Serum TGF-β2 and TGF-β3 Are Increased and Positively Correlated to Pain, Functionality, and Radiographic Staging in Osteoarthritis
Bibliographic record
Abstract
The goal of this study was to verify or reject the hypothesis that systematic differences exist in various profibrotic or antifibrotic factors between osteoarthritic patients and controls, as well as between different stages of osteoarthritis. The study group comprised 63 patients with knee osteoarthritis and 18 controls. Transforming growth factor-beta (TGF-beta)1, -2, -3; tissue inhibitor of metalloproteinase (TIMP)-1 protein levels; and gelatinolytic activity of matrix metalloproteinase (MMP)-1, -2, -3, -9 activities were measured by enzyme-linked immunosorbent assay and gelatin zymography, respectively. Visual analog scale scores, Western Ontario and McMaster Universities Arthritis Index (WOMAC) scores, Lequesne clinical osteoarthritis scales, and Kellgren-Lawrence radiographic grading were recorded for each patient.Transforming growth factor-beta2 and -3 (in contrast to TGF-beta1 and TIMP-1) serum protein levels were significantly higher in osteoarthritic patients compared to controls (210%+/-14% [P<.001] and 232%+/-7% [P<10(-7)], respectively). Additionally, TGF-beta2 and -3 were strongly positively correlated to Kellgren-Lawrence radiographic grading of the disease (P<10(-5) and P<10(-7), respectively). Moreover, TGF-beta2 correlated positively with the WOMAC scale (P=.007). However, TIMP-1 decreased as osteoarthritis progressed clinically, but remained irrelevant to radiographic staging. Furthermore, activities of MMP-2 and -9, but not MMP-1+/-3, were lower in patients with osteoarthritis.
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 | 0.001 | 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".