Smartphone-based fluorescence detector for mHealth

Systematic investigation of the measurement error structure in a smartphone-based spectrophotometer

Smartphones are state-of-the-art devices with several interesting features which make them promising for analytical purposes. After modification to a spectrophotometer (smart spectrophotometer), they can be utilized for the quantitative or qualitative applications. Although smartphones have widely been applied for sensing∖biosensing purposes, the error structure/type of their outputs remained unexplored. Error structure information values the objects/channels in a given data set and variables have the same importance when the noise has identical independent distribution (i.i.d). Otherwise, error structure weights them for further data analysis. In this contribution, a smartphone-based spectrophotometer was constructed integrating simple optical elements-a tungsten lamp as source and a piece of digital versatile disc (DVD) as a reflecting diffraction grating to investigate the error sources of the smartphone-spectrophotometer. For this purpose, error covariance matrices (ECMs) were calculated using a series of replication capturing error information. Afterwards, PCA and MCR-ALS were employed for the decomposition of the ECMs and resolved profiles were translated to the error types. Finally, proportional error as a heteroscedastic noise was highlighted as the most important source of variation in the error structure of the smartphone-based spectrophotometer.

From Point-of-Care Testing to eHealth Diagnostic Devices (eDiagnostics)

Point-of-care devices were originally designed to allow medical testing at or near the point of care by health-care professionals. Some point-of-care devices allow medical self-testing at home but cannot fully cover the growing diagnostic needs of eHealth systems that are under development in many countries. A number of easy-to-use, network-connected diagnostic devices for self-testing are needed to allow remote monitoring of patients' health. This Outlook highlights the essential characteristics of diagnostic devices for eHealth settings and indicates point-of-care technologies that may lead to the development of new devices. It also describes the most representative examples of simple-to-use, point-of-care devices that have been used for analysis of untreated biological samples.

Digital polymerase chain reaction technology - recent advances and future perspectives

Digital polymerase chain reaction (dPCR) technology has remained a "hot topic" in the last two decades due to its potential applications in cell biology, genetic engineering, and medical diagnostics. Various advanced techniques have been reported on sample dispersion, thermal cycling and output monitoring of digital PCR. However, a fully automated, low-cost and handheld digital PCR platform has not been reported in the literature. This paper attempts to critically evaluate the recent developments in techniques for sample dispersion, thermal cycling and output evaluation for dPCR. The techniques are discussed in terms of hardware simplicity, portability, cost-effectiveness and suitability for automation. The present paper also discusses the research gaps observed in each step of dPCR and concludes with possible improvements toward portable, low-cost and automatic digital PCR systems.

Low-Cost Charged-Coupled Device (CCD) Based Detectors for Shiga Toxins Activity Analysis

To improve food safety there is a need to develop simple, low-cost sensitive devices for detection of food-borne pathogens and their toxins. We describe a simple, low-cost webcam-based detector which can be used for various optical detection modalities, including fluorescence, chemiluminescence, densitometry, and colorimetric assays. The portable battery-operated CCD-based detection system consists of four modules: (1) a webcam to measure and record light emission, (2) a sample plate to perform assays, (3) a light emitting diode (LED) for illumination, and (4) a portable computer to acquire and analyze images. To demonstrate the technology, we used a cell based assay for fluorescence detection of the activity of the food borne Shiga toxin type 2 (Stx2), differentiating between biologically active toxin and inactive toxin which is not a risk. The assay is based on Shiga toxin inhibition of cell protein synthesis measured through inhibition of the green fluorescent protein (GFP). In this assay, GFP emits light at 509 nm when excited with a blue LED equipped with a filter at 486 nm. The emitted light is then detected with a green filter at 535 nm. Toxin activity is measured through a reduction in the 509 nm emission. In this system the level of detection (LOD) for Stx2 was 0.1 pg/ml, similar to the LOD of commercial fluorometers. These results demonstrate the utility and potential of low cost detectors for toxin activity. This approach could be readily adapted to the detection of other food-borne toxins.

Improving the Sensitivity and Functionality of Mobile Webcam-Based Fluorescence Detectors for Point-of-Care Diagnostics in Global Health

Resource-poor countries and regions require effective, low-cost diagnostic devices for accurate identification and diagnosis of health conditions. Optical detection technologies used for many types of biological and clinical analysis can play a significant role in addressing this need, but must be sufficiently affordable and portable for use in global health settings. Most current clinical optical imaging technologies are accurate and sensitive, but also expensive and difficult to adapt for use in these settings. These challenges can be mitigated by taking advantage of affordable consumer electronics mobile devices such as webcams, mobile phones, charge-coupled device (CCD) cameras, lasers, and LEDs. Low-cost, portable multi-wavelength fluorescence plate readers have been developed for many applications including detection of microbial toxins such as C. Botulinum A neurotoxin, Shiga toxin, and S. aureus enterotoxin B (SEB), and flow cytometry has been used to detect very low cell concentrations. However, the relatively low sensitivities of these devices limit their clinical utility. We have developed several approaches to improve their sensitivity presented here for webcam based fluorescence detectors, including (1) image stacking to improve signal-to-noise ratios; (2) lasers to enable fluorescence excitation for flow cytometry; and (3) streak imaging to capture the trajectory of a single cell, enabling imaging sensors with high noise levels to detect rare cell events. These approaches can also help to overcome some of the limitations of other low-cost optical detection technologies such as CCD or phone-based detectors (like high noise levels or low sensitivities), and provide for their use in low-cost medical diagnostics in resource-poor settings.

Harnessing QbD, Programming Languages, and Automation for Reproducible Biology

Building robust manufacturing processes from biological components is a task that is highly complex and requires sophisticated tools to describe processes, inputs, and measurements and administrate management of knowledge, data, and materials. We argue that for bioengineering to fully access biological potential, it will require application of statistically designed experiments to derive detailed empirical models of underlying systems. This requires execution of large-scale structured experimentation for which laboratory automation is necessary. This requires development of expressive, high-level languages that allow reusability of protocols, characterization of their reliability, and a change in focus from implementation details to functional properties. We review recent developments in these areas and identify what we believe is an exciting trend that promises to revolutionize biotechnology.