Effects of oral bovine lactoferrin on a mouse model of inflammation associated colon cancer
Bibliographic record
Abstract
Patients with ulcerative colitis or colonic Crohn’s disease have a significantly increased risk of developing colorectal cancer. Bovine lactoferrin (bLF) reportedly inhibited the development of colon cancer in rats and mice, and in a placebo controlled trial, ingestion of bLF inhibited the growth of intestinal polyps. In addition, in a case study, a patient with Crohn’s disease was reported to have remained in remission for over 7 years while ingesting 1 g of bLF daily. Thus, bLF has an inhibitory effect on colon carcinogenesis, and it may also promote remission of Crohn’s disease. The purpose of this study was to investigate the effects of bLF in a mouse model of colorectal cancer related to irritable bowel disease (IBD). The mice were divided into 4 groups: (i) no treatment; (ii) treated with bLF only; (iii) treated with azoxymethane plus dextran sulfate sodium (AOM + DSS); and (iv) treated with AOM + DSS + bLF. AOM was used to initiate intestinal cancer, and DSS was used to induce IBD-like inflammation in the intestine of the C57BL/6 mice. At the end of the study, the mice treated with AOM + DSS + bLF had a better fecal score, fewer lesions in the colon, and less weight loss than the mice treated with AOM + DSS without bLF. However, there were no statistically significant differences between the two groups with respect to tumor burden.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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".