Cortical‐layer‐specific effects of PACAP and<scp>tPA</scp>on interneuron migration during post‐natal development of the cerebellum
Bibliographic record
Abstract
During early post-natal development of the cerebellum, granule neurons (GN) execute a centripetal migration toward the internal granular layer, whereas basket and stellate cells (B/SC) migrate centrifugally to reach their final position in the molecular layer (ML). We have previously shown that pituitary adenylate cyclase-activating polypeptide (PACAP) stimulates in vitro the expression and release of the serine protease tissue-type plasminogen activator (tPA) from GN, but the coordinated role of PACAP and tPA during interneuron migration has not yet been investigated. Here, we show that endogenous PACAP is responsible for the transient arrest phase of GN at the level of the Purkinje cell layer (PCL) but has no effect on B/SC. tPA is devoid of direct effect on GN motility in vitro, although it is widely distributed along interneuron migratory routes in the ML, PCL, and internal granular layer. Interestingly, plasminogen activator inhibitor 1 reduces the migration speed of GN in the ML and PCL, and that of B/SC in the ML. Taken together, these results reveal for the first time that tPA facilitates the migration of both GN and fast B/SC at the level of their intersection in the ML through degradation of the extracellular matrix. Crucial role of tissue plasminogen activator (tPA) in interneuron migration. Interneuron migration is a critical step for normal establishment of neuronal network. This study indicates that, in the post-natal cerebellum, tPA facilitates the opposite migration of immature excitatory granule neurons (GN) and immature inhibitory basket/stellate cells (B/SC) along the same migratory route. These data show that tPA exerts a pivotal role in neurodevelopment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".