Capsaicin Attenuates Amyloid-β-Induced Synapse Loss and Cognitive Impairments in Mice
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most common cause of progressive cognitive impairment in the aged. The aggregation of the amyloid β-protein (Aβ) is a hallmark of AD and is linked to synapse loss and cognitive impairment. Capsaicin, a specific agonist of the transient receptor potential vanilloid 1 (TRPV1), has been proven to ameliorate stress-induced AD-like pathological and cognitive impairments, but it is unclear whether TRPV1 activation can affect cognitive and synaptic functions in Aβ-induced mouse model of AD. In this study, we investigated the effects of TRPV1 activation on spatial memory and synaptic plasticity in mice treated with Aβ. To induce AD-like pathological and cognitive impairments, adult C57Bl/6 mice were microinjected with Aβ42 (100 μM, 2.5 μl/mouse, i.c.v.). Two weeks after Aβ42 microinjection, spatial learning and memory as well as hippocampal long-term potentiation (LTP) were examined. The results showed that Aβ42 microinjection significantly impaired spatial learning and memory in the Morris water maze and novel object recognition tests compared with controls. These behavioral changes were accompanied by synapse loss and impaired LTP in the CA1 area of hippocampus. More importantly, daily capsaicin (1 mg/kg, i.p.) treatment throughout the experiment dramatically improved spatial learning and memory and synaptic function, as reflected by enhanced hippocampal LTP and reduced synapse loss, whereas the TRPV1 antagonist capsazepine (1 mg/kg, i.p.) treatment had no effects on cognitive and synaptic function in Aβ42-treated mice. These results indicate that TRPV1 activation by capsaicin rescues cognitive deficit in the Aβ42-induced mouse model of AD both structurely and functionally.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".