Deciphering the physiopathology of neurodevelopmental disorders using brain organoids
Bibliographic record
Abstract
Neurodevelopmental disorders (NDD) encompass a range of conditions marked by abnormal brain development in conjunction with impaired cognitive, emotional and behavioural functions. Transgenic animal models, mainly rodents, traditionally served as key tools for deciphering the molecular mechanisms driving NDD physiopathology and significantly contributed to the development of pharmacological interventions aimed at treating these disorders. However, the efficacy of these treatments in humans has proven to be limited, due in part to the intrinsic constraint of animal models to recapitulate the complex development and structure of the human brain but also to the phenotypic heterogeneity found between affected individuals. Significant advancements in the field of induced pluripotent stem cells (iPSCs) offer a promising avenue for overcoming these challenges. Indeed, the development of advanced differentiation protocols for generating iPSC-derived brain organoids gives an unprecedented opportunity to explore human neurodevelopment. This review provides an overview of how 3D brain organoids have been used to investigate various NDD (i.e. Fragile X syndrome, Rett syndrome, Angelman syndrome, microlissencephaly, Prader-Willi syndrome, Timothy syndrome, tuberous sclerosis syndrome) and elucidate their pathophysiology. We also discuss the benefits and limitations of employing such innovative 3D models compared to animal models and 2D cell culture systems in the realm of personalized medicine.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".