Type 1 Diabetic Neuropathy and C‐peptide
Bibliographic record
Abstract
The most common microvascular diabetic complication, diabetic peripheral polyneuropathy (DPN), affects type 1 diabetic patients more often and more severely. In recent decades, it has become increasingly clear that perpetuating pathogenetic mechanisms, molecular, functional, and structural changes and ultimately the clinical expression of DPN differ between the two major types of diabetes. Impaired insulin/C-peptide action has emerged as a crucial factor to account for the disproportionate burden affecting type 1 patients. C-peptide was long believed to be biologically inactive. However, it has now been shown to have a number of insulin-like glucose-independent effects. Preclinical studies have demonstrated dose-dependent effects on Na+,K(+)-ATPase activity, endothelial nitric oxide synthase (eNOS), and endoneurial blood flow. Furthermore, it has regulatory effects on neurotrophic factors and molecules pivotal to the integrity of the nodal and paranodal apparatus and modulatory effects on apoptotic phenomena affecting the diabetic nervous system. In animal studies, C-peptide improves nerve conduction abnormalities, prevents nodal degenerative changes, characteristic of type 1 DPN, promotes nerve fiber regeneration, and prevents apoptosis of central and peripheral nerve cell constituents. Limited clinical trials have confirmed the beneficial effects of C-peptide on autonomic and somatic nerve function in patients with type 1 DPN. Therefore, evidence accumulates that replacement of C-peptide in type 1 diabetes prevents and even improves DPN. Large-scale food and drug administration (FDA)-approved clinical trials are necessary to make this natural substance available to the globally increasing type 1 diabetic population.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".