Depth-Profiling of Environmental Pharmaceuticals in Biological Tissue by Solid-Phase Microextraction
Bibliographic record
Abstract
The parallel in vivo measurement of chemicals at various locations in living tissues is an important approach furthering our understanding of biological uptake, transportation, and transformation dynamics. However, from a technical perspective, such measurements are difficult to perform with traditional in vivo sampling techniques, especially in freely moving organisms such as fish. These technical challenges can be well addressed by the proposed depth-profiling solid-phase microextraction (DP-SPME) technique, which utilizes a single soft, flexible fiber with high spatial resolution. The analytical accuracy and depth-profiling capability of DP-SPME was established in vitro within a multilayer gel system and an onion artificially contaminated with pharmaceuticals. In vivo efficacy was demonstrated by monitoring pharmaceutical distribution and accumulation in fish muscle tissue. The DP-SPME method was validated against pre-equilibrium SPME (using multiple small fibers), equilibrium SPME, and liquid extraction methods; results indicated DP-SPME significantly improved precision and data quality due to decreased intersample variation. No significant adverse effects or increases in mortality were observed in comparisons of fish sampled by DP-SPME relative to comparable fish not sampled by this method. Consequently, the simplicity, effectiveness, and improved precision of the technique suggest the potential for widespread application of DP-SPME in the sampling of heterogeneous biotic and abiotic systems.
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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".