Effectiveness of Yi-Zhi-An-Shen granules on cognition and sleep quality in older adults with amnestic mild cognitive impairment: protocol for a randomized, double-blind, placebo-controlled trial
Bibliographic record
Abstract
BACKGROUND: Amnestic mild cognitive impairment (aMCI) is a syndrome characterized by significant forgetfulness that does not meet the criteria of dementia. Individuals with aMCI are at greater risk of progressing to dementia. Current studies suggest that good sleep quality is linked with preserved cognition in the elderly, and sleep complaints are common among the elderly with amnesia. Therefore, improving their sleep may be helpful for maintaining and improving their cognitive capacity. According to the theory of traditional Chinese medicine, Yi-Zhi-An-Shen is an herbal compound which may ameliorate forgetfulness and sleep disorders. As growing evidence indicates that the gut microbiome is associated with major mental symptoms, a hypothesis was proposed that Yi-Zhi-An-Shen granules (YZASG) might work by alternating microbial abundance and diversity. In this study, the investigators intend to assess the efficacy of YZASG on global cognition in the elderly suffering from aMCI and evaluate its safety as well as its potential mechanisms via sleep quality, fecal microbial 16S ribosomal DNA and metagenomics analyses, and serum markers. METHODS/DESIGN: This study is a randomized, double-blind, placebo-controlled clinical trial. A total of 80 patients (aged 60-85 years) will be recruited and allocated randomly to a treatment group and a placebo group in a 1:1 ratio and will then be administered YZASG or isodose placebo three times a day. The intervention course is 16 weeks, with an 18 months follow-up. The primary outcome is the Alzheimer's Disease Assessment Scale-Cognitive Subscale. Secondary outcome measures are the Mini-Mental State Examination, Montreal Cognitive Assessment, Pittsburgh Sleep Quality Index, serum concentrations of immunological factors and inflammatory cytokines, and fecal microbiota. Fecal microbiota will only be collected at the baseline and endpoint of the intervention. DISCUSSION: The results of this trial will be conducive to assessing the safety and effectiveness on cognition of YZASG in intervening aMCI among the elderly and determining if it takes effect via the improvement of sleep quality, regulation of gut microbiota, and concentration of certain serum markers. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03601000 . Registered on 26 July 2018.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".