MicroRNA-511-3p regulates Aβ1–40 induced decreased cell viability and serves as a candidate biomarker in Alzheimer's disease
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disease with high incidence in the elderly population. MicroRNAs have been reported to abnormally expressed in patients with AD. In this study, we investigated the role of inflammation-related miR-511-3p in AD patients and AD cell models. METHOD: The level of miR-511-3p was quantified by Real-Time PCR. The diagnostic value was evaluated by receiver operating characteristic curve (ROC) analysis. The correlation between miR-511-3p expression levels and ini-mental state examination (MMSE) scores, Montreal Cognitive Assessment (MoCA) scores and inflammatory factors was analyzed. The concentrations of IL-1β, IL-6 and TNF-α were measured by Enzyme-Linked Immunosorbent Assay (ELISA) in AD cell model and serum from AD patients. RESULT: on inflammation in AD cells. ROC curve results showed that miR-511-3p had high diagnostic accuracy in distinguishing normal controls from AD patients. CONCLUSION: Our results show that miR-511-3p is down-regulated in AD patients and has high diagnostic value. MiR-511-3p may participate in the development of AD by regulating the levels of neuroinflammatory factors in AD cells. MiR-511-3p may provide a new perspective for the prevention and pathogenesis of AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".