Encapsulation of degraded DNA in alginate hydrogels: Rheological characterization and applicability to forensic science
Bibliographic record
Abstract
• Encapsulation of DNA into forensic biomaterials to enhance mimetic properties. • Conformation and size of DNA oligos influenced rheological properties. • DNA disrupts ionic crosslinking between alginate and calcium cations. • Time since deposition study performed to generate realistic degraded DNA. • Trends observed between degradation of genomic DNA and rheological parameters. Forensic biomaterials are on the rise, with efforts focused on developing realistic tissue and blood mimetics. The incorporation of small and degraded DNA into these materials enhances their realism and functionality, which has implications for research and training across forensic science. It is therefore important to understand the physicochemical and conformational changes that DNA undergoes during ex vivo degradation. Large fragments of highly concentrated genomic and phage DNA in solution have been characterized using rheology; however, this amount and size of DNA are atypical in DNA extracted from forensic evidence. In this work, we investigated how the addition of synthetic DNA oligos and genomic DNA extracted from bloodstains deposited for up to 19 months influenced the rheological properties of polymer systems intended for forensic biomaterial synthesis. We used FTIR spectroscopy to probe interactions between DNA and the encapsulating matrix and automated gel electrophoresis to record DNA quality/quantity metrics, both of which supported our rheological findings. Encapsulating DNA within an alginate-based, ionically crosslinked hydrogel produced the greatest differentiation in rheological profiles among DNA with varying physical properties. The distinct conformations and sizes of encapsulated DNA oligos exhibited significantly different responses during strain amplitude sweeps (p < 0.05). We also observed moderate correlations between the rheological responses of DNA extracts and the time since deposition of corresponding bloodstains ( r = −0.57 to r = 0.62). This indicates that dilute, polydisperse and degraded genomic DNA extracts can modulate the rheological properties of the encapsulating hydrogel, highlighting the need to consider the type of DNA included in forensic biomaterials. Our results demonstrate the potential for rheology to serve as a complementary technique when analyzing encapsulated dilute DNA oligos and degraded DNA.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".