Diabetic Polyneuropathy: New Strategies to Target Sensory Neurons in Dorsal Root Ganglia
Bibliographic record
Abstract
Diabetic polyneuropathy (DPN) is the most common type of diabetic neuropathy, rendering a slowly progressive, symmetrical, and length-dependent dying-back axonopathy with preferential sensory involvement. Although the pathogenesis of DPN is complex, this review emphasizes the concept that hyperglycemia and metabolic stressors directly target sensory neurons in the dorsal root ganglia (DRG), leading to distal axonal degeneration. In this context, we discuss the role for DRG-targeting gene delivery, specifically oligonucleotide therapeutics for DPN. Molecules including insulin, GLP-1, PTEN, HSP27, RAGE, CWC22, and DUSP1 that impact neurotrophic signal transduction (for example, phosphatidylinositol-3 kinase/phosphorylated protein kinase B [PI3/pAkt] signaling) and other cellular networks may promote regeneration. Regenerative strategies may be essential in maintaining axon integrity during ongoing degeneration in diabetes mellitus (DM). We discuss specific new findings that relate to sensory neuron function in DM associated with abnormal dynamics of nuclear bodies such as Cajal bodies and nuclear speckles in which mRNA transcription and post-transcriptional processing occur. Manipulating noncoding RNAs such as microRNA and long-noncoding RNA (specifically MALAT1) that regulate gene expression through post-transcriptional modification are interesting avenues to consider in supporting neurons during DM. Finally, we present therapeutic possibilities around the use of a novel DNA/RNA heteroduplex oligonucleotide that provides more efficient gene knockdown in DRG than the single-stranded antisense oligonucleotide.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".