Effect of physicochemical parameters on the stability and activity of garlic alliinase and its use for in-situ allicin synthesis
Bibliographic record
Abstract
Garlic is a well-known example of natural self-defence system consisting of an inactive substrate (alliin) and enzyme (alliinase) which, when combined, produce highly antimicrobial allicin. Increase of alliinase stability and its activity are of paramount importance in various applications relying on its use for in-situ synthesis of allicin or its analogues, e.g., pulmonary drug delivery, treatment of superficial injuries, or urease inhibitors in fertilizers. Here, we discuss the effect of temperature, pH, buffers, salts, and additives, i.e. antioxidants, chelating agents, reducing agents and cosolvents, on the stability and the activity of alliinase extracted from garlic. The effects of the storage temperature and relative humidity on the stability of lyophilized alliinase was demonstrated. A combination of the short half-life, high reactivity and non-specificity to particular proteins are reasons most bacteria cannot deal with allicin's mode of action and develop effective defence mechanism, which could be the key to sustainable drug design addressing serious problems with escalating emergence of multidrug-resistant (MDR) bacterial strains.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".