Modulation of TGF-B in platelets and neutrophils by dietary lipids
Bibliographic record
Abstract
Dietary lipids have been implicated in the pathology and prevention of variety of chronic illnesses including cancer and atherosclerosis. Transforming growth factor-beta (TGF-$\\beta)$ is a multifunctional cytokine that is believed to modulate the key steps in pathology of these chronic illnesses. Platelets and neutrophils are known to play a role in the development of these illnesses and are also known to transport high concentrations of TGF-$\\beta$ throughout the vascular system. Therefore the possibility that dietary lipids modulate the TGF-$\\beta$ content of these cells was investigated in a series of studies. A preliminary study was conducted with weanling Spragure-Dawley rats to assess TGF-$\\beta$ status of platelets as affected by dietary lipids utilising the CCL-64 bioassay. The growth inhibitory effect of platelet lysate, employing the CCL-64 bioassay, was quantified to reflect TGF-$\\beta$ content. Rats were put on four different high fat diets and one low fat diet for 6 weeks. Diets were composed of 18% test fat and 5% soy oil, by weight. Beef tallow (HFB), fish oil (HFF), corn oil (HFC), and olive oil (HFO) were used for the high fat diets and a low fat soy oil (LFS) diet was included with 5% soy oil. Supplementary studies were conducted with the neutrophils and plasma of the older rats. (Abstract shortened by UMI.)
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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) | 0.003 | 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".