MicroRNA (miRNA) Signaling in the Human CNS in Sporadic Alzheimer’s Disease (AD)-Novel and Unique Pathological Features
Bibliographic record
Abstract
Of the approximately ~2.65 × 10³ mature microRNAs (miRNAs) so far identified in Homo sapiens, only a surprisingly small but select subset-about 35-40-are highly abundant in the human central nervous system (CNS). This fact alone underscores the extremely high selection pressure for the human CNS to utilize only specific ribonucleotide sequences contained within these single-stranded non-coding RNAs (ncRNAs) for productive miRNA-mRNA interactions and the down-regulation of gene expression. In this article we will: (i) consolidate some of our still evolving ideas concerning the role of miRNAs in the CNS in normal aging and in health, and in sporadic Alzheimer's disease (AD) and related forms of chronic neurodegeneration; and (ii) highlight certain aspects of the most current work in this research field, with particular emphasis on the findings from our lab of a small pathogenic family of six inducible, pro-inflammatory, NF-κB-regulated miRNAs including miRNA-7, miRNA-9, miRNA-34a, miRNA-125b, miRNA-146a and miRNA-155. This group of six CNS-abundant miRNAs significantly up-regulated in sporadic AD are emerging as what appear to be key mechanistic contributors to the sporadic AD process and can explain much of the neuropathology of this common, age-related inflammatory neurodegeneration of the human CNS.
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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) | 0.000 | 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".