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Record W4414351258 · doi:10.1093/fsr/owaf025

To test or not to test for body fluids: integration of body fluid identification and direct PCR in one workflow

2025· article· en· W4414351258 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueForensic Sciences Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsHamilton Health Sciences
Fundersnot available
KeywordsBody fluidWorkflowDNA profilingProfiling (computer programming)Identification (biology)Polymerase chain reaction

Abstract

fetched live from OpenAlex

Abstract Frequently at crime scenes, it is possible to find lesser amounts of biological material, which prevents performing all the analyses to make a full identification of the evidence: body fluid identification, DNA extraction, human DNA quantitation, and short tandem repeats (STR) profiling. In these situations, DNA profiling is chosen over body fluid identification. Nowadays, the current advancements in forensic genetics, such as the development of different swab materials and direct polymerase chain reaction (PCR) amplification, allow us to skip the steps of DNA extraction and quantitation, avoiding losing important amounts of genetic material and original evidence. However, DNA profiling is as important as body fluid identification in certain cases. The present study assessed the efficiency of integrating body fluid identification by immunochromatographic tests and genetic profiling into a single workflow using microFLOQ® swabs and evaluating different approaches in bloodstain samples. The findings from this research indicated that the microFLOQ can be used both for sampling directly from the source and for subsampling from swabs of different materials, followed by direct PCR to get good-quality STR profiles, in this case allowing to extract the maximum information from a “unique” source of evidence before destruction, as in body fluid and genetic identification. Future research can expand these results to other body fluids (i.e., semen and saliva) and mixtures. Key points The present work showed that the integration of body fluid identification by immunochromatographic tests and genetic profiling by STR analysis into a single workflow is feasible.Three different strategies for integrating body fluid identification and genetic profile into one single workflow were assessed with different results.No clear correlation was found between hemoglobin concentration and the quality of the STR profiles.A viable solution for low-quantity DNA casework scenarios may be using microFLOQ for subsampling from regular cotton or nylon-flocked (4N6FLOQ) swabs.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.412
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it