KCC2 Enhancers Normalize Reflex Responses and Improve Locomotor Function after Chronic Spinal Cord Injury
Bibliographic record
Abstract
Abstract Within a year after a spinal cord injury (SCI), 75% of individuals develop spasticity. While normal movement relies on the ability to adjust reflexes appropriately, and on reciprocal inhibition of antagonistic muscles, spastic individuals display hyperactive spinal reflexes and involuntary muscle co-contractions. Current anti-spastic medications can suppress uncontrolled movements, but by acting on GABAergic signalling, these medications lead to severe side-effects and weakened muscle force, making them incompatible with activity-based therapies. We have previously shown that pharmacologically enhancing activity of KCC2, a chloride cotransporter, reduces signs of spasticity in anesthetized chronic SCI rats. Here, we examine the effect of enhancing KCC2 in awake animals, using a battery of tests to assess multiple reflex pathways required for normal movement as well as locomotor function. Sprague-Dawley rats were implanted with chronic EMG electrodes bilaterally in ankle flexor and ankle extensor muscles and received a complete spinal transection at T12. Four weeks following SCI, the stretch reflex, the non-nociceptive cutaneous reflex pathway, the flexor withdrawal reflex, and the crossed-extensor reflex pathway as well as locomotor function were evaluated before and after receiving the KCC2 enhancer, CLP290. Our results show that enhancing KCC2 activity normalizes reflex responses in multiple pathways and reduces muscle co-contraction without weakening motor output, thereby improving stepping ability. This work reveals the substantial potential for KCC2 enhancers as a novel antispastic treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".