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1.
Front Biosci (Landmark Ed) ; 27(2): 48, 2022 02 11.
Article in English | MEDLINE | ID: covidwho-1772157

ABSTRACT

BACKGROUND: Thymosin-α1 has been implicated into the treatment of novel respiratory virus Coronavirus Disease 2019 (COVID-19), but the underlying mechanisms are still disputable. AIM: Herein we aimed to reveal a previously unrecognized mechanism that thymosin-α1 prevents COVID-19 by binding with angiotensin-converting enzyme (ACE), which was inspired from the tool of network pharmacology. METHODS: KEGG pathway enrichment of thymosin-α1 treating COVID-19 was analyzed by Database of Functional Annotation Bioinformatics Microarray Analysis, then core targets were validated by ligand binding kinetics assay and fluorometric detection of ACE and ACE2 enzymatic activity. The production of angiotensin I, angiotensin II, angiotensin (1-7) and angiotensin (1-9) were detected by enzyme linked immunosorbent assay. RESULTS: We found that thymosin-α1 impaired the expressions of angiotensin-converting enzyme 2 and angiotensin (1-7) of human lung epithelial cells in a dose-dependent way (p < 0.001). In contrast, thymosin-α1 had no impact on their ACE and angiotensin (1-9) expressions but significantly inhibited the enzymatic activity of ACE (p > 0.05). CONCLUSION: The bioinformatic findings of network pharmacology and the corresponding pharmacological validations have revealed that thymosin-α1 treatment could decrease ACE2 expression in human lung epithelial cells, which strengthens the potential clinical applications of thymosin-α1 to prevent severe acute respiratory syndrome coronavirus 2 infection.


Subject(s)
Angiotensin-Converting Enzyme 2 , COVID-19 , Angiotensin-Converting Enzyme Inhibitors/pharmacology , Angiotensin-Converting Enzyme Inhibitors/therapeutic use , COVID-19/drug therapy , Humans , SARS-CoV-2 , Thymalfasin/pharmacology
2.
2021.
Preprint in English | Other preprints | ID: ppcovidwho-296442

ABSTRACT

ABSTRACT Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb ( https://drugcomb.org/ ) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here we report significant updates of DrugComb, including: 1) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19;2) enhanced algorithms for assessing the sensitivity and synergy of drug combinations;3) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample;and 4) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.

3.
Nucleic Acids Res ; 49(W1): W174-W184, 2021 07 02.
Article in English | MEDLINE | ID: covidwho-1249328

ABSTRACT

Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here, we report significant updates of DrugComb, including: (i) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; (ii) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; (iii) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample and (iv) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.


Subject(s)
Algorithms , Databases, Factual , Drug Evaluation, Preclinical , Drug Therapy, Combination , Internet , COVID-19/drug therapy , Data Visualization , Datasets as Topic , Drug Synergism , Hemorrhagic Fever, Ebola/drug therapy , Humans , Machine Learning , Malaria/drug therapy , Neoplasms/drug therapy
4.
Med Sci Monit ; 27: e929708, 2021 Apr 11.
Article in English | MEDLINE | ID: covidwho-1148368

ABSTRACT

BACKGROUND Since the outbreak of COVID-19 in December 2019, there have been 96 623 laboratory-confirmed cases and 4784 deaths by December 29 in China. We aimed to analyze the risk factors and the incidence of thrombosis from patients with confirmed COVID-19 pneumonia. MATERIAL AND METHODS Eighty-eight inpatients with confirmed COVID-19 pneumonia were reported (31 critical cases, 33 severe cases, and 24 common cases). The thrombosis risk factor assessment, laboratory results, ultrasonographic findings, and prognoses of these patients were analyzed, and compared among groups with different severity. RESULTS Nineteen of the 88 cases developed DVT (12 critical cases, 7 severe cases, and no common cases). In addition, among the 18 patients who died, 5 were diagnosed with DVT. Positive correlations were observed between the increase in D-dimer level (≥5 µg/mL) and the severity of COVID-19 pneumonia (r=0.679, P<0.01), and between the high Padua score (≥4) and the severity (r=0.799, P<0.01). In addition, the CRP and LDH levels on admission had positive correlations with the severity of illness (CRP: r=0.522, P<0.01; LDH: r=0.600, P<0.01). A negative correlation was observed between the lymphocyte count on admission and the severity of illness (r=-0.523, P<0.01). There was also a negative correlation between the lymphocyte count on admission and mortality in critical patients (r=-0.499, P<0.01). Univariable logistic regression analysis showed that the occurrence of DVT was positively correlated with disease severity (crude odds ratio: 3.643, 95% CI: 1.218-10.896, P<0.05). CONCLUSIONS Our report illustrates that critically or severely ill patients have an associated high D-dimer value and high Padua score, and illustrates that a low threshold to screen for DVT may help improve detection of thromboembolism in these groups of patients, especially in asymptomatic patients. Our results suggest that early administration of prophylactic anticoagulant would benefit the prognosis of critical patients with COVID-19 pneumonia and would likely reduce thromboembolic rates.


Subject(s)
COVID-19/complications , Fibrin Fibrinogen Degradation Products/analysis , Venous Thrombosis/epidemiology , Adult , Aged , Asymptomatic Diseases , COVID-19/blood , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19 Testing , China/epidemiology , Female , Hospital Mortality , Humans , Incidence , Lower Extremity/blood supply , Lower Extremity/diagnostic imaging , Male , Middle Aged , Patient Admission , Prognosis , Retrospective Studies , Risk Assessment/methods , Risk Assessment/statistics & numerical data , Risk Factors , SARS-CoV-2/isolation & purification , Severity of Illness Index , Ultrasonography , Venous Thrombosis/blood , Venous Thrombosis/diagnosis , Venous Thrombosis/etiology
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