Molecular Analysis of the Isolates of Acinetobacter baumannii isolated from Tehran Hospitals Using ERIC-PCR Method
Bibliographic record
Abstract
infections, including pneumonia, bacteremia, surgical wound infections, urinary tract infections, and meningitis.In this study, enterobacterial repetitive intergenic consensus sequence-based PCR (ERIC-PCR) technique was used for analysis and molecular typing of Acinetobacter strains, which has high discrimination power compared to phenotypic markers. Methods:In the present study, a total of 40 A. baumanniies strains were isolated from patients hospitalized in Tehran hospitals.After identification and confirmation of the isolates by serotyping and biochemical tests, a single colony of each isolate was cultured on liquid LB medium, and after DNA extraction, PCR was performed.After electrophoresis of PCR product, gel images were stored electronically for analysis and comparison of the isolates. Results:In this study, 40 strains of A. baumannii were analyzed by ERIC-PCR method, of which 29 strains were typed into 10 groups and 11 other strains had no PCR bands or had a band that could not be assigned to any of the above groups.Conclusion: In this study, it was found that A. baumanniie strains could be typed using repetitive sequences.This extent of polymorphism shows that ERIC-PCR is a useful method for analysis of genetic variation of A. baumannii strains.High genetic variation of A. baumannii strains may be due to wide geographical distribution of this species in Iran
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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) | 0.001 | 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) | 0.001 | 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".