Nurr1 Is Not an Essential Regulator of BDNF in Mouse Cortical Neurons
Bibliographic record
Abstract
Nurr1 and brain-derived neurotrophic factor (BDNF) play major roles in cognition. Nurr1 regulates BDNF in midbrain dopaminergic neurons and cerebellar granule cells. Nurr1 and BDNF are also highly expressed in the cerebral cortex, a brain area important in cognition. Due to Nurr1 and BDNF tissue specificity, the regulatory effect of Nurr1 on BDNF in different brain areas cannot be generalized. The relationship between Nurr1 and BDNF in the cortex has not been investigated previously. Therefore, we examined Nurr1-mediated BDNF regulation in cortical neurons in activity-dependent and activity-independent states. Mouse primary cortical neurons were treated with the Nurr1 agonist, amodiaquine (AQ). Membrane depolarization was induced by KCl or veratridine and reversed by nimodipine. AQ and membrane depolarization significantly increased Nurr1 (p < 0.001) and BDNF (pAQ < 0.001, pKCl < 0.01) as assessed by real-time qRT-PCR. However, Nurr1 knockdown did not affect BDNF gene expression in resting or depolarized neurons. Accordingly, the positive correlation between Nurr1 and BDNF expression in AQ and membrane depolarization experiments does not imply co-regulation because Nurr1 knockdown did not affect BDNF gene expression in resting or depolarized cortical neurons. Therefore, in contrast to midbrain dopaminergic neurons and cerebellar granule cells, Nurr1 does not regulate BDNF in cortical neurons.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".