Arsenic trioxide exposure accelerates colon preneoplasic aberrant crypt foci induction regionally through mitochondrial dysfunction
Bibliographic record
Abstract
) twice in two weeks. The third group (D-ATO) had the same as the DMH group with ATO administration starting at week 10. At the end of 14 weeks, colons from sacrificed mice were taken, segmented into distal and proximal and subjected to aberrant crypt foci (ACF), aberrant crypt (AC) counting, alcian blue, H&E and Hoechst histological study and lastly oxidative stress marker analysis as well as mitochondrial swelling assessment. Data showed a significant increase in ACF and AC after DMH treatment, which was further increased after ATO addition. A perturbed histological structure was observed and loss of mucin producing cells in the colon tissue was observed. An important impact on the distal colon compared to the proximal one was noticed. The oxidative stress balance showed a similar pattern with an increase in MPO, NO/l-ornithine balance and MDA, while a decrease was observed in the antioxidant enzymes (CAT, SOD and GSH). In all parameters analyzed, the distal colons showed higher values than proximal. Furthermore, histological cell death analysis in combination with mitochondrial permeability pore opening suggested ATO contribution in the pathological effect. Our study has shown that ATO administration accelerated colon cancer development suggesting the heaviness of such treatments and the need to explore combinations and cycle type formulas.
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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 | 0.001 | 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) | 0.003 | 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".