Studying Human Neurodevelopment and Diseases Using 3D Brain Organoids
Bibliographic record
Abstract
In vitrodifferentiation of pluripotent stem cells provides a systematic platform to study development and disease. Recent advances in brain organoid technology have created new opportunities to investigate the formation and function of the human brain, under physiological and pathological conditions. Brain organoids can be generated to model the cellular and structural development of the human brain, and allow the investigation of the intricate interactions between resident neural and glial cell types. Combined with new advances in gene editing, imaging, and genomic analysis, brain organoid technology can be applied to address questions pertinent to human brain development, disease, and evolution. However, the current iterations of brain organoids also have limitations in faithfully recapitulating thein vivoprocesses. In this perspective, we evaluate the recent progress in brain organoid technology, and discuss the experimental considerations for its utilization. Dual Perspectives Companion Paper: Integrating CRISPR Engineering and hiPSC-Derived 2D Disease Modeling Systems, by Kristina Rehbach, Michael B. Fernando, and Kristen J. Brennand
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".