A non-linear relation between levels of adult hippocampal neurogenesis and expression of the immature neuron marker doublecortin
Bibliographic record
Abstract
ABSTRACT We investigated the mechanisms underlying the effects of the antidepressant fluoxetine on behavior and adult hippocampal neurogenesis (AHN). After confirming our earlier report that the signaling molecule β2-arrestin is required for the antidepressant-like effects of fluoxetine, we found that the effects of fluoxetine on proliferation of neural progenitors and on survival of adult-born granule cells are absent in the β2-arrestin knockout (β2-Arr KO) mice. To our surprise fluoxetine induced a dramatic upregulation of doublecortin (DCX) in the β2-Arr KO mice, indicating that DCX expression can be increased even though AHN is not. We discovered two other conditions where DCX expression is regulated non linearly compared to levels of AHN: a chronic stress model where DCX is upregulated and an inflammation model where DCX is down regulated. We conclude that assessing DCX expression alone to quantify levels of AHN can be misleading and that caution should be applied when label retention techniques are not available. HIGHLIGHTS β2-arrestin (β-Arr2) is required for the antidepressant-like effects of fluoxetine. A dramatic upregulation of doublecortin (DCX) is observed in the β2-Arr KO mice after antidepressant treatment whereas its effects on proliferation of neural progenitors and on survival of adult-born granule cells are absent. DCX is more upregulated than the number of young neurons in a mouse model of depression. DCX is more down regulated than the number of young neurons in a model of inflammation. microRNAs (miRs) may contribute to the regulation of DCX mRNA expression.
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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".