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Single-cell Sequence Analysis Combined with Multiple Machine Learning to Identify Markers in Sepsis Patients: LILRA5.

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Title: Single-cell Sequence Analysis Combined with Multiple Machine Learning to Identify Markers in Sepsis Patients: LILRA5.
Authors: Ning J; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China., Fan X; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China., Sun K; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China., Wang X; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China.; Department of Laboratory, The Second Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China., Li H; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China., Jia K; Department of Pathology, Pathology Department of Shijiazhuang People's Hospital, Shijiazhuang, People's Republic of China., Ma C; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China. macuiqing@hebmu.edu.cn.
Source: Inflammation [Inflammation] 2023 Aug; Vol. 46 (4), pp. 1236-1254. Date of Electronic Publication: 2023 Mar 15.
Publication Type: Journal Article
Journal Info: Publisher: Kluwer Academic/Plenum Publishers Country of Publication: United States NLM ID: 7600105 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-2576 (Electronic) Linking ISSN: 03603997 NLM ISO Abbreviation: Inflammation Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Single-cell Sequence Analysis Combined with Multiple Machine Learning to Identify Markers in Sepsis Patients: LILRA5.
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  Data: <searchLink fieldCode="AU" term="%22Ning+J%22">Ning J</searchLink>; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China.<br /><searchLink fieldCode="AU" term="%22Fan+X%22">Fan X</searchLink>; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China.<br /><searchLink fieldCode="AU" term="%22Sun+K%22">Sun K</searchLink>; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China.<br /><searchLink fieldCode="AU" term="%22Wang+X%22">Wang X</searchLink>; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China.; Department of Laboratory, The Second Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China.<br /><searchLink fieldCode="AU" term="%22Li+H%22">Li H</searchLink>; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China.<br /><searchLink fieldCode="AU" term="%22Jia+K%22">Jia K</searchLink>; Department of Pathology, Pathology Department of Shijiazhuang People's Hospital, Shijiazhuang, People's Republic of China.<br /><searchLink fieldCode="AU" term="%22Ma+C%22">Ma C</searchLink>; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China. macuiqing@hebmu.edu.cn.
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  Data: <searchLink fieldCode="JN" term="%227600105%22">Inflammation</searchLink> [Inflammation] 2023 Aug; Vol. 46 (4), pp. 1236-1254. <i>Date of Electronic Publication: </i>2023 Mar 15.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Kluwer+Academic%2FPlenum+Publishers%22">Kluwer Academic/Plenum Publishers </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>7600105 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1573-2576 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2203603997%22">03603997 </searchLink><i>NLM ISO Abbreviation: </i>Inflammation <i>Subsets: </i>MEDLINE
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        Value: 10.1007/s10753-023-01803-8
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      – TitleFull: Single-cell Sequence Analysis Combined with Multiple Machine Learning to Identify Markers in Sepsis Patients: LILRA5.
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              Text: 2023 Aug
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