Chronic Desipramine Reverses Deficits in Cell Activity, Norepinephrine Innervation, and Anxiety–Depression Phenotypes in Fluoxetine-Resistant cF1ko Mice
Bibliographic record
Abstract
Selective serotonin (5-HT) reuptake inhibitors are only 30% effective for remission in subjects with major depression, and the best treatments for SSRI-resistant patients remain unclear. To model SSRI resistance, we used cF1ko mice with conditional deletion of the repressor Freud-1/CC2D1A in adult 5-HT neurons. Within weeks, this deletion leads to overexpression of 5-HT1A autoreceptors, reduced serotonergic activity, and fluoxetine-resistant anxiety-depression phenotype. We hypothesized that desipramine (DES), which targets norepinephrine (NE), may be effective in cF1ko mice. The actions of chronic DES treatment on behavior, chronic cellular activation, and NE projections were examined in both sexes of cF1ko and WT mice. In contrast to fluoxetine, chronic DES reversed the behavioral phenotypes in cF1ko mice, while in WT littermates DES slightly increased anxiety and depression-like behaviors. Deficits in FosB+ cell counts were seen in the entorhinal cortex, hippocampal CA2/3 layer, and BLA of cF1ko mice and were reversed by chronic DES treatment, especially in GABAergic neurons. In cF1ko mice, widespread reductions were seen in NE axons, varicosities, and especially 30-60% reductions in NE synaptic and triadic contacts, particularly to inhibitory gephyrin-positive sites. DES treatment also reversed these reductions in NE innervation. These results indicate the dynamic plasticity of the adult noradrenergic system within weeks of altering serotonergic function that can be normalized by DES treatment. Accompanying these changes, DES but not fluoxetine reversed the behavioral alterations in cF1ko mice, suggesting a key role for noradrenergic plasticity in antidepressant response in this model of reduced serotonin activity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".