Effect of juvenile pretraining on adolescent structural hippocampal attributes as a substrate for enhanced spatial performance
Bibliographic record
Abstract
Research has demonstrated that Long-Evans rats (LER) display superior mnemonic function over Wistar rats (WR). These differences are correlated with endogenous and input-dependent properties of the hippocampus. The present work sought to determine if juvenile pretraining might enhance hippocampal structural markers and if this would be associated with spatial processing improvements. Male and female WR and LER were either handled or trained on a water maze task from postnatal day 16 (p16) to p26 (pretraining). All animals were then trained on the task from p40 to p44 followed by immunohistochemical assessment of synaptophysin (to mark presynaptic terminals), MAP-2 (to mark dendrites), and the phosphorylated (activated) form of the extracellular signal-regulated kinase-1 (pERK1) in the hippocampus. From p19 to p20, LER (both male and female) showed a dramatic improvement in locating the hidden platform compared to their WR counterparts. On the first day of training at p40, all pretrained groups showed shorter latencies to locate the platform compared to groups without pretraining. Over the next 4 d, only pretrained male LER showed enhanced memory. Immunohistochemical analysis revealed fewer pERK1-labeled neurons in the CA3 hippocampal region in all pretrained groups and fewer pERK1-labeled neurons in the CA1 region of pretrained male LER. Pretrained male LER also showed more MAP-2 staining in CA1 and dentate gyrus regions. Synaptophysin staining revealed a pattern of axonal redistribution in the CA3 region in the pretrained groups. Results suggest a pattern of structural hippocampal alterations that may help to identify network malleability following pretraining protocols.
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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 | 0.000 | 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".