Effect of Eggplant Skin in the Process of Apoptosis in Cancer Cells
Bibliographic record
Abstract
The process of programmed and physiological cell death, or apoptosis, is generally characterized by distinct morphological characteristics and energy-dependent biochemical mechanisms. Apoptosis normally removes old, damaged, excessive and harmful cells and is essential for tissue homeostasis. Cancer is a disease in which damaged cells do go through apoptosis and ultimately uncontrolled cell division results in the formation of a malignant tumor. Stomach cancer is the most common cancer in Iran with about 7751 affected in the year of 1387. This is about 10.17% of the total male and female population per year. Unfortunately, existing treatment options are often aggressive with harmful side effects for the patient. In this study, we decided to prepare anti-cancer drugs from natural ingredients such as eggplant skin which is rich in antioxidants. The primary hypothesis of this study was that due to the presence of compounds containing antioxidants in eggplant skin, the extract should be able to destroy cancer cells by activating their apoptosis. This hypothesis was examined by MTT colorimetric method in two series of cells: gastric cancer cells (AGS) and normal fibroblasts cells (FIB). The yellow MTT salt becomes an insoluble purple formazan by dehydrogenase enzymes in the active mitochondria of cells. The results of this study show that eggplant skin extracts in concentrations of 2.5 μM have a high cytotoxic effect on gastric cancer cell lines, which could be due to the induction of apoptosis in these cells and the least effect on normal fibroblasts cells. Therefore, eggplant skin extract has a positive effect on the apoptosis of cancer cells and can be used in the production of stomach cancer drugs.
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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".