Prolonged inhibition of inflammation in osteoarthritis by triamcinolone acetonide released from a polyester amide microsphere platform
Bibliographic record
Abstract
Controlled biomaterial-based corticosteroid release might circumvent multiple injections and the accompanying risks, such as hormone imbalance and muscle weakness, in osteoarthritic (OA) patients. For this purpose, microspheres were prepared from an amino acid-based polyester amide (PEA) platform and loaded with triamcinolone acetonide (TAA). TAA loaded microspheres were shown to release TAA for over 60 days in PBS. Furthermore, the bioactivity lasted at least 28 days, demonstrated by a 80–95% inhibition of PGE 2 production using TNFα-stimulated chondrocyte culture, indicating inhibition of inflammation. Microspheres loaded with the near infrared marker NIR780-iodide injected in healthy rat joints or joints with mild collagenase-induced OA showed retention of the microspheres up till 70 days after injection. After intra-articular injection of TAA-loaded microspheres, TAA was detectable in the serum until day seven. Synovial inflammation was significantly lower in OA joints injected with TAA-loaded microspheres based on histological Krenn scores. Injection of TAA-loaded nor empty microspheres had no effect on cartilage integrity as determined by Mankin scoring. In conclusion, the PEA platform shows safety and efficacy upon intra-articular injection, and its extended degradation and release profiles compared to the currently used PLGA platforms may render it a good alternative. Even though further in vivo studies may need to address dosing and readout parameters such as pain, no effect on cartilage pathology was found and inflammation was effectively lowered in OA joints.
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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".