Selected article for: "acute infection and machine learning model"

Author: Zhou, Yonggang; Zhang, Jinhe; Wang, Dongyao; Wang, Dong; Guan, Wuxiang; Qin, Jingkun; Xu, Xiuxiu; Fang, Jingwen; Fu, Binqing; Zheng, Xiaohu; Wang, Dongsheng; Zhao, Hong; Chen, Xianxiang; Tian, Zhigang; Xu, Xiaoling; Wang, Guiqiang; Wei, Haiming
Title: Profiling of the immune repertoire in COVID-19 patients with mild, severe, convalescent, or retesting-positive status
  • Cord-id: y48mfuky
  • Document date: 2021_1_14
  • ID: y48mfuky
    Snippet: Forty-seven samples of peripheral blood mononuclear cells from four groups of coronavirus disease (COVID)-19 patients (mild, severe, convalescent, retesting-positive) and healthy controls were applied to profile the immune repertoire of COVID-19 patients in acute infection or convalescence by transcriptome sequencing and immune-receptor repertoire (IRR) sequencing. Transcriptome analyses showed that genes within principal component group 1 (PC1) were associated with infection and disease severit
    Document: Forty-seven samples of peripheral blood mononuclear cells from four groups of coronavirus disease (COVID)-19 patients (mild, severe, convalescent, retesting-positive) and healthy controls were applied to profile the immune repertoire of COVID-19 patients in acute infection or convalescence by transcriptome sequencing and immune-receptor repertoire (IRR) sequencing. Transcriptome analyses showed that genes within principal component group 1 (PC1) were associated with infection and disease severity whereas genes within PC2 were associated with recovery from COVID-19. A “dual-injury mechanism” of COVID-19 severity was related to an increased number of proinflammatory pathways and activated hypercoagulable pathways. A machine-learning model based on the genes associated with inflammatory and hypercoagulable pathways had the potential to be employed to monitor COVID-19 severity. Signature analyses of B-cell receptors (BCRs) and T-cell receptors (TCRs) revealed the dominant selection of longer V–J pairs (e.g., IGHV3-9–IGHJ6 and IGHV3-23–IGHJ6) and continuous tyrosine motifs in BCRs and lower diversity of TCRs. These findings provide potential predictors for COVID-19 outcomes, and new potential targets for COVID-19 treatment.

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