Preventing peritendinous adhesions using lubricious supramolecular hydrogels
Meany, E.; Williams, C. M.; Song, Y. E.; Doulames, V. M.; Bailey, S. J.; Williams, S. C.; Jons, C. K.; Fox, P.; Appel, E.
Show abstract
Of the 1.5 million emergency room visits each year in the United States due to flexor tendon injuries in the hand, over 30-40% result in peritendinous adhesions which can limit range of motion (ROM) and severely impact an individuals quality of life. Adhesions are fibrous scar-like tissues which can form between adjacent tissues in the body in response to injury, inflammation, or during normal healing following surgery. Currently, there is no widespread solution for adhesion prevention in the delicate space of the digit while allowing a patient full ROM quickly after surgery. There is a clear clinical need for a material capable of limiting adhesion formation which is simple to apply, does not impair healing, remains at the application site during motion and initial inflammation (days - weeks), and leaves tendon glide unencumbered. In this work, we developed dynamically crosslinked, bioresorbable supramolecular hydrogels as easy-to-apply lubricious barriers to prevent the formation of peritendinous adhesions. These hydrogels exhibit excellent long-term stability, injectability, and thermally stable viscoelastic properties that allow for simple storage and facile application. We evaluated interactions at the interface of the hydrogels and relevant tissues, including human tendon and skin, in shear and extensional stress modes and demonstrated a unique mechanism of adhesion prevention based on maintenance of a lubricious hydrogel barrier between tissues. Ex vivo studies show that the hydrogels did not impair the gliding behavior nor mechanical properties of tendons when applied in cadaveric human hands following clinically relevant flexor tendon repair. We further applied these hydrogels in a preclinical rat Achilles tendon injury model and observed prolonged local retention at the repair site as well as improved recovery of key functional metrics, including ROM and maximal dorsiflexion. Further, these hydrogels were safe and did not impair tendon strength nor healing compared to the current standard of care. These dynamic, biocompatible hydrogels present a novel solution to the significant problem of peritendinous adhesions with clear translational potential.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Viscoelastic HyA Hydrogel Promotes Recovery of Muscle Quality and Vascularization in a Murine Model of Delayed Rotator Cuff Repair 97%
- Granular hydrogels improve myogenic invasion and repair after volumetric muscle loss 97%
- Self-healing of hyaluronic acid to improve in vivo retention and function 96%
Similar papers in this journal
- Wnt7a-releasing synthetic hydrogel enhances local skeletal muscle regeneration and muscle stem cell engraftment 95%
- Extracellular Matrix Physical Properties Regulate Cancer Cell Morphological Transitions in 3D Hydrogel Microtissues 95%
- Rapid Restoration of Cell Phenotype and Matrix Forming Capacity Following Transient Nuclear Softening 95%
Similar papers in this journal
- Biohybrid tendons enhance the power-to-weight ratio and modularity of muscle-powered robots 96%
- Suspended Tissue Open Microfluidic Patterning (STOMP) 95%
- A 4D Bioprinting Platform to Engineer Anisotropic Musculoskeletal Tissues by Spatially Patterning Microtissues into Temporally Adapting Support Baths 95%
Similar papers in this journal
- Percolation of Microparticle Matrix Promotes Cell Migration and Integration while Supporting Native Tissue Architecture 97%
- Yield Stress and Creep Control Depot Formation and Persistence of Injectable Hydrogels Following Subcutaneous Administration 95%
- 4D bioprinting shape-morphing tissues in granular support hydrogels: Sculpting structure and guiding maturation 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.