Adiponectin alleviated Alzheimer‐like pathologies via autophagy‐lysosomal activation
Why this work is in the frame
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Bibliographic record
Abstract
Adiponectin (APN) deficiency has also been associated with Alzheimer-like pathologies. Recent studies have illuminated the importance of APN signaling in reducing Aβ accumulation, and the Aβ elimination mechanism remains rudimentary. Therefore, we aimed to elucidate the APN role in reducing Aβ accumulation and its associated abnormalities by targeting autophagy and lysosomal protein changes. To assess, we performed a combined pharmacological and genetic approach while using preclinical models and human samples. Our results demonstrated that the APN level significantly diminished in the plasma of patients with dementia and 5xFAD mice (6 months old), which positively correlated with Mini-Mental State Examination (MMSE), and negatively correlated with Clinical Dementia Rating (CDR), respectively. APN deficiency accelerated cognitive impairment, Aβ deposition, and neuroinflammation in 5xFAD mice (5xFAD*APN KO), which was significantly rescued by AdipoRon (AR) treatment. Furthermore, AR treatment also markedly reduced Aβ deposition and attenuated neuroinflammation in APP/PS1 mice without altering APP expression and processing. Interestingly, AR treatment triggered autophagy by mediating AMPK-mTOR pathway signaling. Most importantly, APN deficiency dysregulated lysosomal enzymes level, which was recovered by AR administration. We further validated these changes by proteomic analysis. These findings reveal that APN is the negative regulator of Aβ deposition and its associated pathophysiologies. To eliminate Aβ both extra- and intracellular deposition, APN contributes via the autophagic/lysosomal pathway. It presents a therapeutic avenue for AD therapy by targeting autophagic and lysosomal signaling.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it