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Record W4313543501 · doi:10.1111/bcpt.13834

Growth differentiation factor 15 is dispensable for acetaminophen‐induced liver injury in mice

2023· article· en· W4313543501 on OpenAlexaff
Peng Jiang, Zhenghong Liu, Tingyu Fang, Zhidan Zhang, Yu Zhang, Dongdong Wang, Peter J. Little, Suowen Xu, Jianping Weng

Bibliographic record

VenueBasic & Clinical Pharmacology & Toxicology · 2023
Typearticle
Languageen
FieldMedicine
TopicGDF15 and Related Biomarkers
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
FundersNational Natural Science Foundation of China
KeywordsAcetaminophenLiver injuryPharmacologyCell biologyChemistryMedicineBiology

Abstract

fetched live from OpenAlex

Acetaminophen (APAP)-induced liver injury (AILI) has been recognized as a pivotal contributor to drug-induced liver failure in Western countries, but its molecular mechanism remains poorly understood. Growth differentiation factor 15 (GDF15) is a pleiotropic factor that alleviates non-alcoholic liver steatohepatitis, liver fibrosis and liver injury. The aim of the present study was to examine the possibility whether GDF15 confers protection against AILI. We found that the gene expression of Gdf15 was increased significantly after APAP overdose in mice. Next, the role of Gdf15 in AILI was evaluated by hepatic Gdf15 overexpression (using adeno-associated virus serotype 8), injection with recombinant human GDF15 (rhGDF15) and Gdf15 knockout mice after challenge with APAP. A marked elevation of Gdf15 was observed after AILI. However, there were no significant differences in AILI-related liver injury and JNK phosphorylation after Gdf15 overexpression, rhGDF15 injection or Gdf15 deficiency. Together, we conclude that, despite a noticeable elevation of Gdf15 level after AILI, Gdf15 is dispensable for APAP-induced AILI. Our study further suggests that genomic analysis of mRNA expression after APAP overdose is of limited relevance unless followed up by a functional analysis of candidate genes in vivo.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.086
GPT teacher head0.411
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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