The combination of indirubin and isatin attenuates dextran sodium sulfate induced ulcerative colitis in mice
Bibliographic record
Abstract
Indirubin and isatin have been used in the treatment of inflammatory diseases due to their anti-inflammatory properties. This study aimed to evaluate the combined effect of indirubin and isatin on dextran sulfate sodium (DSS)-induced ulcerative colitis (UC). UC was induced by the administration of 3% (w/v) DSS solution, and then the model mice were administered indirubin (10 mg/kg body mass) and (or) isatin (10 mg/kg body mass) by gavage once daily for 7 days. The results showed that indirubin and isatin, individually or combined, significantly inhibited weight loss, lowered disease activity index (DAI), ameliorated pathological changes, decreased the levels of pro-inflammatory mediators and myeloperoxidase (MPO) activity, increased the expression of anti-inflammatory cytokines and Foxp3, suppressed CD4+ T cell infiltration, and inhibited oxidative stress and epithelial cell apoptosis. Additionally, indirubin and isatin, both individually and combined, can also inhibit activation of the NF-κB and MAPK pathways induced by DSS. The protective effect of combination therapy against UC was superior to that of single-agent treatment. These results suggest that indirubin combined with isatin attenuates DSS-induced UC, and changes to the NF-κB and MAPK signaling pathways may mediate the protective effects of indirubin and isatin in UC.
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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 | 0.001 | 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".