Multiple potential strategies for the application of nisin and derivatives
Bibliographic record
Abstract
Nisin is a naturally occurring bioactive small peptide produced by Lactococcus lactis subsp. lactis and belongs to the Type A (I) lantibiotics. Due to its potent antimicrobial activity, it has been broadly employed to preserve various food materials as well as to combat a variety of microbial pathogens. The present review discusses the antimicrobial properties of nisin and different types of their derivatives employed to treat microbial pathogens with a detailed underlying mechanism of action. Several alternative strategies such as combination, conjugation, and nanoformulations have been discussed in order to address several issues such as rapid degradation, instability, and reduced activity due to the various environmental factors that arise in the applications of nisin. Furthermore, the evolutionary relationship of many nisin genes from different nisin-producing bacterial species has been investigated. A detailed description of the natural and bioengineered nisin variants, as well as the underlying action mechanisms, has also been provided. The chemistry used to apply nisin in conjugation with natural or synthetic compounds as a synergetic mode of antimicrobial action has also been thoroughly discussed. The current review will be useful in learning about recent and past research that has been performed on nisin and its derivatives as antimicrobial agents.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".