Toll-like receptor 4 regulates colitis-associated adenocarcinoma development in interleukin-10-deficient (IL-10−/−) mice
Bibliographic record
Abstract
Chronic inflammatory conditions such as ulcerative colitis and Crohn's disease are associated with an increased risk of developing adenocarcinoma. It has been hypothesized that this increased risk may be related to soluble mediators present in the inflammatory environment and that factors involved in exacerbating the inflammatory response could increase the risk of developing colitis-associated cancer. There is a growing body of evidence from both clinical studies and animal models which suggests that colitis occurs due to an aberrant immune response to enteric flora in genetically susceptible individuals. It is well documented that bacterial toxins such as endotoxin have potent pro-inflammatory effects through activation of TLR4 (Toll-like receptor 4) and therefore this molecule could potentially play a prominent role in the initiation/exacerbation of colitis and adenocarcinoma development. Using genetic mutant mice, we have examined the role of TLR4 in a spontaneously developing mouse model of colitis-associated adenocarcinoma: the IL-10(-/-) (interleukin-10-deficient) mouse. Surprisingly, our evidence suggests that the absence of TLR4 promotes colitis-associated adenocarcinoma in IL-10(-/-) mice. TLR4-dependent chemokine induction may play a part in modulating the development of colitis-associated neoplasia through altered leucocyte recruitment.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".