Application of Solid-Phase Microextraction for In Vivo Laboratory and Field Sampling of Pharmaceuticals in Fish
Bibliographic record
Abstract
Previous field studies utilizing solid-phase microextraction (SPME) predominantly focused on volatile and semivolatile compounds in air or water. Earlier in vivo sampling studies utilizing SPME were limited to the liquid matrix (blood). The present study has expanded the SPME technique to semisolid tissues under laboratory and field conditions through the investigation of both theoretical and applied experimental approaches. Pre-equilibrium extraction and desorption were performed in vivo in two separate animals. Excellent linearity was found between the amounts extracted by SPME from the muscle of living fish and the waterborne concentrations of pharmaceuticals. A simple SPME method is also described to simultaneously determine free and total analyte concentrations in living tissue. The utility of in vivo SPME sampling was evaluated in wild fish collected from a number of different river locations under varying degrees of influence from municipal wastewater effluents. Diphenhydramine and diltiazem were detected in the muscle of fish downstream of a local wastewater treatment plant. Based on this study, SPME demonstrated several important advantages such as simplicity, sensitivity, and robustness under laboratory and in vivo field sampling conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".