Forensic DNA extraction methods for human hard tissue: A systematic literature review and meta-analysis of technologies and sample type
Bibliographic record
Abstract
DNA identification of human remains has a valuable role in the field of forensic science and wider. Although DNA is vital in identification of unknown human remains, post-mortem environmental factors can lead to poor molecular preservation. In this respect, focus has been placed on DNA extraction methodologies for hard tissue samples, as these are the longest surviving. Despite decades of research being conducted on DNA extraction methods for bone and teeth, little consensus has been reached as to the best performing. Therefore, the aim of this study was to conduct a thorough systematic literature review to identify potential DNA extraction technique(s) which perform optimally for forensic DNA profiling from hard tissue samples. PRISMA guidelines were used, by which a search strategy was developed. This included identifying databases and discipline specific journals, keywords, and exclusion and inclusion criteria. In total, 175 articles were identified that detailed over 50 different DNA extraction methodologies. Results of the meta-analysis conducted on 41 articles - meeting further inclusion criteria - showed that statistically significant higher DNA profiling success was associated with solid-phase magnetic bead/resin methods. In addition, incorporating a demineralisation pre-step resulted in significantly higher profiling successes. For hard tissue type, bone outperformed teeth, and even though dense cortical femur samples were more frequently used across the studies, profiling success was comparable, and in some cases, higher in cancellous bone samples. Notably, incomplete data sharing resulted in many studies being excluded, thus an emphasis for minimum reporting standards is made. In conclusion, this study identifies strategies that may improve success rates of forensic DNA profiling from hard tissue samples. Finally, continued improvements to current methods can ensure faster times to resolution and restoring the identity of those who died in obscurity.
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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 | 0.030 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".