A Hyaluronan-binding Peptide (P15-1) Reduces Inflammatory and Catabolic Events in IL-1β-treated Human Articular Chondrocytes
Bibliographic record
Abstract
Inflammation plays a critical role in osteoarthritis (OA). It stimulates catabolic events in articular chondrocytes and prevents chondrogenic precursor cells from repairing cartilage lesions, leading to accelerated cartilage degradation. Therefore, the identification of novel factors that reduce catabolic events in chondrocytes and enhances chondrogenic differentiation of precursor cells in an inflammatory environment may provide novel therapeutic strategies for the treatment of OA. The goal of this study was to determine whether a hyaluronan (HA)-binding peptide (P15-1), via interacting with high molecular weight (HMW)HA can enhance the anti-inflammatory properties of HMWHA and decrease catabolic events in interleukin-1beta (IL-1β)-treated human articular chondrocytes. Treatment with P15-1 decreased catabolic events and stimulated anabolic events in articular chondrocytes cultured in an inflammatory environment. P15-1 pre-mixed with HMWHA was more effective in inhibiting catabolic events and stimulating anabolic events than P15-1 or HMWHA alone. Our findings suggest that P15-1 together with HMWHA inhibits catabolic events in articular chondrocytes via the inhibition of p38 mitogen-activated protein kinases (MAPK) and increasing the thickness of the pericellular matrix (PCM) around chondrocytes thereby decreasing catabolic signaling. Finally, conditioned medium from IL-1β and P15-1-treated human articular chondrocytes was less inhibitory for chondrogenic differentiation of precursor cells than conditioned medium from chondrocytes treated with IL-1β alone. In conclusion, P15-1 is proposed to function synergistically with HMWHA to enhance the protective microenvironment for chondrocytes and mesenchymal stem cells during inflammation and regeneration.
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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 | 0.000 |
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".