Bibliographic record
Abstract
BACKGROUND: Most proteoglycans are heterogeneous molecules composed of a protein core with glycosaminoglycans (GAGs) attached. GAGs are highly negatively charged molecules that readily bind to enzymes, growth factors, cytokines etc. and as such have many functions. The role played by proteoglycans in diabetes has only recently been investigated. METHODS: The importance of proteoglycans and the effects of diabetes on proteoglycans are discussed. Possible strategies for reducing diabetic complications associated with preventing proteoglycan destruction are examined. RESULTS: Proteoglycans are altered in the endothelium, vascular wall, kidney, retina, heart, gut epithelial cells, bone and cartilage with diabetes. A decrease in proteoglycans, associated with hyperglycemic conditions, is reported to be due to a decrease in proteoglycan synthesis or an increase in destruction. Destruction may be a result of an upregulation of enzymes that degrade GAGs or destruction by reactive oxygen species. Several studies suggest that upregulation of heparanase and its destruction of heparan sulfate proteoglycans may be responsible for many of the complications associated with diabetes particularly in the kidney and blood vessels leading to chronic kidney disease, atherosclerosis and acute coronary syndrome. Preliminary studies suggest that administration of GAGs may be beneficial in reducing or delaying the harmful consequences of diabetes in the kidney and retina. CONCLUSIONS: Changes in proteoglycans are partially responsible for diabetic complications. Recent studies demonstrate that administration of GAGs may reduce or delay diabetic complications. Further studies are required to understand the alterations in proteoglycans associated with diabetes, and the protective potential of administered GAGs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".