Intranasal Insulin Ameliorates Experimental Diabetic Neuropathy
Post-publication record
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Bibliographic record
Abstract
OBJECTIVE: We hypothesized that intranasal insulin (I-I) delivery targets the nervous system while avoiding potential adverse systemic effects when compared with subcutaneous insulin (S-I) for experimental streptozotocin-induced diabetic peripheral neuropathy (DPN). RESEARCH DESIGN AND METHODS: I-I or S-I at 0.87 IU daily or placebo were delivered in separate cohorts of diabetic and nondiabetic CD1 mice during 8 months of diabetes. Radiolabeled insulin detection was used to compare delivery and biodistribution for I-I and S-I. Biweekly behavioral testing and monthly electrophysiological and quantitative studies assessed progression of DPN. At and before end point, morphometric analysis of DRG, peripheral nerve, distal epidermal innervation, and specific molecular markers were evaluated. RESULTS: Radiolabeled I-I resulted in more rapid and concentrated delivery to the spinal cord and DRG with less systemic insulin exposure. When compared with S-I or intranasal placebo, I-I reduced overall mouse mortality and sensory loss while improving neuropathic pain and electrophysiological/morphological abnormalities in diabetic mice. I-I restored mRNA and protein levels of phosphoinositide 3-kinase/Akt, cyclic AMP response element-binding protein, and glycogen synthase kinase 3beta to near normal levels within diabetic DRGs. CONCLUSIONS: I-I slows the progression of experimental DPN in streptozotocin mice, avoids adverse effects associated with S-I treatment, and prolongs lifespan when compared with S-I. I-I may be a promising approach for the treatment of DPN.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".