Increased microRNA-34c abundance in Alzheimer's disease circulating blood plasma
Bibliographic record
Abstract
Circulating microRNAs, present either in the cellular component, peripheral blood mononuclear cells (PBMC), or in cell-free plasma, have emerged as biomarkers for age-dependent systemic, disease-associated changes in many organs. Previously, we have shown that microRNA (miR)-34a is increased in circulating PBMC of Alzheimer's disease (AD) patients. In the present study, we show that this microRNA's sister, miR-34c, exhibits even greater increase in both cellular and plasma components of AD circulating blood samples, compared to normal age-matched controls. Statistical analysis shows the accuracy of levels of miR-34c assayed by receiver operating characteristic (ROC) analysis: the area under the curve is 0.99 (p < 0.0001) and the 95% confidence level extends from 0.97 to 1. Pearson correlation between miR-34c levels and mild and moderate AD, as defined by the mini-mental state examination (MMSE), shows an r-value of -0.7, suggesting a relatively strong inverse relationship between the two parameters. These data show that plasma levels of microRNA 34c are much more prominent in AD than those of its sister, miR-34a, or than its own level in PBMC. Transfection studies show that miR-34c, as does its sister miR-34a, represses the expression of several selected genes involved in cell survival and oxidative defense pathways, such as Bcl2, SIRT1, and others, in cultured cells. Taken together, our results indicate that increased levels of miR-34c in both PBMC and plasma may reflect changes in circulating blood samples in AD patients, compared to age-matched normal controls.
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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 | 0.001 |
| 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 | 0.000 |
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".