Role of LncRNA-AF085935, IL-10 and IL-17 in Rheumatoid Arthritis Patients With Chronic Hepatitis C
Bibliographic record
Abstract
BACKGROUND: The current study aimed at testing the effect of corticosteroid therapy on serum levels of interleukin-10 (IL-10) and IL-17 as well as lncRNA-AF085935 in patients of rheumatoid arthritis (RA) associated with hepatitis C virus (HCV) and evaluating the usefulness of using these parameters to predict the therapeutic efficacy of steroids in these patients. METHODS: Thirty healthy control subjects and 65 chronic HCV patients with RA were included in our study. Patients were subjected to clinical examination, abdominal ultrasound, and liver biopsy and received 6-methyl-prednisolone (PDN) 16 mg/day for 48 weeks. Blood samples were collected from all subjects and serum was separated to assess IL-10 and IL-17 by ELISA and HCV RNA and lncRNA-AF085935 by qRT-PCR. RESULTS: Our study revealed that there were significant increases in serum levels of IL-10, IL-17 and lncRNA-AF085935 in RA patients associated with HCV compared with healthy control subjects. Also there were significant increases in serum levels of IL-10 and HCV RNA and a significant decrease in serum level of IL-17 in patients after corticosteroid therapy, while lncRNA-AF085935 is not significantly changed. CONCLUSION: LncRNA-AF085935 might be a useful candidate biomarker for the early detection of RA associated with HCV, providing potential new strategies for early screening and therapy of these patients. IL-17 is a non-invasive prognostic marker to predict the efficacy of corticosteroid therapy in RA patients associated with chronic hepatitis C.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".