Monitoring the supply of vascular endothelial growth factor (VEGF) to ischemic tissues provides information on its biodistribution and delivery to meet the requirements of therapeutic angiogenesis and tissue engineering applications. We herein report the use of microfluidically generated microgels containing VEGFconjugated fluorescent carbon dots (CDs) (VEGF−CDs), a gelatin− phenol conjugate, and silk fibroin for imaging-monitored tracking of VEGF delivery to ischemic muscles. An in vitro release study and a bioactivity assay indicated that the VEGF−CDs were released in a sustained manner with high bioactivity. The microgels showed a high angiogenesis potential, along with a strong fluorescent signal, for the chicken chorioallantoic membrane and chick embryo. Imaging and studies of therapeutic modalities of the composite microgels indicated their effective localization in ischemic tissues and sustained VEGF release, which resulted in enhanced therapeutic angiogenesis of ischemic muscles. This work reveals the success of using VEGF-loaded composite polymer microgels for efficient and monitored VEGF delivery by intramuscular administration for ischemic disease treatment.
Controlling droplet breakup characteristics such as size, frequency, regime, and droplet quality within flow-focusing microfluidic devices is critical for different biomedical applications of droplet microfluidics such as drug delivery, biosensing, and nanomaterial preparation. The development of a prediction platform capable of forecasting droplet breakup characteristics can significantly improve the iterative design and fabrication processes required for achieving desired performance. The present study aims to develop a multipurpose platform capable of predicting the working conditions of user-specific droplet size and frequency and reporting the quality of the generated droplets, regime, and hydrodynamical breakup characteristics in flow-focusing microdevices with different cross-junction tilt angles. Four different neural network-based prediction platforms were compared to accurately estimate capsule size, generation rate, uniformity, and circle metric. The trained capsule size and frequency networks were optimized using the heuristic optimization approach for establishing the Pareto optimal solution plot. To investigate the transition of the droplet generation regime (i.e., squeezing, dripping, and jetting), two different classification models (LDA and MLP) were developed and compared in terms of their prediction accuracy. The MLP model outperformed the LDA model with a crossvalidation measure evaluated as 97.85%, demonstrating that the droplet quality and regime prediction models can provide an engineering judgment for the decision maker to choose between the suggested solutions on the Pareto front. The study followed a comprehensive hydrodynamical analysis of the junction angle effect on the dispersed thread formation, pressure, and velocity domains in the orifice.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.