Alzheimer Disease Pathogenesis: The Role of Autoimmunity
Bibliographic record
Abstract
BACKGROUND: In addition to deposits of amyloid β (Aβ) plaques and neurofibrillary tangles, growing evidence demonstrates that complex and multifaceted biological processes can arise during Alzheimer disease (AD) pathogenesis. The recent failures of clinical trials based on the amyloid hypothesis and the presence of Aβ plaques in cognitively healthy elderly persons without AD point toward a need to explore novel pathobiological mechanisms of AD. CONTENT: In the search for alternative AD mechanisms, numerous genome-wide association studies and mechanistic discoveries suggest a potential immunologic component of the disease. However, new experimental tools are needed to uncover these immunogenic components. The current methods, such as ELISAs or protein microarrays, have limitations of low throughput and/or sensitivity and specificity. In this article, we briefly discuss evidence of potential autoimmune contributions to AD pathobiology, describe the current methods for identifying autoantibodies in patient fluids, and outline our own efforts to develop new techniques for novel autoantibody biomarker discovery. SUMMARY: Uncovering the putative autoimmune components of AD may be crucial in paving the way to new concepts for pathogenesis, diagnosis, and therapy. IMPACT STATEMENT: In addition to deposits of amyloid β plaques and neurofibrillary tangles, growing evidence demonstrates that complex and multifaceted biological processes can arise during Alzheimer disease (AD) pathogenesis. Numerous research directions, including genome-wide association, clinical correlation, and mechanistic studies, have pointed to a potential autoimmunologic contribution to AD pathology. We present research suggesting the association between autoimmunity and AD and demonstrate the need for new laboratory techniques to further characterize potential brain antigen-specific autoantibodies. Uncovering the putative autoimmune components of AD may be crucial in paving the way to new concepts for pathogenesis, diagnosis, and therapy.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".