New insights into a spectrum of developmental malformations related to<scp>mTOR</scp>dysregulations: challenges and perspectives
Bibliographic record
Abstract
In recent years the role of the mammalian target of rapamycin (mTOR) pathway has emerged as crucial for normal cortical development. Therefore, it is not surprising that aberrant activation of mTOR is associated with developmental malformations and epileptogenesis. A broad spectrum of malformations of cortical development, such as focal cortical dysplasia (FCD) and tuberous sclerosis complex (TSC), have been linked to either germline or somatic mutations in mTOR pathway-related genes, commonly summarised under the umbrella term 'mTORopathies'. However, there are still a number of unanswered questions regarding the involvement of mTOR in the pathophysiology of these abnormalities. Therefore, a monogenetic disease, such as TSC, can be more easily applied as a model to study the mechanisms of epileptogenesis and identify potential new targets of therapy. Developmental neuropathology and genetics demonstrate that FCD IIb and hemimegalencephaly are the same diseases. Constitutive activation of mTOR signalling represents a shared pathogenic mechanism in a group of developmental malformations that have histopathological and clinical features in common, such as epilepsy, autism and other comorbidities. We seek to understand the effect of mTOR dysregulation in a developing cortex with the propensity to generate seizures as well as the aftermath of the surrounding environment, including the white matter.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".