The effect of physiological concentrations of six hormones on the growth of breast and prostate cell lines treated with human serum
Bibliographic record
Abstract
Background The majority of cell culture studies assess the effect of hormones on cancer cell growth using media supplemented with charcoal treated serum (CTS). Our objective was to determine the effect of various hormones on the growth of breast and prostate cancer cells incubated in untreated whole human serum (PHS). Methods MCF‐7, MCF‐10A breast and LNCaP prostate cancer cell lines supplemented with PHS were treated with two physiological concentrations of six hormones (17β‐estradiol (E2), dehydroepiandosterone (DHEA), dihydrotestosterone (DHT), testosterone (T), insulin and glucagon). Cell viability was measured after 72 hours using the MTS assay. Results All hormones stimulated growth of MCF‐7 cells (p<0.05). MCF‐10A cell growth was inhibited by DHEA, DHT and T (p<0.05), unaffected by E2 and glucagon, and stimulated by insulin (p<0.05). LNCaP cell growth was stimulated by the highest concentration of DHEA and DHT (p<0.05) and inhibited by the highest concentration of E2 (p<0.05). Insulin and T did not alter LNCaP growth. PHS lowered the magnitude of the hormonal effect by comparison to CTS. Conclusions Unexpectedly, high concentrations of testosterone did not stimulate LNCaP cell growth and we observed for the first time that glucagon stimulates breast cancer cell growth. (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 | 0.000 | 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".