Inhibition of the Ca<sup>2+</sup>-Dependent K<sup>+</sup>Channel,<i>KCNN4</i>/KCa3.1, Improves Tissue Protection and Locomotor Recovery after Spinal Cord Injury
Bibliographic record
Abstract
Spinal cord injury (SCI) triggers inflammatory responses that involve neutrophils, macrophages/microglia and astrocytes and molecules that potentially cause secondary tissue damage and functional impairment. Here, we assessed the contribution of the calcium-dependent K⁺ channel KCNN4 (KCa3.1, IK1, SK4) to secondary damage after moderate contusion lesions in the lower thoracic spinal cord of adult mice. Changes in KCNN4 mRNA levels (RT-PCR), KCa3.1 protein expression (Western blots), and cellular expression (immunofluorescence) in the mouse spinal cord were monitored between 1 and 28 d after SCI. KCNN4 mRNA and KCa3.1 protein rapidly increased after SCI; double labeling identified astrocytes as the main cellular source accounting for this upregulation. Locomotor function after SCI, evaluated for 28 d in an open-field test using the Basso Mouse Scale, was improved in a dose-dependent manner by treating mice with a selective inhibitor of KCa3.1 channels, TRAM-34 (triarylmethane-34). Improved locomotor function was accompanied by reduced tissue loss at 28 d and increased neuron and axon sparing. The rescue of tissue by TRAM-34 treatment was preceded by reduced expression of the proinflammatory mediators, tumor necrosis factor-α and interleukin-1β in spinal cord tissue at 12 h after injury, and reduced expression of inducible nitric oxide synthase at 7 d after SCI. In astrocytes in vitro, TRAM-34 inhibited Ca²⁺ signaling in response to metabotropic purinergic receptor stimulation. These results suggest that blocking the KCa3.1 channel could be a potential therapeutic approach for treating secondary damage after spinal cord injury.
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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) | 0.001 | 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) | 0.001 | 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".