Comparison of methods to examine the endogenous peptides of fetal calf serum
Bibliographic record
Abstract
There is a great desire to relate the patterns of endogenous peptides in blood to human disease and drug response. The best practices for the preparation of blood fluids for analysis are not clear and also relatively few of the peptides in blood have been identified by tandem mass spectrometry. We compared a number of sample preparation methods to extract endogenous peptides including C18 reversed phase, precipitation, and ultrafiltration. We examined the results of these sample preparation methods by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) and liquid chromatography-tandem mass spectrometry (MS/MS) using MALDI-TOF/TOF and electrospray ionization-ion trap. Peptides from solid-phase extraction with C18 in the range of hundreds of femtomoles per spot were detected from the equivalent of 1 μL of serum by MALDI-TOF. We observed endogenous serum peptides from fibrinogen α- and β-chain, complement C3, α-2-HS-glycoprotein, albumin, serine (or cysteine) proteinase inhibitor, factor VIII, plasminogen, immunoglobulin, and other abundant blood proteins. However, we also recorded significant MS/MS spectra from tumor necrosis factor-α-, major histocompatibility complex-, and angiotensin-related peptides, as well as peptides from collagens and other low-abundance proteins. Amino acid substitutions were detected and a phosphorylated peptide was also observed. This is the first time the endogenous peptides of fetal serum have been examined by MS and where peptides from low-abundance proteins, phosphopeptides, and amino acid substitutions were detected.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".