Synergistic Effect between Phyto-Syntesized Silver Nanoparticles and Ciprofloxacin Antibiotic on some Pathogenic Bacterial Strains
Bibliographic record
Abstract
Background: Plant extract as a potential phyto-reducer is used as a simple, non-toxic and ecofriendly green synthesis method of silver nanoparticles (AgNPs). In this study biosynthesis of AgNPs using leaves extract broth of Amaranthus retroflexus as both reducing and stabilizing agent was analyzed. Antibacterial activity toward resistant human pathogenic bacteria Escherichia coli and Pseudomonas aeruginosa and also against plant pathogenic bacteria Pseudomonas syringae, Xanthomonas oryzae, was studied. The biosynthesized AgNPs were also evaluated for their increased antimicrobial activities with Ciprofloxacin antibiotic against some of the tested bacteria. Methods: The formation of green synthesized nanoparticles from aqueous solution of silver nitrate was first screened by measuring the surface plasmon resonance peak at 300-800 nm using UV–vis spectroscopy. The morphology, size and Crystalline structure of the synthesized AgNPs was determined using Transmission Electron Microscope (TEM), DLS and X-ray diffraction analysis. For antibacterial studies two-fold serial dilutions were made in NB medium (Qlab Canada) and the growth of the cultures was monitored by measuring the optical density value at 630 nm (OD630) with microplate reader (Biotech ELX 800) after 24 hours of incubation to obtain the MIC of the AgNPs. Results: The results indicated that the phyto-synthesized AgNPs were spherical with an average size of 48 nm. XRD peaks indicate the presence of a face centered cubic (fcc) structure of crystalline AgNPs. The AgNPs showed highly potent antibacterial activity toward the tested bacteria. Also the combined antibacterial activity of Ciprofloxacin with AgNPs reduced the MIC of antibiotic from 0.125 µg/ml to 0.0625 µg/ml toward P. aeruginosa and Ciprofloxacin MIC against P. syringae decreased from 0.25 to 0.0625 µg/ml in combination with 6.25, 12.5, and 25 µg/ml of AgNPs. Conclusion: Results from the current study suggested that the silver nanoparticles successfully can be synthesized using Amaranth leaf extract. The phyto-synthesized nanoparticles could have potential antibacterial applications and show synergistic effect in combination with Ciprofloxacin antibiotic.
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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".