Minimally Invasive Chemical Biopsy Needle with Self‐Wettable Extraction Phase For In Vivo Tissue Sampling During Medical Procedures
Bibliographic record
Abstract
Chemical biopsy by solid-phase microextraction (SPME) with sorbent-coated fibers offers monitoring of biological processes in a significantly less invasive manner compared to conventional tissue biopsy. The developed device features a self-protective design by using an acupuncture needle coated with biocompatible material only in a recessed section. The coating comprises micron-sized naturally wettable sorbent particles embedded in a durable, biocompatible binder, ensuring broad analyte extraction coverage without the need for solvent activation. This design allows professionals in biochemical research and medical staff to use the device for in vivo monitoring of tissue concentrations of endogenous and/or exogenous substances without introducing activation solvent to the investigated system. The device is successfully used to sample anti-cancer drugs in both animal models and human patients undergoing in vivo lung perfusion (IVLP) surgery, and then determine the drug concentration level by LC/MS. Finally, a proof-of-concept experiment using a microfluidic open interface (MOI) to directly desorb and introduce the extract analytes from the coating to MS detection is proposed for potential on-site real-time drug monitoring during the surgery.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".