Involvement of micro<scp>RNA</scp>s in epileptogenesis
Bibliographic record
Abstract
Patients who have sustained brain injury or had developmental brain lesions present a non-negligible risk for developing delayed epilepsy. Finding therapeutic strategies to prevent development of epilepsy in at-risk patients represents a crucial medical challenge. Noncoding microRNA molecules (miRNAs) are promising candidates in this area. Indeed, deregulation of diverse brain-specific miRNAs has been observed in animal models of epilepsy as well as in patients with epilepsy, mostly in temporal lobe epilepsy (TLE). Herein we review deregulated miRNAs reported in epilepsy with potential roles in key molecular and cellular processes underlying epileptogenesis, namely neuroinflammation, cell proliferation and differentiation, migration, apoptosis, and synaptic remodeling. We provide an up-to-date listing of miRNAs altered in epileptogenesis and assess recent functional studies that have interrogated their role in epilepsy. Last, we discuss potential applications of these findings for the future development of disease-modifying therapeutic strategies for antiepileptogenesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".