Integrated System for High-Throughput Protein Identification Using a Microfabricated Device Coupled to Capillary Electrophoresis/Nanoelectrospray Mass Spectrometry
Bibliographic record
Abstract
An integrated microsystem providing rapid analyses of trace-level tryptic digests for proteomics application is presented. This modular microsystem includes an autosampler and a microfabricated device comprising a sample introduction port and an array of separation channels together with a low dead-volume facility enabling an interface to nanoelectrospray mass spectrometry. Sequential injection and separation of peptide standards and tryptic digests was achieved with a throughput of up to 30 samples per hour with less than 3% sample carryover. Replicate injections of peptide mixtures indicated that reproducibility of migration time was typically better than 2.3% relative standard deviation (RSD), whereas RSD values of 3.7–11.8% were observed on peak height. Mass spectral detection of sub-micromolar protein digests (< 7 fmol per injection) was achieved using a quadrupole/time-of-flight instrument in less than 2 min per sample with peak widths of 1.8–7.0 s. The analytical potential of this integrated device for the identification of gel-isolated proteins from Neisseria meningitidis immunotype L3 has been demonstrated using both peptide mass-fingerprint database searching and on-line tandem mass spectrometry.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".