The toxic effect of <i>Vu</i>-Defr, a defensin from <i>Vigna unguiculata</i> seeds, on <i>Leishmania amazonensis</i> is associated with reactive oxygen species production, mitochondrial dysfunction, and plasma membrane perturbation
Bibliographic record
Abstract
Plant defensins are plant antimicrobial peptides that present diverse biological activities in vitro, including the elimination of Leishmania amazonensis. Plant defensins are considered promising candidates for the development of new drugs. This protozoan genus has great epidemiological importance and the mechanism behind the protozoan death by defensins is unknown, thus, we chose L. amazonensis for this study. The aim of the work was to analyze the possible toxic mechanisms of Vu-Defr against L. amazonensis. For analyses, the antimicrobial assay was repeated as previously described, and after 24 h, an aliquot of the culture was tested for viability, membrane perturbation, mitochondrial membrane potential, reactive oxygen species (ROS) and nitric oxide (NO) inductions. The results of these analyses indicated that after interaction with L. amazonensis, the Vu-Defr causes elimination of promastigotes from culture, membrane perturbation, mitochondrial membrane collapse, and ROS induction. Our analysis demonstrated that NO is not produced after Vu-Defr and L. amazonensis interaction. In conclusion, our work strives to help to fill the gap relating to effects caused by plant defensins on protozoan and thus better understand the mechanism of action of this peptide against L. amazonensis.
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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) | 0.001 | 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".