Neonatal inflammation produces selective behavioural deficits and alters <i>N</i>‐methyl‐<scp>d</scp>‐aspartate receptor subunit mRNA in the adult rat brain
Bibliographic record
Abstract
Peripheral inflammation causes production of central cytokines that alter transmission at the N-methyl-D-aspartate receptor (NR). During development, NRs are important for synaptic plasticity and network connectivity. We therefore asked if neonatal inflammation would alter expression of NRs in the brain and behavioural performance in adulthood. We gave lipopolysaccharide (LPS) (100 microg/kg, i.p.) or saline to male rats on postnatal day (P)5, P14, P30 or P77. Subsequently we assessed mRNA levels of the NR1, NR2A, B, C and D subunits in the hippocampus and cortex either acutely (2 h) or in adulthood using real-time reverse transcriptase-polymerase chain reaction. We explored learning and memory behaviours in adult rats using the Morris water maze and contextual fear conditioning paradigms. Hippocampal NR1 mRNA was acutely increased in the P5- and P77-treated rats but was reduced in adults treated with LPS at P5, P30 and P77. P14 LPS-treated rats showed few acute changes but showed pronounced increases in NR2A, B, C and D subunit mRNA later in adulthood. The cortex displayed relatively few acute changes in expression in the neonatal-treated rats; however, it showed robust changes in NR2B, C and D mRNA in all groups given LPS in adulthood. Behavioural deficits were observed specifically in the P5 and P30 LPS-treated groups in the water maze probe trial and fear conditioning tests, consistent with hippocampal NR1 mRNA down-regulation. Thus, a single bout of inflammation during development can programme specific and persistent differences in NR mRNA subunit expression in the hippocampus, which could be associated with behavioural and cognitive deficits in adulthood.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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".