Target organ toxicity in rats after subchronic oral exposure to soil extracts containing a complex mixture of contaminants
Bibliographic record
Abstract
Abstract Complex mixtures of unknown contaminants present a challenge to identify toxicological risks without using large numbers of animals and labour-intensive screens of all organs. This study examined soil extracts from a legacy contaminated pesticide packaging and blending site. HepG2 cytotoxicity was used as an initial screen of 18 soil samples to identify the most toxic extracts for testing in animals. Then, target organ toxicities were identified following biweekly oral gavage for one month of three soil extracts (0.1% in polyethylene glycol or PEG) compared to vehicle control in male Sprague Dawley rats (n = 9–10/group). Exposure to extract A significantly increased neutrophils and lymphocytes compared to control. In contrast, all extracts increased plasma α-2 macroglobulin and caused mild to moderate lymphocytic proliferation within the spleen white pulp, all indicative of inflammation. Rats exposed to all soil extracts exhibited acute tubular necrosis. Cholinesterase activity was significantly reduced in plasma, but not brain, after exposure to extract A compared to control. Increased hepatic ethoxyresorufin-o-deethylase activity compared to control was observed following exposure to extracts A and B. Exposure to soil extract C in rats showed a prolonged QTc interval in electrocardiography as well as increased brain lipid peroxidation. Candidate contaminants are organochlorine, organophosphate/carbamate pesticides or metabolites. Overall, HepG2 cytotoxicity did not successfully predict the neurotoxicity and cardiotoxicity observed with extract C, but was more successful with suspected hydrocarbon toxicities in extracts A and B. Caution should be taken when extrapolating the observation of no effects from in vitro cell culture to in vivo toxicity and better cell culture lines or assays should be explored.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".