Novel glycan-related biomarker discovery by total glycomic and focused protein glycomic analyses
Bibliographic record
Abstract
The cell surface is covered with a variety of glycan subtypes (sub-glycans) such as N-glycans, O-glycans, glycosphingolipid-glycans, and glycosaminoglycans, which are collectively called the glycocalyx. The expression patterns of sub-glycans change in response to various biological events during disease pathogenesis; however, the structures of all major sub-glycans and their relative concentrations in a cell have been hardly reported. Total glycomic analysis, which comprehensively measures all major sub-glycans, is a powerful tool to discover cellular and clinical biomarkers. In this review, we provide an overview of the analytical methods for sub-glycans and the total glycome in cultured cell lines, human serum, mouse brain tissue, and human osteoarthritis cartilage. This approach not only facilitates characterization of cells, but also has applications for hierarchical clustering analysis, glycan-related biomarker discovery, and investigation of the relationship between sub-glycans and gene expression levels using the total glycome. Moreover, we discuss our recent research focused on identifying potential biomarkers of nonalcoholic fatty liver disease. These glycomic technologies are expected to contribute to diagnostics and drug development for rare diseases in the future.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".