Modeling Epilepsy Using Human Induced Pluripotent Stem Cells-Derived Neuronal Cultures Carrying Mutations in Ion Channels and the Mechanistic Target of Rapamycin Pathway
Bibliographic record
Abstract
Epilepsy is a neurological disorder that affects over 65 million people globally. It is characterized by periods of seizure activity of the brain as a result of excitation and inhibition (E/I) imbalance, which is regarded as the core underpinning of epileptic activity. Both gain- and loss-of-function (GOF and LOF) mutations of ion channels, synaptic proteins and signaling molecules along the mechanistic target of rapamycin (mTOR) pathway have been linked to this imbalance. The pathogenesis of epilepsy often has its roots in the early stage of brain development. It remains a major challenge to extrapolate the findings from many animal models carrying these GOF or LOF mutations to the understanding of disease mechanisms in the developing human brain. Recent advent of the human pluripotent stem cells (hPSCs) technology opens up a new avenue to recapitulate patient conditions and to identify druggable molecular targets. In the following review, we discuss the progress, challenges and prospects of employing hPSCs-derived neural cultures to study epilepsy. We propose a tentative working model to conceptualize the possible impact of these GOF and LOF mutations in ion channels and mTOR signaling molecules on the morphological and functional remodeling of intrinsic excitability, synaptic transmission and circuits, ultimately E/I imbalance and behavioral phenotypes in epilepsy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".