Roles of gap junctions, connexins, and pannexins in epilepsy
Bibliographic record
Abstract
Enhanced gap junctional communication (GJC) between neurons is considered a major factor underlying the neuronal synchrony driving seizure activity. In addition, the hippocampal sharp wave ripple complexes, associated with learning and seizures, are diminished by GJC blocking agents. Although gap junctional blocking drugs inhibit experimental seizures, they all have other non-specific actions. Besides interneuronal GJC between dendrites, inter-axonal and inter-glial GJC is also considered important for seizure generation. Interestingly, in most studies of cerebral tissue from animal seizure models and from human patients with epilepsy, there is up-regulation of glial, but not neuronal gap junctional mRNA and protein. Significant changes in the expression and post-translational modification of the astrocytic connexin Cx43, and Panx1 were observed in an in vitro Co(++) seizure model, further supporting a role for glia in seizure-genesis, although the reasons for this remain unclear. Further suggesting an involvement of astrocytic GJC in epilepsy, is the fact that the expression of astrocytic Cx mRNAs (Cxs 30 and 43) is several fold higher than that of neuronal Cx mRNAs (Cxs 36 and 45), and the number of glial cells outnumber neuronal cells in mammalian hippocampal and cortical tissue. Pannexin expression is also increased in both animal and human epileptic tissues. Specific Cx43 mimetic peptides, Gap 27 and SLS, inhibit the docking of astrocytic connexin Cx43 proteins from forming intercellular gap junctions (GJs), diminishing spontaneous seizures. Besides GJs, Cx membrane hemichannels in glia and Panx membrane channels in neurons and glia are also inhibited by traditional gap junctional pharmacological blockers. Although there is no doubt that connexin-based GJs and hemichannels, and pannexin-based membrane channels are related to epilepsy, the specific details of how they are involved and how we can modulate their function for therapeutic purposes remain to be elucidated.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".