Impaired hippocampal LTP in inbred mouse strains can be rescued by β‐adrenergic receptor activation
Bibliographic record
Abstract
Long-term potentiation (LTP), an activity-dependent enhancement of synaptic strength, and memory can be influenced by neuromodulatory transmitters such as norepinephrine (NE) and also by genetic background. beta-Adrenergic receptor activation can facilitate the expression of hippocampal CA1 LTP induced by weak stimulus patterns, but its influence on LTP induced by stronger stimulus patterns is unclear. We examined neural NE and dopamine (DA) levels, beta-adrenergic receptor expression and hippocampal LTP in genetically diverse inbred mouse strains. Brain tissue levels of NE were significantly lower in strains 129S1/SvImJ (129), BALB/cByJ (BALB) and C3H/HeJ (C3H) than in C57BL/6NCrlBR (B6). Western blot analysis showed that hippocampal beta(1)-adrenergic receptor expression was similar in strains B6, 129 and C3H, but was increased in BALB. LTP was induced in area CA1 of hippocampal slices by four trains of high-frequency stimulation (HFS) of the Schaeffer collaterals in the four inbred strains. Two hours after induction, LTP was significantly reduced in strains 129, BALB and C3H compared to B6, correlating with neural NE levels. We rescued hippocampal LTP in strains 129, BALB and C3H to levels seen in B6 by bath application of 1 microm isoproterenol, a beta-adrenergic receptor agonist, during HFS. Propranolol, a beta-adrenergic receptor antagonist, blocked this rescue in 129, BALB and C3H but did not affect LTP in strain B6. Thus, although this form of multitrain LTP does not rely on beta-adrenergic receptor activation, our data show that pharmacological activation of beta-adrenergic receptors during multiple trains of HFS can rescue CA1 LTP in genetically diverse strains with impaired LTP.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".