Anticancer Effects of <i>α</i> -Pinene in AGS Gastric Cancer Cells
Bibliographic record
Abstract
Gastric cancer is the fifth most common cancer globally and the third leading cause of cancer-related mortality. Existing treatment strategies for gastric cancer often present numerous side effects. Consequently, recent studies have shifted toward devising new treatments grounded in safer natural substances. α-Pinene, a natural terpene found in the essential oils of various plants, such as Lavender angustifolia and Satureja myrtifolia, displays antioxidant, antibiotic, and anticancer properties. Yet, its impact on gastric cancer remains unexplored. This research assessed the effects of α-pinene in vitro using a human gastric adenocarcinoma cell-line (AGS) human gastric cancer cells and in vivo via a xenograft mouse model. The survival rate of AGS cells treated with α-pinene was notably lower than that of the control group, as revealed by the 3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide assay. This decline in cell viability was linked to apoptosis, as verified by 4′,6-diamidino-2-phenylindole and annexin V/propidium iodide staining. The α-pinene–treated group exhibited elevated cleaved-poly (ADP-ribose) polymerase and B cell lymphoma 2 (Bcl-2)–associated X (Bax) levels and reduced Bcl-2 levels compared with the control levels. Moreover, α-pinene triggered the activation of extracellular signal-regulated kinase, c-Jun N-terminal kinase, and p38 within the mitogen-activated protein kinase (MAPK) pathway. In the xenograft mouse model, α-pinene induced apoptosis through the MAPK pathway, devoid of toxicity. These findings position α-pinene as a promising natural therapeutic for gastric cancer.
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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".