Neuroinflammation as a core pathogenic mechanism of Alzheimer’s disease: recent research advances and related therapeutical approaches
Bibliographic record
Abstract
Alzheimer's disease (AD) is characterized by amyloid-beta (Aβ) accumulation, tau deposition, oxidative stress, and neuroinflammation. However, recent research focused more on neuroinflammation and oxidative stress than protein deposits, as the therapeutical effect against the latter has been relatively disappointing over the years. Since then, many novel findings have emerged, indicating neuroinflammation as another crucial contributor in AD pathogenesis rather than a secondary effect of protein deposition, and shed some light on the therapeutical design for AD. This paper will discuss some new understanding of neuroinflammation in AD. Unlike what is previously known as an immune-privileged site, the brain has a solid immune response upon detecting foreign pathogens and protein deposits, mediated mainly by glial cells. This immune response then ends with chronic neuroinflammation, which accelerates AD progression. Thus, this paper will highlight the role of glial cells in neuroinflammation and bring up the usually neglected contribution of periphery immune system and microbiomes infection as well, together depict a broader picture of neuroinflammation in the pathogenesis of AD, along with potential therapeutical advance with an emphasis on immunotherapy.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".