Fluoxetine attenuates prepulse inhibition deficit induced by neonatal administration of MK-801 in mice
Bibliographic record
Abstract
Increasing evidence supports schizophrenia may be a neurodevelopmental and neurodegenerative disorder. Fluoxetine, a selective serotonin reuptake inhibitor, has been reported to have neuroprotective effects and be effective in treating neurodegenerative disorders including schizophrenia. The objective of the present study was to evaluate the effect and underlying neuroprotective mechanism of fluoxetine on the sensorimotor gating deficit, a schizophrenia-like behavior in a neurodevelopmental schizophrenic mouse model induced by MK-801, an N-methyl-D-aspartate glutamate receptor antagonist. On postnatal day 7, mouse pups were treated with a total seven subcutaneous daily injections of MK-801 (1 mg/kg/day), followed by intraperitoneal injection of fluoxetine (5 or 10 mg/kg/day) starting on postnatal day 14 in the MK-801-injected mice for 4 weeks. The sensorimotor gating deficit in mice was measured by prepulse inhibition (PPI) behavioral test on postnatal day 43. After the behavioral test, the protein expression of brain-derived neurotrophic factor (BDNF) was measured by western blot or ELISA in the frontal cortex of mice. Our results showed fluoxetine attenuated PPI deficit and the decrease of cerebral BDNF expression in the MK-801-injected mice. These results suggest that fluoxetine can be used to treat sensorimotor gating deficit in a neurodevelopmental mouse model of schizophrenia, and the attenuating effect of fluoxetine on sensorimotor gating deficit may be related to fluoxetine's neuroprotective effect targeting on the modulation of cerebral BDNF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".