Murine Appendectomy Model of Chronic Colitis Associated Colorectal Cancer by Precise Localization of Caecal Patch
Bibliographic record
Abstract
The human appendix has been recently implicated to play important biological roles in the pathogenesis of various complex diseases, such as colorectal cancer, inflammatory bowel disease, and Parkinson's disease. To study the function of the appendix, a gut disease-associated murine appendectomy model has been established and its step-by-step protocol is described here. This report introduces a facile protocol for caecal patch removal in mice followed by the chemical induction of chronic colitis-associated colorectal cancer using a combination of dextran sulfate sodium (DSS) and azoxymethane (AOM). IgA specific cells and IgA concentration were significantly reduced upon removal of the caecal patch in male C57BL/6 mice compared to those in the sham group. Simultaneously administering 2% DSS and AOM resulted in nearly 80% mice survival in both sham and appendectomy groups without significant body weight loss. Histological results confirmed colonic inflammation and different degrees of adenocarcinoma. This model can be used for the study of the functional role of the appendix in maintaining gut microbiota homeostasis and pathogenesis of gut colitis and malignancies, as well as for the potential development of drug targeting therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".