Concurrent Titration and Determination of Antibiotic Resistance in <i>Ureaplasma</i> Species with Identification of Novel Point Mutations in Genes Associated with Resistance
Bibliographic record
Abstract
Antibiotic resistance determination of Ureaplasma spp. (Ureaplasma parvum and Ureaplasma urealyticum) usually requires predetermination of bacterial titer, followed by antibiotic interrogation using a set bacterial input. This 96-well method allows simultaneous quantification of bacteria in the presence and absence of antibiotics. A method for determining precise MICs and a method for screening against multiple antibiotics using breakpoint thresholds are detailed. Of the 61 Ureaplasma-positive clinical isolates screened, one (1.6%) was resistant to erythromycin (MIC, >64 mg/liter) and clarithromycin (MIC, 4 mg/liter), one to ciprofloxacin (1.6%), and one to tetracycline/doxycycline (1.6%). Five isolates were also consistently found to have an elevated MIC of 8 mg/liter for erythromycin, but this may not represent true antibiotic resistance, as no mutations were found in the 23S rRNA operons or ribosome-associated L4 and L22 proteins for these strains. However, two amino acids (R66Q67) were deleted from the L4 protein of the erythromycin-/clarithromycin-resistant strain. The tetM genetic element was detected in the tetracycline-resistant clinical isolate as well as in the positive control Vancouver strain serotype 9. The tetM gene was also found in a fully tetracycline-susceptible Ureaplasma clinical isolate, and no mutations were found in the coding region that would explain its failure to mediate tetracycline resistance. An amino acid substitution (D82N) was found in the ParC subunit of the ciprofloxacin-resistant isolate, adjacent to the S83L mutation reported by other investigators in many ciprofloxacin-resistant Ureaplasma isolates. It is now possible to detect antibiotic resistance in Ureaplasma within 48 h of positive culture without prior knowledge of bacterial load, identifying them for further molecular analysis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".