Coupling of Solid-Phase Microextraction and Capillary Isoelectric Focusing with Laser-Induced Fluorescence Whole Column Imaging Detection for Protein Analysis
Bibliographic record
Abstract
A coupling method of solid-phase microextraction (SPME) and capillary isoelectric focusing (CIEF) with laser-induced fluorescence (LIF) whole column imaging detection (WCID) was developed for the analysis of proteins. Unlike other liquid-phase separation methods and conventional CIEF, proteins are focused into stationary bands within a pH gradient in CIEF-WCID. Thus, CIEF-WCID is the most compatible liquid-phase separation method for coupling with SPME, which can effectively resolve the problems associated with the slow desorption kinetics of SPME in a liquid phase. By combining SPME and CIEF-WCID, the desorption time can be as long as necessary, allowing complete desorption without any band broadening and analyte carryover. By using this method, R-phycoerythrin in water can be extracted by SPME in 10 min, and subsequently analyzed by CIEF-LIF-WCID within 20 min, providing a limit of detection of 3.5 x 10(-12) M (S/N = 3). The feasibility of the SPME-CIEF-LIF-WCID method was demonstrated by extracting and analyzing extracellular phycoerythrins in cultured cyanobacteria samples. Extracellular phycoerythrins at the nanomolar level were extracted and analyzed in 30 min, while avoiding the interference of the cyanobacteria cells.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".