Immunopathogenesis of systemic lupus erythematosus and rheumatoid arthritis: the role of aberrant expression of non-coding RNAs in T cells
Bibliographic record
Abstract
Non-coding RNAs (ncRNAs), including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), are RNA molecules that do not translate into protein. Both miRNAs and lncRNAs are known to regulate gene expression and to play an essential role in T cell differentiation and function. Both systemic lupus erythematosus (SLE), a prototypic systemic autoimmune disease, and rheumatoid arthritis (RA), a representative disease of inflammatory arthritis, are characterized by a complex dysfunction in the innate and adaptive immunity. T cells play a central role in cell-mediated immune response and multiple defects in T cells from patients with SLE and RA have been observed. Abnormality in T cell signalling, cytokine and chemokine production, T cell activation and apoptosis, T cell differentiation and DNA methylation that are associated closely with the aberrant expression of a number of miRNAs and lncRNAs have been implicated in the immunopathogenesis of SLE and RA. This review aims to provide an overview of the current state of research on the abnormal expression of miRNAs and lncRNAs in T cells and their roles in the immunopathogenesis of SLE and RA. In addition, by comparing the differences in aberrant expression of miRNAs and lncRNAs in T cells between patients with SLE and RA, controversial areas are highlighted that warrant further investigation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".