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PMC:7247521 / 12240-13867 JSONTXT

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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
206 53-61 Disease denotes COVID-19 MESH:C000657245
207 70-91 Disease denotes robustness of disease MESH:D003141
216 122-130 Disease denotes COVID-19 MESH:C000657245
217 669-677 Disease denotes COVID-19 MESH:C000657245
218 741-749 Disease denotes COVID-19 MESH:C000657245
219 793-797 Disease denotes SARS MESH:D045169
220 932-940 Disease denotes COVID-19 MESH:C000657245
221 1108-1111 Disease denotes XCH
222 1284-1292 Disease denotes COVID-19 MESH:C000657245
223 1488-1508 Disease denotes Banxia tianma baizhu

LitCovid-PMC-OGER-BB

Id Subject Object Predicate Lexical cue
T157 19-23 CHEBI:23888;CHEBI:23888 denotes drug
T158 53-61 SP_7 denotes COVID-19
T159 122-130 SP_7 denotes COVID-19
T160 304-309 CHEBI:23888;CHEBI:23888 denotes drugs
T161 381-386 CHEBI:23888;CHEBI:23888 denotes drugs
T162 471-476 CHEBI:23888;CHEBI:23888 denotes drugs
T163 569-573 CHEBI:23888;CHEBI:23888 denotes drug
T164 641-646 CHEBI:23888;CHEBI:23888 denotes drugs
T165 669-677 SP_7 denotes COVID-19
T166 741-749 SP_7 denotes COVID-19
T167 774-783 GO:0010467 denotes expressed
T168 784-789 SO:0000704 denotes genes
T169 793-797 SP_10 denotes SARS
T170 932-940 SP_7 denotes COVID-19
T171 1205-1209 CHEBI:23888;CHEBI:23888 denotes drug
T172 1284-1292 SP_7 denotes COVID-19
T173 1433-1438 CHEBI:23888;CHEBI:23888 denotes drugs

LitCovid-PD-FMA-UBERON

Id Subject Object Predicate Lexical cue fma_id
T37 728-737 Body_part denotes cytokines http://purl.org/sig/ont/fma/fma84050

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T39 53-61 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T40 122-130 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T41 669-677 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T42 741-749 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T43 793-797 Disease denotes SARS http://purl.obolibrary.org/obo/MONDO_0005091
T44 932-940 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T45 1284-1292 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T95 751-753 http://purl.obolibrary.org/obo/CLO_0050509 denotes 27
T96 784-789 http://purl.obolibrary.org/obo/OGG_0000000002 denotes genes

LitCovid-PD-CHEBI

Id Subject Object Predicate Lexical cue chebi_id
T63 19-23 Chemical denotes drug http://purl.obolibrary.org/obo/CHEBI_23888
T64 304-309 Chemical denotes drugs http://purl.obolibrary.org/obo/CHEBI_23888
T65 381-386 Chemical denotes drugs http://purl.obolibrary.org/obo/CHEBI_23888
T66 471-476 Chemical denotes drugs http://purl.obolibrary.org/obo/CHEBI_23888
T67 569-573 Chemical denotes drug http://purl.obolibrary.org/obo/CHEBI_23888
T68 641-646 Chemical denotes drugs http://purl.obolibrary.org/obo/CHEBI_23888
T69 1205-1209 Chemical denotes drug http://purl.obolibrary.org/obo/CHEBI_23888
T70 1433-1438 Chemical denotes drugs http://purl.obolibrary.org/obo/CHEBI_23888

LitCovid-sentences

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.

2_test

Id Subject Object Predicate Lexical cue
32554251-31986264-6393503 751-753 31986264 denotes 27
32554251-30809144-6393504 1475-1477 30809144 denotes 12