Molecular Identification of Pathogenic Enterococci and Evaluation of Multi-drug Resistance in Enterococcus Species Isolated From Clinical samples of Some Hospitals in Tehran, Iran
Bibliographic record
Abstract
Enterococcus faecalis Enterococcus faeciumMulti-drug Resistance Polymerase Chain Reaction Background and Objectives: Multidrug-resistant (MDR) enterococci cause many problems for physicians and infection control specialists in the recent years.Hence, by identification of antibiotic resistance patterns of enterococci in different geographical regions, an appropriate strategy can be developed to prevent bacterial antibiotic resistance and provide effective treatments.The current study aimed at identifying enterococci via molecular methods and evaluating multi-drug resistance patterns in Enterococcus species isolated from nosocomial samples of some hospitals in Tehran, Iran. Material and Methods:The current study was conducted on 300 nosocomial samples from different hospitals in Tehran, Iran.The identified Enterococcus species of E. faecalis and E. faecium were isolated via biochemical testing and confirmed using polymerase chain reaction (PCR).The antibiotic resistance pattern was determined using the disc diffusion method according to the Clinical and Laboratory Standards Institute (CLSI) guidelines.Results: The highest antibiotic resistance was observed against quinupristindalfopristin, tetracycline, and erythromycin.Minimum inhibitory concentration (MIC) of vancomycin against the isolated antibiotic resistant Enterococcus spp. was ≤ 256 µg/mL.According to the results of the current study, 69.6% of E. faecalis and 80% of E. faecium isolates showed multi-drug resistance.Conclusion: Increase of antibiotic resistant bacteria, especially MDR species, is a severe health threatening problem worldwide.The increase of MDR bacteria limited the therapeutic solutions to the patients with enterococcal infection, increased treatment costs, and led to transmission of resistant genes among bacteria.It is highly important to find antibiotic resistant patterns to compile guidelines for infectious diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".