Modeling the Effects of Yoga on the Progression of Alzheimer’s Disease in a Dish
Bibliographic record
Abstract
Alzheimer's disease (AD) accounts for 80% of all dementia cases, making it the most common form of dementia. Aging serves as the main risk factor for AD, but early onset AD can also occur in individuals younger than 65 years. AD results from progressive neurodegeneration leading to dysfunctional synaptic transmission in the brain. The cascade hypothesis of AD states that amyloid precursor protein (APP) metabolism becomes impaired either by mutation or an interleukin-mediated stress response to injury, resulting in the splicing of harmful oligomeric forms of amyloid beta (Aβ). These oligomers disrupt extracellular receptor binding, intracellular function, and cellular membrane integrity. Yoga and meditative practices slow the progression of the cognitive decline associated with AD. However, the biological mechanisms underlying this therapeutic effect remain elusive. Here, we investigated the ability of neurotransmitters released during yoga and meditative practices to rescue neurons from synaptic dysfunction in an in vitro Alzheimer's model created by culturing basal forebrain cholinergic neurons with physiologically relevant levels of the I-42 isoform of oligomeric Aβ (OΑβI-42). We found that the neurotransmitters dopamine and histamine produce a cooperative action with serotonin to reverse the loss of choline acetyltransferase (CHaT) by OΑβI-42. The loss of ChaT, the enzyme responsible for processing the cholinergic neurotransmitter acetylcholine, contributes to the synaptic dysfunction experienced during AD. These neurotransmitters inhibit nitric oxide synthesis caused by OΑβI-42, preventing oxidative and nitrosative stress. Serotonin activates an alternate cleavage of APP to produce a fragment with known neurotrophic effects, giving it the unique ability to inhibit the OΑβI-42 production cycle. We hypothesize here that these concerted actions lead to the protection of cholinergic synaptic transmission in AD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".