Laser-induced fluorescence detection of non-covalently labeled protein in capillary isoelectric focusing
Bibliographic record
Abstract
Non-Covalent labeling for fluorescence detection of proteins has been investigated to increase the sensitivity of capillary isoelectric focusing using laser-induced fluorescence (LIF) detection. Non-covalently labeling fluorescent dyes, NanoOrange, Sypro red, Sypro orange, and Sypro tangerine were explored for the coupling of bovine serum albumin (BSA) and hemoglobin. Labeled proteins were studied by two complementary detection methods, viz. whole column UV and LIF detection instruments. The studies using a commercial capillary isoelectric focusing (CIEF) instrument with UV detection gave accurate pI point determination of the labeled protein, and it was confirmed that non-covalently labeled BSA focused to well characterized peaks and the related calculated pI values did not change significantly. The axial LIF detection system confirmed the formation of fluorescent labeled BSA, and an improvement of detection sensitivity of at least 10 times was achieved using LIF as compared to the UV absorption instrument.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".