Diabetic neuropathy and the sensory neuron: New aspects of pathogenesis and their treatment implications
Bibliographic record
Abstract
Diabetic polyneuropathy (DPN) continues to be generally considered as a "microvascular" complication of diabetes mellitus alongside nephropathy and retinopathy. The microvascular hypothesis, however, might be tempered by the concept that diabetes directly targets dorsal root ganglion sensory neurons. This neuron-specific concept, supported by accumulating evidence, might account for important features of DPN, such as its early sensory neuron degeneration. Diabetic sensory neurons develop neuronal atrophy alongside a series of messenger ribonucleic acid (RNA) changes related to declines in structural proteins, increases in heat shock protein, increases in the receptor for advanced glycation end-products, declines in growth factor signaling and other changes. Insulin is recognized as a potent neurotrophic factor, and insulin ligation enhances neurite outgrowth through activation of the phosphoinositide 3-kinase-protein kinase B pathway within sensory neurons and attenuates phenotypic features of experimental DPN. Several interventions, including glucagon-like peptide-1 agonism, and phosphatase and tensin homolog inhibition to activate growth signals in sensory neurons, or heat shock protein overexpression, prevent or reverse neuropathic abnormalities in experimental DPN. Diabetic sensory neurons show a unique pattern of microRNA alterations, a key element of messenger RNA silencing. For example, let-7i is widely expressed in sensory neurons, supports their growth and is depleted in experimental DPN; its replenishment improves features of DPN models. Finally, impairment of pre-messenger RNA splicing in diabetic sensory neurons including abnormal nuclear RNA metabolism and structure with loss of survival motor neuron protein, a neuron survival molecule, and overexpression of CWC22, a splicing factor, offer further novel insights. The present review addresses these new aspects of DPN sensory neurodegeneration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".