Toward Analysis at the Point of Need: A Digital Microfluidic Approach to Processing Multi‐Source Sexual Assault Samples
Bibliographic record
Abstract
Forensic case samples collected in sexual assaults typically contain DNA from multiple sources, which complicates short-tandem repeat (STR) profiling. These samples are typically sent to a laboratory to separate the DNA from sperm and non-sperm sources prior to analysis. Here, the automation and miniaturization of these steps using digital microfluidics (DMF) is reported, which may eventually enable processing sexual assault samples outside of the laboratory, at the point of need. When applied to vaginal swab samples collected up to 12 h post-coitus (PC), the new method identifies single-source (male) STR profiles. When applied to samples collected 24-72 h PC, the method identifies mixed STR profiles, suggesting room for improvement and/or potential for data deconvolution. In sum, an automated, miniaturized sample pre-processing method for separating the DNA contained in sexual assault samples is demonstrated. This type of automated processing using DMF, especially when combined with Rapid DNA Analysis, has the potential to be used for processing of sexual assault samples in hospitals, police offices, and other locations outside of the laboratory.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".