The complexity of molecular processes in osteoarthritis of the knee joint
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it