Elevated levels of interleukin-1β, interleukin-6, tumor necrosis factor-α and vascular endothelial growth factor in patients with knee articular cartilage injury
Bibliographic record
Abstract
BACKGROUND: Inflammatory cytokines play a vital role in the occurrence of osteoarticular injury and inflammation. Whether inflammation-associated factors interleukin-1β (IL-1β), IL-6, tumor necrosis factor-α (TNF-α) and vascular endothelial growth factor (VEGF) are involved in the pathogenesis of keen articular cartilage injury remains poorly understood. AIM: To measure the levels of inflammatory factors [IL-1β, IL-6, TNF-α and VEGF] in patients with knee articular cartilage injury. METHODS: = 16)] according to disease severity and X-ray examinations. Meanwhile, 30 healthy individuals who underwent physical examination were selected as the control group. The levels of IL-1β, IL-6, TNF-α and VEGF were measured by ELISA and immunohistochemical staining. RESULTS: < 0.05). After treatment, the scores of visual analogue scale and the Western Ontario and McMaster University of Orthopaedic Index in patient groups were 2.26 ± 1.13 and 15.56 ± 7.12 points, respectively, which were significantly lower than those before treatment (6.98 ± 1.32 and 49.48 ± 8.96). Correlation analysis suggested that IL-1β and TNF-α were positively correlated with VEGF. CONCLUSION: IL-1β, IL-6, TNF-α and VEGF levels are increased in patients with knee articular cartilage injury, and are associated with the disease severity, indicating they might play an important role in the occurrence and development of knee articular cartilage injury. Furthermore, therapeutically targeting them might be a novel approach for the treatment of keen articular cartilage injury.
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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".