The Synthesis and Characterizes of Nano-Metallic Particles Against Antibiotic Resistant Bacteria, Isolated from Rasoul-e-Akram Hospital’s Patients, Tehran, Iran
Bibliographic record
Abstract
The emergence of antimicrobial resistance of microorganisms to antibiotics, Also, an increase in nosocomial infections, particularly by Methicillin Resistant Staphylococcus aureus (MRSA), Pseudomonas aeruginosa, the need to discover new antibacterial agents with a mechanism of action different from killing bacteria were more than ever before. The Ag nanoparticles (NPs), ZnO (NPs) and Ag/ZnO (NPs) were synthesized through the thermal decomposition of the precursor of oxalate. Gram-negative antibiotic resistant bacteria and Gram-positive antibiotic resistant bacteria were prepared from the Central laboratory of Rasoul-e-Akram hospital. All of isolates were confirmed by biochemical tests. For determine of antibiotic resistance patterns of isolated, disk diffusion method in accordance with the standard CLSI were used, again. Antibacterial effects of (NPs) against antibiotic resistance bacteria were conducted by MIC and MBC tests. The particles size was less of 50 nm, approximately. Curiously, the silver (NPs) was not exposed the antibacterial properties against all of isolated bacteria. Also, klebsiella pneumonia and MRSA had greatest sensitivity to the ZnO (NPs). Also, Gram-positive antibiotic resistant bacteria showed high sensitivity to Ag/ZnO (NPs), compared to other bacteria. Interestingly, The MBC for ZnO (NPs) against Pseudomonas aeruginosa >= 8192 was observed. The Ag (NPs) had not the ability to inhibit the nosocomial infection. klebsiella pneumonia and MRSA had greatest sensitivity to the ZnO (NPs). Also, Gram-positive antibiotic-resistant bacteria showed high sensitivity to Ag/ZnO (NPs), compared to other bacteria. The Ag/ZnO (NPs) was ability to kill antibiotics resistant bacteria. The antibacterial agents can open a new leaf in our life in the treatment of nosocomial infections.
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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".