LitCovid-sentences-v1  

PubMed:31986264 JSONTXT 21 Projects

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Id Subject Object Predicate Lexical cue
TextSentencer_T1 0-83 Sentence denotes Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.
TextSentencer_T2 84-95 Sentence denotes BACKGROUND:
TextSentencer_T3 96-227 Sentence denotes A recent cluster of pneumonia cases in Wuhan, China, was caused by a novel betacoronavirus, the 2019 novel coronavirus (2019-nCoV).
TextSentencer_T4 228-368 Sentence denotes We report the epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes of these patients.
TextSentencer_T5 369-377 Sentence denotes METHODS:
TextSentencer_T6 378-464 Sentence denotes All patients with suspected 2019-nCoV were admitted to a designated hospital in Wuhan.
TextSentencer_T7 465-619 Sentence denotes We prospectively collected and analysed data on patients with laboratory-confirmed 2019-nCoV infection by real-time RT-PCR and next-generation sequencing.
TextSentencer_T8 620-806 Sentence denotes Data were obtained with standardised data collection forms shared by WHO and the International Severe Acute Respiratory and Emerging Infection Consortium from electronic medical records.
TextSentencer_T9 807-924 Sentence denotes Researchers also directly communicated with patients or their families to ascertain epidemiological and symptom data.
TextSentencer_T10 925-1047 Sentence denotes Outcomes were also compared between patients who had been admitted to the intensive care unit (ICU) and those who had not.
TextSentencer_T11 1048-1057 Sentence denotes FINDINGS:
TextSentencer_T12 1058-1175 Sentence denotes By Jan 2, 2020, 41 admitted hospital patients had been identified as having laboratory-confirmed 2019-nCoV infection.
TextSentencer_T13 1176-1383 Sentence denotes Most of the infected patients were men (30 [73%] of 41); less than half had underlying diseases (13 [32%]), including diabetes (eight [20%]), hypertension (six [15%]), and cardiovascular disease (six [15%]).
TextSentencer_T14 1384-1426 Sentence denotes Median age was 49·0 years (IQR 41·0-58·0).
TextSentencer_T15 1427-1493 Sentence denotes 27 (66%) of 41 patients had been exposed to Huanan seafood market.
TextSentencer_T16 1494-1523 Sentence denotes One family cluster was found.
TextSentencer_T17 1524-1803 Sentence denotes Common symptoms at onset of illness were fever (40 [98%] of 41 patients), cough (31 [76%]), and myalgia or fatigue (18 [44%]); less common symptoms were sputum production (11 [28%] of 39), headache (three [8%] of 38), haemoptysis (two [5%] of 39), and diarrhoea (one [3%] of 38).
TextSentencer_T18 1804-1919 Sentence denotes Dyspnoea developed in 22 (55%) of 40 patients (median time from illness onset to dyspnoea 8·0 days [IQR 5·0-13·0]).
TextSentencer_T19 1920-1960 Sentence denotes 26 (63%) of 41 patients had lymphopenia.
TextSentencer_T20 1961-2026 Sentence denotes All 41 patients had pneumonia with abnormal findings on chest CT.
TextSentencer_T21 2027-2191 Sentence denotes Complications included acute respiratory distress syndrome (12 [29%]), RNAaemia (six [15%]), acute cardiac injury (five [12%]) and secondary infection (four [10%]).
TextSentencer_T22 2192-2253 Sentence denotes 13 (32%) patients were admitted to an ICU and six (15%) died.
TextSentencer_T23 2254-2377 Sentence denotes Compared with non-ICU patients, ICU patients had higher plasma levels of IL2, IL7, IL10, GSCF, IP10, MCP1, MIP1A, and TNFα.
TextSentencer_T24 2378-2393 Sentence denotes INTERPRETATION:
TextSentencer_T25 2394-2578 Sentence denotes The 2019-nCoV infection caused clusters of severe respiratory illness similar to severe acute respiratory syndrome coronavirus and was associated with ICU admission and high mortality.
TextSentencer_T26 2579-2735 Sentence denotes Major gaps in our knowledge of the origin, epidemiology, duration of human transmission, and clinical spectrum of disease need fulfilment by future studies.
TextSentencer_T27 2736-2744 Sentence denotes FUNDING:
TextSentencer_T28 2745-2920 Sentence denotes Ministry of Science and Technology, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, and Beijing Municipal Science and Technology Commission.