Loss of GABAergic Interneurons in Laminae I–III of the Spinal Cord Dorsal Horn Contributes to Reduced GABAergic Tone and Neuropathic Pain after Spinal Cord Injury
Bibliographic record
Abstract
Abstract In this study we explore if loss of GABAergic inhibitory interneurons in the superficial dorsal horn of the spinal cord contributes to reduced GABAergic tone and neuropathic pain following spinal cord injury (SCI). A moderate contusion injury to T11 resulted in the development of mechanical hyperalgesia and thermal hyperalgesia below the level of the lesion in gad1:GFP mice that were alleviated by IP administration of the GABA transporter antagonist tiagabine. Six weeks following SCI a decreased number of GFP(+) neurons were observed in the dorsal horn of SCI animals relative to sham mice. Tissue from a mouse 2 weeks post-SCI was subsequently observed to express activated caspase-3, indicative of apoptosis, co-localized to some GFP(+) GABAergic neurons. Glutamate decarboxylase (GAD)65 and GAD67 immunohistochemical staining was reduced in the dorsal horn of SCI animals. This observation was confirmed in Western blots showing reduced immunoreactivity for GAD67, as well as GABA transporter (GAT)1. Reversal of post-SCI neuropathic pain by tiagabine suggests that reduced GABAergic tone may contribute to hyperalgesia symptoms. This is supported by the subsequent observation that SCI reduced the number of GFP(+) inhibitory neurons, and the finding that some GABAergic GFP(+) neurons undergo cell death at a time point consistent with the development of neuropathic pain following SCI. Concordantly, reductions in both GAD65 and GAD67 and GAT1 immunoreactivity also support the observation of a loss of GABAergic inhibition and the associated spinal interneurons.
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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".