Behavioral and cellular responses to circadian disruption and prenatal immune activation in mice
Bibliographic record
Abstract
Most individuals with neurodevelopmental disorders (NDDs), including schizophrenia and autism spectrum disorders, experience disruptions in sleep and circadian rhythms. Epidemiological studies indicate that exposure to prenatal infection increases the risk of developing NDDs. We studied how environmental circadian disruption contributes to NDDs using maternal immune activation (MIA) in mice, which models prenatal infection. Pregnant dams were injected with viral mimetic poly IC (or saline) at E9.5. Adult poly IC- and saline-exposed offspring were subjected to 4 weeks of each of the following: standard lighting (LD1), constant light (LL) and standard lighting again (LD2). Behavioral tests were conducted in the last 12 days of each condition. Poly IC exposure led to significant behavioral differences, including reduced sociability (males only) and deficits in prepulse inhibition. Interestingly, poly IC exposure led to reduced sociability specifically when males were tested after LL exposure. Mice were exposed again to either LD or LL for 4 weeks and microglia were characterized. Notably, poly IC exposure led to increased microglial morphology index and density in dentate gyrus, an effect attenuated by LL exposure. Our findings highlight interactions between circadian disruption and prenatal infection, which has implications in informing the development of circadian-based therapies for individuals with NDDs.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".