Evaluation of the anti-cancer effect of Dianthin-30 on MCF-7 breast cancer cell line in 3D cell cultures
Bibliographic record
Abstract
Objectives: Breast cancer is one of the most common types of cancer among women.One of these toxins that inhibits the growth of breast cancer cells in 2D cell culture and has significant anti-tumor effects is Dianthin-30.Therefore, in this manuscript, for the first time, the anti-cancer effect of Dianthin-30 toxin against breast cancer cells (MCF-7) in 3D culture has been studied.Materials and Methods: In order to evaluate the anti-cancer effects and cytotoxicity of the toxin at concentrations of 1.25, 2.5, 5 and 10μg/ml, MTT methods were used and a Neutral red test was used to validate the results of this test.Nitric oxide, Catalase, GSH assays, cytochrome c, Caspase-3 and Comet assay tests were also used to determine the type of mortality in cancer cells.Results: This toxin did not induce nitric oxide production, but at concentrations higher than 5μg/ml increased catalase production compared to the control.However, the level of GSH produced in all of the concentrations was significant compared to the control.In addition, Dianthin-30 increased cytochrome 30 and activation of caspase-3 in the above concentrations, but this effect was not significant compared to the control.The results of alkaline comet test also showed that the rate of induction of apoptosis by toxin was upward compared to the control.Conclusion: The results of this study show that Dianthin-30 has anti-cancer effects and has caused death in breast cancer cells and this toxin probably induced apoptosis in cancer cells more than the non-mitochondrial pathway.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".