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Träfflista för sökning "WFRF:(Zhao Hai He) srt2:(2020-2024)"

Search: WFRF:(Zhao Hai He) > (2020-2024)

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  • Liu, Zhengtao, et al. (author)
  • Multi-omics network analysis on samples from sequential biopsies reveals vital role of proliferation arrest for Macrosteatosis related graft failure in rats after liver transplantation
  • 2023
  • In: Genomics. - : Elsevier BV. - 0888-7543 .- 1089-8646. ; 115:6
  • Journal article (peer-reviewed)abstract
    • To investigate the molecular impact of graft MaS on post-transplant prognosis, based on multi-omics integrative analysis. Rats were fed by methionine-choline deficient diet (MCD) for MaS grafts. Samples were collected from grafts by sequential biopsies. Transcriptomic and metabolomic profilings were assayed. Post-transplant MaS status showed a close association with graft failure. Differentially expressed genes (DEGs) for in-vivo MaS were mainly enriched on pathways of cell cycle and DNA replication. Post-transplant MaS caused arrests of graft regeneration via inhibiting the E2F1 centered network, which was confirmed by an in vitro experiment. Data from metabolomics assays found insufficient serine/creatine which is located on one‑carbon metabolism was responsible for MaS-related GF. Pre-transplant MaS caused severe fibrosis in long-term survivors. DEGs for grafts from long-term survivors with pre-transplant MaS were mainly enriched in pathways of ECM-receptor interaction and focal adhesion. Transcriptional regulatory network analysis confirmed SOX9 as a key transcription factor (TF) for MaS-related fibrosis. Metabolomic assays found elevation of aromatic amino acid (AAA) was a major feature of fibrosis in long-term survivors. Graft MaS in vivo increased post-transplant GF via negative regulations on graft regeneration. Pre-transplant MaS induced severe fibrosis in long-term survivors via activations on ECM-receptor interaction and AAA metabolism.
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3.
  • Menkveld, Albert J., et al. (author)
  • Nonstandard Errors
  • 2024
  • In: JOURNAL OF FINANCE. - : Wiley-Blackwell. - 0022-1082 .- 1540-6261. ; 79:3, s. 2339-2390
  • Journal article (peer-reviewed)abstract
    • In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
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