Extraction efficiency and bioactive evaluation of Tamarix nilotica and Arthrocnemum macrostachyum extracts for anti-cancer potential
Bibliographic record
Abstract
This study aimed to evaluate the potential of phytochemicals from two native UAE plant species, Arthrocnemum macrostachyum and Tamarix nilotica, as anti-cancer agents. The plant extracts were obtained using two methods, maceration, and microwave-assisted extraction (MAE), and were subsequently evaluated for their in vitro cytotoxicity against three cancer cell lines: breast (MDA-MB-231), colon (HCT-116), and lung (A-549). Results suggest that: 1) MAE is more efficient than maceration in recovering metabolites from plant biomass based on measurements of total phenolic content, radical scavenging activity, and bioactivity of extracts based on in vitro cytotoxicity. 2) Only T. nilotica extracts were found to be bioactive based on cytotoxicity measurements. 3) Cancer cell lines displayed differential sensitivity to T. nilotica crude extracts, with breast cancer cells being the most sensitive and lung cancer cells being the least sensitive. 4) Solid-phase fractionation of T. nilotica crude extract using different percentages of methanol resulted in several fractions that were 100-fold more cytotoxic compared to the crude unfractionated extract. The 30% and 70% methanol fractions exhibited the highest cytotoxicity towards breast and colon cancer cell lines, respectively. 5) Untargeted metabolomics using UHPLC-Q-ToF-MS of T. nilotica crude extracts revealed 909 molecular features, of which only 327 were annotated using MS/MS fragmentation. The results suggest that T. nilotica extracts have potential as anti-cancer agents and that MAE is an efficient method for extracting phytochemicals from plant biomass. The study also revealed that cancer cell lines exhibited differential sensitivity to the extracts and that solid-phase fractionation of crude extract using different percentages of methanol can yield fractions that are more cytotoxic than the crude extract.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".