Interpretation and applicability of microRNA data to the context of Alzheimer's and age-related diseases
Bibliographic record
Abstract
Generated by the ribonuclease III Dicer, microRNAs (miRNAs) are predicted to regulate up to 90% of the genes in humans, suggesting that they may control every cellular processes in all cells and tissues of the human body! Likely to play a central role in health and disease, a dysfunctional miRNA-based regulation of gene expression may represent the main etiologic factor underlying age-related diseases affecting major organs, such as the brain. Here, we discuss some of the limitations associated to the interpretation and applicability of miRNA data, based on our recent study on the etiology of Alzheimer's disease (AD). Using transiently transfected murine neuronal N2a cells in culture, in parallel to a mouse model of AD, we were able to demonstrate a role for two miRNAs (miR-298 and miR-328) in the regulation of beta-amyloid (Abeta) precursor protein (APP)-converting enzyme (BACE) messenger RNA (mRNA) translation, thereby providing key insights into the molecular basis underlying BACE deregulation in AD. However, whether miRNA data can be extrapolated and transposed to the human context of age-related diseases, such as AD, not only requires caution, but also warrants several considerations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".