Amelioration of Both Central and Peripheral Neuropathy in Mouse Models of Type 1 and Type 2 Diabetes by the Neurogenic Molecule NSI-189
Bibliographic record
Abstract
While peripheral neuropathy is the most common complication of long-term diabetes, cognitive deficits associated with encephalopathy and myelopathy also occur. Diabetes is a risk factor for Alzheimer disease (AD) and increases the risk of progression from mild cognitive impairment to AD. The only current recommendation for preventing or slowing the progression of peripheral neuropathy is to maintain close glycemic control, while there is no recommendation for central nervous system disorders. NSI-189 is a new chemical entity that when orally administered promotes neurogenesis in the adult hippocampus, increases hippocampal volume, enhances synaptic plasticity, and reduces cognitive dysfunction. To establish the potential for impact on peripheral neuropathy, we first showed that NSI-189 enhances neurite outgrowth and mitochondrial functions in cultured adult rat primary sensory neurons. Oral delivery of NSI-189 to murine models of type 1 (female) and type 2 (male) diabetes prevented multiple functional and structural indices of small and large fiber peripheral neuropathy, increased hippocampal neurogenesis, synaptic markers and volume, and protected long-term memory. NSI-189 also halted progression of established peripheral and central neuropathy. NSI-189, which is currently in clinical trials for treatment of major depressive disorder, offers the opportunity for the development of a single therapeutic agent against multiple indices of central and peripheral neuropathy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".