Possible Role of Dynorphins in Alzheimer's Disease and Age-Related Cognitive Deficits
Bibliographic record
Abstract
BACKGROUND/AIMS: Expression of dynorphin, an endogenous opioid peptide, increases with age and has been associated with cognitive deficits in rodents. Elevated dynorphin levels have been reported in postmortem samples from Alzheimer's disease (AD) patients, and prodynorphin (PDYN) gene polymorphisms might be linked to cognitive function in the elderly. Activation of κ-opioid receptors by dynorphins has been associated with stress-related memory impairments. Interestingly, these peptides can also modulate glutamate neurotransmission and may affect synaptic plasticity underlying memory formation. N-methyl-D-aspartate (NMDA) and α-amino-3-hydroxy-5-methyl-4-isoxazol-propionate (AMPA) ionotropic glutamate receptor levels generally decrease with aging, and their function is impaired in AD. METHODS: Here, we compared the impact of aging on ionotropic glutamate receptor levels in the hippocampal formation of wild-type (WT) and Pdyn knock-out (KO) mice. RESULTS: We observed a significant reduction in GluR1 and GluR2 AMPA receptor subunits in the hippocampal formation of 18- to 25-month-old WT mice in comparison with 6-month-old mice. Conversely, the GluR1 protein level was maintained in old Pdyn KO mice, and the NMDA NR2B subunit level was increased by 42% when compared to old WT animals. CONCLUSIONS: These results suggest that elevated dynorphin expression occurring during aging and AD may mediate cognitive deficits by altering the glutamatergic system integrity.
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) | 0.000 | 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 | 0.000 |
| 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".