Mechanisms of infantile epileptic spasms syndrome: What have we learned from animal models?
Bibliographic record
Abstract
The devastating developmental and epileptic encephalopathy of infantile epileptic spasms syndrome (IESS) has numerous causes, including, but not limited to, brain injury, metabolic, and genetic conditions. Given the stereotyped electrophysiologic, age-dependent, and clinical findings, there likely exists one or more final common pathways in the development of IESS. The identity of this final common pathway is unknown, but it may represent a novel therapeutic target for infantile spasms. Previous research on IESS has focused largely on identifying the neuroanatomic substrate using specialized neuroimaging techniques and cerebrospinal fluid analysis in human patients. Over the past three decades, several animal models of IESS were created with an aim to interrogate the underlying pathogenesis of IESS, to identify novel therapeutic targets, and to test various treatments. Each of these models have been successful at recapitulating multiple aspects of the human IESS condition. These animal models have implicated several different molecular pathways in the development of infantile spasms. In this review we outline the progress that has been made thus far using these animal models and discuss future directions to help researchers identify novel treatments for drug-resistant IESS.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".