Human Serum Proteins Preseparated by Electrophoresis or Chromatography Followed by Tandem Mass Spectrometry
Bibliographic record
Abstract
Electrophoretic and chromatographic sample preparations were compared and together detected the presence of some 600 types of protein products in human serum. Proteins from crude serum preseparated by ionic electrophoresis, chromatography, or a combination of both were analyzed. Proteins were digested with trypsin or chymotrypsin. Naturally occurring peptides were also collected by reversed-phase chromatography. The resulting peptides were identified by tandem mass spectrometry. The peptides were either desorbed by a laser from a metal chip into a quadrupole-time-of-flight mass spectrometer or ionized as an electro-spray from reversed-phase chromatography via a metal needle under voltage into an ion-trap mass spectrometer. All of the commonly known proteins associated with serum were detected, and the two mass spectrometers agreed on the identity of abundant serum proteins. Preseparation of serum proteins prior to digestion markedly enhanced the capacity to detect un-common proteins from blood. Electrophoretic- and chromatography-based experiments were found to be complementary. Many novel cellular proteins not previously associated with serum were recorded.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".