The complexity of molecular processes in osteoarthritis of the knee joint
Bibliographic record
Abstract
Osteoarthritis (OA) is a common medical problem leading to chronic pain and physical disability among the world's population. Analyzing the molecular background of the degenerative arthritis creates the potential for developing novel targeted methods of treatment. Fifty samples of meniscus, anterior cruciate ligaments (ACLs) and articular surfaces were collected from patients who underwent total knee arthroplasty in 2016. Enzyme-linked immunosorbent assay was used to assess the levels of interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF), transforming growth factor-β1 and LUMINEX for MMP-1, MMP-2, MMP-3, MMP-9 and MMP-13. The collected data were correlated with the severity of radiological OA, demographic data and clinical scales. Strong positive correlations in the concentration of metalloproteinases and proinflammatory cytokines, TNF-α (MMP-2 and MMP-13) and IL-6 (MMP-13), were identified. MMP-13 had a positive correlation with the concentration of MMP-1, MMP-2 and MMP-9. Negative correlation coefficient exists between clinical conditions measured with the Western Ontario and McMaster Universities Osteoarthritis Index scale and the level of TNF-α and MMP-1. The TNF-α concentration was lower in the cartilage of the articular surface among patients who took non-steroidal anti-inflammatory drugs periodically. The decrease in MMP-2 in the cartilage of the articular surface corresponded with the severity of radiological OA on the Kellgren-Lawrence scale. Current treatment methods for OA do not stop disease progression. Identifying signaling pathways and molecular particles engaged in OA and their correlations with the patient's clinical condition brings new therapeutic possibilities.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".