Validating digital polymerase chain reaction for 16S rRNA gene amplification from low biomass environmental samples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Digital polymerase chain reaction (dPCR) is a DNA quantification technology that offers absolute quantification of DNA templates. In this study, we optimized and validated a chip-based dPCR EvaGreen assay with commonly used 16S rRNA gene primer pairs and compared its performance to quantitative real-time PCR (qPCR). We compared measurements of low amounts of template DNA using a newly designed synthetic DNA standard to assess precision, accuracy, and sensitivity. Optimization approaches were tested to minimize partitions with intermediate fluorescence levels between true positive and true negative partitions (so-called “rain”) for dPCR. Both dPCR and qPCR demonstrated similar quantification performance, with variability in accuracy increasing for samples containing fewer than 30 copies μl−1 template concentrations. Both tested 16S rRNA gene primer sets amplified non-target template contaminants within both qPCR and dPCR mixtures, which could not be eliminated by ultraviolet light or DNAse treatment and negatively affected the apparent sensitivity of both PCR assays. Digital PCR was less susceptible to common PCR inhibitors, such as ethanol and humic acids, but was more susceptible to tannic acid inhibition than qPCR. These findings demonstrate the suitability of dPCR for 16S rRNA gene quantification of low biomass environmental samples.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it