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PMC:7128131 JSONTXT

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LitCovid_AGAC

Id Subject Object Predicate Lexical cue
p86294s30 982-995 NegReg denotes insufficiency
p86294s38 1030-1040 NegReg denotes negatively

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T1 30-38 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T2 112-136 Disease denotes coronavirus disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T3 138-146 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T4 547-555 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T5 908-918 Disease denotes infections http://purl.obolibrary.org/obo/MONDO_0005550
T6 1261-1274 Disease denotes infections in http://purl.obolibrary.org/obo/MONDO_0005550
T7 1411-1419 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T8 1667-1675 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T9 1772-1780 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T10 2151-2159 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T11 2160-2184 Disease denotes coronavirus disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T12 3213-3221 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T1 463-466 http://purl.obolibrary.org/obo/CL_0000990 denotes CDC
T2 963-966 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T3 1089-1092 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T4 1157-1158 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T5 1160-1161 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T6 1275-1276 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T7 1337-1338 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T8 1521-1522 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T9 1725-1726 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T10 1836-1837 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T11 1955-1959 http://purl.obolibrary.org/obo/UBERON_0000473 denotes test
T12 2584-2587 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T13 2691-2694 http://purl.obolibrary.org/obo/CLO_0051582 denotes has

LitCovid-PD-CHEBI

Id Subject Object Predicate Lexical cue chebi_id
T1 1324-1333 Chemical denotes indicator http://purl.obolibrary.org/obo/CHEBI_47867

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T1 0-86 Sentence denotes Potential association between COVID-19 mortality and health-care resource availability
T2 88-217 Sentence denotes The ongoing epidemic of coronavirus disease 2019 (COVID-19) is devastating, despite extensive implementation of control measures.
T3 218-386 Sentence denotes The outbreak was sparked in Wuhan, the capital city of Hubei province in China, and quickly spread to different regions of Hubei and across all other Chinese provinces.
T4 387-819 Sentence denotes As recorded by the Chinese Center for Disease Control and Prevention (China CDC), by Feb 16, 2020, there had been 70 641 confirmed cases and 1772 deaths due to COVID-19, with an average mortality of about 2·5%.1 However, in-depth analysis of these data show clear disparities in mortality rates between Wuhan (>3%), different regions of Hubei (about 2·9% on average), and across the other provinces of China (about 0·7% on average).
T5 820-1164 Sentence denotes We postulate that this is likely to be related to the rapid escalation in the number of infections around the epicentre of the outbreak, which has resulted in an insufficiency of health-care resources, thereby negatively affecting patient outcomes in Hubei, while this has not yet been the situation for the other parts of China (figure A, B ).
T6 1165-1366 Sentence denotes If we assume that average levels of health care are similar throughout China, higher numbers of infections in a given population can be considered an indirect indicator of a heavier health-care burden.
T7 1367-1632 Sentence denotes Plotting mortality against the incidence of COVID-19 (cumulative number of confirmed cases since the start of the outbreak, per 10 000 population) showed a significant positive correlation (figure C), suggesting that mortality is correlated with health-care burden.
T8 1633-1713 Sentence denotes Figure Mortality and incidence of COVID-19 in Hubei and other provinces of China
T9 1714-1877 Sentence denotes Mortality (A) and cumulative number of confirmed cases of COVID-19 since the start of the outbreak per 10 000 population (B) in Hubei and other provinces of China.
T10 1878-2055 Sentence denotes Horizontal lines represent median and IQR. p values were from Mann-Whitney U test. (C) Correlation between mortality and number of cases per 10 000 population (Spearman method).
T11 2056-2150 Sentence denotes Data were obtained from the Chinese Center for Disease Control and Prevention to Feb 16, 2020.
T12 2151-2185 Sentence denotes COVID-19=coronavirus disease 2019.
T13 2186-2560 Sentence denotes In reality, there are substantial regional disparities in health-care resource availability and accessibility in China.2 Such disparities might partly explain the low mortality rates—despite high numbers of cases—in the most developed southeastern coastal provinces, such as Zhejiang (0 deaths among 1171 confirmed cases) and Guangdong (four deaths among 1322 cases [0·3%]).
T14 2561-2832 Sentence denotes The Chinese government has realised the logistical hurdles associated with medical supplies in the epicentre of the outbreak, and has strived to accelerate deliveries, mobilise the country's large and strong medical forces, and rapidly build new local medical facilities.
T15 2833-2985 Sentence denotes These measures are essential for controlling the epidemic, protecting health workers on the front line, and mitigating the severity of patient outcomes.
T16 2986-3191 Sentence denotes Acknowledging the potential association of mortality with health-care resource availability might help other regions of China, which are now beginning to struggle with this outbreak, to be better prepared.
T17 3192-3452 Sentence denotes More importantly, as COVID-19 is already affecting at least 29 countries and territories worldwide, including one north African country, the situation in China could help to inform other resource-limited regions on how to prepare for possible local outbreaks.3
T18 3454-3488 Sentence denotes We declare no competing interests.

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
2 30-38 Disease denotes COVID-19 MESH:C000657245
3 39-48 Disease denotes mortality MESH:D003643
6 112-136 Disease denotes coronavirus disease 2019 MESH:C000657245
7 138-146 Disease denotes COVID-19 MESH:C000657245
10 1640-1649 Disease denotes Mortality MESH:D003643
11 1667-1675 Disease denotes COVID-19 MESH:C000657245
16 1714-1723 Disease denotes Mortality MESH:D003643
17 1772-1780 Disease denotes COVID-19 MESH:C000657245
18 1985-1994 Disease denotes mortality MESH:D003643
19 2160-2184 Disease denotes coronavirus disease 2019 MESH:C000657245
30 1051-1058 Species denotes patient Tax:9606
31 533-539 Disease denotes deaths MESH:D003643
32 547-555 Disease denotes COVID-19 MESH:C000657245
33 573-582 Disease denotes mortality MESH:D003643
34 666-675 Disease denotes mortality MESH:D003643
35 908-918 Disease denotes infections MESH:D007239
36 1261-1271 Disease denotes infections MESH:D007239
37 1376-1385 Disease denotes mortality MESH:D003643
38 1411-1419 Disease denotes COVID-19 MESH:C000657245
39 1584-1593 Disease denotes mortality MESH:D003643
46 2968-2975 Species denotes patient Tax:9606
47 2353-2362 Disease denotes mortality MESH:D003643
48 2473-2479 Disease denotes deaths MESH:D003643
49 2528-2534 Disease denotes deaths MESH:D003643
50 3029-3038 Disease denotes mortality MESH:D003643
51 3213-3221 Disease denotes COVID-19 MESH:C000657245