Meme kanseri hücre dizisinde (MCF-7) selenyumun rolü
Bibliographic record
Abstract
Breast cancer is among the common causes of death in the world, it is known that genetic, endocrine and environmental factors play a role in its etiology.Intake of supplements in nutrition is important for the development of new agents for breast cancer treatment or to increase the effectiveness of existing drugs.It is suggested that micronutrients such as selenium taken from vegetable and animal foods such as seafood, legumes, meat, milk, and nuts may play a role in preventing or suppressing cancer by supporting the effectiveness of anticancer agents.In our study, 200 nM selenium was applied to a human breast cancer cell (MCF-7) line for 48 hours.Cell viability by trypan blue method, cell proliferation by XTT, total antioxidant (TAS)-oxidant capacity (TOS) and oxidative stress index (OSI) values were analyzed by ELISA method.A decrease was detected in cell viability, proliferation, TAS, TOS values in the selenium applied group compared to the control group not to statistically significant and an increase was observed in the OSI value.According to the results obtained in our study, it was determined that selenium concentration, which appears to be effective in normal cells, does not show the same effect on breast cancer cells.
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 | 0.000 | 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) | 0.005 | 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".