Tracking neuroinflammation in Alzheimer’s disease: the role of positron emission tomography imaging
Bibliographic record
Abstract
Alzheimer's disease (AD) has been reconceptualized as a dynamic pathophysiological process, where the accumulation of amyloid-beta (Aβ) is thought to trigger a cascade of neurodegenerative events resulting in cognitive impairment and, eventually, dementia. In addition to Aβ pathology, various lines of research have implicated neuroinflammation as an important participant in AD pathophysiology. Currently, neuroinflammation can be measured in vivo using positron emission tomography (PET) with ligands targeting diverse biological processes such as microglial activation, reactive astrocytes and phospholipase A2 activity. In terms of therapeutic strategies, despite a strong rationale and epidemiological studies suggesting that the use of non-steroidal anti-inflammatory drugs (NSAIDs) may reduce the prevalence of AD, clinical trials conducted to date have proven inconclusive. In this respect, it has been hypothesized that NSAIDs may only prove protective if administered early on in the disease course, prior to the accumulation of significant AD pathology. In order to test various hypotheses pertaining to the exact role of neuroinflammation in AD, studies in asymptomatic carriers of mutations deterministic for early-onset familial AD may prove of use. In this respect, PET ligands for neuroinflammation may act as surrogate markers of disease progression, allowing for the development of more integrative models of AD, as well as for the measuring of target engagement in the context of clinical trials using NSAIDs. In this review, we address the biological basis of neuroinflammatory changes in AD, underscore therapeutic strategies using anti-inflammatory compounds, and shed light on the possibility of tracking neuroinflammation in vivo using PET imaging ligands.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".