Metal nanoparticles assisted revival of Streptomycin against MDRS Staphylococcus aureus
Bibliographic record
Abstract
The ability of microorganisms to generate resistance outcompetes with the generation of new and efficient antibiotics. Therefore, it is critically required to develop novel antibiotic agents and treatments to control bacterial infections. Green synthesized metallic and metal oxide nanoparticles are considered as the potential means to target bacteria as an alternative to antibiotics. Nanoconjugates have also attracted attention because of their increased biological activity as compared to free antibiotics. In the present investigation, silver nanoparticles (AgNPs), zinc oxide nanoparticles (ZnO NPs), copper oxide nanoparticles (CuO NPs), and iron oxide nanoparticles (FeO NPs) have been synthesized by using leaf extract of Ricinus communis. Characterization of nanoparticles was done by using UV-Vis Spectroscopy, Fourier Transform Infrared Spectroscopy, Scanning Electron Microscopy, Energy Dispersive X-Ray Analyzer, X-ray Diffraction Analysis, and Dynamic Light Scattering Particle Size Analyzer. Interestingly, Streptomycin when combined with AgNPs, ZnO NPs, CuO NPs, and FeO NPs showed enhanced antibacterial activity against clinical isolates of S. aureus which suggested synergism between the nanoparticles and antibiotics. The highest enhanced antibacterial potential of Streptomycin was observed in conjugation with ZnO NPs (11 ± 0.5 mm) against S. aureus. Minimum inhibitory concentration of conjugates of AgNPs, ZnO NPs, CuO NPs, and FeO NPs with streptomycin against S. aureus was found to be 3.12, 2.5,10, and 12.5 μg/mL respectively. The considerable point of the present investigation is that S. aureus, which was resistant to streptomycin becomes highly susceptible to the same antibiotic when combined with nanoparticles. This particular observation opens up windows to mitigate the current crisis due to antibiotic resistance to combat antimicrobial infections efficiently.
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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".