Nematicidal Activity of 2-Thiophenecarboxaldehyde and Methylisothiocyanate from Caper (<i>Capparis spinosa</i>) against <i>Meloidogyne incognita</i>
Bibliographic record
Abstract
New pesticides based on plant extracts have recently gained interest in the development of nontoxic crop protection chemicals. Numerous research studies are focused on the isolation and identification of new active compounds derived from plants. In this manuscript we report about the use of the Mediterranean species Capparis spinosa as a potent natural nematicidal agent against the root knot nematodes Meloidogyne incognita. Leaves, stems, and caper buds of Capparis spinosa were used to obtain their methanol extracts (LME, SME, BME) that were successively in vitro tested against second stage nematode juveniles (J2). In terms of paralysis induction, the methanol extract of the stem part (SME) was found more effective against M. incognita and then the caper methanol buds and leaves extracts. The chemical composition analysis of the extracts carried out by GC/MS and LC/MS techniques showed that methylisothiocyanate was the main compound of SME. The EC50 for SME after 3 days of immersion was 215 ± 36 mg/L. The constituent components of SME such as 2-thiophenecarboxaldehyde and methylisothiocyanate were successively in vitro tested for their nematicidal activity against J2. Both compounds induced paralysis on root knot nematodes ranking first (EC50 = 7.9 ± 1.6, and 14.1 ± 1.9 mg/L respectively) for M. incognita. Moreover, 2-thiophenecarboxaldehyde showed a strong fumigant activity.
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) | 0.000 | 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".