JNK Activation in Alzheimer’s Disease Is Driven by Amyloid β and Is Associated with Tau Pathology
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide c-Jun N-terminal kinase 3 (JNK3) is suggested to play a key role in neurodegenerative disorders, especially in Alzheimer’s disease (AD). However, it remains unclear whether JNK or amyloid β (Aβ) appears first in the disease onset. Postmortem brain tissues from four dementia subtypes of patients (frontotemporal dementia, Lewy body dementia, vascular dementia, and AD) were used to measure activated JNK (pJNK) and Aβ levels. pJNK expression is significantly increased in AD; however, similar pJNK expression was found in other dementias. Furthermore, there was a significant correlation, co-localization, and direct interaction between pJNK expression and Aβ levels in AD. Significant increased levels of pJNK were also found in Tg2576 mice, a model of AD. In this line, Aβ 42 intracerebroventricular injection in wild-type mice was able to induce a significant elevation of pJNK levels. JNK3 overexpression, achieved by intrahippocampal injection of an adeno-associated viral vector expressing this protein, was enough to induce cognitive deficiencies and precipitate Tau aberrant misfolding in Tg2576 mice without accelerating amyloid pathology. JNK3 overexpression may therefore be triggered by increased Aβ. The latter, together with subsequent involvement of Tau pathology, may be underlying cognitive alterations in early stages of AD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".