Serum Interleukin-15 Levels Are Associated with Severity of Pain in Patients with Knee Osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Inflammation plays a crucial role in the development and progression of osteoarthritis (OA). Interleukin-15 (IL-15) is a well-known proinflammatory cytokine. OBJECTIVE: We aimed at evaluating the relationship between serum IL-15 levels and the severity of pain as well as radiographic progression in patients with knee OA. METHODS: Two hundred and twenty-six OA patients and 106 controls were enrolled in this study. The symptomatic/radiological severity of OA was assessed by the Western Ontario McMaster University Osteoarthritis Index- (WOMAC-)pain scores/Kellgren-Lawrence (KL) grading system. Serum IL-15 levels were measured by enzyme-linked immunosorbent assay (ELISA). RESULTS: Serum IL-15 levels were significantly higher in OA patients compared with controls. Serum IL-15 levels were independently and positively correlated with WOMAC-pain scores but not KL grades in OA patients. CONCLUSIONS: We demonstrated that increased serum IL-15 levels were independently correlated with self-reported greater pain in knee OA patients. These results suggest that IL-15 might play a crucial role in the pathogenesis of OA related pain and therapeutic interventions by blocking IL-15 signaling pathways to delay the degenerative process of OA related pain which warrants further investigations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.002 | 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".