<i>Acalypha indica</i> aqueous leaf extract as potential nematicide against the root-knot nematode, <i>Meloidogyne incognita</i> : in vitro and molecular docking studies
Bibliographic record
Abstract
Root-knot nematode (RKN) (Meloidogyne incognita) is a major plant parasitic nematode that severely damages crops, leading to significant yield losses and substantial economic impact globally. This study aims to investigate an environmentally sustainable biological strategy for mitigating parasitic populations of the root-knot nematode, M. incognita. Specifically, the research focuses on assessing the nematicidal efficacy of Acalypha indica against M. incognita mortality and second-stage juveniles’ (J2) hatching under controlled in vitro conditions. A. indica leaf aqueous extract was applied at varying concentrations (250, 500, 750, and 1000 ppm) to J2s and egg masses of M. incognita. Notably, at 1000 ppm, a significant increase in J2 mortality and hatching inhibition was observed, while 250 ppm concentration showed the least favorable outcome; with mortality rates ranging from 22–82%. Chemical analysis via gas chromatography-mass spectroscopy (GC-MS) identified Benzoic acid, Cyclooctasiloxane, and 3-Isopropoxy-1,1,1,7,7,7-hexamethyl-3,5,5-tris (trimethylsiloxy) tetrasiloxane as predominant compounds. The nematicidal activity of A. indica leaf extract was further validated through in silico molecular docking, revealing that benzoic acid, Cyclooctasiloxane, and 3-Isopropoxy-1,1,1,7,7,7-hexamethyl-3,5,5-tris (trimethylsiloxy) tetrasiloxane bind to the ODR 3 protein of M. incognita with binding energies of −15.72, −8.91, and −7.35 kJ/mol, respectively. These findings hold promise for environmentally benign root-knot nematode management, contributing to improved soil health.
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 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 | 0.001 |
| 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) | 0.000 | 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".