<i>In Vivo</i> Solid-Phase Microextraction For Tissue Bioanalysis
Bibliographic record
Abstract
Conventional in vitro or ex vivo bioanalytical quantitative sample preparation methods for the determination of compounds in biological tissues are often coupled with challenges in obtaining an assay representative of the system of interest. The rising interest in in vivo microsampling bioanalytical methods is due to the unique advantages they offer over their in vitro counterparts. In vivo solid-phase microextraction (SPME), a diffusion-based microsampling tool, has been successfully applied in recent studies to various biological systems. This review presents recent trends in tissue bioanalysis using in vivo SPME as a sample preparation tool. Efforts were made to discuss the various bioapplications of the method while highlighting possible strategies for improved sensitivity where needed. In vivo SPME devices currently employed for the various applications have also been described. In addition, we highlight selectivity of a new class of biocompatible coatings that can potentially improve the coverage of metabolites for untargeted metabolomics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".