A Simple and Efficient Method for Extracting DNA From Old and Burned Bone
Bibliographic record
Abstract
It has been a challenge to extract DNA from bones previously soaked in water, burned, or buried for a long time, due to the reduced quality and quantity of DNA in the bone samples. The dramatic degradation of the DNA and the presence of PCR inhibitors in the collagen significantly complicate the process of DNA identification in dated and charred bones. In this article, we present a novel strategy to obtain DNA from bones based on the use of cetyltrimethylammonium bromide (CTAB) lysis buffer and isoamyl alcohol-chloroform extraction with subsequent DNA purification using the DNA IQ System, or alternatively the QIAquick system. When applied to bones soaked, burned or buried for up to nine years, this method increases the purity and yield of DNA with respect to the traditional phenol-chloroform method and significantly improves multiplex STR genotyping using fluorescence-based methods. The results of this research will assist forensic scientists in the identification of DNA from victims whose bodies underwent significant trauma or burning, precluding the utilization of traditional forensic DNA identification techniques.
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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 | 0.001 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".