Quantification of Greenland halibut serum vitellogenin: a trip from the deep sea to the mass spectrometer
Bibliographic record
Abstract
This paper focuses on the sequential steps involved in developing a technique for quantifying Greenland halibut vitellogenin, a serum protein biomarker, using a comprehensive mass spectrometric approach. In the first phase of this study, in-gel trypsin digestions of serum proteins separated by sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) were analyzed by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). A characteristic band around a molecular mass of 185 kDa, present in the mature female specimens, but absent in the male samples, was identified as vitellognin according to the peptide mass fingerprint obtained by MALDI-MS. Subsequently, MALDI and electrospray ionization tandem mass spectrometry (ESI-MS/MS) analyses were performed on the digest of the vitellogenin band for de novo sequencing. From these studies, a characteristic 'signature' peptide (sequence: FFGQEIAFANIDK) was selected from a list of candidate peptides as a surrogate analytical standard used for quantification purposes. Sample preparation for vitellogenin quantification consisted of a simple one-step overnight trypsin digestion. Samples were spiked with an isotopologue signature peptide standard and analyzed by high-performance liquid chromatography (HPLC) coupled in-line to an electrospray quadrupole-hexapole-quadrupole tandem mass spectrometer, operated in selective reaction monitoring mode. Transitions [(m/z 750.0 --> 1020.4 and 750.0 --> 1205.4) and (754.8 --> 1028.6 and 754.8 --> 1213.2)] were monitored for the signature peptide and the internal standard, respectively. Samples obtained from the field showed that vitellogenin levels were in accordance with fish maturity determined by macroscopic examination of the gonad, proving this technique suitable for measuring vitellogenin as a serum protein biomarker for reproductive maturity in female fish.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".