Circuit Reconstruction after Traumatic Spinal Cord Injury by Cellular and Pharmacological Approaches
Bibliographic record
Abstract
Extensive neuronal loss, progressive neurodegeneration, and circuit impairment are the key pathological features of spinal cord injury (SCI). The intrinsic capacity of the injured spinal cord to replenish damaged neurons and reassemble the disrupted spinal circuitry is restricted. Transplantation of exogenous neural precursor cells (NPCs) has shown promise to structurally repair the injured spinal network. However, proper maturation and integration of newly generated neurons from NPC graft within the zhost spinal circuit has remained challenging. Here, in adult female Sprague Dawley rats, we demonstrate that blockade of CSPG/LAR/PTP-σ axis in combination with neuromodulation by activation of 5-HT 1/2/7 receptors augment the generation of spinal cord-specific neurons by NPC grafts and supports the maturity and functional connectivity of NPC-derived neurons with the host local spinal network as well as major descending motor pathways that culminate in recovery of locomotion and sensorimotor integration. Taken together, this novel cellular and pharmacological approach facilitates functional restoration of neural networks within the damaged spinal circuitry that addresses a critical gap in cell-based repair strategies for SCI.
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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) | 0.002 | 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".