Nanoliter-Volume Selective Sampling of Peptides Based on Isoelectric Points for MALDI-MS
Bibliographic record
Abstract
A simple way to selectively isolate peptides based on their isoelectric points (pI) for MALDI mass spectral analysis is described. An applied voltage was used to electromigrate peptides into a capillary. The capillary was modified with a zwitterionic surfactant, 1,2-dilauroyl-sn-phosphatidylcholine (DLPC), to suppress the electroosmotic flow (EOF) during injection. Hence, either the cationic or the anionic peptides were introduced, depending on the voltage polarity. By controlling the pH, selective loading of peptides was performed to isolate trace components from a mixture. The injected sample plugs were subsequently spotted in nanoliter volumes for MALDI-MS analysis. No significant sample losses resulting from selective sampling were detected. Low attomole-level detection of peptides (adrenocorticotropic hormone fragment 18-39, pI 4.25) was achieved from a mixture containing other peptides (angiotensin I, pI 6.92, and bradykinin, pI 12.00) at 100 000-fold higher concentrations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".