Glycosaminoglycans, Hyperglycemia, and Disease
Bibliographic record
Abstract
SIGNIFICANCE: Diabetes is a widespread disease with many clinical pathologies. Despite numerous pharmaceutical strategies for treatment, the incidence of diabetes continues to increase. Hyperglycemia, observed in diabetes, causes endothelial injury resulting in microvascular and macrovascular complications such as nephropathy, retinopathy, neuropathy, and increased atherosclerosis. RECENT ADVANCES: Proteoglycans are chemically diverse macromolecules consisting of a protein core with glycosaminoglycans (GAGs) attached. Heparan sulfate proteoglycans are important compounds found on the endothelial cell membrane and in the extracellular matrix, which play an important role in growth regulation and serve as a reservoir for cytokines and other bioactive molecules. Endothelial cells are altered in hyperglycemia by a reduction in heparan sulfate and upregulation and secretion of heparanase, an enzyme that degrades heparan sulfate GAGs on proteoglycans. Reactive oxygen species, increased in diabetes, also destroy GAGs. CRITICAL ISSUES: Preservation of heparan sulfate proteoglycans on endothelial cells may be a strategy to prevent angiopathy associated with diabetes. The use of GAGs and GAG-like compounds may increase endothelial heparan sulfate and prevent an increase in the heparanase enzyme. FUTURE DIRECTIONS: Elucidating the mechanisms of GAG depletion and its significance in endothelial health may help to further understand, prevent, and treat cardiovascular complications associated with diabetes. Further studies examining the role of GAGs and GAG-like compounds in maintaining endothelial health, including their effect on heparanase, will determine the feasibility of these compounds in diabetes treatment. Preservation of heparan sulfate by decreasing heparanase may have important implications not only in diabetes, but also in cardiovascular disease and tumor biology.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".