Alzheimer’s disease and multiple sclerosis: a possible connection through the viral demyelinating neurodegenerative trigger (vDENT)
Bibliographic record
Abstract
Alzheimer's disease (AD) and multiple sclerosis (MS) are two CNS disorders affecting millions of people, for which no cure is available. AD is usually diagnosed in individuals age 65 and older and manifests with accumulation of beta amyloid in the brain. MS, a demyelinating disorder, is most commonly diagnosed in its relapsing-remitting (RRMS) form in young adults (age 20-40). The lack of success in a number of recent clinical trials of immune- or amyloid-targeting therapeutics emphasizes our incomplete understanding of their etiology and pathogenesis. Evidence is accumulating that infectious agents such as viruses may contribute either directly or indirectly. With the emerging recognition that demyelination plays a role in risk and progression of AD, we propose that MS and AD are connected by sharing a common environmental factor (a viral infection such as HSV-1) and pathology (demyelination). In the viral DEmyelinating Neurodegenerative Trigger (vDENT) model of AD and MS, the initial demyelinating viral (e.g., HSV-1) infection provokes the first episode of demyelination that occurs early in life, with subsequent virus reactivations/demyelination and associated immune/inflammatory attacks resulting in RRMS. The accumulating damage and/or virus progression deeper into CNS leads to amyloid dysfunction, which, combined with the inherent age-related defects in remyelination, propensity for autoimmunity, and increased blood-brain barrier permeability, leads to the development of AD dementia later in life. Preventing or diminishing vDENT event(s) early in life, thus, may have a dual benefit of slowing down the progression of MS and reducing incidence of AD at an older age.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".