α-Melanocyte Stimulating Hormone Prevents GABAergic Neuronal Loss and Improves Cognitive Function in Alzheimer's Disease
Bibliographic record
Abstract
In Alzheimer's disease (AD), appropriate excitatory-inhibitory balance required for memory formation is impaired. Our objective was to elucidate deficits in the inhibitory GABAergic system in the TgCRND8 mouse model of AD to establish a link between GABAergic dysfunction and cognitive function. We sought to determine whether the neuroprotective peptide α-melanocyte stimulating hormone (α-MSH) attenuates GABAergic loss and thus improves cognition. TgCRND8 mice with established β-amyloid peptide pathology and nontransgenic littermates were treated with either α-MSH or vehicle via daily intraperitoneal injections for 28 d. TgCRND8 mice exhibited spatial memory deficits and altered anxiety that were rescued after α-MSH treatment. The expression of GABAergic marker glutamic acid decarboxylase 67 (GAD67) and the number of GABAergic GAD67+ interneurons expressing neuropeptide Y and somatostatin are reduced in the hippocampus in vehicle-treated TgCRND8 mice. In the septohippocampal pathway, GABAergic deficits are observed before cholinergic deficits, suggesting that GABAergic loss may underlie behavior deficits in vehicle-treated TgCRND8 mice. α-MSH preserves GAD67 expression and prevents loss of the somatostatin-expressing subtype of GABAergic GAD67+ inhibitory interneurons. Without decreasing β-amyloid peptide load in the brain, α-MSH improves spatial memory in TgCRND8 mice and prevents alterations in anxiety. α-MSH modulated the excitatory-inhibitory balance in the brain by restoring GABAergic inhibition and, as a result, improved cognition in TgCRND8 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 | 0.000 |
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".