Sodium Channel Toxins - Receptor Targeting and Therapeutic Potential
Bibliographic record
Abstract
Sodium channels underlie propagated electrical signalling in most excitable cells, including neurons and the myocytes of skeletal muscle and heart. These proteins are targeted by a variety of current therapeutic drugs to combat such maladies as pain, myotonias, epilepsies and cardiac arrhythmias. Typically, these problems are associated with overactivity of sodium channels leading to hyperexcitability in the relevant tissue. More than ten distinct but closely related molecular isoforms of mammalian sodium channel are now known to be specifically expressed in different cell types and tissues. Therapeutic attenuation of sodium channel activity must be effected with great precision in both targeting and the degree of reduction in channel activity if a malfunction is to be corrected without introducing deleterious or even catastrophic side effects. Numerous natural toxins have evolved to target sodium channels, either by blocking current through the pore or by modifying channel gating. Among the well studied toxins, the peptide conotoxins from cone snail venoms show a remarkable ability to discriminate among closely related forms of sodium channel, as well as exhibiting a variety of modes of action. Here, we examine the molecular basis of action of different Na channel targeted conotoxins and explore their potential as models for the future design of more specifically targeted drugs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".