Tissue-Specific In Vivo Bioconcentration of Pharmaceuticals in Rainbow Trout (<i>Oncorhynchus mykiss</i>) Using Space-Resolved Solid-Phase Microextraction
Bibliographic record
Abstract
The space-resolved solid-phase microextraction (SR-SPME) technique was employed to study the tissue-specific bioconcentration of pharmaceuticals in live fish. The segmented design of the SPME fibers allowed for the simultaneous determination of pharmaceutical residues in fish dorsal-epaxial muscle and adipose tissue with a single SPME fiber. The miniaturized fiber endowed the technique with high spatial resolution allowing for quantification of analytes within adjacent, relatively small tissues of immature rainbow trout. The pre-equilibrium sampling and kinetic calibration approach yielded efficient and accurate quantitation of pharmaceuticals in fish tissue. The ability of the SPME method to repeatedly sample the same fish circumvents problems arising from interanimal variation, thus improving the precision of generated bioconcentration kinetic profiles. In vivo monitoring with SR-SPME was validated with in vitro liquid extraction of tissue samples using methanol. Of the nine compounds evaluated, five (atrazine, gemfibrozil, carbamazepine, ibuprofen, and fluoxetine) bioconcentrated in adipose and muscle tissue over the eight exposure days. Although the accumulation of analytes in both tissues was positively correlated, each compound partitioned with differing affinities as modified by their hydrophobicity and unique molecular structure. Water samples analyzed using the SPME technique yielded results similar to those determined by solid-phase extraction (SPE); however, SPME was more rapid and operationally much simpler. This study illustrates the application conditions for in situ SR-SPME while demonstrating the potential of these miniaturized SPME fibers for simultaneous in vivo repeated sampling of multiple tissues.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".