Top-Down Fabricated microPlates for Prolonged, Intra-articular Matrix Metalloproteinase 13 siRNA Nanocarrier Delivery to Reduce Post-traumatic Osteoarthritis
Bibliographic record
Abstract
Post-traumatic osteoarthritis (PTOA) associated with joint injury triggers a degenerative cycle of matrix destruction and inflammatory signaling, leading to pain and loss of function. Here, prolonged RNA interference (RNAi) of matrix metalloproteinase 13 (MMP13) is tested as a PTOA disease modifying therapy. MMP13 is upregulated in PTOA and degrades the key cartilage structural protein type II collagen. Short interfering RNA (siRNA) loaded nanoparticles (siNPs) were encapsulated in shape-defined poly(lactic- co -glycolic acid) (PLGA) based microPlates (μPLs) to formulate siNP-μPLs that maintained siNPs in the joint significantly longer than delivery of free siNPs. Treatment with siNP-μPLs against MMP13 (siMMP13-μPLs) in a mechanical load-induced mouse model of PTOA maintained potent (65–75%) MMP13 gene expression knockdown and reduced MMP13 protein production in joint tissues throughout a 28-day study. MMP13 silencing reduced PTOA articular cartilage degradation/fibrillation, meniscal deterioration, synovial hyperplasia, osteophytes, and pro-inflammatory gene expression, supporting the therapeutic potential of long-lasting siMMP13-μPL therapy for PTOA.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".