Additional file 1 of Initial gut microbiota composition is a determining factor in the promotion of colorectal cancer by oral iron supplementation: evidence from a murine model
Bibliographic record
Abstract
Additional file 1. Supplementary Figure S1. Dietary iron supplementation promotes duodenal tumorigenesis in ApcMin/+mice independently of fecal microbiota transplant origin. Supplementary Figure S2: Representative pictures of the colon of ApcMin/+mice.Supplementary Figure S3: CRC associated gut microbiota differ from healthy control gut microbiota. Supplementary Figure S4: Bray-Curtis distances. Supplementary Figure S5: Beta diversity analysis of FMT-HC and FMT-CRC as a function of the dietary iron and initial gut microbiota composition. Supplementary Figure S6: Alpha-diversity indexes. Supplementary Figure S7: Quantification of B. pseudolongum and R. ilealis in the gut microbiota of FMT-CRC ApcMin/+mice. Supplementary Figure S8: Quantification of SCFA in the colon of FMT-CRC ApcMin/+mice supplemented withA. inops and B. pseudolongum. Supplementary Figure S9: Colonic cytokines levels in FMT-CRC mice. Supplementary Table S1: Demographic, clinical, and perioperative data of patients with colorectal cancer and healthy controls Supplementary Table S2: Erythroid parameters of patients with colorectal cancer. Supplementary Table S3: Primers for real-time PCR. Supplementary Table S4: Significant changes in relative abundances of microbial taxa at the family level.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.864 | 0.129 |
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".