-9.3) and neo-przewaquinone a (-10), Wikstrosin (-9.7), xilingsaponin A (-9.6), ardisianoside G (-9.6), and 23-epi-26-deoxyactein (-9.6), respectively. Four compounds (Casuarictin, Heterophylliin D, Protohypericin, and Glansrin B) could interact with S protein mutation sites of Lambda and Delta mutants, respectively, and MD simulation results showed that four plant chemicals and spike protein have good energy stable complex formation ability. In addition, protein-protein docking was carried out to evaluate the changes in ACE2 binding ability caused by the formation of four plant chemicals and S protein complexes. The analysis showed that the binding of four plant chemicals to the S protein could reduce the stability of the binding to ACE2, thereby reducing the replication ability of the virus. Conclusion: To sum up, the study concluded that four phytochemicals (Casuarictin, Heterophylliin D, Protohypericin, and Glansrin B) had significant effects on the binding sites of the SARS-CoV-2 S protein. This study needs further in vitro and in vivo experimental validation of these major phytochemicals to assess their potential anti-SARS-CoV-2. Graphical abstract.

LightR-YOLOv5: A compact rotating detector for SARS-CoV-2 antigen-detection rapid diagnostic test results.

Nucleic acid testing is currently the golden reference for coronaviruses (SARS-CoV-2) detection, while the SARS-CoV-2 antigen-detection rapid diagnostic tests (RDT) is an important adjunct. RDT can be widely used in the community or regional screening management as self-test tools and may need to be verified by healthcare authorities. However, manual verification of RDT results is a time-consuming task, and existing object detection algorithms usually suffer from high model complexity and computational effort, making them difficult to deploy. We propose LightR-YOLOv5, a compact rotating SARS-CoV-2 antigen-detection RDT results detector. Firstly, we employ an extremely light-weight L-ShuffleNetV2 network as a feature extraction network with a slight reduction in recognition accuracy. Secondly, we combine semantic and texture features in different layers by judiciously combining and employing GSConv, depth-wise convolution, and other modules, and further employ the NAM attention to locate the RDT result detection region. Furthermore, we propose a new data augmentation approach, Single-Copy-Paste, for increasing data samples for the specific task of RDT result detection while achieving a small improvement in model accuracy. Compared with some mainstream rotating object detection networks, the model size of our LightR-YOLOv5 is only 2.03MB, and it is 12.6%, 6.4%, and 7.3% higher in mAP@.5:.95 metrics compared to RetianNet, FCOS, and R3Det, respectively.

The impact of wearing powered air purifying respirators or N95 masks on the olfactory function in healthcare workers: A randomized controlled trial.

BACKGROUND: With the Coronavirus disease 2019 epidemic, wearing a mask has become routine to prevent and control the virus's spread, especially for healthcare workers. However, the impact of long-term mask wear on the human body has not been adequately investigated. This study aimed to investigate whether Powered Air Purifying Respirators and N95 masks impact the olfaction in healthcare workers. METHODS: We recruited fifty-six healthcare workers and randomly divided them into 2 groups, wearing a powered air purifying respirator (PAPR) (experiment group, N = 28) and an N95 mask (control group, N = 28). Olfactory discrimination and threshold tests were performed before and after wearing the masks. SPSS 26.0 (SPSS Inc., Chicago, Illinois) software was used for the statistical analyses. RESULTS: There was a statistical difference in the olfactory threshold test after wearing the mask in both PAPR Group (Z = -2.595, P = .009) and N95 Group (Z = -2.120, P = .034), with no significant difference between the 2 (χ2 = 0.29, P = .589). There was no statistical difference in the discrimination test scores in both 2 groups after wearing the masks. CONCLUSION: Wearing a mask affects the healthcare workers' olfaction, especially odor sensitivity. Healthcare workers have a higher olfactory threshold after long-term mask wear, whether wearing PAPRs or N95 masks.

The impact of wearing powered air purifying respirators or N95 masks on the olfactory function in healthcare workers: A randomized controlled trial

Background: With the Coronavirus disease 2019 epidemic, wearing a mask has become routine to prevent and control the virus's spread, especially for healthcare workers. However, the impact of long-term mask wear on the human body has not been adequately investigated. This study aimed to investigate whether Powered Air Purifying Respirators and N95 masks impact the olfaction in healthcare workers. Methods: We recruited fifty-six healthcare workers and randomly divided them into 2 groups, wearing a powered air purifying respirator (PAPR) (experiment group, N = 28) and an N95 mask (control group, N = 28). Olfactory discrimination and threshold tests were performed before and after wearing the masks. SPSS 26.0 (SPSS Inc., Chicago, Illinois) software was used for the statistical analyses. Results: There was a statistical difference in the olfactory threshold test after wearing the mask in both PAPR Group (Z = –2.595, P = .009) and N95 Group (Z = –2.120, P = .034), with no significant difference between the 2 (χ2 = 0.29, P = .589). There was no statistical difference in the discrimination test scores in both 2 groups after wearing the masks. Conclusion: Wearing a mask affects the healthcare workers' olfaction, especially odor sensitivity. Healthcare workers have a higher olfactory threshold after long-term mask wear, whether wearing PAPRs or N95 masks.

Modeling Kaempferol as a Potential Pharmacological Agent for COVID-19/PF Co-Occurrence Based on Bioinformatics and System Pharmacological Tools.

Objective: People suffering from coronavirus disease 2019 (COVID-19) are prone to develop pulmonary fibrosis (PF), but there is currently no definitive treatment for COVID-19/PF co-occurrence. Kaempferol with promising antiviral and anti-fibrotic effects is expected to become a potential treatment for COVID-19 and PF comorbidities. Therefore, this study explored the targets and molecular mechanisms of kaempferol against COVID-19/PF co-occurrence by bioinformatics and network pharmacology. Methods: Various open-source databases and Venn Diagram tool were applied to confirm the targets of kaempferol against COVID-19/PF co-occurrence. Protein-protein interaction (PPI), MCODE, key transcription factors, tissue-specific enrichment, molecular docking, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to clarify the influential molecular mechanisms of kaempferol against COVID-19 and PF comorbidities. Results: 290 targets and 203 transcription factors of kaempferol against COVID-19/PF co-occurrence were captured. Epidermal growth factor receptor (EGFR), proto-oncogene tyrosine-protein kinase SRC (SRC), mitogen-activated protein kinase 3 (MAPK3), mitogen-activated protein kinase 1 (MAPK1), mitogen-activated protein kinase 8 (MAPK8), RAC-alpha serine/threonine-protein kinase (AKT1), transcription factor p65 (RELA) and phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform (PIK3CA) were identified as the most critical targets, and kaempferol showed effective binding activities with the above critical eight targets. Further, anti-COVID-19/PF co-occurrence effects of kaempferol were associated with the regulation of inflammation, oxidative stress, immunity, virus infection, cell growth process and metabolism. EGFR, interleukin 17 (IL-17), tumor necrosis factor (TNF), hypoxia inducible factor 1 (HIF-1), phosphoinositide 3-kinase/AKT serine/threonine kinase (PI3K/AKT) and Toll-like receptor signaling pathways were identified as the key anti-COVID-19/PF co-occurrence pathways. Conclusion: Kaempferol is a candidate treatment for COVID-19/PF co-occurrence. The underlying mechanisms may be related to the regulation of critical targets (EGFR, SRC, MAPK3, MAPK1, MAPK8, AKT1, RELA, PIK3CA and so on) and EGFR, IL-17, TNF, HIF-1, PI3K/AKT and Toll-like receptor signaling pathways. This study contributes to guiding development of new drugs for COVID-19 and PF comorbidities.

The psychological impact of COVID-19 pandemic on healthcare workers.

Background: As unprecedented and prolonged crisis, healthcare workers (HCWs) are at high risk of developing psychological disorders. We investigated the psychological impact of COVID-19 pandemic on HCWs. Methods: This cross-sectional study randomly recruited 439 HCWs in Hunan Cancer Hospital via a web-based sampling method from June 1st 2021 to March 31st 2022. Anxiety and depression levels were measured using Hospital Anxiety and Depression Scale (HADS). The Post Traumatic Stress Disorder (PTSD) Checklist for DSM-5 (PCL-5) was used to assess the presence and severity of PTSD. Fear was measured by modified scale of SARS. Data were collected based on these questionnaires. Differences in fear, anxiety, depression and PTSD among HCWs with different clinical characteristics were analyzed using a multivariate analysis of variance. The Cronbach's alpha scores in our samples were calculated to evaluate the internal consistency of HADS, fear scale and PCL-5. Results: The prevalence of anxiety, depression, and PTSD in HCWs was 15.7, 9.6, and 12.8%, respectively. Females and nurses were with higher fear level (P < 0.05) and higher PTSD levels (P < 0.05). Further analysis of female HCWs revealed that PTSD levels in the 35-59 years-old age group were higher than that in other groups; while married female HCWs were with increased fear than single HCWs. The internal consistency was good, with Cronbach's α = 0.88, 0.80 and 0.84 for HADS, fear scale, and PCL, respectively. Conclusion: Gender, marital status, and age are related to different level of psychological disorders in HCWs. Clinical supportive care should be implemented for specific group of HCWs.

The prevalence of psychological disorders among cancer patients during the COVID-19 pandemic: A meta-analysis.

PURPOSE: We aimed to assess the prevalence rate (PR) of depression, anxiety, posttraumatic stress disorder (PTSD), insomnia, distress, and fear of cancer progression/recurrence among patients with cancer during the COVID-19 pandemic. METHODS: Studies that reported the PR of six psychological disorders among cancer patients during the COVID-19 pandemic were searched in PubMed, Embase, PsycINFO, and Web of Science databases, from January 2020 up to 31 January 2022. Meta-analysis results were merged using PR and 95% confidence intervals, and heterogeneity among studies was evaluated using I² and Cochran's Q test. Publication bias was examined using funnel plots and Egger's tests. All data analyses were performed using Stata14.0 software. RESULTS: Forty studies with 27,590 participants were included. Pooled results showed that the PR of clinically significant depression, anxiety, PTSD, distress, insomnia, and fear of cancer progression/recurrence among cancer patients were 32.5%, 31.3%, 28.2%, 53.9%, 23.2%, and 67.4%, respectively. Subgroup analysis revealed that patients with head and neck cancer had the highest PR of clinically significant depression (74.6%) and anxiety (92.3%) symptoms. Stratified analysis revealed that patients with higher education levels had higher levels of clinically significant depression (37.2%). A higher level of clinically significant PTSD was observed in employed patients (47.4%) or female with cancer (27.9%). CONCLUSION: This meta-analysis evaluated the psychological disorders of cancer patients during the COVID-19 outbreak. Therefore, it is necessary to develop psychological interventions to improve the mental health of cancer patients during the pandemic.

Modeling Kaempferol as a Potential Pharmacological Agent for COVID-19/PF Co-Occurrence Based on Bioinformatics and System Pharmacological Tools

Objective: People suffering from coronavirus disease 2019 (COVID-19) are prone to develop pulmonary fibrosis (PF), but there is currently no definitive treatment for COVID-19/PF co-occurrence. Kaempferol with promising antiviral and anti-fibrotic effects is expected to become a potential treatment for COVID-19 and PF comorbidities. Therefore, this study explored the targets and molecular mechanisms of kaempferol against COVID-19/PF co-occurrence by bioinformatics and network pharmacology. Methods: Various open-source databases and Venn Diagram tool were applied to confirm the targets of kaempferol against COVID-19/PF co-occurrence. Protein-protein interaction (PPI), MCODE, key transcription factors, tissue-specific enrichment, molecular docking, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to clarify the influential molecular mechanisms of kaempferol against COVID-19 and PF comorbidities. Results: 290 targets and 203 transcription factors of kaempferol against COVID-19/PF co-occurrence were captured. Epidermal growth factor receptor (EGFR), proto-oncogene tyrosine-protein kinase SRC (SRC), mitogen-activated protein kinase 3 (MAPK3), mitogen-activated protein kinase 1 (MAPK1), mitogen-activated protein kinase 8 (MAPK8), RAC-alpha serine/threonine-protein kinase (AKT1), transcription factor p65 (RELA) and phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform (PIK3CA) were identified as the most critical targets, and kaempferol showed effective binding activities with the above critical eight targets. Further, anti-COVID-19/PF co-occurrence effects of kaempferol were associated with the regulation of inflammation, oxidative stress, immunity, virus infection, cell growth process and metabolism. EGFR, interleukin 17 (IL-17), tumor necrosis factor (TNF), hypoxia inducible factor 1 (HIF-1), phosphoinositide 3-kinase/AKT serine/threonine kinase (PI3K/AKT) and Toll-like receptor signaling pathways were identified as the key anti-COVID-19/PF co-occurrence pathways. Conclusion: Kaempferol is a candidate treatment for COVID-19/PF co-occurrence. The underlying mechanisms may be related to the regulation of critical targets (EGFR, SRC, MAPK3, MAPK1, MAPK8, AKT1, RELA, PIK3CA and so on) and EGFR, IL-17, TNF, HIF-1, PI3K/AKT and Toll-like receptor signaling pathways. This study contributes to guiding development of new drugs for COVID-19 and PF comorbidities.

Acute stress disorder in patients with accidental traumatic fractures: What can we do.

AIM: To evaluate the potential influencing factors of acute stress disorder (ASD) in patients with accidental traumatic fractures to provide evidence for clinical nursing care. DESIGN: A retrospective study. METHODS: Patients with traumatic fractures treated in our hospital from 1 January 2020 to 30 November 2021 were included. The characteristics of ASD and no ASD patients were assessed. RESULTS: A total of 468 patients with traumatic fractures were included, the incidence of ASD was 28.20%. Logistic regression analysis showed that age ≤50 years (OR2.918, 95% CI1.994 ~ 3.421), female (OR2.074, 95% CI1.489 ~ 3.375), AIS-ISS at admission ≥20 (OR3.981, 95% CI2.188 ~ 5.091), VAS at admission≥7 (OR2.804, 95% CI2.027 ~ 3.467), introverted personality (OR1.722, 95%CI1.314 ~ 2.432) and CD-RISC at admission≤60 (OR3.026, 95% CI2.338 ~ 4.769) were the risk factors of ASD in patients with traumatic fractures (all p < .05). CONCLUSIONS: The development of ASD in patients with traumatic fractures is affected by multiple factors. Medical workers should take early and timely management and nursing measures for related risk factors to reduce the occurrence of ASD.

Insights into potential mechanisms of asthma patients with COVID-19: A study based on the gene expression profiling of bronchoalveolar lavage fluid.

BACKGROUND: The 2019 novel coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently a major challenge threatening the global healthcare system. Respiratory virus infection is the most common cause of asthma attacks, and thus COVID-19 may contribute to an increase in asthma exacerbations. However, the mechanisms of COVID-19/asthma comorbidity remain unclear. METHODS: The "Limma" package or "DESeq2" package was used to screen differentially expressed genes (DEGs). Alveolar lavage fluid datasets of COVID-19 and asthma were obtained from the GEO and GSV database. A series of analyses of common host factors for COVID-19 and asthma were conducted, including PPI network construction, module analysis, enrichment analysis, inference of the upstream pathway activity of host factors, tissue-specific analysis and drug candidate prediction. Finally, the key host factors were verified in the GSE152418 and GSE164805 datasets. RESULTS: 192 overlapping host factors were obtained by analyzing the intersection of asthma and COVID-19. FN1, UBA52, EEF1A1, ITGB1, XPO1, NPM1, EGR1, EIF4E, SRSF1, CCR5, PXN, IRF8 and DDX5 as host factors were tightly connected in the PPI network. Module analysis identified five modules with different biological functions and pathways. According to the degree values ranking in the PPI network, EEF1A1, EGR1, UBA52, DDX5 and IRF8 were considered as the key cohost factors for COVID-19 and asthma. The H2O2, VEGF, IL-1 and Wnt signaling pathways had the strongest activities in the upstream pathways. Tissue-specific enrichment analysis revealed the different expression levels of the five critical host factors. LY294002, wortmannin, PD98059 and heparin might have great potential to evolve into therapeutic drugs for COVID-19 and asthma comorbidity. Finally, the validation dataset confirmed that the expression of five key host factors were statistically significant among COVID-19 groups with different severity and healthy control subjects. CONCLUSIONS: This study constructed a network of common host factors between asthma and COVID-19 and predicted several drugs with therapeutic potential. Therefore, this study is likely to provide a reference for the management and treatment for COVID-19/asthma comorbidity.

Black carbon-climate interactions regulate dust burdens over India revealed during COVID-19.

India as a hotspot for air pollution has heavy black carbon (BC) and dust (DU) loadings. BC has been identified to significantly impact the Indian climate. However, whether BC-climate interactions regulate Indian DU during the premonsoon season is unclear. Here, using long-term Reanalysis data, we show that Indian DU is positively correlated to northern Indian BC while negatively correlated to southern Indian BC. We further identify the mechanism of BC-dust-climate interactions revealed during COVID-19. BC reduction in northern India due to lockdown decreases solar heating in the atmosphere and increases surface albedo of the Tibetan Plateau (TP), inducing a descending atmospheric motion. Colder air from the TP together with warmer southern Indian air heated by biomass burning BC results in easterly wind anomalies, which reduces dust transport from the Middle East and Sahara and local dust emissions. The premonsoon aerosol-climate interactions delay the outbreak of the subsequent Indian summer monsoon.

A Practical Strategy for Exploring the Pharmacological Mechanism of Luteolin Against COVID-19/Asthma Comorbidity: Findings of System Pharmacology and Bioinformatics Analysis.

Asthma patients may increase their susceptibility to SARS-CoV-2 infection and the poor prognosis of coronavirus disease 2019 (COVID-19). However, anti-COVID-19/asthma comorbidity approaches are restricted on condition. Existing evidence indicates that luteolin has antiviral, anti-inflammatory, and immune regulation capabilities. We aimed to evaluate the possibility of luteolin evolving into an ideal drug and explore the underlying molecular mechanisms of luteolin against COVID-19/asthma comorbidity. We used system pharmacology and bioinformatics analysis to assess the physicochemical properties and biological activities of luteolin and further analyze the binding activities, targets, biological functions, and mechanisms of luteolin against COVID-19/asthma comorbidity. We found that luteolin may exert ideal physicochemical properties and bioactivity, and molecular docking analysis confirmed that luteolin performed effective binding activities in COVID-19/asthma comorbidity. Furthermore, a protein-protein interaction network of 538 common targets between drug and disease was constructed and 264 hub targets were obtained. Then, the top 6 hub targets of luteolin against COVID-19/asthma comorbidity were identified, namely, TP53, AKT1, ALB, IL-6, TNF, and VEGFA. Furthermore, the enrichment analysis suggested that luteolin may exert effects on virus defense, regulation of inflammation, cell growth and cell replication, and immune responses, reducing oxidative stress and regulating blood circulation through the Toll-like receptor; MAPK, TNF, AGE/RAGE, EGFR, ErbB, HIF-1, and PI3K-AKT signaling pathways; PD-L1 expression; and PD-1 checkpoint pathway in cancer. The possible "dangerous liaison" between COVID-19 and asthma is still a potential threat to world health. This research is the first to explore whether luteolin could evolve into a drug candidate for COVID-19/asthma comorbidity. This study indicated that luteolin with superior drug likeness and bioactivity has great potential to be used for treating COVID-19/asthma comorbidity, but the predicted results still need to be rigorously verified by experiments.

Host Factor Interaction Networks Identified by Integrative Bioinformatics Analysis Reveals Therapeutic Implications in COPD Patients With COVID-19

Background: The COVID-19 pandemic poses an imminent threat to humanity, especially for those who have comorbidities. Evidence of COVID-19 and COPD comorbidities is accumulating. However, data revealing the molecular mechanism of COVID-19 and COPD comorbid diseases is limited. Methods: We got COVID-19/COPD -related genes from different databases by restricted screening conditions (top500), respectively, and then supplemented with COVID-19/COPD-associated genes (FDR<0.05, ;LogFC;≥1) from clinical sample data sets. By taking the intersection, 42 co-morbid host factors for COVID-19 and COPD were finally obtained. On the basis of shared host factors, we conducted a series of bioinformatics analysis, including protein-protein interaction analysis, gene ontology and pathway enrichment analysis, transcription factor-gene interaction network analysis, gene-microRNA co-regulatory network analysis, tissue-specific enrichment analysis and candidate drug prediction. Results: We revealed the comorbidity mechanism of COVID-19 and COPD from the perspective of host factor interaction, obtained the top ten gene and 3 modules with different biological functions. Furthermore, we have obtained the signaling pathways and concluded that dexamethasone, estradiol, progesterone, and nitric oxide shows effective interventions. Conclusion: This study revealed host factor interaction networks for COVID-19 and COPD, which could confirm the potential drugs for treating the comorbidity, ultimately, enhancing the management of the respiratory disease.

Phillyrin for COVID-19 and Influenza Co-infection: A Potential Therapeutic Strategy Targeting Host Based on Bioinformatics Analysis.

Background: The risk of co-epidemic between COVID-19 and influenza is very high, so it is urgent to find a treatment strategy for the co-infection. Previous studies have shown that phillyrin can not only inhibit the replication of the two viruses, but also has a good anti-inflammatory effect, which is expected to become a candidate compound against COVID-19 and influenza. Objective: To explore the possibility of phillyrin as a candidate compound for the treatment of COVID-19 and influenza co-infection and to speculate its potential regulatory mechanism. Methods: We used a series of bioinformatics network pharmacology methods to understand and characterize the pharmacological targets, biological functions, and therapeutic mechanisms of phillyrin in COVID-19 and influenza co-infection and discover its therapeutic potential. Results: We revealed potential targets, biological processes, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and upstream pathway activity of phillyrin against COVID-19 and influenza co-infection. We constructed protein-protein interaction (PPI) network and identified 50 hub genes, such as MMP9, IL-2, VEGFA, AKT, and HIF-1A. Furthermore, our findings indicated that the treatment of phillyrin for COVID-19 and influenza co-infection was associated with immune balance and regulation of hypoxia-cytokine storm, including HIF-1 signaling pathway, PI3K-Akt signaling pathway, Ras signaling pathway, and T cell receptor signaling pathway. Conclusion: For the first time, we uncovered the potential targets and biological pathways of phillyrin for COVID-19 and influenza co-infection. These findings should solve the urgent problem of co-infection of COVID-19 and influenza that the world will face in the future, but clinical drug trials are needed for verification in the future.

Drug target inference by mining transcriptional data using a novel graph convolutional network framework

A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest. Supplementary Information The online version contains supplementary material available at 10.1007/s13238-021-00885-0.

Structure-Based Virtual Screening and Identification of Potential Inhibitors of SARS-CoV-2 S-RBD and ACE2 Interaction.

The emergence and rapid spread of SARS-CoV-2 have caused a worldwide public health crisis. Designing small molecule inhibitors targeting SARS-CoV-2 S-RBD/ACE2 interaction is considered as a potential strategy for the prevention and treatment of SARS-CoV-2. But to date, only a few compounds have been reported as SARS-CoV-2 S-RBD/ACE2 interaction inhibitors. In this study, we described the virtual screening and experimental validation of two novel inhibitors (DC-RA016 and DC-RA052) against SARS-CoV-2 S-RBD/ACE2 interaction. The NanoBiT assays and surface plasmon resonance (SPR) assays demonstrated their capabilities of blocking SARS-CoV-2 S-RBD/ACE2 interaction and directly binding to both S-RBD and ACE2. Moreover, the pseudovirus assay revealed that these two compounds possessed significant antiviral activity (about 50% inhibition rate at maximum non-cytotoxic concentration). These results indicate that the compounds DC-RA016 and DC-RA052 are promising inhibitors against SARS-CoV-2 S-RBD/ACE2 interaction and deserve to be further developed.