PMC:7247521 / 12240-13867 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"206","span":{"begin":53,"end":61},"obj":"Disease"},{"id":"207","span":{"begin":70,"end":91},"obj":"Disease"},{"id":"216","span":{"begin":122,"end":130},"obj":"Disease"},{"id":"217","span":{"begin":669,"end":677},"obj":"Disease"},{"id":"218","span":{"begin":741,"end":749},"obj":"Disease"},{"id":"219","span":{"begin":793,"end":797},"obj":"Disease"},{"id":"220","span":{"begin":932,"end":940},"obj":"Disease"},{"id":"221","span":{"begin":1108,"end":1111},"obj":"Disease"},{"id":"222","span":{"begin":1284,"end":1292},"obj":"Disease"},{"id":"223","span":{"begin":1488,"end":1508},"obj":"Disease"}],"attributes":[{"id":"A206","pred":"tao:has_database_id","subj":"206","obj":"MESH:C000657245"},{"id":"A207","pred":"tao:has_database_id","subj":"207","obj":"MESH:D003141"},{"id":"A216","pred":"tao:has_database_id","subj":"216","obj":"MESH:C000657245"},{"id":"A217","pred":"tao:has_database_id","subj":"217","obj":"MESH:C000657245"},{"id":"A218","pred":"tao:has_database_id","subj":"218","obj":"MESH:C000657245"},{"id":"A219","pred":"tao:has_database_id","subj":"219","obj":"MESH:D045169"},{"id":"A220","pred":"tao:has_database_id","subj":"220","obj":"MESH:C000657245"},{"id":"A222","pred":"tao:has_database_id","subj":"222","obj":"MESH:C000657245"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"2.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network\nSince 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. Specifically, the disturbing effect of drugs on diseases is simulated by deleting disease network nodes. 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. Firstly, COVID-19 disease network was constructed based on specific cytokines of COVID-19 [27] and differentially expressed genes of SARS (GSE36969, GSE51387, GSE68820). 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. And the five formulae (MSXG, SGMH, XCH, WLS and Others) disturbance scores are calculated according to the changes before and after drug intervention. 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]. We take Banxia tianma baizhu decoction (BXTM) as negative control; and another efficient formula Yi du bi fei decoction (YDBF) as positive control."}

{"project":"LitCovid-PMC-OGER-BB","denotations":[{"id":"T157","span":{"begin":19,"end":23},"obj":"CHEBI:23888;CHEBI:23888"},{"id":"T158","span":{"begin":53,"end":61},"obj":"SP_7"},{"id":"T159","span":{"begin":122,"end":130},"obj":"SP_7"},{"id":"T160","span":{"begin":304,"end":309},"obj":"CHEBI:23888;CHEBI:23888"},{"id":"T161","span":{"begin":381,"end":386},"obj":"CHEBI:23888;CHEBI:23888"},{"id":"T162","span":{"begin":471,"end":476},"obj":"CHEBI:23888;CHEBI:23888"},{"id":"T163","span":{"begin":569,"end":573},"obj":"CHEBI:23888;CHEBI:23888"},{"id":"T164","span":{"begin":641,"end":646},"obj":"CHEBI:23888;CHEBI:23888"},{"id":"T165","span":{"begin":669,"end":677},"obj":"SP_7"},{"id":"T166","span":{"begin":741,"end":749},"obj":"SP_7"},{"id":"T167","span":{"begin":774,"end":783},"obj":"GO:0010467"},{"id":"T168","span":{"begin":784,"end":789},"obj":"SO:0000704"},{"id":"T169","span":{"begin":793,"end":797},"obj":"SP_10"},{"id":"T170","span":{"begin":932,"end":940},"obj":"SP_7"},{"id":"T171","span":{"begin":1205,"end":1209},"obj":"CHEBI:23888;CHEBI:23888"},{"id":"T172","span":{"begin":1284,"end":1292},"obj":"SP_7"},{"id":"T173","span":{"begin":1433,"end":1438},"obj":"CHEBI:23888;CHEBI:23888"}],"text":"2.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network\nSince 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. Specifically, the disturbing effect of drugs on diseases is simulated by deleting disease network nodes. 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. Firstly, COVID-19 disease network was constructed based on specific cytokines of COVID-19 [27] and differentially expressed genes of SARS (GSE36969, GSE51387, GSE68820). 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. And the five formulae (MSXG, SGMH, XCH, WLS and Others) disturbance scores are calculated according to the changes before and after drug intervention. 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]. We take Banxia tianma baizhu decoction (BXTM) as negative control; and another efficient formula Yi du bi fei decoction (YDBF) as positive control."}

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T37","span":{"begin":728,"end":737},"obj":"Body_part"}],"attributes":[{"id":"A37","pred":"fma_id","subj":"T37","obj":"http://purl.org/sig/ont/fma/fma84050"}],"text":"2.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network\nSince 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. Specifically, the disturbing effect of drugs on diseases is simulated by deleting disease network nodes. 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. Firstly, COVID-19 disease network was constructed based on specific cytokines of COVID-19 [27] and differentially expressed genes of SARS (GSE36969, GSE51387, GSE68820). 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. And the five formulae (MSXG, SGMH, XCH, WLS and Others) disturbance scores are calculated according to the changes before and after drug intervention. 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]. We take Banxia tianma baizhu decoction (BXTM) as negative control; and another efficient formula Yi du bi fei decoction (YDBF) as positive control."}

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T39","span":{"begin":53,"end":61},"obj":"Disease"},{"id":"T40","span":{"begin":122,"end":130},"obj":"Disease"},{"id":"T41","span":{"begin":669,"end":677},"obj":"Disease"},{"id":"T42","span":{"begin":741,"end":749},"obj":"Disease"},{"id":"T43","span":{"begin":793,"end":797},"obj":"Disease"},{"id":"T44","span":{"begin":932,"end":940},"obj":"Disease"},{"id":"T45","span":{"begin":1284,"end":1292},"obj":"Disease"}],"attributes":[{"id":"A39","pred":"mondo_id","subj":"T39","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A40","pred":"mondo_id","subj":"T40","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A41","pred":"mondo_id","subj":"T41","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A42","pred":"mondo_id","subj":"T42","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A43","pred":"mondo_id","subj":"T43","obj":"http://purl.obolibrary.org/obo/MONDO_0005091"},{"id":"A44","pred":"mondo_id","subj":"T44","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A45","pred":"mondo_id","subj":"T45","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"}],"text":"2.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network\nSince 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. Specifically, the disturbing effect of drugs on diseases is simulated by deleting disease network nodes. 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. Firstly, COVID-19 disease network was constructed based on specific cytokines of COVID-19 [27] and differentially expressed genes of SARS (GSE36969, GSE51387, GSE68820). 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. And the five formulae (MSXG, SGMH, XCH, WLS and Others) disturbance scores are calculated according to the changes before and after drug intervention. 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]. We take Banxia tianma baizhu decoction (BXTM) as negative control; and another efficient formula Yi du bi fei decoction (YDBF) as positive control."}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T95","span":{"begin":751,"end":753},"obj":"http://purl.obolibrary.org/obo/CLO_0050509"},{"id":"T96","span":{"begin":784,"end":789},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"}],"text":"2.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network\nSince 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. Specifically, the disturbing effect of drugs on diseases is simulated by deleting disease network nodes. 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. Firstly, COVID-19 disease network was constructed based on specific cytokines of COVID-19 [27] and differentially expressed genes of SARS (GSE36969, GSE51387, GSE68820). 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. And the five formulae (MSXG, SGMH, XCH, WLS and Others) disturbance scores are calculated according to the changes before and after drug intervention. 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]. We take Banxia tianma baizhu decoction (BXTM) as negative control; and another efficient formula Yi du bi fei decoction (YDBF) as positive control."}

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T63","span":{"begin":19,"end":23},"obj":"Chemical"},{"id":"T64","span":{"begin":304,"end":309},"obj":"Chemical"},{"id":"T65","span":{"begin":381,"end":386},"obj":"Chemical"},{"id":"T66","span":{"begin":471,"end":476},"obj":"Chemical"},{"id":"T67","span":{"begin":569,"end":573},"obj":"Chemical"},{"id":"T68","span":{"begin":641,"end":646},"obj":"Chemical"},{"id":"T69","span":{"begin":1205,"end":1209},"obj":"Chemical"},{"id":"T70","span":{"begin":1433,"end":1438},"obj":"Chemical"}],"attributes":[{"id":"A63","pred":"chebi_id","subj":"T63","obj":"http://purl.obolibrary.org/obo/CHEBI_23888"},{"id":"A64","pred":"chebi_id","subj":"T64","obj":"http://purl.obolibrary.org/obo/CHEBI_23888"},{"id":"A65","pred":"chebi_id","subj":"T65","obj":"http://purl.obolibrary.org/obo/CHEBI_23888"},{"id":"A66","pred":"chebi_id","subj":"T66","obj":"http://purl.obolibrary.org/obo/CHEBI_23888"},{"id":"A67","pred":"chebi_id","subj":"T67","obj":"http://purl.obolibrary.org/obo/CHEBI_23888"},{"id":"A68","pred":"chebi_id","subj":"T68","obj":"http://purl.obolibrary.org/obo/CHEBI_23888"},{"id":"A69","pred":"chebi_id","subj":"T69","obj":"http://purl.obolibrary.org/obo/CHEBI_23888"},{"id":"A70","pred":"chebi_id","subj":"T70","obj":"http://purl.obolibrary.org/obo/CHEBI_23888"}],"text":"2.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network\nSince 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. Specifically, the disturbing effect of drugs on diseases is simulated by deleting disease network nodes. 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. Firstly, COVID-19 disease network was constructed based on specific cytokines of COVID-19 [27] and differentially expressed genes of SARS (GSE36969, GSE51387, GSE68820). 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. And the five formulae (MSXG, SGMH, XCH, WLS and Others) disturbance scores are calculated according to the changes before and after drug intervention. 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]. We take Banxia tianma baizhu decoction (BXTM) as negative control; and another efficient formula Yi du bi fei decoction (YDBF) as positive control."}

{"project":"LitCovid-sentences","denotations":[{"id":"T76","span":{"begin":0,"end":99},"obj":"Sentence"},{"id":"T77","span":{"begin":100,"end":341},"obj":"Sentence"},{"id":"T78","span":{"begin":342,"end":446},"obj":"Sentence"},{"id":"T79","span":{"begin":447,"end":659},"obj":"Sentence"},{"id":"T80","span":{"begin":660,"end":829},"obj":"Sentence"},{"id":"T81","span":{"begin":830,"end":1072},"obj":"Sentence"},{"id":"T82","span":{"begin":1073,"end":1223},"obj":"Sentence"},{"id":"T83","span":{"begin":1224,"end":1479},"obj":"Sentence"},{"id":"T84","span":{"begin":1480,"end":1627},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"2.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network\nSince 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. Specifically, the disturbing effect of drugs on diseases is simulated by deleting disease network nodes. 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. Firstly, COVID-19 disease network was constructed based on specific cytokines of COVID-19 [27] and differentially expressed genes of SARS (GSE36969, GSE51387, GSE68820). 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. And the five formulae (MSXG, SGMH, XCH, WLS and Others) disturbance scores are calculated according to the changes before and after drug intervention. 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]. We take Banxia tianma baizhu decoction (BXTM) as negative control; and another efficient formula Yi du bi fei decoction (YDBF) as positive control."}

{"project":"2_test","denotations":[{"id":"32554251-31986264-6393503","span":{"begin":751,"end":753},"obj":"31986264"},{"id":"32554251-30809144-6393504","span":{"begin":1475,"end":1477},"obj":"30809144"}],"text":"2.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network\nSince 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. Specifically, the disturbing effect of drugs on diseases is simulated by deleting disease network nodes. 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. Firstly, COVID-19 disease network was constructed based on specific cytokines of COVID-19 [27] and differentially expressed genes of SARS (GSE36969, GSE51387, GSE68820). 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. And the five formulae (MSXG, SGMH, XCH, WLS and Others) disturbance scores are calculated according to the changes before and after drug intervention. 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]. We take Banxia tianma baizhu decoction (BXTM) as negative control; and another efficient formula Yi du bi fei decoction (YDBF) as positive control."}