Influence of anticoagulant on the spectroscopic analysis of drying blood pools
Bibliographic record
Abstract
Over the course of a few minutes, untreated whole blood undergoes a clotting cascade making forensic research that investigates “fresh” bloodletting events difficult. In bloodstain pattern analysis research, whole blood treated with anticoagulant is often used to prolong the usability of the blood and allow for transport and experimentation to simulate pattern formation using “fresh” clot-free blood. Anticoagulants bind to components of this cascade making them unavailable to participate in coagulation, preventing the formation of clots. In this work, we investigate the spectral implications of anticoagulant addition for time since deposition (TSD) estimation methods, particularly of larger volume blood pools. We characterized the differences in spectral profiles of blood pools with and without a citrate-based anticoagulant using visible absorbance spectroscopy, attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy, and X-ray photoelectron spectroscopy (XPS). Across all methods, notable spectral differences were observed, namely the red-shift in the Soret peak maxima (visible), delayed increase in the 1532 cm-1 peak (ATR-FTIR), and increased accessibility of iron (XPS) in pools treated with ACD-A. Principal component analysis and partial least squares discriminant analysis (PLS-DA) were used to further assess the variation in the visible absorbance and ATR-FTIR spectra over time. The blood pools differed most significantly in the first week following deposition due to the addition of the water in the anticoagulant, slowed desiccation, and lack of clotting in the treated blood pools. At timepoints exceeding one week following deposition, the spectral profiles of the pools regained similarity. In summary, the fluid choice, and particularly the inclusion of anticoagulant is an important consideration during experimental design and TSD estimation method development.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".