Diosgenin-rich Yam (rhizome of Dioscorea batatas) extract ameliorates cognitive functions and plasma biomarkers for mild cognitive impairment and mild Alzheimer's disease: A randomized controlled trial
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Bibliographic record
Abstract
In Alzheimer's disease (AD), neural circuit disconnection primarily causes memory dysfunction, which leads to the hypothesis that repairing neural networks may be an appropriate treatment strategy for AD. We previously found that diosgenin stimulated neurite regeneration and synapse formation in AD model mice, and diosgenin-rich Yam extract enhanced cognitive function in healthy people and normal mice. In this study, we investigated the efficacy and safety of Yam extract in patients with mild cognitive impairment (MCI) and mild AD. Patients were administered either diosgenin-rich Yam extract or placebo, respectively, for 24 weeks. The primary outcome was cognitive function, which was evaluated using the Mini Mental State Examination Japanese version (MMSE-J), Alzheimer's Disease Assessment Scale-Cognition (ADAS-Cog), and verbal fluency test; the secondary outcomes were plasma biomarker levels [neurofilament light chain (NfL); axonal marker, glial fibrillary acidic protein levels; reactive astrocyte marker, and the Aβ42/40 ratio; marker of brain Aβ]. Diosgenin-rich Yam extract improved the ADAS-Cog Praxis domain score and plasma NfL (12 to 24 weeks) in all patients. The number of patients with improved/maintained ADAS-Cog total, Praxis or Memory scores was high in the Yam extract group, but not in placebo group. Divided by severity of pathology, diosgenin-rich Yam extract also improved the total and Memory domain in ADAS-Cog, and plasma NfL levels in patients with MCI. In the Yam extract group, improvement of MMSE-J was related to plasma Aβ42/40 increase, and improvement of plasma NfL was related to better MMSE-J. Treatment with diosgenin-rich Yam extract for 24 weeks resulted in modest cognitive improvement and a decrease in plasma NfL levels, which may suggest axonal protection in the brain.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it