Quantification of DNA in Neonatal Dried Blood Spots by Adenine Tandem Mass Spectrometry
Bibliographic record
Abstract
Newborn screening programs have expanded to include molecular-based assays as first-tier tests and the success of these assays depends on the quality and yield of DNA extracted from neonatal dried blood spots (DBS). To meet high throughput and rapid turnaround time requirements, newborn screening laboratories adopted rapid DNA extraction methods that produce crude extracts. Quantification of DNA in neonatal DBS is not routinely performed due to technical challenges; however, this may enhance the performance of assays that are sensitive to amounts of input DNA. In this study, we developed a novel high throughput method to quantify total DNA in DBS. It is based on specific acid-catalyzed depurination of DNA followed by mass spectrometric quantification of adenine. The amount of adenine was used to calculate DNA quantity per 3.2 mm DBS. Reference intervals were established using archived, neonatal DBS (n = 501) and a median of 130.6 ng of DNA per DBS was obtained, which is in agreement with literature values. The intra- and interday variations were <15%. The limits of detection and quantification were 12.5 and 37.8 nmol/L adenine, respectively. We demonstrated that DNA from neonatal DBS can be successfully quantified in high throughput settings using instruments currently deployed in NBS laboratories.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".