Plasticity of Mouse Dorsal Root Ganglion Neurons by Innate Immune Activation Is Influenced by Electrophysiological Activity
Bibliographic record
Abstract
The complex relationship between inflammation, its effects on neuronal excitability and the ensuing plasticity of dorsal root ganglion (DRG) sensory neurons remains to be fully explored. In this study, we have employed a system of experiments assessing the impact of inflammatory conditioned media derived from activated immune cells on the excitability and activity of DRG neurons and how this relates to subsequent growth responses of these cells. We show here that an early phase of increased neuronal activity in response to inflammatory conditioned media is critical for the engagement of plastic processes and that neuronal excitability profiles are linked through time to the structural phenotype of individual neurons. Pharmacological blockade of neuronal activity was able to abolish the growth-promoting effects of inflammatory media. Our results suggest that targeting the activity of DRG neurons may provide a novel therapeutic avenue to manipulate their growth status and potential for plasticity in response to inflammation. Importantly, the same pharmacological blockade in vivo abolished pain responses in a mouse model of multiple sclerosis. While further studies are needed to fully elucidate the underlying mechanisms of the relationship between neural activity and growth status, a more complete understanding of this relationship may ultimately lead to the development of new treatments for neuropathic pain in disorders associated with heightened immune responses such as rheumatoid arthritis and multiple sclerosis.
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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) | 0.001 | 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".