Targeting microglia for the treatment of Alzheimer’s disease
Bibliographic record
Abstract
INTRODUCTION: Activated microglia are associated with the progression of Alzheimer's disease (AD), as well as many other neurodegenerative diseases of aging. Microglia are therefore key targets for therapeutic intervention. AREAS COVERED: β-amyloid (Aβ) deposits activate the complement system, which, in turn, stimulates microglia to release neurotoxic materials. Research has focused primarily on anti-inflammatory agents to temper this toxic effect. More recently there has been a focus on converting microglia from this M1 state to an M2 state in which the toxic effects are reduced and their phagocytic activity toward Aβ enhanced. Studies in transgenic mice have suggested a number of possible anti-inflammatory approaches but they may not always be a good model. An example is vaccination with antibodies to Aβ, which is effective in mouse models, but has repeatedly failed in clinical trials. Biomarker studies indicate that AD commences many years prior to clinical onset. EXPERT OPINION: A hopeful approach to a disease-modifying treatment of AD is to administer agents that inhibit the inflammatory stimulation of microglia or successfully convert them to an M2 state. However, any such treatment must be started early in the disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".