Objective: Patients with severe symptomatic aortic stenosis are assessed for coronary artery disease (CAD) prior to transcatheter aortic valve implantation (TAVI) with treatment implications. Invasive coronary angiography (ICA) is the recommended modality but is associated with peri-procedural complications. Integrating machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) into existing TAVI-planning CT protocol may aid exclusion of significant CAD and thus avoiding ICA in selected patients. Materials and Methods: A single-center, retrospective study was conducted, 41 TAVI candidates with both TAVI-planning CT and ICA performed were analyzed. CT datasets were evaluated by a ML-based CT-FFR software. Beta-blocker and nitroglycerin were not administered in these patients. The primary outcome was to identify significant CAD. The diagnostic performance of CT-FFR was compared against ICA. Results: On per-patient level, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy were 89%, 94%, 80%, 97% and 93%, respectively. On per-vessel level, the sensitivity, specificity, PPV, NPV and diagnostic accuracy were 75%, 94%, 67%, 96% and 92%, respectively. The area under the receiver operative characteristics curve per individual coronary vessels yielded overall 0.90 (95% confidence interval 85%–95%). ICA may be avoided in up to 80% of patients if CT-FFR results were negative. Conclusion ML-based CT-FFR can provide accurate screening capabilities for significant CAD thus avoiding ICA. Its integration to existing TAVI-planning CT is feasible with the potential of improving the safety and efficiency of pre-TAVI CAD assessment.

Nanoparticles are considered to be a powerful approach for the delivery of poorly water-soluble drugs. One of the main challenges is developing an appropriate method for preparation of drug nanoparticles. As a simple, rapid and scalable method, the flash nanoprecipitation (FNP) has been widely used to fabricate these drug nanoparticles, including pure drug nanocrystals, polymeric micelles, polymeric nanoparticles, solid lipid nanoparticles, and polyelectrolyte complexes. This review introduces the application of FNP to produce poorly water-soluble drug nanoparticles by controllable mixing devices, such as confined impinging jets mixer (CIJM), multi-inlet vortex mixer (MIVM) and many other microfluidic mixer systems. The formation mechanisms and processes of drug nanoparticles by FNP are described in detail. Then, the controlling of supersaturation level and mixing rate during the FNP process to tailor the ultrafine drug nanoparticles as well as the influence of drugs, solvent, anti-solvent, stabilizers and temperature on the fabrication are discussed. The ultrafine and uniform nanoparticles of poorly water-soluble drug nanoparticles prepared by CIJM, MIVM and microfluidic mixer systems are reviewed briefly. We believe that the application of microfluidic mixing devices in laboratory with continuous process control and good reproducibility will be benefit for industrial formulation scale-up.