The Effects of Serum from Prostate Cancer Patients with Elevated Body Mass Index on Prostate Cancer Cells in Vitro
Bibliographic record
Abstract
We examined whether serum from obese, compared to non-obese, PCa (prostate cancer) patients creates a growth-enhancing tumor micro-environment in vitro. Serum from 80 subjects was divided into four groups: normal weight men with and without PCa and overweight/obese men with and without PCa. Cell proliferation, migration, and invasion were measured in LNCaP, and PC3 cells treated with patient serum were obtained from the above groups. The results reveal that proliferation of LNCaP cells was significantly (P = 0.05) greater with serum from non-obese (mean = 1.26 ± 0.20) compared to that from obese patients (mean = 1.16 ± 0.19). Serum from obese PCa patients compared to non-obese PCa patients induced significantly greater amounts of cell migration (P < 0.01) in PC3 cells. Serum from obese patients induced significantly (P < 0.01) lower amounts of cell invasion (mean = 8.2 ± 4.5) compared to non-obese patients (mean = 18.1 ± 5.0) when treated on PC3 cells. Serum TNF-α (tumor necrosis factor alpha) levels correlated with LNCaP cell proliferation in vitro in non-obese PCa (P < 0.01) and non-obese control groups (P = 0.05). All statistical calculations controlled for age, since the PCa patient groups were significantly older than the control groups (P < 0.01). In conclusion, serum from obese PCa patients induced greater PCa cell migration and lower cell proliferation and invasion in vitro.
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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".