Effects of<i>Thymus serpyllum</i>Extract on Cell Proliferation, Apoptosis and Epigenetic Events in Human Breast Cancer Cells
Bibliographic record
Abstract
Thymus (T.) serpyllum (wild thyme) is an aromatic medicinal plant due to its several biological properties, including anticancer activity. Breast cancer is one of the most common malignancies and increasing evidence supports that it is not only a genetic but also an epigenetic disease. Epigenetics investigates changes in gene expression caused by mechanisms that do not involve alterations in DNA sequence. DNA methylation and histone acetylation are the most widely studied epigenetic changes in cancer cells. This study evaluated the effects of T. serpyllum on apoptosis and epigenetic events in breast cancer cells. XTT cell viability assay was used to determine cytotoxicity. DNA fragmentation and caspase 3/7 activity assays were used in the assesment of apoptosis. DNA methyltransferase (DNMT) and histone deacetylase (HDAC) activities were evaluated by ELISA and verified by qRT-PCR. T. serpyllum extract induced significant cytotoxicity in breast cancer cells (MCF-7 and MDA-MB-231) but not in normal cells. It also induced apoptosis and inhibited the DNMT and HDAC activities in MDA-MB-231 cells. In the present study, the first preliminary data on the effects of the methanolic extract of T. serpyllum in normal and breast cancer cells were obtained and suggest that T. serpyllum may be a promising candidate in the development of novel therapeutic drugs for breast cancer treatment.
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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".