Development of chitosan‐based nanoparticles encapsulating <scp> <i>Bacillus velezensis</i> CMRP4490 </scp> metabolites for enhanced in vitro control of <scp> <i>Sclerotinia sclerotiorum</i> </scp>
Bibliographic record
Abstract
Abstract Modern agriculture seeks to control pests and diseases in the field while maintaining production, reducing the use of dangerous chemical molecules, and resorting to more sustainable solutions. One of the ways to achieve these objectives is biological control. Furthermore, combined with biological control, the use of nanoencapsulation techniques of biological control agents with materials that are bioavailable and biodegradable in the environment has proven to be an alternative to reducing the use of non‐renewable materials. Therefore, the present work aimed to develop a nanoparticle system for biological control of Sclerotinia sclerotiorum . The nanoparticle system was produced using the ionotropic gelation technique using chitosan (CHI) as a polymer. The cell‐free supernatant (CFS) of the microorganism Bacillus velezensis CMRP4490 was used to produce nanoparticles, as preliminary studies show that its metabolites act in biological control. The nanoparticles produced were prepared in different concentrations of CHI and CFS and their antimicrobial activity was evaluated against the fungus S . sclerotiorum . The encapsulated samples have a concentration of 20%–80% of CFS and 0.25% and 0.8% of CHI and showed a 100% inhibitory effect against S . Sclerotiorum , and the results obtained indicate a synergistic effect between CHI and CFS.
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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 | 0.000 | 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 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".