Effects of natural Lithium and Lithium isotopes on voltage gated sodium channel activity in SH-SY5Y and IPSC derived cortical neurons
Bibliographic record
Abstract
Although lithium (Li) is a widely used treatment for bipolar disorder, its exact mechanisms of action remain elusive. Research has shown that the two stable Li isotopes, which differ in their mass and nuclear spin, can induce distinct effects in both in vivo and in vitro studies. Since sodium (Na+) channels are the primary pathway for Li+ entry into cells, we examined how Li+ affects the current of Na+ channels using whole-cell patch-clamp techniques on SH-SY5Y neuroblastoma cells and human iPSC-derived cortical neurons. Our findings indicate that mammalian Na+ channels in both neuronal models studied here display no selectivity between Na+ and Li+, unlike previously reported bacterial Na+ channels. We observed differences between the two neuronal models in three measured parameters ( $$\:{V}_{\text{h}\text{a}\text{l}\text{f}},\:{G}_{\text{m}\text{a}\text{x}},\:z$$ ). We saw no statistically significant differences between any ions in SH-SY5Y cells, but small differences in the half-maximum activation potential ( $$\:{V}_{\text{h}\text{a}\text{l}\text{f}}$$ ) between Na+ and 6Li+ and between 7Li+ and 6Li+ were found in iPSC-derived cortical neurons. Although Na+ channels are widely expressed and important in neuronal function, the very small differences observed in this work suggest that Li+ regulation through Na+ channels is likely not the primary mechanism underlying Li+ isotope differentiation.
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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 | 0.001 |
| 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".