Reducing Hepatocyte Injury and Necrosis in Response to Paracetamol Using Noncoding RNAs
Bibliographic record
Abstract
UNLABELLED: The liver performs multiple functions within the human body. It is composed of numerous cell types, which play important roles in organ physiology. Our study centers on the major metabolic cell type of the liver, the hepatocyte, and its susceptibility to damage during drug overdose. In these studies, hepatocytes were generated from a renewable and genetically defined resource. In vitro-derived hepatocytes were extensively profiled and exposed to varying levels of paracetamol and plasma isolated from liver-failure patients, with a view to identifying noncoding microRNAs that could reduce drug- or serum-induced hepatotoxicity. We identified a novel anti-microRNA, which reduced paracetamol-induced hepatotoxicity and glutathione depletion. Additionally, we identified a prosurvival role for anti-microRNA-324 following exposure to plasma collected from liver failure patients. We believe that these studies represent an important advance for the field, demonstrating the power of stem cell-derived systems to model human biology "in a dish" and identify novel noncoding microRNAs, which could be translated to the clinic in the future. SIGNIFICANCE: The liver performs vital functions within the human body and is composed of numerous cell types. The major metabolic cell type of the liver, the hepatocyte, is susceptible to damage during drug overdose. In these studies, hepatocytes were generated from a renewable resource and exposed to varying levels of paracetamol, with a view to identifying interventions that could reduce or attenuate drug-induced liver toxicity. A novel noncoding RNA that reduced paracetamol-induced hepatocyte toxicity was identified. These findings may represent an important advance for the field.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".