The Use of Hemastix<sup>®</sup> and the Subsequent Lack of DNA Recovery Using the Promega DNA IQ<sup>TM</sup> System
Bibliographic record
Abstract
Following implementation of our automated process incorporating the Promega DNA IQ system as a DNA extraction method, a large number of blood-containing exhibits failed to produce DNA. These exhibits had been tested with the Hemastix reagent strip, commonly used by police investigators and forensic laboratories as a screening test for blood. Some exhibits were even tainted green following transfer of the presumptive test reagents onto the samples. A series of experiments were carried out to examine the effect of the Hemastix chemistries on the DNA IQ system. Our results indicate that one or more chemicals imbedded in the Hemastix reagent strip severely reduce the ability to recover DNA from any suspected stain using the DNA IQ magnetic bead technology. The 3,3',5,5'-tetramethylbenzidine (TMB) used as the reporting dye appears to interact with the magnetic beads to prevent DNA recovery. Hydrogen peroxide does not seem to be involved. The Hemastix chemistries do not interfere in any way with DNA extraction performed using phenol-chloroform. The incompatibility of the Hemastix chemistries on the DNA IQ system forced us to adopt an indirect approach using filter paper to carry out the presumptive test.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".