Comparison of qPCR versus culture for the detection and quantification of Clostridium difficile environmental contamination
Bibliographic record
Abstract
Contaminated surfaces serve as an important reservoir for Clostridium difficile transmission. Current strategies to detect environmental contamination of C. difficile rely heavily on culture, and often only indicate presence versus absence of spores. The goal of this study was to compare quantitative PCR (qPCR) to culture for the detection and quantification of C. difficile from inert surfaces. First, we compared the limit of detection (LOD) of a 16S rRNA gene and toxin B gene qPCR assay for detection of C. difficile in solution. Second, we compared the LODs of 16S rRNA gene qPCR versus culture for detection of C. difficile from surfaces. Solution experiments were performed by direct seeding of spores into neutralizing broth, whereas surface experiments involved seeding of spores onto plastic test surfaces, and recovery using sponge swabs. Both experiments were conducted using spores expressing short (NAP1) and long (NAP4) hair lengths. Combining data from both strains, the overall LOD for C. difficile cells in solution was 1.4 cells for 16S rRNA gene and 23.6 cells for toxin B gene qPCR (p<0.001). The overall LOD for C. difficile cells from surfaces was 17.1 cells for 16S rRNA gene qPCR and 54.5 cells for culture (p = 0.05), and was not statistically different between strains for each method (p = 0.52). Overall, the proportion of C. difficile cells recovered from surfaces was good when detected by 16S rRNA gene qPCR and culture (qPCR: 76%, culture: 67%, p = 0.36), but, 16S rRNA gene qPCR was capable of detecting lower levels of surface contamination. Future work attempting to measure the presence of C. difficile on environmental surfaces should consider using qPCR.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".