Detection Strategies for Sialic Acid and Sialoglycoconjugates
Bibliographic record
Abstract
Glycoconjugates are a vast class of biomolecules implicated in biological processes important for human health and disease. The structural complexity of glycoconjugates remains a challenge to deciphering their precise biological roles and for their development as biomarkers and therapeutics. Human glycoconjugates on the outside of the cell are modified with sialic (neuraminic) acid residues at their termini. The enzymes that install sialic acids are sialyltransferases (SiaTs), a family of 20 different isoenzymes. The removal and degradation of sialic acids is mediated by neuraminidase (NEU; sialidase) enzymes, of which there are four isoenzymes. In this review, we discuss chemical and biochemical approaches for the detection and analysis of sialoglycoconjugate (SGC) structures and their enzymatic products. The most common methods include affinity probes and synthetic substrates. Fluorogenic and radiolabelled substrates are also important tools for many applications, including screening for enzyme inhibitors. Strategies that give insight into the native substrate-specificity of enzymes that regulate SGCs (SiaT & NEU) are necessary to improve our understanding of the role of sialic acid metabolism in health and 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".