Lipidomic profiling of subchronic As4S4 exposure identifies inflammatory mediators as sensitive biomarkers in rats
Bibliographic record
Abstract
Arsenic sulfide compounds provide nearly all of the world's supply of arsenic. However, the risk of arsenic trisulfide exposure is still not fully investigated. Here, we systemically assessed the toxicology of As4S4 in rats by combining arsenic metabolite detection, routine testing and lipidomic profiling. It was revealed that the oral administration of As4S4 for two months increased the total arsenic content in the liver reaching a saturation level. Further analysis by anion exchange chromatography coupled with inductively coupled plasma mass spectrometry (ICP-MS) technology showed no trace of inorganic arsenic, but there was significant presence of dimethylarsinic acid (DMA), in the livers of rats. This arsenic metabolite was less toxic to rats and did not induce overt liver pathology and functional injury. In contrast, lipidomic profiling provided a comprehensive map of lipids and uncovered a more complex inflammatory response, exhibiting more sensitive change to arsenic exposure. We observed that metabolites of cyclooxygenase, including PGF2α, dhk PGF2α, 15k PGF2α, 8-iso-PGF2a, PGE2, dhk PGE2, PGD2, 15d-PGD2, and PGJ2, were significantly elevated. But mediators from lipoxygenase, cytochrome P450, docosahexaenoic acid, and eicosapentaenoic acid pathways were not markedly affected. In summary, we identified DMA as the predominant arsenic species in the livers of rats, and found cyclooxygenase-derived lipids as the inflammatory mediators before the development of overt liver injury for subchronic As4S4 exposure. These mediators could translate into potential metabolic biomarkers in early arsenic risk assessment and as targets for therapeutic intervention.
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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 | 0.000 |
| 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 | 0.000 |
| 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".