Screening of biological samples by SWATH acquisition and forensic toxicological applications
Bibliographic record
Abstract
Fast and accurate screening of biological samples is an important step in forensic toxicology for further decisions regarding possible quantification of the detected compounds in body fluids or tissues, investigations and prosecution. Based on Quadrupole Time-of-Flight (QqTOF) mass spectrometers – which are capable of acquiring complete MS and MS/MS spectra at a rate of 20 Hz or more and high mass accuracy – a new screening methodology was developed, validated and implemented in the forensic toxicological workflow. Urine samples were diluted with a mixture of water / acetonitrile / formic acid / ammonium formate (97.5 / 2.5 / 0.1% / 2.5 mM) and three internal standards were added (EME-D3, Tramadol-D3C13, THC-D3). Blood or plasma samples were prepared by protein precipitation. The samples were injected onto a core shell column (Phenomenex Kinetex C8, 50 x 2.1 mm, 2.6 μm) and analyzed on a QqTOF instrument (5600 TripleTOF, AB Sciex, Concord, Canada) with typical run times of 15 minutes from injection to injection. The methodology is based on sequential window acquisition of all theoretical fragment ions spectra (SWATH) and high resolution/high accuracy reference spectra which are used for data processing. Several forensic cases and applications are presented and the impact of this mass spectrometry based technique in the field of forensic toxicology is discussed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".