Reproducibility in growth of breast and prostate cells stimulated with serum taken at different points in time from individuals on their habitual diets
Bibliographic record
Abstract
Background While dietary components have been linked to cancer, the total impact of specific diets on cancer remains controversial. Therefore, we undertook a series of studies to develop a reliable ex vivo bioassay for assessing the effect of diet on cancer cell growth. Various types of human cancer cell lines were treated with serum from subjects on their habitual diets. Methods Eighteen subjects (10 males; 8 females) provided us with three fasting blood samples, each separated by one week. Subjects were asked to maintain their habitual diet and exercise regimen for the length of the study. Three cell lines (MCF‐7, MCF‐10A, LNCaP) were then incubated in media containing the serum samples (10%). After 72 hours, cell growth was measured using the MTS colorimetric assay. Results The stimulation of cell growth was relatively constant for a given individual over the 3‐week period. There were, however, statistically significant inter‐individual differences in serum‐stimulated cell growth in all three cell lines. Subject age and body mass index (BMI) were not correlated with cell growth. Conclusion These findings suggest that the consistency of results for a given individual will allow differences in breast and prostate cell growth resulting from specific diets to be detected using this method. (Funding provided by Canada Research Chair discretionary funds)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".