Role of Microbes in the Development of Alzheimer’s Disease: State of the Art – An International Symposium Presented at the 2017 IAGG Congress in San Francisco
Bibliographic record
Abstract
This article reviews research results and ideas presented at a special symposium at the International Association of Gerontology and Geriatrics (IAGG) Congress held in July 2017 in San Francisco. Five researchers presented their results related to infection and Alzheimer’s disease (AD). Prof. Itzhaki presented her work on the role of viruses, specifically HSV-1, in the pathogenesis of AD. She maintains that although it is true that most people harbor HSV-1 infection, either latent or active, nonetheless aspects of herpes infection can play a role in the pathogenesis of AD, based on extensive experimental evidence from AD brains and infected cell cultures. Dr. Miklossy presented research on the high prevalence of bacterial infections that correlate with AD, specifically spirochete infections, which have been known for decades to be a significant cause of dementia (e.g., in syphilis). She demonstrated how spirochetes drive senile plaque formation, which are in fact biofilms. Prof. Balin then described the involvement of brain tissue infection by the Chlamydia pneumoniae bacterium, with its potential to use the innate immune system in its spread, and its initiation of tissue damage characteristic of AD. Prof. Fülöp described the role of AD-associated amyloid beta (A) peptide as an antibacterial, antifungal and antiviral innate immune effector produced in reaction to microorganisms that attack the brain. Prof. Barron put forward the novel hypothesis that, according to her experiments, there is a strong sequence-specific binding between the AD-associated A and another ubiquitous and important human innate immune effector, the cathelicidin peptide LL-37. Given this binding, LL-37 expression in the brain will decrease amyloid beta deposition via formation of non-toxic, soluble A/LL-37 complexes. Therefore, a chronic under-expression of LL-37 could be the factor that simultaneously permits chronic infections in brain tissue and allows for pathological accumulation of A. This first-of-its-kind symposium opened the way for a paradigm shift in studying the pathogenesis of AD, from the “amyloid cascade hypothesis”, which so far has been quite unsuccessful, to a new “infection hypothesis”, or perhaps more broadly, “innate immune system dysregulation hypothesis”, which may well permit and lead to the discovery of new treatments for AD patients.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".