Can an Infection Hypothesis Explain the Beta Amyloid Hypothesis of Alzheimer’s Disease?
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most frequent type of dementia. The pathological hallmarks of the disease are extracellular senile plaques composed of beta-amyloid peptide (Aβ) and intracellular neurofibrillary tangles composed of pTau. These findings led to the "beta-amyloid hypothesis" that proposes that Aβ is the major cause of AD. Clinical trials targeting Aβ in the brain have mostly failed, whether they attempted to decrease Aβ production by BACE inhibitors or by antibodies. These failures suggest a need to find new hypotheses to explain AD pathogenesis and generate new targets for intervention to prevent and treat the disease. Many years ago, the "infection hypothesis" was proposed, but received little attention. However, the recent discovery that Aβ is an antimicrobial peptide (AMP) acting against bacteria, fungi, and viruses gives increased credence to an infection hypothesis in the etiology of AD. We and others have shown that microbial infection increases the synthesis of this AMP. Here, we propose that the production of Aβ as an AMP will be beneficial on first microbial challenge but will become progressively detrimental as the infection becomes chronic and reactivates from time to time. Furthermore, we propose that host measures to remove excess Aβ decrease over time due to microglial senescence and microbial biofilm formation. We propose that this biofilm aggregates with Aβ to form the plaques in the brain of AD patients. In this review, we will develop this connection between Infection - Aβ - AD and discuss future possible treatments based on this paradigm.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".