Anticancer, antioxidant, and antibacterial activity of chemically fingerprinted extract from Cyclamen persicum Mill.
Bibliographic record
Abstract
In the last few decades, researchers have thoroughly studied the use of plants in Palestine, one of them is Cyclamen persicum Mill. (C. persicum). Cyclamen persicum has been historically cultivated since the 1700s due to its tuber. The tuber is known to stimulate the nasal receptors, thus triggering the sensory neurons. Cyclamen persicum has anti-inflammatory effects, reduces cholesterol levels, treats diabetes, and inhibits tumor growth. In this respect, in-vitro examination of antibacterial and anticancer activities and antioxidative potency of C. persicum ethanolic extract were evaluated. The antioxidative potency of the extracted plant material was determined spectrophotometrically using the DPPH free radical scavenging method and the HPLC-PDA method to evaluate its total phenolic content (TPC) and total flavonoid content (TFC). The experimental results revealed weak antibacterial activity of C. persicum extract against both gram negative (E. coli) and gram positive (Streptococcus aureus and S. aureus) bacterial strains, with the zones of inhibition found to be less than 8 mm. On the other hand, powerful activity against MCF7 breast cancer as well as HT29 colon cancer cell lines was obtained. The findings also revealed potent inhibition of free radicals and the presence of maximal levels of natural products such as phenolic compounds and flavonoids, which supportits biological activities and powerful ability to scavenge free radicals. HPLC results showed the presence of numerous flavonoid and phenolic compounds such as rutin, chlorogenic acid, kaempferol, trans-cinnamic acid, quercetin, sinapic acid, and p-coumaric acid.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".