A Review on the Physicochemical and Biological Aspects of the Chitosan Antifungal Activity in Agricultural Applications
Bibliographic record
Abstract
The antifungal activity of the chitosan biopolymer has been extensively studied for several decades. However, the mechanisms of action associated with this process have not been fully clarified yet. To a large extent, this situation is due to the lack of systematization with which, in general terms, the subject has been approached. However, it seems to have begun to change in recent years with the appearance of several papers reviewing the accumulated knowledge on the beneficial effects shown by chitosan in agricultural applications and putting forward it in a more systematic mode. In this work, the most relevant mechanisms of action proposed for chitosan regarding its antifungal activity will be briefly presented, i.e., disruption and changes in the fungal plasma membrane, alteration of gene expression, inhibition of RNA and protein synthesis, Ca2+ channel blocker, to then address the main factors that influence this antifungal activity, observed mainly in studies focused on phytopathogenic species, which have been grouped into three main blocks: those related exclusively to the chitosan molecules, those associated to the fungal itself and those having to do with the environment where the processes take place. Additionally, a brief section addressing some possibilities on which future studies on this topic should focus is also included.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".