Faculty Opinions recommendation of Methylglyoxal modification of Nav1.8 facilitates nociceptive neuron firing and causes hyperalgesia in diabetic neuropathy.
Bibliographic record
Abstract
This study establishes a mechanism for metabolic hyperalgesia based on the glycolytic metabolite methylglyoxal. We found that concentrations of plasma methylglyoxal above 600 nM discriminate between diabetes-affected individuals with pain and those without pain. Methylglyoxal depolarizes sensory neurons and induces post-translational modifications of the voltage-gated sodium channel Na(v)1.8, which are associated with increased electrical excitability and facilitated firing of nociceptive neurons, whereas it promotes the slow inactivation of Na(v)1.7. In mice, treatment with methylglyoxal reduces nerve conduction velocity, facilitates neurosecretion of calcitonin gene-related peptide, increases cyclooxygenase-2 (COX-2) expression and evokes thermal and mechanical hyperalgesia. This hyperalgesia is reflected by increased blood flow in brain regions that are involved in pain processing. We also found similar changes in streptozotocin-induced and genetic mouse models of diabetes but not in Na(v)1.8 knockout (Scn10(-/-)) mice. Several strategies that include a methylglyoxal scavenger are effective in reducing methylglyoxal- and diabetes-induced hyperalgesia. This previously undescribed concept of metabolically driven hyperalgesia provides a new basis for the design of therapeutic interventions for painful diabetic neuropathy. PMID: 22581285 Funding information This work was supported by: NIDDK NIH HHS, United States Grant ID: 2R56DK33861-21
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.452 | 0.191 |
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".