Low-Mg<sup>2+</sup> treatment increases sensitivity of voltage-gated Na<sup>+</sup> channels to Ca<sup>2+</sup>/calmodulin-mediated modulation in cultured hippocampal neurons
Bibliographic record
Abstract
Culture of hippocampal neurons in low-Mg(2+) medium (low-Mg(2+) neurons) results in induction of continuous seizure activity. However, the underlying mechanism of the contribution of low Mg(2+) to hyperexcitability of neurons has not been clarified. Our data, obtained using the patch-clamp technique, show that voltage-gated Na(+) channel (VGSC) activity, which is associated with a persistent, noninactivating Na(+) current (INa,P), was modulated by calmodulin (CaM) in a concentration-dependent manner in normal and low-Mg(2+) neurons, but the channel activity was more sensitive to Ca(2+)/CaM regulation in low-Mg(2+) than normal neurons. The increased sensitivity of VGSCs in low-Mg(2+) neurons was partially retained when CaM12 and CaM34, CaM mutants with disabled binding sites in the N or C lobe, were used but was diminished when CaM1234, a CaM mutant in which all four Ca(2+) sites are disabled, was used, indicating that functional Ca(2+)-binding sites from either lobe of CaM are required for modulation of VGSCs in low-Mg(2+) neurons. Furthermore, the number of neurons exhibiting colocalization of CaM with the VGSC subtypes NaV1.1, NaV1.2, and NaV1.3 was significantly higher in low- Mg(2+) than normal neurons, as shown by immunofluorescence. Our main finding is that low-Mg(2+) treatment increases sensitivity of VGSCs to Ca(2+)/CaM-mediated regulation. Our data reveal that CaM, as a core regulating factor, connects the functional roles of the three main intracellular ions, Na(+), Ca(2+), and Mg(2+), by modulating VGSCs and provides a possible explanation for the seizure discharge observed in low-Mg(2+) neurons.
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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) | 0.000 | 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".