Alginate/Xanthan gum hydrogels as forensic blood substitutes for bloodstain formation and analysis.
Bibliographic record
Abstract
Understanding the behaviour of human blood outside of the body has important implications in forensic research, especially related to bloodstain pattern analysis (BPA). The design of forensic blood substitutes (FBSs) can provide many advantages, including the incorporation of multiple physiological components for use as safe and reliable materials for forensic applications. In this work, we present the design of synthetic alginate and xanthan gum-based hydrogels that contain electrosprayed microparticles (MPs) with and without crosslinked DNA. In addition to the MPs, the alginate/xanthan gum FBS materials include fillers to alter the physical appearance and fluid properties of the material. The optimized FBS consisted of alginate (1% w/v) and xanthan gum (5.0 x10-3 w/v %), 2 mM CaCl2, ferric citrate (0.5% w/v), magnesium silicate (0.25% w/v), Allura red (2% w/v), 0.025% v/v Tween 20 and 9.5% v/v MPs. The FBS was tested in passive dripping experiments relevant to BPA scenarios at various impact angles. The spreading ratio (Ds/D0) was determined for 90 degree stains made on a paper surface and compared to bovine blood where the FBS was shown to simulate accurate and predictable spreading behaviour. In addition, we simulated other common BPA scenarios (e.g., impact patterns) and evidence processing potential. The FBS could be swabbed, and the DNA could be extracted, amplified, and genotyped analogous to human blood evidence. A stability test was also conducted which revealed a shelf-life of up to 4 weeks where the material remains relevant to human blood at physiological temperature.
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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 | 0.000 |
| 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 | 0.000 |
| 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".