Nobiletin, a flavone from <i>Citrus depressa</i>, induces gene expression and increases the protein level and activity of neprilysin in SK-N-SH cells
Bibliographic record
Abstract
Neprilysin (NEP) is one of the candidate amyloid β protein (Aβ) degrading enzymes affecting brain Aβ clearance. This enzyme declines in the brain with age, which leads to the increased Aβ deposition in Alzheimer's disease (AD). Pharmacological activation of NEP during the aging process, therefore, represents a potential strategy to prevent the development of AD. To examine the influence of nobiletin on neprilysin activity, we measured cellular NEP activity in SK-N-SH cells. Moreover, NEP expression was examined by using reverse transcription - polymerase chain reaction and Western blotting. Measurement of cellular NEP activity showed that nobiletin stimulated this in a dose- and time-dependent manner in SK-N-SH cells. Moreover, nobiletin increased the expression of NEP mRNA, and then the levels of NEP protein, also in a dose- and time-dependent manner. Our findings showed that nobiletin promoted NEP gene and protein expression, resulting in enhancement of cellular NEP activity in SK-N-SH cells. This compound could be a novel Aβ-degrading compound for use in the development of disease-modifying drugs to prevent and (or) cure 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 | 0.000 | 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".