PMC:7247521 / 12240-13867 JSONTXT 9 Projects

Annnotations TAB TSV DIC JSON TextAE

Id Subject Object Predicate Lexical cue
T76 0-99 Sentence denotes 2.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network
T77 100-341 Sentence denotes Since QFPD effects on COVID-19 via multi-component and multi-target, we evaluate the potential efficacy of QFPD through TCMATCOV platform, which uses the quantitative evaluation algorithm of multi-target drugs to disturb the disease network.
T78 342-446 Sentence denotes Specifically, the disturbing effect of drugs on diseases is simulated by deleting disease network nodes.
T79 447-659 Sentence denotes The disturbance rate of drugs is calculated by comparing the changes of network topology characteristics before and after drug intervention, which is used to evaluate the intervention effect of drugs on diseases.
T80 660-829 Sentence denotes Firstly, COVID-19 disease network was constructed based on specific cytokines of COVID-19 [27] and differentially expressed genes of SARS (GSE36969, GSE51387, GSE68820).
T81 830-1072 Sentence denotes Then, this platform uses four kinds of network topology characteristics to evaluate the robustness of COVID-19 network, including network average connectivity, network average shortest path, connectivity centrality and compactness centrality.
T82 1073-1223 Sentence denotes And the five formulae (MSXG, SGMH, XCH, WLS and Others) disturbance scores are calculated according to the changes before and after drug intervention.
T83 1224-1479 Sentence denotes Finally, the disturbance effect of the five formulae on the COVID-19 network was compared with null models with the total score of the disturbance, and the higher the value is, the higher the damage degree of drugs to the stability of the network is [12].
T84 1480-1627 Sentence denotes We take Banxia tianma baizhu decoction (BXTM) as negative control; and another efficient formula Yi du bi fei decoction (YDBF) as positive control.