Bibliographic record
Abstract
Diabetes is a complex disorder responsible for the mortality and morbidity of millions of individuals worldwide. Although many approaches have been used to understand and treat diabetes, the role of proteoglycans, in particular heparan sulfate proteoglycans (HSPGs), has only recently received attention. The HSPGs are heterogeneous, highly negatively charged, and are found in all cells primarily attached to the plasma membrane or present in the extracellular matrix (ECM). HSPGs are involved in development, cell migration, signal transduction, hemostasis, inflammation, and antiviral activity, and regulate cytokines, chemokines, growth factors, and enzymes. Hyperglycemia, accompanying diabetes, increases reactive oxygen species and upregulates the enzyme heparanase that degrades HSPGs or affects the synthesis of the HSPGs altering their structure. The modified HSPGs in the endothelium and ECM in the blood vessel wall contribute to the nephropathy, cardiovascular disease, and retinopathy seen in diabetes. Besides the blood vessel, other cells and tissues in the heart, kidney, and eye are affected by diabetes. Although not well understood, the adipose tissue, intestine, and brain also reveal HSPG changes associated with diabetes. Further, HSPGs are significantly involved in protecting the β cells of the pancreas from autoimmune destruction and could be a focus of prevention of type I diabetes. In some circumstances, HSPGs may contribute to the pathology of the disease. Understanding the role of HSPGs and how they are modified by diabetes may lead to new treatments as well as preventative measures to reduce the morbidity and mortality associated with this complex condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".