The emerging role of glycans and the importance of sialylation in cardiovascular disease
Bibliographic record
Abstract
Glycosylation is the process by which glycans (i.e. 'sugars') are enzymatically attached to proteins or lipids to form glycoconjugates. Growing evidence points to glycosylation playing a central role in atherosclerosis. Glycosylation occurs in all human cells and post-translationally modifies many signalling molecules that regulate cardiovascular disease, affecting their binding and function. Glycoconjugates are present in abundance on the vascular endothelium and on circulating lipoproteins, both of which have well-established roles in atherosclerotic plaque development. Sialic acid is a major regulator of glycan function and therefore the process of sialylation, in which sialic acid is added to glycans, is likely to be entwined in any regulation of atherosclerosis. Glycans and sialylation regulators have the potential to present as new biomarkers that predict atherosclerotic disease or as targets for pharmacological intervention, as well as providing insights into novel cardiovascular mechanisms. Moreover, the asialoglycoprotein receptor 1 (ASGR1), a glycan receptor, is emerging as an exciting new regulator of lipid metabolism and coronary artery disease. This review summarises the latest advances in the growing body of evidence that supports an important role for glycosylation and sialylation in the regulation of atherosclerosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 0.002 |
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".