镜头(地质)
材料科学
横截面
粘附
模数
抗弯强度
弯曲模量
压缩(物理)
胶粘剂
生物医学工程
光学
复合材料
结构工程
物理
图层(电子)
医学
工程类
作者
Kazuo Ichikawa,Kei Ichikawa,Naoki Yamamoto,Rie Horai
标识
DOI:10.3390/medicina59071282
摘要
Background and Objectives: In this study, we aimed to compare the physical properties of hole-implantable collamer lenses (H-ICLs) and implantable phakic contact lenses (IPCLs) and investigate their flexural and cell adhesion characteristics. Materials and Methods: Transverse compression load to achieve lens flexion and static Young's modulus were measured in H-ICLs and IPCLs using designated equipment. Load was measured both with and without restraining the optic section of the lenses. Adhesion of iHLEC-NY2 cells to the lens surfaces was examined using phase-contrast microscopy, and cell proliferation activity was evaluated using WST-8 assay. Results: The H-ICL showed a greater tendency for transverse compression load compared to IPCL, while the IPCL showed a higher Young's modulus with respect to the force exerted on the center of the anterior surface of the optic section. The joint between the optic section and haptic support in the IPCL was found to mitigate the effects of transverse compression load. Both lens types showed minimal cell adhesion. Conclusions: Our findings indicate that H-ICLs and IPCLs exhibit distinct physical properties and adhesive characteristics. The IPCL demonstrated higher Young's modulus and unique structural features, while the H-ICL required greater transverse compression load to achieve the flexion required to tuck the haptic supports into place behind the iris to fix the lens. The observed cell non-adhesive properties for both lens types are promising in terms of reducing complications related to cell adhesion. However, further investigation and long-term observation of IPCL are warranted to assess its stability and potential impact on the iris. These findings contribute to a better understanding of the performance and potential applications of H-ICLs and IPCLs in ophthalmology.
科研通智能强力驱动
Strongly Powered by AbleSci AI