Microwave‐assisted protein staining: mass spectrometry compatible methods for rapid protein visualisation
Bibliographic record
Abstract
The effects of microwave irradiation on the staining of electrophoresed and electroblotted proteins have been assessed using currently available detection methods. Although the absorption of microwave radiation was found to be uneven, band intensity following microwave-assisted protein staining (MAPS) was comparable and in some cases exceeded the intensity of the bands visualised by the original staining methods. It was found that microwave treatment drastically reduced the duration of the staining protocols for visualisation of the proteins separated by both one- and two-dimensional electrophoresis. Application of MAPS methods did not affect peptide mass fingerprinting analysis by mass spectrometry and subsequent identification of the protein by database searching. The peptide mass maps corresponding to the proteins visualised using both the conventional and MAPS methods did not show significant difference in signal/noise ratio. Moreover, it appeared that microwave treatment of the gels resulted in the increased recovery of the peptides following in-gel trypsin digestion. Briefly, microwave-assisted protein staining methods were rapid, compatible with mass spectrometry and were equally effective on thin (0.75-mm) and thick (1.5-mm) gels (such as those used in 2D electrophoresis).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.002 | 0.002 |
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".