Development and validation of a ddPCR assay to detect and quantify tobacco DNA in smoke and smokeless tobacco and tobacco-free products
Bibliographic record
Abstract
The last decade, smoke and smokeless products claiming to be tobacco-free, including herbal cigarettes and herbal shisha, became available on the European market and gained popularity. This study proposes a new digital droplet PCR (ddPCR) method, designed based on a previously developed real-time PCR (qPCR) method being currently used by the U.S. Food and Drug Administration (FDA) to specifically detect the presence of tobacco DNA in targeting a sequence from the Nicotiana tabacum nia-1 gene. To ensure a harmonized and reliable control by enforcement laboratories, both of these qPCR and ddPCR methods were then evaluated and validated for their compliance to an international standard. First, the performance of these PCR-based methods was successfully assessed as specific and sensitive, and in line with minimum performance requirements from international standard. Secondly, the transferability to external laboratory was confirmed for these PCR-based methods. Finally, the applicability of these PCR-based methods was demonstrated using 7 ground tobacco reference materials from the Tobacco Research Center (TRC) Toronto University as well as 6 commercial smokeless and tobacco-free smoke and smokeless products. Based on this study, the previously developed qPCR method was confirmed as complying with international standard, ensuring a efficient and harmonize use by enforcement laboratories for tobacco control on the European market. Moreover, this study proposed to enforcement laboratories the possibility to use a ddPCR method, enabling the simultaneous detection and absolute quantification of tobacco DNA as well as a limited impact of PCR inhibitors.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".