Polymer particles for the intra-articular delivery of drugs to treat osteoarthritis
Bibliographic record
Abstract
Abstract Osteoarthritis (OA) is a leading cause of chronic disability. It is a progressive disease, involving pathological changes to the entire joint, resulting in joint pain, stiffness, swelling, and loss of mobility. There is currently no disease-modifying pharmaceutical treatment for OA, and the treatments that do exist suffer from significant side effects. An increasing understanding of the molecular pathways involved in OA is leading to many potential drug targets. However, both current and new therapies can benefit from a targeted approach that delivers drugs selectively to joints at therapeutic concentrations, while limiting systemic exposure to the drugs. Delivery systems including hydrogels, liposomes, and various types of particles have been explored for intra-articular drug delivery. This review will describe progress over the past several years in the development of polymer-based particles for OA treatment, as well as their in vitro, in vivo , and clinical evaluation. Systems based on biopolymers such as polysaccharides and polypeptides, as well as synthetic polyesters, poly(ester amide)s, thermoresponsive polymers, poly(vinyl alcohol), amphiphilic polymers, and dendrimers will be described. We will discuss the role of particle size, biodegradability, and mechanical properties in the behavior of the particles in the joint, and the challenges to be addressed in future research.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".