Effects of Zinc Compounds on the Enzymatic Activities of Lysozyme and Peroxidase and Their Antifungal Activities
Bibliographic record
Abstract
This study aimed to investigate the effects of zinc compounds on the enzymatic activities of lysozyme, peroxidase, and the glucose oxidase-mediated peroxidase (GO-PO) system and their antifungal activities. Four different zinc compounds (zinc chloride, gluconate, lactate, and sulfate) were incubated with hen egg-white lysozyme (HEWL), bovine lactoperoxidase (bLPO), the GO-PO system, and human unstimulated whole saliva in solution and on a hydroxyapatite surface. Enzymatic activities of lysozyme, peroxidase, and the GO-PO system were measured through the hydrolysis of Micrococcus lysodeikticus, oxidation of fluorogenic 2',7'-dichlorofluorescin, and glucose assay, respectively. Interactions between zinc and enzymes were analyzed by surface plasmon resonance (SPR). The minimum inhibitory concentration (MIC) and candidacidal activities of zinc compounds were examined against three Candida albicans strains. Zinc gluconate and sulfate significantly increased the enzymatic activities of salivary lysozyme in the solution assay and of HEWL and salivary lysozyme on the hydroxyapatite surface. However, all examined zinc compounds significantly decreased the enzymatic activities of bLPO and salivary peroxidase in solution and on the surface. SPR analyses revealed binding of zinc to lysozyme and peroxidase, with affinity differing according to the zinc compounds. The MIC of zinc compounds against C. albicans was 1.0-2.4 mM. Candidacidal activities were 17.7-38.8% and 23.7-47.0% at 1.0 and 10 mM concentrations, respectively. In conclusion, zinc compounds enhanced lysozyme activity but inhibited peroxidase activity. Zinc compounds exhibited concentration-dependent candidacidal activity against C. albicans. Zinc compounds are potential therapeutic agents for oral health, especially for geriatric patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".