Upregulation of Micro RNA-146a (miRNA-146a), A Marker for Inflammatory Neurodegeneration, in Sporadic Creutzfeldt–Jakob Disease (sCJD) and Gerstmann–Straussler–Scheinker (GSS) Syndrome
Bibliographic record
Abstract
A mouse- and human-brain-abundant, nuclear factor (NF)-кB-regulated, micro RNA-146a (miRNA-146a) is an important modulator of the innate immune response and inflammatory signaling in specific immunological and brain cell types. Levels of miRNA-146a are induced in human brain cells challenged with at least five different species of single- or double-stranded DNA or RNA neurotrophic viruses, suggesting a broad role for miRNA-146a in the brain's innate immune response and antiviral immunity. Upregulated miRNA-146a is also observed in pro-inflammatory cytokine-, Aβ42 peptide- and neurotoxic metal-induced, oxidatively stressed human neuronal-glial primary cell cocultures, in murine scrapie and in Alzheimer's disease (AD) brain. In AD, miRNA-146a levels are found to progressively increase with disease severity and co-localize to brain regions enriched in inflammatory neuropathology. This study provides evidence of upregulation of miRNA-146a in extremely rare (incidence 1-10 per 100 million) human prion-based neurodegenerative disorders, including sporadic Creutzfeldt-Jakob disease (sCJD) and Gerstmann-Straussler-Scheinker syndrome (GSS). The findings suggest that an upregulated miRNA-146a may be integral to innate immune or inflammatory brain cell responses in prion-mediated infections and to progressive and irreversible neurodegeneration of both the murine and human brain.
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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 | 0.001 | 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".