Neonatal Behavioral Changes in Rats With Gestational Exposure to Lipopolysaccharide: A Prenatal Infection Model for Developmental Neuropsychiatric Disorders
Bibliographic record
Abstract
Exposure to prenatal infections has been widely associated with the increased risk for neuropsychiatric disorders of developmental origin such as schizophrenia and autism. Although several behavioral and cognitive deficits have been detected during adulthood in rodent models of prenatal infections, early behavioral changes have not been well characterized. In a prenatal lipopolysaccharide (LPS) model, we have previously observed significant alterations in the neuronal cytoarchitecture during early postnatal life. In the present study, we aimed to investigate the potential effects of prenatal immune activation on early neurophenotypic presentations using a set of behavioral test battery. Female Sprague-Dawley rats were administered with 100 μg/kg LPS (intraperitoneally) at gestational days 15 and 16. During the first postnatal week, we found no significant effect on maternal behavior or mother-pup interaction by this treatment. Also, no major changes in physical developmental milestones of pups were noted from postnatal (P) days P6 to P16. Importantly, prenatal LPS-exposed pups had a significant decrease in the number and duration of ultrasonic vocalization calls at P3 and P5. Prenatal LPS treatment also led to impairments in nest-seeking behavior and odor-stroke associative learning in neonatal rats at P8 and P9. At the molecular level, we detected significant decrease in the expression of cortical 5HT1A and 5HT1B messenger RNA at P3. These data suggest that prenatal exposure to an immune activator can significantly impair the social/communicative behavior in the neonate offspring, which may be relevant to childhood and premorbid abnormalities reported in autism and schizophrenia subjects.
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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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".