Antimicrobial and anti-biofilm activity of titanium dioxide nanoparticles alone and in combination with erythromycin against methicillin-resistant Staphylococcus aureus
Bibliographic record
Abstract
Background: S. aureus is a major pathogen and the predominant bacteria that causes hospital-acquired infections. TiO2NPs have unique characteristics and expanding use for different applications in nanomedicine and have attracted enormous interest in the various rising nanoproducts. Objective: To evaluate the anti-bacterial and anti-biofilm activities of TiO2NPs alone and in combination with the macrolide class of drugs (Erythromycin) against S. aureus isolated from different clinical specimens. Methods: Kirby-Bauer’s disk diffusion technique was applied for antimicrobial susceptibility testing against S. aureus isolates. The Minimum inhibitory concentrations of TiO2NPs and erythromycin were performed with the broth microdilution method, while biofilm formation was investigated by the Calgary technique. Results: At a total of eighty-five strains of S. aureus clinical isolates from patients who are in hospitals in the medical city of Baghdad, 34 (40%) of S. aureus were sensitive to penicillin class (Methicillin) while 51 (60%) were resistant to methicillin, with statistical significance between both groups (P <0.05). In urine samples, the majority of S. aureus isolates were 21 (24.7%), followed by sputum with 14 (16.5%) samples. Patients infected with S. aureus were significant among age groups of 45–54 years old and patients with Ages between 15 and 35 indicate a lower susceptibility to S. aureus infection.mThe results showed that the prevalence of infection with S. aureus was significantly higher among female patients 30 (35.3%) rather than male patients 21 (24.7%), and the male/female ratio was 0.46/1. Vancomycin and imipenem were the most active antibiotics against MSSA and MRSA, with sensitivity of 85 (100%) and 82 (96.5%) for vancomycin and imipenem, respectively, whereas MRSA and MSSA exhibited marked resistance to ciprofloxacin and azithromycin, with sensitivity of 64 (75.3%) and 38 (44.7%), respectively.mTiO2NPs showed excellent biofilm inhibitory activity against MRSA and MSSA isolates, and results showed that TiO2 NPs alone with ½MIC can inhibit biofilm formation by about 40% of MRSA and about 60% of MSSA. Moreover, the combination of TiO2 NPs with erythromycin inhibits biofilm formation by approximately 80–90% for MRSA and MSSA, respectively.
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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".