Biosynthesis of Silver Nanoparticles Using Stenocereus queretaroensis Fruit Peel Extract: Study of Antimicrobial Activity
Bibliographic record
Abstract
The synthesis and application of nanomaterials as antioxidants and cytotoxic agents has increased in recent years. Biological methods go beyond the chemical and physical synthesis that is expensive and not friendly to the environment. Foodborne pathogens and microorganisms causing candidiasis are responsible of 5–10% hospitalized patients. The nutritional properties of the fruit called pitaya, from the Stenocereus queretaroensis species, have been little explored. Therefore, in this study the phytochemical composition of S. queretaroensis peel was evaluated and silver nanoparticles (AgNPs) were synthesized biologically in an environmentally friendly way by S. queretaroensis peel aqueous extract that contains phytochemicals capable of reducing silver nitrate. The antimicrobial activity of the AgNPs was tested by determining the minimum inhibitory concentration (MIC), minimal bactericidal concentration (MBC) and time-kill kinetics. AgNPs were characterized visually, by UV-visible spectroscopy and TEM. FTIR spectroscopy identified metabolites responsible for the AgNPs formation. AgNPs showed potent antimicrobial activity against gram-negative and gram-positive bacteria, against fungi, and a methicillin-resistant strain of S. aureus. MIC and MBC values were as low as 0.078 and 0.156 μg/mL using AgNPs biosynthesized by S. queretaroensis fruit peel and the time kill assay started a log reduction in CFU/mL at 1 × MIC and 2 × MIC. S. queretaroensis-mediated AgNPs could be the basis for the formulation of biofilms for packaging products or as disinfectants for use on different surfaces.
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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) | 0.000 | 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".