Mass spectrometric study of N‐glycans from serum of woodchucks with liver cancer
Bibliographic record
Abstract
Woodchucks have been a preferred lab animal model of chronic hepatitis B viral infection. The model recapitulates the disease progression of HBV infection to hepatocellular carcinoma (HCC) and has documented similarities in protein glycosylation with human HCC. This study examined N-glycans in serum of animals with(out) HCC. Oligosaccharides were released enzymatically using PNGaseF from total serum or from serum partially fractionated by extraction. Two different extraction procedures - reversed-phase high-performance liquid chromatography (RP-HPLC) and solid-phase extraction (SPE) on a cation-exchange/reversed-phase STRATA-XC cartridge - were used with the purpose of confirming glycosylation profiles. Oligosaccharides were analyzed by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) after derivatization with phenylhydrazine and/or permethylation. Characteristic fragment ions produced under MS/MS conditions allowed discrimination between isomeric structures of oligosaccharides, including those sialylated with two types of acidic residues. The complementary methods allowed structural characterization of oligosaccharides from various N-glycan classes. Furthermore, to validate results, glycosylation profiles of woodchuck sera were compared to glycans obtained from mouse serum on the same conditions. In summary, we have identified 40 N-glycan structures in the serum of woodchucks and some types of oligosaccharide structures appeared to increase in HCC samples following protease digest. The study provides improved tools for the characterization of N-glycans from total serum in the progression of liver disease.
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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) | 0.000 | 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 | 0.000 | 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".