Steady state levels of transforming growth factor‐β1 and ‐β2 mRNA and protein expression are elevated in colonic tumors <i>in vivo</i> irrespective of dietary lipids intervention
Bibliographic record
Abstract
Colonic tumors of human origin produce abundant transforming growth factor (TGF)-beta suggesting that TGF-beta is critical to their growth. Dietary lipids regulate a number of growth factors including TGF-beta. Whether elevated TGF-beta levels are consistently expressed in colonic tumors irrespective of the environmental milieu in an in vivo model is not known and forms the main objective of the present study. Male F344 rats were injected with azoxymethane, 10 weeks later, rats bearing preneoplastic lesions were fed a low fat (5% corn oil) diet and 3 high fat (5% corn oil with 18% corn oil, fish oil or beef tallow) diets for 16 weeks. Colonic tumors and mucosae were processed and assessed for TGF-beta status. TGF-beta1 and -beta2 mRNA levels were upregulated in colonic tumors more than in mucosae of all diet groups. Dietary lipids modulated TGF-beta mRNA in both tumors and mucosae, high corn and fish oil diets upregulated TGF-beta1 significantly more than the low fat corn oil or high fat beef tallow diets. Immunohistochemical assessments of tissues with different biological features revealed that TGF-beta1 and -beta2 were elevated in tumors and in selected microscopic preneoplastic lesions compared to normal mucosae. This is the first in vivo study, documenting that developing colonic tumors acquire upregulated TGF-beta phenotype even in the presence of lipid environments capable of differentially regulating TGF-beta in normal mucosae. Elevated expression of TGF-beta in a selected subset of microscopic preneoplastic lesions suggests that TGF-beta plays an important role on both early and late stages of colon carcinogenesis.
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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) | 0.001 | 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".