The ketamine metabolite ( <i>2R,6R</i> )‐hydroxynorketamine rescues hippocampal mRNA translation, synaptic plasticity and memory in mouse models of Alzheimer's disease
Bibliographic record
Abstract
INTRODUCTION: Impaired brain protein synthesis, synaptic plasticity, and memory are major hallmarks of Alzheimer's disease (AD). The ketamine metabolite (2R,6R)-hydroxynorketamine (HNK) has been shown to modulate protein synthesis, but its effects on memory in AD models remain elusive. METHODS: We investigated the effects of HNK on hippocampal protein synthesis, long-term potentiation (LTP), and memory in AD mouse models. RESULTS: HNK activated extracellular signal-regulated kinase 1/2 (ERK1/2), mechanistic target of rapamycin (mTOR), and p70S6 kinase 1 (S6K1)/ribosomal protein S6 signaling pathways. Treatment with HNK rescued hippocampal LTP and memory deficits in amyloid-β oligomers (AβO)-infused mice in an ERK1/2-dependent manner. Treatment with HNK further corrected aberrant transcription, LTP and memory in aged APP/PS1 mice. DISCUSSION: Our findings demonstrate that HNK induces signaling and transcriptional responses that correct synaptic and memory deficits in AD mice. These results raise the prospect that HNK could serve as a therapeutic approach in AD. HIGHLIGHTS: The ketamine metabolite HNK activates hippocampal ERK/mTOR/S6 signaling pathways. HNK corrects hippocampal synaptic and memory defects in two mouse models of AD. Rescue of synaptic and memory impairments by HNK depends on ERK signaling. HNK corrects aberrant transcriptional signatures in APP/PS1 mice.
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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) | 0.001 | 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 | 0.001 |
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".