Combinatorial <i>Prg4</i> and <i>Il-1ra</i> Gene Therapy Protects Against Hyperalgesia and Cartilage Degeneration in Post-Traumatic Osteoarthritis
Bibliographic record
Abstract
Osteoarthritis (OA) is a degenerative disease of synovial joints characterized by progressive loss of articular cartilage, subchondral bone remodeling, and intra-articular inflammation with synovitis that results in chronic pain and motor impairment. Despite the economic and health impacts, current medical therapies are targeted at symptomatic relief of OA and fail to alter its progression. Given the complexity of OA pathogenesis, we hypothesized that a combinatorial gene therapy approach, designed to inhibit inflammation with interleukin-1 receptor antagonist (IL-1Ra) while promoting chondroprotection using lubricin (PRG4), would improve preservation of the joint compared to monotherapy alone. Employing two surgical techniques to model mild, moderate and severe posttraumatic OA, we found that combined delivery of helper-dependent adenoviruses (HDVs), expressing IL-1Ra and PRG4, preserved articular cartilage better than either monotherapy in both models as demonstrated by preservation of articular cartilage volume and surface area. This improved protection was associated with increased expression of proanabolic and cartilage matrix genes together with decreased expression of catabolic genes and inflammatory mediators. In addition to improvements in joint tissues, this combinatorial gene therapy prolonged protection against thermal hyperalgesia compared to either monotherapy. Taken together, our results show that a combinatorial strategy is superior to monotherapeutic approaches for treatment of posttraumatic OA.
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 | 0.000 |
| 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".