TGF-β 3 as a potential biomarker of therapeutic efficacy of hyaluronic acid injections in knee osteoarthritis
Bibliographic record
Abstract
Knee osteoarthritis (OA) is a common disease often treated with intra-articular injections of hyaluronic acid (HA); however, clinical response to such therapy can vary. Preliminary data suggest that serum levels of TGF-β3 may serve as a biomarker of therapeutic efficacy. The aim of this study was to evaluate the influence of serum TGF-β3 levels on clinical symptoms of knee OA following intra-articular HA injections. Materials and methods. The study included 40 women with knee OA stages I-III. Serum TGF-β3 levels were assessed prior to HA injection and 6 months after. Clinical outcomes such as pain, joint function, stiffness were monitored according to Western Ontario and McMaster University Osteoarthritis Index (WOMAC) and need for NSAIDs for one year. Depending on the duration of the therapeutic effect of local HA therapy, patients were divided into two groups: group 1 (17 individuals) – with a good response (reduction in pain and stiffness, improvement in joint function over one year), and group 2 (23 patients) – with no or minimal response to HA injection. Results. In patients of group 1 with a good response to HA injection, a higher serum level of TGF-β3 was observed both at baseline and after 6 months compared to group 2 (p₁-₂ = 0,047 and p₁-₂ = 0,009, respectively). All patients in group 1 (17/17) demonstrated a reduced frequency of NSAIDs use (p<0,001), a significant reduction in WOMAC (p<0,001). Patients with concomitant varicose veins of the lower extremities exhibited lower TGF-β3 levels (p=0,007, rs=-0,428). Conclusions. Higher serum TGF-β3 values in patients with knee OA may be associated with a more significant and prolonged therapeutic effect of intra-articular HA injection. Varicose veins of the lower extremities combined with low TGF-β3 levels in patients with knee OA may be associated with reduced efficacy of local HA therapy.
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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".