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Rezvani, A.

Publications and source records attributed to Rezvani, A..

2 recordsLinked to original sources

Precise Control of Sodium Alginate Microdroplets through pH-Sensitive Cross-Linkers and Geometric Factors

This study presents a novel approach to achieve internal and on-chip gelation of sodium alginate microdroplets using pH-sensitive water-insoluble cross-linkers. Gelation is initiated by precisely controlling the introduction of acetic acid, which decreases the pH, through the generation of microdroplets via step emulsification. Unlike current approaches in microfluidic droplet generation, where droplet size depends on various external factors such as flow rate ratio and fluid properties, our strategy utilizes the devices geometry to regulate droplet size. Importantly, this approach ensures that there is no aggregation in the channels following microgel creation. The empirical investigation focuses on exploring the impact of geometrical factors on alginate droplet formation in our customized microfluidic device. To expedite the production of polydimethylsiloxane (PDMS) microfluidic devices, we utilized 3D-printed molds, which provide effective control over channel height. Using our method, we successfully generate microdroplets with a coefficient of variation (CV) below 3% and diameters ranging from 100 to 200 {micro}m. It is worth noting that these microdroplets experience an average shrinkage of 16% following gelation, indicating the successful formation of stable gels. By combining pH-sensitive cross-linkers, precise control over acetic acid introduction, and the geometric regulation of droplet size, our approach demonstrates great potential for the development of microfluidic devices capable of producing uniform and stable microdroplets for various applications, including drug delivery, tissue engineering, and biotechnology.

bioengineering↗

Data cleaning for image-based profiling enhancement

With the advent of high-throughput assays, a large number of biological experiments can be carried out. Image-based assays are among the most accessible and inexpensive technologies for this purpose. Indeed, these assays have proved to be effective in characterizing unknown functions of genes and small molecules. Image analysis pipelines have a pivotal role in translating raw images that are captured in such assays into useful and compact representation, also known as measurements. CellProfiler is a popular and commonly used tool for this purpose through providing readily available modules for the cell/nuclei segmentation, and making various measurements, or features, for each cell/nuclei. Single cell features are then aggregated for each treatment replica to form treatment "profiles." However, there may be several sources of error in the CellProfiler quantification pipeline that affects the downstream analysis that is performed on the profiles. In this work, we examined various preprocessing approaches to improve the profiles. We consider identification of drug mechanisms of action as the downstream task to evaluate such preprocessing approaches. Our enhancement steps mainly consist of data cleaning, cell level outlier detection, toxic drug detection, and regressing out the cell area from all other features, as many of them are widely affected by the cell area. We also examined unsupervised and weakly-supervised deep learning based methods to reduce the feature dimensionality, and finally suggest possible avenues for future research.

cell biology↗