Yemeni Sidr Honey Inhibits Cell Proliferation and Promotes Apoptosis in Human Cancer and Mouse-Derived Cell Lines
Bibliographic record
Abstract
Honey has become popular as a potential treatment for several ailments, including cancer. Honeys from different parts of the world have been shown to have different anti-proliferative, immune-modulatory, and anti-inflammatory actions. Yemeni Sidr honey (YSH) is world-renowned for its anti-inflammatory activity and has been suggested to have anti-cancer activity, although empirical evidence is lacking. We tested three YSH samples by HPLC to show they contained similar sugars and an overlapping group of phenolic and flavonoid components, as described previously. YSH’s apoptotic and anti-proliferative activities were measured in in vitro models of cancer growth. The treatment of breast cancer cell lines (MDA-MB-231 and MCF-7), a cervical cancer cell line (HeLa), and mouse melanoma cells (B16-BL6) with 1% (w/v) YSH in media for 48–72 h almost completely inhibited cell proliferation and promoted cell apoptosis. In contrast, a non-malignant HBL-100 cell line was more resistant to treatment with YSH. This suggests that YSH may be a good candidate as an anti-cancer treatment, which requires further study.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".