Maternal immune activation accelerates pup reflex development and alters immune proteins in pup stomach contents and brain
Bibliographic record
Abstract
Prenatal infection increases the risk for neurodevelopmental disorders including autism spectrum disorder and schizophrenia. To better understand this link, a number of maternal immune activation (MIA) rodent models have been studied. However, the majority of these studies focus on adult behavioural outcomes that mirror adult symptoms related to neurodevelopmental disorders. There is little research reporting the effects of MIA on early postnatal development and even fewer using outbred mouse strains. Here, we use a modified version of the Fox scale to assess the effects of two MIA models, a bacterial model (LPS) and a viral model (PolyIC), on overall mouse pup sensorimotor development in CD-1 mice. Surprisingly, both bacterial and viral MIA models resulted in early reflex development when compared with control pups. To better characterize potential factors related to these changes, we examined indicators of sickness/inflammation in the immune-activated dams and in their pups. Sickness behaviour in the dams resulting from immune activation was assessed using a telemetry implant that allowed for continuous recording of temperature and activity in dams exposed to bacterial or viral immune activation. Although MIA dams showed reduced activity on the day immediately following MIA compared to controls, there was no evidence of fever. All dams showed elevated cytokines/chemokines associated with parturition, but this resolved by day 10 post-parturition and was unaffected by previous immune activation. Although circulating cytokines/chemokines in the dams were similar across MIA treatments, there were differences in the amount of interleukin-12p70 and interleukin-13 present in milk taken from milk bands in MIA pups, and interleukin-4 was overall decreased in LPS pup brain. These findings demonstrate that bacterial and viral models of MIA can result in similar precocious development in mice but differing long-term effects on inflammatory markers in both the milk provided to the pups and in their brains.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".