Growth differentiation factor 15 is dispensable for acetaminophen‐induced liver injury in mice
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".