Metabolomics‐based parallel discovery of xenobiotics and induced endogenous metabolic dysregulation in clinical toxicology
Bibliographic record
Abstract
Abstract Intoxication by xenobiotics triggers the perturbation of metabolic fingerprints in biofluids, including the accumulation of xenobiotic compounds and the dysregulation of endogenous metabolites. In this work, an untargeted metabolomics workflow was developed to simultaneously profile both xenobiotic and endogenous metabolites for the identification of the xenobiotic origin and an in‐depth understanding of the intoxication mechanism. This workflow was demonstrated in a real‐world clinical case. Plasma samples were collected from four intoxicated children and another three healthy children. Untargeted metabolomics analysis was performed using ultraperformance liquid chromatography (UPLC) coupled to a high‐resolution mass spectrometer (HRMS) with data‐independent MS E acquisition. LC–MS E data was processed using an untargeted metabolomics data interpretation workflow, in which the identities of xenobiotics and altered endogenous metabolic features were determined via database searching. Five xenobiotic chemicals and 19 endogenous metabolites were found to be dysregulated. Combined with the clinical evidence, penfluridol was confirmed as the xenobiotic toxin. Furthermore, a mechanistic hypothesis was developed to explain the dysregulation of the four endogenous acyl‐carnitines. This workflow can be readily applied to a wide range of clinical toxicology cases, offering a powerful and convenient means of simultaneous discovery of intoxication source and the understanding of intoxication mechanisms.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".