Comparison of ORAGENE® and Mouthwashed-Based Saliva Collection Methods for Genomic DNA Isolation
Bibliographic record
Abstract
In recent years, saliva has been used as a non-invasive method of obtaining genomic DNA. Two common collection methods include mouthwash and commercially produced saliva kits. Here, a novel comparison between these two collection methods, using Scope® mouthwash and the Oragene®-Discover kit (OGR-250) from DNA Genotek Inc., was conducted to analyze differences in the quantity and quality of the DNA isolated, and cost effectiveness. The Oragene® kit yielded greater quantity of DNA, while Scope® mouthwash was more cost effective. The difference in yield was attributed to the larger volume of saliva obtained from the Oragene® kit. Isolation from both collection methods resulted in similar DNA quality. Depuis quelques années, la salive est utilisée comme une méthode non-invasive pour obtenir de l’ADN génomique. Deux méthodes de collection communes sont par rince-bouche et par des trousses commerciales de collection de salive. Ici, une comparaison entre ces deux méthodes, utilisant la rince-bouche Scope et la trousse Oragene-Discover (OGR-250) de DNA Genotek Ink, a été conduite afin d’analyser les différences dans la quantité et la qualité d’ADN isolée ainsi que dans l’efficacité du coût. La trousse Oragene a recueilli plus d’ADN, alors que Scope était moins cher. La différence en quantité est attribuée au plus grand volume de salive qui est obtenu grâce à l’Oragene. L’isolation par les deux méthodes résultait en une qualité similaire d’ADN.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".