Semi-Targeted Profiling of Bile Acids by High-Resolution Mass Spectrometry in a Rat Model of Drug-Induced Liver Injury
Bibliographic record
Abstract
Using a semi-targeted approach, we have investigated the effect of acetaminophen on circulating bile acid profiles in rats, including many known bile acids and potential isomeric structures, as well as glucuronide and sulfate conjugates. The chromatographic separation was based on an optimized reverse-phase method exhibiting excellent resolution for a complex mix of bile acids using a solid-core C18 column, coupled to a high-resolution quadrupole time-of-flight system. The semi-targeted workflow consisted of first assigning all peaks detectable in samples from 46 known bile acids contained in a standard mix, as well as additional peaks for other bile acid isomers. The presence of glucuronide and sulfate conjugates was also examined based on their elemental formulae and detectable peaks with matching exact masses were added to the list of features for statistical analysis. In this study, rats were administered acetaminophen at four different doses, from 75 to 600 mg/kg, with the highest dose being a good model of drug-induced liver injury. Statistically significant changes were found by comparing bile acid profiles between dosing levels. Some tentatively assigned conjugates were further elucidated using in vitro metabolism incubations with rat liver fractions and standard bile acids. Overall, 13 identified bile acids, 23 tentatively assigned bile acid isomers, and 9 sulfate conjugates were found to increase significantly at the highest acetaminophen dose, and thus could be linked to drug-induced liver injury.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".