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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".