PAK Inactivation Impairs Social Recognition in 3xTg-AD Mice without Increasing Brain Deposition of Tau and A
Bibliographic record
Abstract
Defects in p21-activated kinase (PAK) are suspected to play a role in cognitive symptoms of Alzheimer's disease (AD). Dysfunction in PAK leads to cofilin activation, drebrin displacement from its actin-binding site, actin depolymerization/severing, and, ultimately, defects in spine dynamics and cognitive impairment in mice. To determine the role of PAK in AD, we first quantified PAK by immunoblotting in homogenates from the parietal neocortex of subjects with a clinical diagnosis of no cognitive impairment (n = 12), mild cognitive impairment (n = 12), or AD (n = 12). A loss of total PAK, detected in the cortex of AD patients (-39% versus controls), was correlated with cognitive impairment (r(2) = 0.148, p = 0.027) and deposition of total and phosphorylated tau (r(2) = 0.235 and r(2) = 0.206, respectively), but not with Aβ42 (r(2) = 0.056). Accordingly, we found a decrease of total PAK in the cortex of 12- and 20-month-old 3xTg-AD mice, an animal model of AD-like Aβ and tau neuropathologies. To determine whether PAK dysfunction aggravates AD phenotype, 3xTg-AD mice were crossed with dominant-negative PAK mice. PAK inactivation led to obliteration of social recognition in old 3xTg-AD mice, which was associated with a decrease in cortical drebrin (-25%), but without enhancement of Aβ/tau pathology or any clear electrophysiological signature. Overall, our data suggest that PAK decrease is a consequence of AD neuropathology and that therapeutic activation of PAK may exert symptomatic benefits on high brain function.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".