Effects of <i>Perilla</i> seed oil in combination with the nobiletin-rich ponkan powder on the cognitive function in healthy Japanese elderly: Possible supplementation for brain health in the elderly.
Bibliographic record
Abstract
We recently reported the potential impacts of perilla seed oil (PO), a rich source of a-linolenic acid (ALA, C18:3, n-3), on cognitive function in healthy elderly Japanese individuals. Here, supplements containing either PO alone or PO with nobiletin-rich air-dried immature ponkan powder were examined for their effects on brain functions in 49 healthy elderly Japanese individuals. PO group or the PO + ponkan powder (POPP) group for a 12-month randomized, double-blind, parallel-armed study. Taking a total of fifteen capsules daily for twelve months was not associated with any clinically significant side effects. From baseline to 12-months intervention, taking POPP significantly increased the cognitive index scores obtained by the three evaluation methods more than taking PO alone supplementation. The increased cognitive function observed in the POPP group was accompanied by increases in the levels of a-linolenic acid and docosahexaenoic acid in the erythrocyte plasma membranes and serum levels of brain-derived neurotropic factor (BDNF) and biological antioxidant potential. Our results demonstrate that a 12-months intervention with PO plus nobiletin-rich ADPP supplementations enhances serum BDNF and the antioxidant potential and may improve the age-related cognitive decline in healthy elderly individuals by enhancing the erythrocyte w-3 fatty acid levels.
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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 | 0.000 | 0.000 |
| 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".