Perspective on in vivo SPME for human applications: Starting from monitoring doxorubicin during lung chemo-perfusion
Bibliographic record
Abstract
In vivo solid-phase microextraction (SPME) is a non-destructive and minimally invasive sampling technique for living systems that facilitates the acquisition of representative metabolome profiles while offering detection of low abundance, short-lived, and unstable species that not easily captured by traditional methods. Recently, following over 10 years of adventure in ex vivo and in vivo animal studies, SPME was successfully applied for in vivo analysis of human tissue. The proposed in vivo SPME method was coupled to LC-MS for monitoring of doxorubicin during in vivo lung perfusion (IVLP) with temporal and spatial information. In view of this breakthrough and considering the already comprehensive body of research on animal models in the literature, we provide here future perspectives on in vivo SPME from three different aspects: optimization and development of SPME features, direct coupling with MS for real-time monitoring, and future applications.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".