Orchestrating Neural Development Through <scp>mRNA</scp> Translation Regulation
Bibliographic record
Abstract
Neural development is a highly intricate process that relies on the precise regulation of gene expression. While a significant focus has been placed on understanding the transcriptional control of brain development, the regulation of mRNA translation plays a fundamental role in controlling gene expression. mRNA translation in subcellular compartments distant from the cell body, such as neuronal growth cones and astrocytic processes, allows for a rapid response to the local environment. Thus, the regulation of mRNA translation influences neurodevelopmental mechanisms such as cell fate decisions, neural stem cell proliferation and differentiation, and axon guidance. As such, the dysregulation of mRNA translation can have profound consequences for neural development, leading to conditions like microcephaly, cortical malformations, autism spectrum disorders, and fragile X syndrome. This review provides an overview of mRNA translation mechanisms that control prenatal brain development and identifies significant knowledge gaps. Specifically, we focus on mRNA translation regulation through signaling cascades such as the mammalian/mechanistic target of rapamycin complex 1 (mTORC1), the integrated stress response, Fragile X Messenger Ribonucleoprotein 1 (FMRP) and eukaryotic elongation factor 2/kinase (eEF2/eEF2K), all of which are critical for mRNA translational regulation and have been previously studied regarding brain development.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".