All-Trans Retinoic Acid Ameliorates Trinitrobenzene Sulfonic Acid-Induced Colitis by Shifting Th1 to Th2 Profile
Bibliographic record
Abstract
Inflammatory bowel disease is characterized with uncontrolled immune response in inflamed mucosa, with dominance of Th1 cells. Recently, all-trans retinoic acid has been shown that can lead T-cell response by suppressing Th17 development via retinoic acid receptor (RAR), but it is still unknown whether all-trans retinoic acid can modulate Th1 response of inflammatory bowel disease. In the experiment, we investigated the effect of all-trans retinoic acid on trinitrobenzene sulfonic acid (TNBS)-induced murine colitis, and the possible mechanism. Mice were intraperitoneally treated daily with all-trans retinoic acid (the agonist of RAR-alpha) or LE135 (the antagonist of RAR-alpha) or medium, and sacrificed 6 days later. Colon was collected for histological analysis and myeloperoxidase (MPO) activity measurement. Lamina propria mononuclear cells (LPMCs) were isolated, cultured, and assayed for the expressions of T-bet and GATA-3 by the use of Western blot and for cytokine levels by the use of ELISA. All-trans retinoic acid treatment inhibited inflammatory responses as shown by lower histological inflammatory scores and MPO activity, compared with LE135 and medium groups. Furthermore, in LPMCs culture supernatants, the levels of Th1 cytokines (INF-gamma, IL-12, and TNF-alpha) were decreased while those of Th2 cytokines (IL-4 and IL-10) were increased significantly in all-trans retinoic acid-treated mice. In addition, T-bet expression in LPMCs was inhibited and GATA-3 expression was up-regulated in all-trans retinoic acidtreated mice. On the contrary, LE135 showed the reverse effects in colon inflammation and cytokine profile. By shifting Th1 to Th2 profile in inflamed mucosa, all-trans retinoic acid down-regulates inflammatory response and ameliorates acute TNBS-induced colitis, which suggests the ligand of RAR-alpha-based pharmaceutical strategies for managing inflammatory bowel disease.
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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 | 0.000 | 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) | 0.001 | 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".