Diabetic Sensory Neurons, Dorsal Root Ganglia, and Neuropathy
Bibliographic record
Abstract
Sensory neurons are critical targets in diabetes mellitus (DM). Diabetic polyneuropathy (DPN) can be considered a unique form of sensory predominant neurodegeneration that renders loss of distal axon terminals, especially those in the skin, with relative preservation of their cell body (perikarya). Patients experience loss of sensation (numbness), gait instability with falls, unrecognized injury of insensate limbs with skin ulceration, and neuropathic pain. Sensory neurons reside in paraspinal dorsal root (and trigeminal) ganglia (DRGs) possessing unique microvascular and barrier properties that lead to greater vulnerability from DM. The molecular responses of sensory neurons differ from those of axotomized peripheral neurons. In DM the changes emphasize downregulation of key structural proteins, shifts in ion channel expression, and attenuated growth proteins all indicative of chronic neurotoxic stress. Changes in several differentially expressed mRNAs and miRNAs of DRG neurons in DPN, such as CWC22 and mmu-Let-7i, may contribute to sensory dysfunction. Finally, molecular strategies emphasizing regenerative impacts, including topical approaches, have the capacity to reverse features of DPN including loss of skin innervation. These have included local insulin (intrathecal, intranasal, near nerve, intrahindpaw) given in doses that do not alter hyperglycemia, GLP-1 agonists, PTEN (phosphatase and tensin homolog deleted on chromosome 10) inhibition or knockdown, and muscarinic antagonists. Several additional and novel strategies are emerging that may influence axonal degeneration of distal sensory terminals or axon regeneration specifically. Despite a limited clinical trial track record over several decades, new mechanistic insights for translation in DPN offer hope for better trial results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".