The role of calcium and cyclic nucleotide signaling in cerebellar granule cell migration under normal and pathological conditions
Bibliographic record
Abstract
In the developing brain, immature neurons migrate from their sites of origin to their final destination, where they reside for the rest of their lives. This active movement of immature neurons is essential for the formation of normal neuronal cytoarchitecture and proper differentiation. Deficits in migration result in the abnormal development of the brain, leading to a variety of neurological disorders. A myriad of extracellular guidance molecules and intracellular effector molecules is involved in controlling the migration of immature neurons in a cell type, cortical layer and birth-date-specific manner. To date, little is known about how extracellular guidance molecules transfer their information to the intracellular effector molecules, which regulate the migration of immature neurons. In this article, to fill the gap between extracellular guidance molecules and intracellular effector molecules, using the migration of cerebellar granule cells as a model system of neuronal cell migration, we explore the role of second messenger signaling (specifically Ca(2+) and cyclic nucleotide signaling) in the regulation of neuronal cell migration. We will, first, describe the cortical layer-specific changes in granule cell migration. Second, we will discuss the roles of Ca(2+) and cyclic nucleotide signaling in controlling granule cell migration. Third, we will present recent studies showing the roles of Ca(2+) and cyclic nucleotide signaling in the deficits in granule cell migration in mouse models of fetal alcohol spectrum disorders and fetal Minamata disease.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".