Alcohol Extracts From Ganoderma lucidum Delay the Progress of Alzheimer’s Disease by Regulating DNA Methylation in Rodents
Bibliographic record
Abstract
Age-related changes in methylation are involved in the occurrence and development of tumors, autoimmune disease, and nervous system disorders, including Alzheimer’s disease (AD), in elderly individuals; hence, modulation of these methylation changes may be an effective strategy to delay the progression of AD pathology. In this study, the AD model rats were used to screen the main active fractions from the mushroom, Ganoderma lucidum, for anti-aging properties, and their effects on DNA methylation evaluated. The results of evaluation of rats treated with 100 mg/kg/d of D-galactose to induce accelerated aging showed that alcohol extracts of G. lucidum contained the main active anti-aging fraction. The effects on DNA methylation of these G. lucidum extracts were then evaluated using SAMP8 and APP/PS1 AD model mice by whole genome bisulfite sequencing. Molecular docking analysis was carried out to screen for molecules regulated by specific components, including ganoderic acid Mk, ganoderic acid C6, and lucidone A, which may be active ingredients of G. lucidum, including the methylation regulators of Histone H3, MYT, DNMT3A, and DNMT3B. Auxiliary tests also demonstrated that G. lucidum alcohol extracts could improve learning and memory function, ameliorate neuronal apoptosis and brain atrophy, and down-regulate the expression of the AD intracellular marker, Aβ1–42. We concluded that alcohol extracts from G. lucidum, including ganoderic acid and lucidone A, are the main fractions involved in delaying AD progression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".