Plasma miR-146a and miR-365 expression and inflammatory factors in patients with osteoarthritis.
Bibliographic record
Abstract
OBJECTIVE: To investigate the expression levels of micro-ribonucleic acid (miR)-146a and miR-365 in the plasma of osteoarthritis (OA) patients, to study their expression with the inflammatory factors and the severity of disease in patients and to analyse their diagnostic significance. MATERIALS AND METHODS: A total of 42 OA patients diagnosed with OA and treated in our hospital from January 2017 to January 2018 were selected as the subjects, and 28 healthy people were enrolled as controls. The expressions of interleukin-1 beta (IL-1β) and IL-6 in the plasma of OA patients were detected via immunohistochemical staining. Moreover, the knee joint function of OA patients was evaluated by Lysholm score, Western Ontario and McMaster Universities (WOMAC) score and Visual Analogue Scale (VAS) score. The expression levels of plasma miR-146a and miR-365 in OA patients were measured through RT-PCR. Besides, the significance of the expression levels of miR-146a and miR-365 for the diagnosis of OA was analysed by ROC curves. RESULTS: As compared with healthy people, OA patients had elevated expression levels of plasma IL-1β and IL-6, decreased Lysholm score, increased WOMAC and VAS scores as well as significantly up-regulated levels of plasma miR-146a and miR-365, which were of important significance for diagnosis. CONCLUSION: The expression levels of plasma miR-146a, miR-365 and inflammatory factors are notably higher, the disease is more severe, and the function of knee joint movement is weaker in OA patients than those in healthy controls. It can be concluded that the levels of both miR-146a and miR-365 can serve as biomarkers of OA diagnosis.
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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 | 0.000 | 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".