Effect of human serum on cancer cell growth
Bibliographic record
Abstract
Background: Specific Dietary components have been linked to promotion or inhibition of cancer. We have therefore undertaken a series of studies to develop reliable ex vivo methodology for assessing the effect of diet on cancer development, by incubating serum from subjects, who have participated in dietary intervention studies, with various types of human cancer cell lines. Hypothesis: Serum from a given subject will constantly result in a consistant pattern of in vitro cell growth, different from other individuals providing diet and lifestyle patterns are unchanged. Methods: Blood will be taken from 20 healthy male and postmenopausal female volunteers once a week for a period of three weeks. The sera from subjects will be incubated with MCF‐7, MCF‐10A, and LNCaP cells. After 72 hours of incubation cell viability will be measured with the use of MTS assay. In addition, the effects of glucagon, insulin, E2, DHEA, DHT and testosterone will be tested in all cell lines. Results: The study is currently in progress. The results from both the human study and hormonal analysis will be presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.004 | 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".