A novel GoldNano Carb test for rapid phenotypic detection of carbapenemases, particularly OXA type, in Enterobacteriaceae, Pseudomonas aeruginosa and Acinetobacter spp.
Bibliographic record
Abstract
Objectives: To develop a simple gold nanoparticle (AuNP)-based colorimetric test, GoldNano Carb (GoldC), for detecting carbapenemase production in Gram-negative bacteria, compared with updated Carba NP (CNP) and CarbAcineto NP (CAcNP) tests by using PCR methods as gold standard. Methods: Ninety-nine carbapenemase-producing Enterobacteriaceae (CPE), Pseudomonas spp. and Acinetobacter spp. isolates and 89 non-CPE isolates were tested by the GoldC and CNP. Additionally, the CAcNP was performed in the Acinetobacter spp. isolates. The final imipenem (imipenem/cilastatin form) concentration was 5 mg/mL for all three tests. For the GoldC, the imipenem powder was added directly to bacterial cell suspension in distilled water prior to detection of acid product by the citrate-capped AuNP solution. An AuNP change from red to purple, blue or green indicates carbapenemase activity. Results: The GoldC detected all carbapenemase producers except one OXA-23-like producer (99.0% sensitivity), whereas 11 carbapenemase producers (10 Acinetobacter and 1 P. aeruginosa) were CNP negative (88.9% sensitivity). However, the GoldC and CNP provided 100% and 98.6% sensitivity, respectively, for the CPE and Pseudomonas spp. Both tests gave one false positive from CTX-M-1-like-producing Enterobacter spp. (98.9% specificity). The GoldC and CAcNP detected 96.7% and 93.3% of the Acinetobacter spp. isolates, respectively. Interestingly, times to positivity by the GoldC were markedly shorter than those by the CNP (76.8% versus 36.2% positive at 5 min) and CAcNP (43.3% at 5 min versus 20% within 30 min). Conclusions: The GoldC is fast, easy, highly sensitive and inexpensive (∼$0.25 per test), suggesting that it may be suitable for routine carbapenemase detection in low-resource settings for infection control or epidemiological purposes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".