Spatial training preserves associative memory capacity with augmentation of dendrite ramification and spine generation in Tg2576 mice
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disorder and there is currently no efficient cure for this devastating disease. Cognitive stimulation can delay memory loss during aging and in patients with mild cognitive impairment. In 3 × Tg-AD mice, training decreased the neuropathologies with transient amelioration of memory decline. However, the neurobiological mechanisms underlying the learning-improved memory capacity are poorly understood. Here, we found in Tg2576 mice spatial training in Morris water maze (MWM) remarkably improved the subsequent associative memory acquisition detected by contextual fear conditioning. We also found that spatial training enhanced long term potentiation, dendrite ramification and spine generation in hippocampal dentate gyrus (DG) and CA1 neurons at 24 h after the training. In the molecular level, the MWM training remarkably activated calcium/calmodulin-dependent protein kinase II (CaMKII) with elevation of glutamate AMPA receptor GluA1 subunit (GluA1), postsynaptic density protein 93 (PSD93) and postsynaptic density protein 95 (PSD95) in the hippocampus. Finally, the training also significantly ameliorated AD-like tau and amyloid pathologies. We conclude that spatial training in MWM preserves associative memory capacity in Tg2576 mice, and the mechanisms involve augmentation of dendrite ramification and spine generation in hippocampus.
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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 | 0.001 | 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) | 0.001 | 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".