Infections contribute to proteinopathies in neurodegenerative diseases and in some post‐viral persistent symptoms
Bibliographic record
Abstract
Abstract Background The failure of therapies directed against amyloid‐beta makes us reconsider the theory of the amyloid cascade in Alzheimer’s disease (AD). Infections are suspected to contribute to the disease process. Co‐infection with herpes simplex virus type 1 (HSV‐1) and cytomegalovirus (CMV) results in a higher odds ratio than an individual heterozygous for APOE 4 to develop AD. The objective is to propose a simplified biological model considering the scientific literature to explain the contribution of infections to proteinopathies resulting in neurodegenerative disease for possible integration into a predictive model. Method We performed a narrative review of the literature on the cellular mechanisms affected by HSV‐1, CMV and AD. Result We propose an hypothetical model supporting that infections can contribute significantly to the different pathophysiological processes of AD via an increase in the failure of misfolding required to obtain a functional conformation of proteins and a decrease in the clearance of proteins with a potentially toxic conformation. Some important changes also involve immunosenescence, as well as compromise of neurogenesis. Immunosenescence allows latent infections, such as HSV‐1 and CMV, to reactivate, spread, and cause cellular changes favorable to AD development. Conclusion The model explains the cellular changes observed in AD. Given that effective treatments already exist for many of the infections, additional studies are urgently needed to detail the specific contribution of each infection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".