The efficacy of anti-microbial agents against Actinomyces species isolated from patients with dental caries
Bibliographic record
Abstract
Introduction: Destruction of teeth is a serious problem in oral and general health of the human being, and dental caries is one of the major defects of dental hard tissue.Objectives: The objective of this research was to isolate Actinomyces species from dental caries using polymerase chain reaction technology, and to examine the in vitro antibiotic resistance of isolates using a specific 16S rRNA primer to investigate the effect of several anti-bacterial agents, such as amikacin, gentamicin, ciprofloxacin, amoxicillin, ceftriaxone, cefixime, vancomycin, and erythromycin against Actinomyces species.Material and methods: Eighty samples from dental carious lesions were collected and tested for the current study between March and June, 2021.GP-ID cards with 64 biochemical tests were utilized in the automated Vitek-2 Compact system to confirm bacterium isolates.The basis of polymerase chain reaction technology was DNA polymerase's capacity to manufacture a new DNA strand.Kirby-Bauer disc diffusion method was applied to examine the in vitro antibiotic resistance of isolates taken from dental caries using a specific 16S rRNA.Results: Actinomyces meyeri and A. viscosus were detected with 16S rRNA genes using PCR technique.All 18 (100%) A. meyeri identified by Vitek-2 system provided positive results for specific 16S rRNA gene at 519 bp, and all 19 (100%) A. viscosus identified by Vitek-2 system provided positive results for specific 16S rRNA gene at 787 bp.The use of selective antibiotic tests, such as amikacin, gentamicin, ciprofloxacin, amoxicillin, ceftriaxone, cefixime, vancomycin, and erythromycin demonstrated the impact of antibiotics against A. meyeri and A. viscosus isolates. Conclusions:The detection of Actinomyces spp. with the use of PCR analysis appears to be extreme accurate.Amoxicillin should be considered as the first choice antibiotic for Actinomyces spp.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".