The neuregulin receptor, ErbB4, is not required for normal development and adult maintenance of the substantia nigra pars compacta
Bibliographic record
Abstract
Degeneration of dopaminergic neurons in the substantia nigra is associated with one of the most prominent human neurological disorders, Parkinson's disease. It is therefore of high interest to identify molecules with trophic effects on this neuronal population. We show here that the neuregulin receptor ErbB4 is differentially expressed in mesencephalic dopaminergic neurons, found in the substantia nigra and in a subregion of the ventral tegmentum but not in the retrorubral field. Early developmental onset and continued expression of ErbB4 into the adult and the presence of two high affinity ligands, neuregulin-1 and betacellulin, in the basal ganglia, suggested that these molecules might participate in the differentiation and/or maintenance of the nigrostriatal system. In order to address this hypothesis, we used a loxP flanked ErbB4 allele in combination with a nestin-Cre transgene and generated brain-specific ErbB4 null mice. These mutant animals survived into adulthood. The distribution of dopaminergic cell bodies in the midbrain, the expression of numerous genes specific to mesencephalic dopaminergic neurons, and the axonal projection to the basal ganglia all appeared normal. Finally, an assessment of their motor function revealed no behavioral deficits. The apparent lack of any mutant phenotype suggests the presence of a strong compensatory mechanism.
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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 | 0.000 |
| 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".