Valve-Adjustable Optofluidic Bio-Imaging Platform for Progressive Stenosis Investigation.
Longfei ChenLe YuYantong LiuHongshan XuWei LiFang WangJiaomeng ZhuKezhen YiLinlu MaHui XiaoFuling ZhouMing ChenYan-Xiang ChengFu-Bing WangChengliang ZhuXuan XiaoYi YangPublished in: ACS sensors (2023)
The clinical evidence has proven that valvular stenosis is closely related to many vascular diseases, which attracts great academic attention to the corresponding pathological mechanisms. The investigation is expected to benefit from the further development of an in vitro model that is tunable for bio-mimicking progressive valvular stenosis and enables accurate optical recognition in complex blood flow. Here, we develop a valve-adjustable optofluidic bio-imaging recognition platform to fulfill it. Specifically, the bionic valve was designed with in situ soft membrane, and the internal air-pressure chamber could be regulated from the inside out to bio-mimic progressive valvular stenosis. The developed imaging algorithm enhances the recognition of optical details in blood flow imaging and allows for quantitative analysis. In a prospective clinical study, we examined the effect of progressive valvular stenosis on hemodynamics within the typical physiological range of veins by this way, where the inhomogeneity and local enhancement effect in the altered blood flow field were precisely described and the optical differences were quantified. The effectiveness and consistency of the results were further validated through statistical analysis. In addition, we tested it on fluorescence and noticed its good performance in fluorescent tracing of the clotting process. In virtue of theses merits, this system should be able to contribute to mechanism investigation, pharmaceutical development, and therapeutics of valvular stenosis-related diseases.
Keyphrases
- blood flow
- high resolution
- aortic valve
- atrial fibrillation
- multiple sclerosis
- mitral valve
- randomized controlled trial
- aortic stenosis
- transcatheter aortic valve replacement
- oral anticoagulants
- machine learning
- high throughput
- clinical trial
- mass spectrometry
- transcription factor
- working memory
- deep learning
- single molecule
- inferior vena cava