Biochemical Analyses of Bioactive Extracts from Plants Native to Lampedusa, Sicily Minor Island
Bibliographic record
Abstract
Major threats to the human lifespan include cancer, infectious diseases, diabetes, mental degenerative conditions and also reduced agricultural productivity due to climate changes, together with new and more devastating plant diseases. From all of this, the need arises to find new biopesticides and new medicines. Plants and microorganisms are the most important sources for isolating new metabolites. Lampedusa Island host a rich contingent of endemic species and subspecies. Seven plant species spontaneously growing in Lampedusa, i.e., Atriplex halimus L. (Ap), Daucus lopadusanus Tineo (Dl), Echinops spinosus Fiori (Es) Glaucium flavum Crantz (Gf) Hypericum aegypticum L: (Ha), Periploca angustifolia Labill (Pa), and Prasium majus L. (Pm) were collected, assessed for their metabolite content, and evaluated for potential applications in agriculture and medicine. The HPLC-MS analysis of n-hexane (HE) and CH2Cl2 (MC) extracts and the residual aqueous phases (WR) showed the presence of several metabolites in both organic extracts. Crude HE and MC extracts from Dl and He significantly inhibited butyrylcholinesterase, as did WR from the extraction of Dl and Pa. HE and MC extracts showed a significant toxicity towards hepatocarcinoma Huh7, while Dl, Ha and Er HE extracts were the most potently cytotoxic to ileocecal colorectal adenocarcinoma HCT-8 cell lines. Most extracts showed antiviral activity. At the lowest concentration tested (1.56 μg/mL), Dl, Gf and Ap MC extracts inhibited betacoronavirus HCoV-OC43 infection by> 2 fold, while the n-hexane extract of Pm was the most potent. In addition, at 1.56 μg/mL, potent inhibition (>10 fold) of dengue virus was detected for Dl, Er, and Pm HE extracts, while Pa and Ap MC extracts dampened infections to undetectable levels. Regarding to phytotoxicity, MC extracts from Er, Ap and Pm were more effective in inhibiting tomato rootlet elongation; the same first two extracts also inhibited seed cress germination while its radicle elongation, due to high sensitivity, was affected by all the extracts. Es and Gf MC extracts also inhibited seed germination of Phelipanche ramosa. Thus, we have uncovered that many of these Lampedusa plants displayed promising biopesticide, antiviral, and biological properties.
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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 | 0.001 | 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 | 0.000 |
| 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".