Blocking the eIF2α Kinase (PKR) Enhances Positive and Negative Forms of Cortex-Dependent Taste Memory
Bibliographic record
Abstract
Age-associated memory deterioration (and the decline in ability to acquire new information) is one of the major diseases of our era. Cognitive enhancement can be achieved by using psycho-stimulants, such as caffeine or nicotine, but very little is known about drugs that can enhance the consolidation phase of memories in the cortex, the brain structure considered to store, at least partially, long-term memories. We used cortex-dependent taste-learning paradigms to test the hypothesis that pharmacological manipulation of the translation initiation eIF2α, which plays a role in hippocampus-dependent memory, can enhance positive or negative forms of taste memories. We found that dephosphorylation (Ser51) of eIF2α, specifically in the cortex, is both correlated with and necessary for normal memory consolidation. To reduce eIF2α phosphorylation and improve memory consolidation, we pharmacologically inhibited one of the eIF2α kinases, PKR, which is known to be involved in brain aging and Alzheimer's disease. Systemic or local microinjection of PKR inhibitor to the gustatory cortex enhanced both positive and negative forms of taste memory in rats and mice. Our results provide clear evidence that PKR plays a major role in cortex-dependent memory consolidation and, therefore, that pharmacological inhibition of PKR is a potential target for drugs to enhance cognition.
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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) | 0.000 | 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 | 0.001 |
| 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".