resaCPE: A Rapid, Low-Cost Colourimetric Assay for the Detection of Carbapenemase-Producing Enterobacterales
Bibliographic record
Abstract
Abstract The use of carbapenem antibiotics is threatened by the global spread of carbapenemase-producing Enterobacterales (CPE), bacterial pathogens which hydrolyze these last-resort antimicrobials. Rapid detection of CPE is vital to ensure timely administration of antimicrobial therapy to infected patients, as well as the implementation of infection control measures to prevent outbreaks in healthcare settings. In this study, we report the development of resaCPE, a rapid, low-cost CPE detection method that couples the inactivation of an imipenem disk with a cell viability assay employing a carbapenem-hyper-susceptible Escherichia coli strain. Results are interpreted by a simple colourimetric readout in which CPE-positive samples turn pink, while CPE-negative samples remain purple. The assay was validated with a panel of 91 CPE and non-CPE isolates, and its performance compared to two widely used CPE detection strategies, the modified carbapenem inactivation method (mCIM) and the CARBA-NP test. The resaCPE test demonstrated the same level of performance as the mCIM (100 % sensitivity and specificity), while providing results in significantly less time (3.5 h vs. 20-24 h, respectively). We also observed that the resaCPE test outperformed the CARBA-NP test (95.3 % sensitivity, 100 % specificity), specifically when applied to the detection of CPE isolates producing carbapenemases with weaker hydrolytic activity. Due to the rapid turnaround time, minimal setup requirements, and low cost (∼$1 USD/sample), the resaCPE test is a potentially attractive option for primary CPE screening, particularly in lower resource settings.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".