Inflammation-induced miRNA-155 inhibits self-renewal of neural stem cells via suppression of CCAAT/enhancer binding protein β (C/EBPβ) expression
Bibliographic record
Abstract
Intracerebral inflammation resulting from injury or disease is implicated in disruption of neural regeneration and may lead to irreversible neuronal dysfunction. Analysis of inflammation-related microRNA profiles in various tissues, including the brain, has identified miR-155 among the most prominent miRNAs linked to inflammation. Here, we hypothesize that miR-155 mediates inflammation-induced suppression of neural stem cell (NSC) self-renewal. Using primary mouse NSCs and human NSCs derived from induced pluripotent stem (iPS) cells, we demonstrate that three important genes involved in NSC self-renewal (Msi1, Hes1 and Bmi1) are suppressed by miR-155. We also demonstrate that suppression of self-renewal genes is mediated by the common transcription factor C/EBPβ, which is a direct target of miR-155. Our study describes an axis linking inflammation and miR-155 to expression of genes related to NSC self-renewal, suggesting that regulation of miR-155 may hold potential as a novel therapeutic strategy for treating neuroinflammatory diseases.
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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) | 0.001 | 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".