Molecular Detection of Bacteria Producing Newer Types of β-Lactamases
Bibliographic record
Abstract
In Gram-negative pathogens, β-lactamase production remains the most important contributing factor to β- lactam resistance. β-lactamases are bacterial enzymes that inactivate β-lactam antibiotics by hydrolysis, which results in ineffective compounds. The three major groups usually referred to as the newer β-lactamases are plasmid-mediated AmpC enzymes, extended-spectrum β-lactamases (ESBLs) and carbapenem-hydrolyzing enzymes (including metallo-β- lactamases [MBLs]). Molecular methods that include simple and multiplex PCR, real-time PCR, DNA sequencing and various hybridization-based techniques are used widely in research and reference laboratories for the detection of organisms producing newer β-lactamases. The routine screening in clinical diagnostic laboratories of organisms producing TEM, SHV and OXA types of ESBLs using genotypic methods remains problematic, while the detection of CTX-Ms, plasmid-mediated AmpCs and MBLs shows clinical usefulness. Molecular methods have advantages over phenotypic tests by accurately detecting resistant genes in a rapid fashion and by defining the precise genetic basis of the resistance mechanism providing important information valuable to the early introduction of infection control practices. Molecular assays have the potential to complement conventional phenotypic susceptibility techniques and impact directly on patient care. Keywords: Molecular assays, detection, extended-spectrum β-lactamases, plasmid-mediated AmpC β-lactamases, metallo-β-lactamases
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".