Intraspinal Grafting of Serotonergic Neurons Modifies Expression of Genes Important for Functional Recovery in Paraplegic Rats
Bibliographic record
Abstract
Serotonin (5-hydroxytryptamine; 5-HT) plays an important role in control of locomotion, partly through direct effects on motoneurons. Spinal cord complete transection (SCI) results in changes in 5-HT receptors on motoneurons that influence functional recovery. Activation of 5-HT2Aand 5-HT7receptors improves locomotor hindlimb movements in paraplegic rats. Here, we analyzed the mRNA of 5-HT2Aand 5-HT7receptors (encoded byHtr2aandHtr7genes, resp.) in motoneurons innervating tibialis anterior (TA) and gastrocnemius lateralis (GM) hindlimb muscles and the tail extensor caudae medialis (ECM) muscle in intact as well as spinal rats. Moreover, the effect of intraspinal grafting of serotonergic neurons onHtr2aandHtr7gene expression was examined to test the possibility that the graft origin 5-HT innervation in the spinal cord of paraplegic rats could reverse changes in gene expression induced by SCI. Our results indicate that SCI at the thoracic level leads to changes inHtr2aandHtr7gene expression, whereas transplantation of embryonic serotonergic neurons modifies these changes in motoneurons innervating hindlimb muscles but not those innervating tail muscles. This suggests that the upregulation of genes critical for locomotor recovery, resulting in limb motoneuron plasticity, might account for the improved locomotion in grafted animals.
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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".