Amyloid beta and tau are associated with the dual effect of neuroinflammation on neurodegeneration
Bibliographic record
Abstract
INTRODUCTION: Current literature presents conflicting results regarding the impact of neuroinflammation on Alzheimer's disease (AD)-related neurodegeneration. While some studies suggest that neuroinflammation potentiates neurodegeneration, others indicate a protective effect. METHODS: We evaluated 145 individuals with positron emission tomography (PET) for amyloid beta (Aβ), tau, and translocator protein (TSPO), a proxy of neuroinflammation, to test the hypothesis that Aβ and tau are associated with the dual effect of neuroinflammation on neurodegeneration across the AD continuum. RESULTS: The detrimental effects of neuroinflammation on gray matter density occurred in two waves. The first neuroinflammation-related detrimental wave was associated with brain Aβ deposition, while the second was with widespread tau tangle pathology. Furthermore, the concomitant presence of neuroinflammation, Aβ, and tau was associated with faster cognitive decline over 2 years. CONCLUSIONS: Our results support a model in which Aβ- and tau-associated neuroinflammation are related to two waves of deleterious effects on AD-related neurodegeneration. HIGHLIGHTS: Two waves of detrimental neuroinflammation effects on brain density associated with Aβ or tau. Aβ associated with deleterious effect of neuroinflammation on brain density in early AD. Tau associated with deleterious effect of neuroinflammation on brain density in late AD. Interactions of Aβ, tau, and neuroinflammation are associated with cognitive decline.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".