Patient-Derived Stem Cells, Another in vitro Model, or the Missing Link Toward Novel Therapies for Autism Spectrum Disorders?
Bibliographic record
Abstract
Autism Spectrum Disorders (ASDs) is a multigenic and multifactorial neurodevelopmental group of disorders diagnosed in early childhood, leading to deficits in social interaction, verbal and non-verbal communication and characterized by restricted and repetitive behaviours and interests. To date, genetic, descriptive and mechanistic aspects of the ASDs have been investigated using mouse models and post mortem brain tissue. More recently, the technology to generate stem cells from patients’ samples has brought a new avenue for modelling ASD through 2D and 3D neuronal models that are derived from a patient's own cells, with the goal of building new therapeutic strategies for treating ASDs. This review analyses how studies performed on mouse models and human samples can complement each other, advancing our current knowledge into the pathophysiology of the ASDs. Regardless of the genetic and phenotypic heterogeneities of ASDs, convergent information regarding the molecular and cellular mechanisms involved in these disorders can be extracted from these models. Thus, considering the complexities of these disorders, patient-derived models have immense potential to elucidate molecular deregulations that contributed to the different autistic phenotypes. Through these direct investigations with the human in vitro models, they offer the potential for opening new therapeutic avenues that can be translated into the clinic.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".