Spinal GABAergic Transplants Attenuate Mechanical Allodynia in a Rat Model of Neuropathic Pain
Bibliographic record
Abstract
Injury to the spinal cord or peripheral nerves can lead to the development of allodynia due to the loss of inhibitory tone involved in spinal sensory function. The potential of intraspinal transplants of GABAergic cells to restore inhibitory tone and thus decrease pain behaviors in a rat model of neuropathic pain was investigated. Allodynia of the left hind paw was induced in rats by unilateral L5- 6 spinal nerve root ligation. Mechanical sensitivity was assessed using von Frey filaments. Postinjury, transgenic fetal green fluorescent protein mouse GABAergic cells or human neural precursor cells (HNPCs) expanded in suspension bioreactors and differentiated into a GABAergic phenotype were transplanted into the spinal cord. Control rats received undifferentiated HNPCs or cell suspension medium only. Animals that received either fetal mouse GABAergic cell or differentiated GABAergic HNPC intraspinal transplants demonstrated a significant increase in paw withdrawal thresholds at 1 week post-transplantation that was sustained for 6 weeks. Transplanted fetal mouse GABAergic cells demonstrated immunoreactivity for glutamic acid decarboxylase and GABA that colocalized with green fluorescent protein. Intraspinally transplanted differentiated GABAergic HNPCs demonstrated immunoreactivity for GABA and beta-III tubulin. In contrast, intraspinal transplantation of undifferentiated HNPCs, which predominantly differentiated into astrocytes, or cell suspension medium did not affect any behavioral recovery. Intraspinally transplanted GABAergic cells can reduce allodynia in a rat model of neuropathic pain. In addition, HNPCs expanded in a standardized fashion in suspension bioreactors and differentiated into a GABAergic phenotype may be an alternative to fetal cells for cell-based therapies to treat chronic pain syndromes.
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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 | 0.000 |
| 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".