Blue Laser-Activated Silver Nanoparticles from Grape Seed Extract for Photodynamic Antimicrobial Therapy Against <i>Escherichia coli</i> and <i>Staphylococcus aureus</i>
Bibliographic record
Abstract
Introduction: Living organisms, particularly humans, frequently encounter microorganisms like bacteria, fungi, and viruses in their surroundings. Silver nanoparticles are widely used in biomedical devices due to their antibacterial, antifungal, and antiviral properties. The study evaluates the efficacy of blue laser and silver nanoparticles from grape seed extract (AgNPs-GSE) in reducing gram-negative Escherichia coli and gram-positive Staphylococcus aureus bacteria causing infections. Methods: The sample consisted of four groups: a control without laser irradiation (T0), E. coli samples (A1 and A2) irradiated with a 405 nm diode laser at different times and concentrations of silver nanoparticles, and S. aureus samples (A3 and A4) irradiated with a 405 nm diode laser at different times and concentrations. Bacteria in groups A2 and A4 were treated with a photosensitizer (PS) made from grape seed extracts, incubated for 10 minutes, and then irradiated for 90, 120, 150, and 180 seconds. The samples were cultured on Tryptic Soy Agar (TSA) media, incubated at 37 °C, counted by using a Quebec colony counter, and analyzed using ANOVA and Tukey tests with a significance level of P<0.05. Results: The study found that 10 µl of AgNPs-GSE, when combined with exposure to a blue laser at 405 nm and a dose of 3.44 J/cm², can effectively photoinactivate E. coli and S. aureus bacteria. The addition of AgNPs-GSE to E. coli bacteria led to a significant reduction in their viability, with a reduction of 73.93%, 80.96%, and 83.80%, respectively. Similarly, when S. aureus bacteria were irradiated for 180 seconds by adding 1 mM, 1.5 mM, and 2 mM AgNPs-GSE, bacterial viability was reduced by 70.87%, 78.04%, and 87.01%, respectively. Conclusion: The findings from the present study indicate that at an energy density of 3.44 J/cm², it was possible to inactivate E. coli by 83.80% and S. aureus by 87.01%.
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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".