Hyaluronic Acid Microplates for Intra-articular Lubrication and Cartilage Protection in Post-traumatic Osteoarthritis
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Osteoarthritis (OA) is the most common joint disorder, characterized by a vicious cycle of synovial inflammation and cartilage degradation. Intra-articular injection of hyaluronic acid (HA)-based products, one of the currently available treatments, provides only temporary symptomatic relief without addressing the underlying inflammation. Here, we engineered several configurations of 20 × 5 μm square-shaped HA-based hydrogel microparticles (μHA) by photopolymerizing HA–methacrylate chains within a sacrificial template. The μHA mechano-pharmacological properties were tuned by adjusting the HA concentration, molecular weight, and degree of methacrylation, resulting in microparticles with a Young’s modulus ranging from a few tens (30 kPa) to a few hundred (200 kPa) kilopascals; a structure stable for over a month under oxidative stress conditions; and reduced friction in simulated synovial fluids. Under H 2 O 2 -induced oxidative conditions, μHA decreased the production of proinflammatory cytokines (IL-6, IL-1β, and TNF-α) in human chondrocytes to basal levels. In a three-dimensional OA cartilage model, μHA reduced glycosaminoglycan release and matrix metalloproteinase-13 activity, demonstrating chondroprotective effects. In a rigorous murine model of early-stage post-traumatic OA, a single intra-articular injection of μHA lowered proinflammatory gene expression in the synovium to basal levels. In summary, μHA offers a drug-free approach to managing OA by enhancing lubrication and reducing inflammation, providing a sustained therapeutic activity over several weeks.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.004 | 0.001 |
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".