Promise of Mindfulness-Based Interventions as Therapies to Prevent Cognitive Decline
Bibliographic record
Abstract
Background: Alzheimer’s disease (AD) is a neurodegenerative disorder that affects 5 million United States citizens. Many authors have proposed that mindfulness-based interventions (MBIs) have the potential to effectively prevent AD-associated pathology and symptomology. However, the fact that both meditation and AD are complex processes that involve a great number of biological pathways has made these phenomena particularly challenging to dissect. This review advocates the use of gene expression to investigate the mechanisms by which MBIs combat AD pathology. Methods: Searches were performed using Thomson Reuters Web of Science. Ultimately, 85 journal articles were selected for their content as it pertains to the purpose of this review. Summary: Peripheral blood mononuclear cells (PBMCs) may provide reliable measures of cerebral gene expression. Profiling their gene expression has demonstrated that MBIs may produce gene expression changes in many of the same pathways (inflammation, cellular stress, proliferation, synaptic function) and often in the opposite direction of disease-related deregulation. While AD is marked by shortened telomeres resulting in genetic turmoil, meditation has been documented to exhibit a positive effect on telomere maintenance. A comprehensive gene expression investigation is invaluable to reveal relevant molecular mechanisms and provide the foundation for exploring the interaction between MBI and 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".