TRPV1 activation alleviates cognitive and synaptic plasticity impairments through inhibiting AMPAR endocytosis in APP23/PS45 mouse model of Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is one of the most common causes of neurodegenerative diseases in the elderly. The accumulation of amyloid-β (Aβ) peptides is one of the pathological hallmarks of AD and leads to the impairments of synaptic plasticity and cognitive function. The transient receptor potential vanilloid 1 (TRPV1), a nonselective cation channel, is involved in synaptic plasticity and memory. However, the role of TRPV1 in AD pathogenesis remains largely elusive. Here, we reported that the expression of TRPV1 was decreased in the brain of APP23/PS45 double transgenic AD model mice. Genetic upregulation of TRPV1 by adeno-associated virus (AAV) inhibited the APP processing and Aβ deposition in AD model mice. Meanwhile, upregulation of TRPV1 ameliorated the deficits of hippocampal CA1 long-term potentiation (LTP) and spatial learning and memory through inhibiting GluA2-containing α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) endocytosis. Furthermore, pharmacological activation of TRPV1 by capsaicin (1 mg/kg, i.p.), an agonist of TRPV1, dramatically reversed the impairments of hippocampal CA1 LTP and spatial learning and memory in AD model mice. Taken together, these results indicate that TRPV1 activation effectively ameliorates cognitive and synaptic functions through inhibiting AMPAR endocytosis in AD model mice and could be a novel molecule for AD treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".