Altered transcription of glutamatergic and glycinergic receptors in spinal cord dorsal horn following spinal cord transection is minimally affected by passive exercise of the hindlimbs
Bibliographic record
Abstract
Gene expression is altered following a spinal transection (STx) in both motor and sensory systems. Exercise has been shown to influence gene expression in both systems post-STx. Gene expression alterations have also been shown in the dorsal root ganglia and nociceptive laminae of the spinal cord following either an incomplete spinal cord injury (SCI) or a contusive SCI. However, the effect of STx and exercise on gene expression in spinal cord laminae I-III has not fully been examined. Therefore, the purpose of this study was to determine whether gene expression in laminae I-III is altered following STx and determine whether superimposed passive exercise of the hindlimbs would influence gene expression post-STx in laminae I-III. Laser capture microdissection was used to selectively harvest laminae I-III of lumbar spinal cord sections, and quantitative RT-PCR was used to examine relative expression of 23 selected genes in samples collected from control, STx and STx plus exercise rats. We demonstrate that post-STx, gene expression for metabotropic glutamate receptors 1, 5 and 8 were up-regulated, whereas ionotropic glutamatergic receptor (Glur2) and glycinergic subunit GLRA1 expression was down-regulated. Daily exercise attenuated the down-regulation of Glur2 gene expression in laminae I-III. Our results demonstrate that in a STx model, gene expression is altered in laminae I-III and that although passive exercise influences gene expression in both the motor and sensory systems, it had a minimal effect on gene expression in laminae I-III post-STx.
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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 | 0.000 |
| 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".