DNA quantitation and degradation assessment: a quantitative PCR protocol designed for small forensic genetics laboratories
Bibliographic record
Abstract
For over 10 years, quantitative PCR (qPCR) for DNA quantitation has been reported in forensics. However, assays have not been described for small qPCR platforms. Thus, technological advancement is not always implemented in small forensic genetics laboratories. A duplex qPCR assay is reported, using a StepOne instrument and targeting a short and a long human DNA region. This study was performed according to international validation guidelines, including sensitivity, repeatability, reproducibility, precision, accuracy, contamination assessment, known and case-type samples, and degradation studies. Characterization of the genetic markers, species specificity, and population studies had already been conducted. Moreover, case-type samples were quantified, amplified using commercial kits and the number of alleles detected was recorded. Sensitivity was shown to be 10 pg/µL. Standard curve replicates demonstrated the assay is accurate, precise, as well as fairly repeatable and reproducible. The NGM Detect kit was shown to yield higher peaks than Identifiler Plus and NGM Select for degraded samples. Moreover, quality sensors were always present and proved useful. The quantification values of the large target showed a correlation with the number of alleles detected in the STR profiles for known and casework samples. The degradation index was shown to be informative, with a value of 10 or higher indicating dropout. It is suggested that after quantitation, samples with low or degraded DNA be amplified using newer amplification kits containing quality sensors to confirm that the low-quality profile was not affected by inhibition.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.013 |
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".