Thiorphan reprograms neurons to promote functional recovery after spinal cord injury
Bibliographic record
Abstract
Abstract We previously identified an embryonic shift in the corticospinal motor neuronal transcriptome after spinal cord injury associated with successful axonal regeneration 1 . Exploiting this transcriptional regenerative ‘signature’, here we used in silico screens to identify small molecules that generate similar shifts in the transcriptome, and identified thiorphan—a neutral endopeptidase inhibitor—as a lead candidate. In a new adult motor cortex neuronal in vitro screen 2 , thiorphan increased neurite outgrowth 1.8-fold ( P < 0.001). We then infused thiorphan into the central nervous system beginning 2 weeks after severe C5 spinal cord contusions and, when combined with a neural stem cell graft, thiorphan elicited significant improvements in forelimb function ( P < 0.005) and corticospinal regeneration ( P < 0.05). Extending clinical relevance, thiorphan significantly increased neurite outgrowth in primary cortical neuronal cultures from a 56-year-old human. These findings represent a new path for drug discovery, starting from in silico screens to proof-of-concept in adult human brain cultures.
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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) | 0.000 | 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".