Improvement of Cognitive Functions And Neurotrophic Effects through by Α5‐GABAA Receptor Positive Allosteric Modulation in Animal Models of Risk for Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Background Reduced GABA/somatostatin (SST) signaling is reported in psychiatric, stress‐related and neurodegenerative disorders. SST+ interneurons from cortical layers and the hippocampus inhibit the dendrites of excitatory neurons, largely through α5‐containing GABAA receptors (α5‐GABAAR). Recently, we showed that an α5‐positive allosteric modulator (α5‐PAM) alleviates working memory deficits and reverses neuronal atrophy in old mice. We then started to investigate the behavioral and neurotrophic effects of this α5‐PAM in animal models of aging, chronic stress, and β‐amyloid load, as risk factor for developing Alzheimer’s disease. Method Three studies are presented, with ∼12 mice/group, 50% female: 1) Young C57BL6 subjected to unpredictable chronic mild stress (UCMS) to induce cognitive deficits. 2) 22 month‐old C57BL6 developing an age‐related cognitive decline. 3) 5xFAD transgenic mice with progressive amyloid‐related cognitive decline. In all studies, efficacy of chronic administration of GL‐II‐73 (30mg/kg, p.o, for 4 weeks) at rescuing cognitive deficits across 3 domains was assessed. Working memory was assessed in an alternation task, spatial memory in the water maze, and cognitive flexibility in a set‐shifting assay. Brains were then stained using Golgi‐Cox technique (n = 4brain/group; 8cell/brain) for quantification of dendritic length and spine density in the prefrontal cortex and hippocampus (NeuroLucida). Result Chronic treatment in all models reversed cognitive deficits across domains (ps<0.01), with a strong effect on working memory. Chronic treatment also significantly reversed UCMS‐, age‐, or amyloid‐induced dendritic shrinkage and reduction of spine density at apical and basal dendrites (p<0.001 in PFC and CA1). Conclusion Together, results support that selective α5 targeting of GABAA receptors overcomes chronic stress‐, aging‐, or amyloid‐related cognitive deficits and detriments in neuronal morphology. This represents the first intervention targeting the GABAergic system to have a symptomatic and disease‐modifying therapeutic potential in a transdiagnostic manner. This could represent a major avenue for clinical development for patients suffering from cognitive deficits across brain disorders.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".