{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T23:47:44Z","timestamp":1768520864683,"version":"3.49.0"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"D1","funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1K99LM014024-01"],"award-info":[{"award-number":["1K99LM014024-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>LitCovid\u00a0(https:\/\/www.ncbi.nlm.nih.gov\/research\/coronavirus\/)\u2014first launched in February 2020\u2014is a first-of-its-kind literature hub for tracking up-to-date published research on COVID-19. The number of articles in LitCovid has increased from 55 000 to \u223c300 000 over the past 2.5\u00a0years, with a consistent growth rate of \u223c10 000 articles per month. In addition to the rapid literature growth, the COVID-19 pandemic has evolved dramatically. For instance, the Omicron variant has now accounted for over 98% of new infections in the United\u00a0States. In response to the continuing evolution of the COVID-19 pandemic, this article describes significant updates to LitCovid over the last 2\u00a0years. First, we introduced the long Covid collection consisting of the articles on COVID-19 survivors experiencing ongoing multisystemic symptoms, including respiratory issues, cardiovascular disease, cognitive impairment, and profound fatigue. Second, we provided new annotations on the latest COVID-19 strains and vaccines mentioned in the literature. Third, we improved several existing features with more accurate machine learning algorithms for annotating topics and classifying articles relevant to COVID-19. LitCovid has been widely used with millions of accesses by users worldwide on various information needs and continues to play a critical role in collecting, curating\u00a0and standardizing the latest knowledge on the COVID-19 literature.<\/jats:p>","DOI":"10.1093\/nar\/gkac1005","type":"journal-article","created":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T16:33:56Z","timestamp":1668011636000},"page":"D1512-D1518","source":"Crossref","is-referenced-by-count":26,"title":["LitCovid in 2022: an information resource for the COVID-19 literature"],"prefix":"10.1093","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6036-1516","authenticated-orcid":false,"given":"Qingyu","family":"Chen","sequence":"first","affiliation":[{"name":"National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , MD, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2706-9054","authenticated-orcid":false,"given":"Alexis","family":"Allot","sequence":"additional","affiliation":[{"name":"National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , MD, USA"}]},{"given":"Robert","family":"Leaman","sequence":"additional","affiliation":[{"name":"National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , MD, USA"}]},{"given":"Chih-Hsuan","family":"Wei","sequence":"additional","affiliation":[{"name":"National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , MD, USA"}]},{"given":"Elaheh","family":"Aghaarabi","sequence":"additional","affiliation":[{"name":"National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , MD, USA"},{"name":"Towson University , Towson, MD, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6547-9137","authenticated-orcid":false,"given":"John\u00a0J","family":"Guerrerio","sequence":"additional","affiliation":[{"name":"National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , MD, USA"},{"name":"Dartmouth College , Hanover, NH, USA"}]},{"given":"Lilly","family":"Xu","sequence":"additional","affiliation":[{"name":"National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , MD, USA"},{"name":"Land O\u2019 Lakes High School, FL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9998-916X","authenticated-orcid":false,"given":"Zhiyong","family":"Lu","sequence":"additional","affiliation":[{"name":"National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , MD, USA"}]}],"member":"286","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"key":"2023010804302775700_B1","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1146\/annurev-biodatasci-021821-061045","article-title":"Artificial intelligence in action: addressing the COVID-19 pandemic with natural language processing","volume":"4","author":"Chen","year":"2021","journal-title":"Annu. 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