Rapid Detection of Carbapenemase-Producing Enterobacterales Using a Luminescent Whole-Cell Biosensor
Bibliographic record
Abstract
Abstract Carbapenemase-producing Enterobacterales (CPE) pose an urgent global health threat due to their ability to inactivate carbapenems, a group of last-resort antibiotics. Infections caused by these pathogens are associated with poor patient outcomes, high mortality rates, and added burden to infection prevention and control programs, making early detection vital to ensure optimal antimicrobial therapy and appropriate implementation of infection control practices. In this study, we report the application of a luminescent whole-cell biosensor for the rapid detection of CPE. This biosensor provides positive test results within 2.5 h, inclusive of setup time, and has been validated with a panel of laboratory and clinical isolates producing a diverse range of carbapenemases (KPC, NDM, IMP, VIM, and OXA-48-like). The assay identified 81/83 CPE isolates tested with a sensitivity of 97.5%, including strains producing weak OXA-48-like carbapenemases, which are sometimes missed by currently used detection methods. The assay also demonstrated a specificity of 100%, with all non-CPE clinical isolates testing negative under the optimized assay conditions. Due to the rapid time-to-positivity, minimal setup requirements, and high sensitivity, this test could serve as an attractive alternative to CPE detection methods currently employed by clinical microbiology laboratories and could also facilitate CPE screening in other settings (e.g., environmental and agricultural).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".