Recovery of Locomotor Function with Combinatory Drug Treatments Designed to Synergistically Activate Specific Neuronal Networks
Bibliographic record
Abstract
Spinal cord injury (SCI) is a devastating condition generally leading to a permanent and irreversible loss of sensory and motor functions. We have identified recently a number of serotonergic, adrenergic and dopaminergic receptor agonists or precursors that can acutely elicit some motor and locomotor-like movements in completely spinal cord-transected (thoracic level) animals. However, only partial central network-activating effects were found with single molecules since none administered separately could elicit weight-bearing and functional stepping movements in Tx animals. In turn, a recent breakthrough revealed that full spinal locomotor network-activating effects may be induced with synergistic drug combinations. Indeed, specific cocktails comprising some of these agonists and precursors were found, indeed, to powerfully generate weight-bearing stepping with plantar foot placement in untrained, non-assisted and non-sensory-stimulated Tx mice. This significant finding provides clear evidence suggesting that combinatorial approaches based on drug-drug synergistic interactions may constitute innovative solutions for the design and development of novel pharmacological therapies in the field of SCI and related neurological disorders.
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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) | 0.001 | 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".