Gestational exposure to a ketogenic diet increases sociability in CD-1 mice.
Bibliographic record
Abstract
Postnatal administration of high-fat, low-carbohydrate ketogenic diets (KDs) is an established and effective treatment option for refractory epilepsy, with more recently identified therapeutic potential across a wide range of preclinical models of neurological and psychiatric disorders. However, the impact of gestational exposure to a KD (GKD) on offspring development remains unclear. Previous work has found that GKD exposure reduces depression- and anxiety-like behaviors in CD-1 mice, whereas postnatal KD improves sociability in several different rodent models of autism. Here we examined how sociability is impacted by GKD. Given that the neuropeptide oxytocin positively regulates affect, anxiety, and sociability, we also examined the effects of GKD on brain oxytocin expression. Male and female CD-1 mice exposed to either a standard diet (SD) or a KD gestationally were cross-fostered with SD dams at birth and remained on a SD from that point onward. These offspring were then tested for sociability and social novelty (three-chambered test) and depressive-like behaviors (forced swim test) at 10 weeks of age. At the conclusion of testing, brain tissue was collected and immunohistochemically processed for oxytocin expression in hypothalamic and limbic areas. We found that GKD increased sociability and reduced depressive-like symptoms, without affecting oxytocin expression in quantified areas. By expanding the scope of the lasting impact of gestational exposure to a ketogenic diet to include positive effects on sociability, these results indicate that GKDs may have novel therapeutic applications for individuals at risk for developmental disorders of social behavior, including autism and schizophrenia. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".