Establishing a standardized murine orthotopic intra-rectal model for the study of colorectal adenocarcinoma
Bibliographic record
Abstract
Background: Orthotopic models offer a more accurate representation of colorectal cancer (CRC) compared to subcutaneous models. Despite promising results from the reported intra-rectal models, establishing a standardized method for CRC research remains challenging due to model variability, hindering comprehensive studies on CRC pathogenesis and treatment modalities, such as brachytherapy. This study aimed to establish a standardized workflow for an orthotopic intra-rectal animal model to induce the growth of colorectal adenocarcinoma in male and female mice. Methods: HT-29 colorectal adenocarcinoma cells were injected into the rectal mucosa of female (n=21) and male (n=26) non-obese diabetic severe combined immunodeficiency (NOD SCID) gamma (NSG) mice. Mice were placed on a 45° wedge elevating their pelvis for better visualization of the anus. Tumor growth and localization were monitored using a 7-T magnetic resonance imaging (MRI) scanner with rapid acquisition with relaxation echo (RARE) sequence at weeks 1, 2, and 3 post-cell instillation. Once tumors reached 5-8 mm in diameter, the mice were euthanized. Histopathology and immunohistochemical analyses confirmed the tumors' morphology, including necrosis, vascularity (CD-31) and apoptosis (cleaved caspase-3). Results: There was a 92% and 95% tumor growth success rate in male and female mice, respectively. Tumors grew to 5-8 mm in diameter within ~20 days. No significant difference in tumor size was observed between genders. Tumor morphology was consistent across cases. Most tumors exhibited a lack of central blood vessels, accompanied by varying degrees of necrosis and apoptosis, whereas external portions were highly vascularized. Conclusions: An orthotopic intra-rectal model was successfully developed. This model will be used in future studies to evaluate the efficacy of CRC treatments.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".