Fast In Vivo Microextraction: A New Tool for Clinical Analysis
Bibliographic record
Abstract
BACKGROUND: We sought to develop a technique with the potential to partly replace current methods of analysis based on blood draws. To achieve this goal, we developed an in vivo microextraction technique that is faster than conventional methods, interferes minimally with the investigated system, minimizes errors associated with sample preparation, and limits exposure to hazardous biological samples. METHODS: Solid-phase microextraction devices based on hydrophilic polypyrrole and polyethylene glycol coatings were used for direct extraction of drugs from the flowing blood of beagle dogs, over a period of 8 h. The drugs extracted on the probes were subsequently quantified by liquid chromatography coupled to tandem mass spectrometry. Two calibration strategies--external and standard on the fiber--were used to correlate the amount extracted with the in vivo concentration. RESULTS: Diazepam and its metabolites were successfully monitored over the course of a pharmacokinetic study, repeated 3 times on 3 beagles. The fast microextraction technique was validated by comparison with conventional plasma analysis, and a correlation factor of 0.99 was obtained. In addition to total concentrations, the method was useful for determining free drug concentrations. CONCLUSIONS: The proposed technique has several advantages and is suitable for fast clinical analyses. This approach could be used not only for drugs, but for any other endogenous or exogenous compounds.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".