Microfluidic technology for cell biology-related applications: a review.

Fluid flow at the microscale level exhibits a unique phenomenon that can be explored to fabricate microfluidic devices integrated with components that can perform various biological functions. In this manuscript, the importance of physics for microscale fluid dynamics using microfluidic devices has been reviewed. Microfluidic devices provide new opportunities with regard to spatial and temporal control over cell growth. Furthermore, the manuscript presents an overview of cellular stimuli observed by combining surfaces that mimic the complex biochemistries and different geometries of the extracellular matrix, with microfluidic channels regulating the transport of fluids, soluble factors, etc. We have also explained the concept of mechanotransduction, which defines the relation between mechanical force and biological response. Furthermore, the manipulation of cellular microenvironments by the use of microfluidic systems has been highlighted as a useful device for basic cell biology research activities. Finally, the article focuses on highly integrated microfluidic platforms that exhibit immense potential for biomedical and pharmaceutical research as robust and portable point-of-care diagnostic devices for the assessment of clinical samples.

Combined electromechanically driven pulsating flow of nonlinear viscoelastic fluids in narrow confinements.

Controlled microscale transport is at the core of many scientific and technological advancements, including medical diagnostics, separation of biomolecules, etc., and often involves complex fluids. One of the challenges in this regard is to actuate flows at small scales in an energy efficient manner, given the strong viscous forces opposing fluid motion. We try to address this issue here by probing a combined time-periodic pressure and electrokinetically driven flow of a viscoelastic fluid obeying the simplified linear Phan-Thien-Tanner model, using numerical as well as asymptotic tools, in view of the fact that oscillatory fields are less energy intensive. We establish that the interplay between oscillatory electrical and mechanical forces can lead to complex temporal mass flow rate variations with short-term bursts and peaks in the flow rate. We further demonstrate that an oscillatory pressure gradient or an electric field, in tandem with another steady actuating force can indeed change the net throughput significantly-a paradigm that is not realized in Newtonian or other simpler polymeric liquids. Our results reveal that the extent of augmentation in the flow rate strongly depends on the frequency of the imposed actuating forces along with their waveforms. We also evaluate the streaming potential resulting from an oscillatory pressure-driven flow and illustrate that akin to the volume throughput, the streaming potential also shows complex temporal variations, while its time average gets augmented in the presence of a time-periodic pressure gradient in a nonlinear viscoelastic medium.

Real-Time Analytics for the Healthcare Industry: Arrhythmia Detection.

It is time for the healthcare industry to move from the era of "analyzing our health history" to the age of "managing the future of our health." In this article, we illustrate the importance of real-time analytics across the healthcare industry by providing a generic mechanism to reengineer traditional analytics expressed in the R programming language into Storm-based real-time analytics code. This is a powerful abstraction, since most data scientists use R to write the analytics and are not clear on how to make the data work in real-time and on high-velocity data. Our paper focuses on the applications necessary to a healthcare analytics scenario, specifically focusing on the importance of electrocardiogram (ECG) monitoring. A physician can use our framework to compare ECG reports by categorization and consequently detect Arrhythmia. The framework can read the ECG signals and uses a machine learning-based categorizer that runs within a Storm environment to compare different ECG signals. The paper also presents some performance studies of the framework to illustrate the throughput and accuracy trade-off in real-time analytics.