Imaging systems and algorithms to analyze biological samples in real-time using mobile phone microscopy.

Miniaturized imaging devices have pushed the boundaries of point-of-care imaging, but existing mobile-phone-based imaging systems do not exploit the full potential of smart phones. This work demonstrates the use of simple imaging configurations to deliver superior image quality and the ability to handle a wide range of biological samples. Results presented in this work are from analysis of fluorescent beads under fluorescence imaging, as well as helminth eggs and freshwater mussel larvae under white light imaging. To demonstrate versatility of the systems, real time analysis and post-processing results of the sample count and sample size are presented in both still images and videos of flowing samples.

Hepatitis C Point-of-Care Testing in Vulnerable Populations: A Human Factors Study.

One third of all hepatitis C virus (HCV) cases in the United States are incarcerated in jails and prisons. Hepatitis C virus testing is primarily accomplished through a clinical laboratory, yet point-of-care (POC) testing is less invasive and results are available in 20 minutes compared with up to 3 weeks. The purpose of this article was to describe the findings of a collaborative project between the Colleges of Engineering and Nursing at the University of Massachusetts Amherst in executing a human factors study for HCV antibody testing and screening. Observation and recording of three-step human factors data included length of time and resources required to execute a POC test and technology use data. In the three-step process, more time is spent on filling out paperwork (4.27 minutes) than is spent on the procedure (1.24 minutes) or on counselling (0.55 minutes). The majority of high-risk respondents had access to smart technology within the previous 3 years. Human factors data will enhance the capabilities of testing, data storage, self-management, and aid in formulating an efficient screening model for marginalized patients with liver disease.

Lensless fluorescence imaging with height calculation.

Lensless fluorescence imaging (LFI) is the imaging of fluorescence from cells or microspheres using an image sensor with no external lenses or filters. The simplicity of the hardware makes it well suited to replace fluorescence microscopes and flow cytometers in lab-on-a-chip applications, but the images captured by LFI are highly dependent on the distance between the sample and the sensor. This work demonstrates that not only can samples be accurately detected across a range of sample-sensor separations using LFI, but also that the separation can be accurately estimated based on the shape of fluorescence in the LFI image. First, a theoretical model that accurately predicts LFI images of microspheres is presented. Then, the experimental results are compared to the model and an image processing method for accurately predicting sample-sensor separation from LFI images is presented. Finally, LFI images of microspheres and cells passing through a microfluidic channel are presented.