A77 THE INFLUENCE OF IRON SUPPLEMENTATION IN EARLY LIFE ON COLORECTAL CANCER PROGRESSION
Bibliographic record
Abstract
Abstract Background During the first years of life, the immune system develops simultaneously with the gut microbiota, and it has been previously shown that perturbations to gut microbiota development can lead to altered immune phenotypes later in life. One factor that may influence the gut microbiota in early life, and therefore immune development, is iron supplementation. Aims We aim to evaluate how iron supplementation in early life affects immune system development and therefore cancer progression in adulthood. Methods BALB/c mice were given either an iron sufficient or an iron excess diet beginning at weaning until adulthood at 8 weeks of age. All mice were then placed on the iron sufficient diet for two weeks prior to beginning the experimental model in order to establish the effect of iron only during the early life period. Mice were then injected subcutaneously with the murine colon carcinoma cell line CT-26. Results Mice that recieved iron supplementation in early life had significantly higher levels of iron in both the liver and spleen, indicating that this diet was successful in increasing iron levels. Furthermore, mice given iron supplementation had significantly more sebaceous glands per mm ear tissue than mice given the iron sufficient diet. Sebaceous glands are structures in the skin important for coordination of the innate immune response and regulation of the skin microbiota. We next measured several cytokines by ELISA. Mice supplemented with iron had significantly lower levels of interleukin (IL)-4, IL-10, and interferon (IFN)-γ. Conclusions Considering that mice given the iron excess diet had significantly altered skin immune structures and significantly decreased basal cytokine levels in ear tissue, we conclude that iron supplementation in early life modulates immune system development. Next, we will evaluate how iron supplementation in early life affects colorectal cancer development and progression. Funding Agencies CIHRFRQS, NSERC
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".