Effects of subcutaneous vs. oral nanoparticle-mediated insulin delivery on hemostasis disorders in type 1 diabetes: A rat model study
Bibliographic record
Abstract
Complications associated with Type 1 diabetes (T1D) have complex origins that revolve around chronic hyperglycemia; these complications involve hemostasis disorders, coagulopathies, and vascular damage. Our study aims to develop innovative approaches to minimize these complications and to compare the outcomes of the new approach with those of traditional methods. To achieve our objective, we designed novel nanoparticles comprising covalent organic frameworks (nCOF) loaded with insulin, termed nCOF/Insulin, and compared it to subcutaneous insulin to elucidate the influence of insulin delivery methods on various parameters, including bleeding time, coagulation factors, platelet counts, cortisol plasma levels, lipid profiles, and oxidative stress parameters. Traditional subcutaneous insulin injections exacerbated hemostasis disorder and vascular injuries in streptozotocin (STZ)-induced diabetic rats through increasing plasma triglycerides and lipid peroxidation. Conversely, oral delivery of nCOF/Insulin ameliorated hemostatic disorders and restored the endothelial oxidant/antioxidant balance by reducing lipid peroxidation and enhancing the lipid profile. Our study pioneers the understanding of how STZ-induced diabetes disrupts bleeding time, induces a hypercoagulable state, and causes vascular damage through lipid peroxidation. Additionally, it provides the first evidence for the involvement of subcutaneous insulin treatment in exacerbating vascular and hemostasis disorders in type 1 diabetes (T1D). Introducing an innovative oral insulin delivery via the nCOF approach represents a potential paradigm shift in diabetes management and patient care and promises to improve treatment strategies for type 1 Diabetes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".