A251 AZOXYMETHANE INDUCES INTESTINAL CRYPT ABSCESSES IN KAISO TRANSGENIC (KAISOTG) MICE
Bibliographic record
Abstract
Colitis associated cancer (CAC) is a poorly characterized subgroup of colorectal cancer (CRC) that afflicts ~20% patients suffering from inflammatory bowel disease (IBD). The limited understanding of CAC stems from the lack of suitable mammalian model systems, as well as a general gap in research involving the molecular mechanisms of this disease. Studies have shown that increased expression of the transcription factor Kaiso causes intestinal inflammation and tumorigenesis in mice. The objective of this research is to determine the effects of environmental carcinogens on disease progression (IBD to CAC) in Kaiso overexpressing transgenic (KaisoTg ) mice. We hypothesize that treatment with the carcinogen, azoxymethane (AOM), will exacerbate the Kaiso-mediated intestinal inflammation and lead to colitis-associated cancer (i.e. polyp formation). We injected KaisoTg or wildtype (WT) mice with either AOM, or a PBS vehicle control once a week for 6 weeks and sacrificed them one week after the last injection. Their intestinal tissues have been collected and assessed macroscopically for inflammation and polyp formation. Immunohistochemistry (IHC) analysis was then performed for established inflammation- tumourigenesis-associated proteins (e.g. p53, NF-kB, p120ctn, b-catenin). Our preliminary findings show that the intestinal tissues of AOM-treated mice exhibited atypical hyperplasia and aberrant crypt foci (ACF), which represent precursors to polyp formation. The mice also exhibited a higher quantity of NF-kB positive nuclei compared to the control, indicating increased inflammation in the experimental group. Interestingly, KaisoTg mice were found to have decreased levels of acetylated-p53 (K381), indicating higher p53 survival rates in overexpressing mice. The findings from our pilot study suggests that the KaisoTg mouse model may hold potential as a novel genetic model for IBD-CAC progression, compared to the popular AOM/DSS mouse model that currently represents the gold standard for CAC. KaisoTg mice closely follow the natural inflammatory progression in mammals[SR1], and, when treated with AOM, can be used to molecularly characterize the transition from IBD to CAC. [SR2] Mice in our expanded study of AOM-treated KaisoTg will be examined in December 2017[SR3], and we anticipate that they will exhibit increased inflammation and phenotypes akin to CAC. NSERC, McMaster University
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".