Changes in expression of Kv7.5 and Kv7.2 channels in dorsal root ganglion neurons in the streptozotocin rat model of painful diabetic neuropathy
Bibliographic record
Abstract
Diabetic peripheral neuropathic pain (DPNP), the most debilitating complication of diabetes mellitus, is resistant to current therapy. The pathogenesis of DPNP is still elusive, but several mechanisms have been proposed including abnormal hyperexcitability of dorsal root ganglion (DRG) neurons. The underlying molecular mechanisms of such aberrant hyperexcitability are incompletely understood. Using the streptozotocin (STZ) rat model of DPNP, we have recently provided evidence implicating neuronal K v 7 channels that normally exert a powerful stabilizing influence on neuronal excitability, in the abnormal hyperexcitability of DRG neurons and in pain hypersensitivity associated with DPNP. In the present immunohistochemical study, we sought to determine whether K v 7.2 and/or K v 7.5 channel expression is altered in DRG neurons in STZ rats. We found 35 days post-STZ: (1) a significant decrease in K v 7.5-immunoreactivity in small (<30 μm) DRG neurons (both IB4 positive and IB4 negative) and medium-sized (30−40 μm) neurons, and (2) a significant increase in K v 7.2-immunoreactivity in small (<30 μm) neurons, and a non-significant increase in medium/large neurons. The decrease in K v 7.5 channel expression in small and medium-sized DRG neurons in STZ rats is likely to contribute to the mechanisms of hyperexcitability of these neurons and thereby to the resulting pain hypersensitivity associated with DPNP. The upregulation of K v 7.2 subunit in small DRG neurons may be an activity dependent compensatory mechanism to limit STZ-induced hyperexcitability of DRG neurons and the associated pain hypersensitivity. The findings support the notion that K v 7 channels may represent a novel target for DPNP treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".