Investigation of Degradation and Biocompatibility of Indirect 3D-Printed Bile Duct Stents.
Ming-Chan LeeCheng-Tang PanRuo-Jiun HuangHsin-You OuChun-Yen YuYow-Ling Shirley ShiuePublished in: Bioengineering (Basel, Switzerland) (2024)
This study proposes a bile duct stent based on indirect 3D printing technology. Four ratio materials were synthesized from lactic acid (LA) and glycolide (GA) monomers by melt polymerization: PLA, PLGA (70:30), PLGA (50:50), and PLGA (30:70). The four kinds of material powders were preliminarily degraded, and the appearance was observed with an optical microscope (OM) and a camera. The weight and appearance of the four materials changed significantly after four weeks of degradation, which met the conditions for materials to be degraded within 4-6 weeks. Among them, PLGA (50:50) lost the most-the weight dropped to 13.4%. A stent with an outer diameter of 10 mm and an inner diameter of 8 mm was successfully manufactured by indirect 3D printing technology, demonstrating the potential of our research. Then, the degradation experiment was carried out on a cylindrical stent with a diameter of 6 mm and a height of 3 mm. The weight loss of the sample was less than that of the powder degradation, and the weight loss of PLGA (50:50) was the largest-the weight dropped to 79.6%. The nano-indenter system measured the mechanical properties of materials. Finally, human liver cancer cells Hep-3B were used to conduct in vitro cytotoxicity tests on the scaffolds to test the biocompatibility of the materials. A bile duct stent meeting commercial size requirements has been developed, instilling confidence in the potential of our research for future medical applications.
Keyphrases
- weight loss
- drug delivery
- bariatric surgery
- drug release
- body mass index
- roux en y gastric bypass
- bone regeneration
- gastric bypass
- lactic acid
- physical activity
- weight gain
- optic nerve
- tissue engineering
- metabolic syndrome
- type diabetes
- high resolution
- tyrosine kinase
- body weight
- adipose tissue
- human health
- gestational age
- deep learning
- climate change
- obese patients