Back

Recovery of cryo-injured rabbit urethras by biofabricated C-shaped adipose-derived mesenchymal cell structures

Gautam Silwal, S.; Imamura, T.; Shimamura, M.; Minagawa, T.; Nakazawa, M.; Ogawa, T.; Ishizuka, O.

2020-04-03 bioengineering
10.1101/2020.04.02.022715 bioRxiv
Show abstract

Urethral tissue damage can cause stress urinary incontinence or urinary retention, and there are few long-term, effective treatments. For structural and functional recovery of urethral tissues, we designed and constructed C-shaped structures composed of adipose-derived mesenchymal cells (AMCs) by using a three-dimensional (3D)-bioprinter system, which could be transplanted in damaged urethras without obstructing the lumen. We determined if transplantation of the biofabricated structures could reconstruct the urethral tissues. AMCs were harvested from rabbits, cultured and labeled with PKH26 to form spheroids. The spheroids were assembled on a custom-designed C-shaped support by a 3D bioprinter. Urethras of rabbits were cryo-injured by spraying with liquid nitrogen for 20 seconds, incised and biofabricated structure was autologously transplanted. Control rabbits were treated similarly but without transplantation structure. Two and four weeks after surgery, the control urethras were partially constricted; however, the structure-transplanted urethras were patent. The AMCs within the structures differentiated into skeletal muscle, smooth muscle, nerve, or endothelial cells. Some cells contained growth factors and cytokines. Therefore, biofabricated C-shaped AMC structures have potential to be an effective treatment for urethral recovery. Summary StatementThree-dimensional bioprinter was used to biofabricate novel C-shaped structures composed of adipose-derived mesenchymal cells (AMCs) and have the potential to be an effective treatment for urethral recovery.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.