Functionalized Nanogels with Endothelin-1 and Bradykinin Receptor Antagonist Peptides Decrease Inflammatory and Cartilage Degradation Markers of Osteoarthritis in a Horse Organoid Model of Cartilage
Bibliographic record
Abstract
Osteoarthritis (OA) is a degenerative and heterogeneous disease that affects all types of joint structures. Current clinical treatments are only symptomatic and do not manage the degenerative process in animals or humans. One of the new orthobiological treatment strategies being developed to treat OA is the use of drug delivery systems (DDS) to release bioactive molecules over a long period of time directly into the joint to limit inflammation, control pain, and reduce cartilage degradation. Two vasoactive peptides, endothelin-1 and bradykinin, play important roles in OA pathogenesis. In this study, we investigated the effects of two functionalized nanogels as DDS. We assessed the effect of chitosan functionalized with a type A endothelin receptor antagonist (BQ-123-CHI) and/or hyaluronic acid functionalized with a type B1 bradykinin receptor antagonist (R-954-HA). The biocompatibility of these nanogels, alone or in combination, was first validated on equine articular chondrocytes cultured under different oxic conditions. Further, in an OA equine organoid model via induction with interleukin-1 beta (IL-1β), a combination of BQ-123-CHI and R-954-HA (BR5) triggered the greatest decrease in inflammatory and catabolic markers. In basal and OA conditions, BQ-123-CHI alone or in equimolar combinations with R-954-HA had weak pro-anabolic effects on collagens synthesis. These new nanogels, as part of a composite DDS, show promising attributes for treating OA.
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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) | 0.000 | 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".