Neonatal programming of the rat neuroimmune response: stimulus specific changes elicited by bacterial and viral mimetics
Bibliographic record
Abstract
Recently, it has been shown that the neonatal immune environment can have significant programming effects on the adult neuroimmune response. A single neonatal immune challenge with the bacterial mimetic lipopolysaccharide (LPS) can alter the neuroendocrine, neurochemical and febrile responses to a subsequent, homotypic (LPS) immune challenge as adults. As the programming effects of viral stimuli during this neonatal period are unknown, we tested whether the viral mimetic polyinosinic-polycytidylic acid (PolyIC), administered on postnatal day 14 (P14) would alter the adult neuroimmune responses to a subsequent PolyIC challenge. Our results show that animals treated neonatally with PolyIC had significantly attenuated febrile responses to an adult PolyIC challenge, which coincided with a heightened corticosteroid response. When the corticosteroid receptor blocker RU486 was administered prior to the adult PolyIC challenge, animals treated neonatally with PolyIC no longer displayed attenuated febrile responses. Similar responses to an adult LPS challenge have been seen in animals that were exposed neonatally to LPS, indicating that both neonatal immune stimuli elicit highly similar programming effects on the adult neuroimmune responses. However, we find that neither neonatal PolyIC nor neonatal LPS challenges led to an alteration in the adult febrile or corticosteroid responses to a heterotypic adult immune challenge, indicating that the programming effects of the neonatal immune environment are stimulus specific, and do not alter the adult responses to other immune stimuli.
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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 | 0.000 |
| 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".