Integrated microfluidic devices with enhanced separation performance: Application to phosphoproteome analyses of differentiated cell model systems
Bibliographic record
Abstract
This work reports on the application of a microfluidic device integrating nanoscale LC to nanoelectrospray MS (nano-LC-chip-MS) for the analysis of complex protein digests. Peak profile analyses of more than 700 peptide ions, reproducibly detected across replicate nano-LC-chip-MS runs (n = 5), indicated that the system provided RSD values of 0.24% on retention time, +/- 30 ppm on m/z measurement and +/- 30% variation on intensity over three orders of magnitude. RP adsorbant media with different alkyl chains and particle size packed in both trapping and separation channels were investigated to improve the chromatographic performance of this system. A two-fold improvement in chromatographic peak capacity was achieved using microfluidic devices comprising a 5 mircrom C3 trap with 2.5 microm C18 trap separation channel compared to the traditional 5 microm C18 stationary phase. Enhanced sample selectivity for the identification of phosphopeptides was obtained by combining immobilized metal affinity media prior to peptide separation on the RP microfluidic device. This system was evaluated in the context of differential phosphoproteome analyses to identify changes in signaling events and protein expression of human monocytes following the administration of phorbol ester.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".