Biocompatible Solid-Phase Microextraction Nanoelectrospray Ionization: An Unexploited Tool in Bioanalysis
Bibliographic record
Abstract
In recent years, different geometrical configurations of solid-phase microextraction (SPME) have been directly coupled to mass spectrometry, resulting in benefits such as diminishing matrix effects, improvement of detection limits, and considerable enhancement of analysis throughput. Although SPME fibers have been used for years, their potential for quantitative analysis when directly combined with mass spectrometry has not been explored to its full extent. In this study, we present the direct coupling of biocompatible SPME (Bio-SPME) fibers to mass spectrometry via nanoelectrospray ionization (nano-ESI) emitters as a powerful tool for fast quantitative analysis of target analytes in biofluids. Total sample preparation time does not exceed 2 min, and by selecting an appropriate fiber length and sample vessel, sample volumes ranging between 10 and 1500 μL can be used. Despite the short extraction time of the technique, limits of detection in the subnanogram per milliliter with good accuracy (≥90%) and linearity (R(2) > 0.999) were attained for all the studied probes in phosphate-buffered saline (PBS), urine, and whole blood. Given that Bio-SPME-nano-ESI efficiently integrates sampling with analyte extraction/enrichment, sample cleanup (including elimination of matrix effects in the form of particles), and ionization, our results demonstrated that it is an advantageous configuration for bioanalytical applications such as therapeutic drug monitoring, doping in sports, and pharmacological studies in various matrixes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".