Technical note: Removal of metal ion inhibition encountered during DNA extraction and amplification of copper‐preserved archaeological bone using size exclusion chromatography
Bibliographic record
Abstract
A novel technique for the removal of metal ions inhibiting DNA extraction and PCR of archaeological bone extracts is presented using size exclusion chromatography. Two case studies, involving copper inhibition, demonstrate the effective removal of metal ion inhibition. Light microscopy, SEM, elemental analysis, and genetic analysis were used to demonstrate the effective removal of metal ions from samples that previously exhibited molecular inhibition. This research identifies that copper can cause inhibition of DNA polymerase during DNA amplification. The use of size exclusion chromatography as an additional purification step before DNA amplification from degraded bone samples successfully removes metal ions and other inhibitors, for the analysis of archaeological bone. The biochemistry of inhibition is explored through chemical and enzymatic extraction methodology on archaeological material. We demonstrate a simple purification technique that provides a high yield of purified DNA (>95%) that can be used to address most types of inhibition commonly associated with the analysis of degraded archaeological and forensic samples. We present a new opportunity for the molecular analysis of archaeological samples preserved in the presence of metal ions, such as copper, which have previously yielded no DNA results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".