Standard microplate based enzyme-linked immunosorbent assays (ELISA) are widely utilized for various nanomedicine, molecular sensing, and disease screening applications, and this multiwell plate batched analysis dramatically reduces diagnosis costs per patient compared to nonbatched or nonstandard tests. However, their use in resource-limited and field-settings is inhibited by the necessity for relatively large and expensive readout instruments. To mitigate this problem, we created a hand-held and cost-effective cellphone-based colorimetric microplate reader, which uses a 3D-printed opto-mechanical attachment to hold and illuminate a 96-well plate using a light-emitting-diode (LED) array. This LED light is transmitted through each well, and is then collected via 96 individual optical fibers. Captured images of this fiber-bundle are transmitted to our servers through a custom-designed app for processing using a machine learning algorithm, yielding diagnostic results, which are delivered to the user within ∼1 min per 96-well plate, and are visualized using the same app. We successfully tested this mobile platform in a clinical microbiology laboratory using FDA-approved mumps IgG, measles IgG, and herpes simplex virus IgG (HSV-1 and HSV-2) ELISA tests using a total of 567 and 571 patient samples for training and blind testing, respectively, and achieved an accuracy of 99.6%, 98.6%, 99.4%, and 99.4% for mumps, measles, HSV-1, and HSV-2 tests, respectively. This cost-effective and hand-held platform could assist health-care professionals to perform high-throughput disease screening or tracking of vaccination campaigns at the point-of-care, even in resource-poor and field-settings. Also, its intrinsic wireless connectivity can serve epidemiological studies, generating spatiotemporal maps of disease prevalence and immunity.

DNA imaging techniques using optical microscopy have found numerous applications in biology, chemistry and physics and are based on relatively expensive, bulky and complicated set-ups that limit their use to advanced laboratory settings. Here we demonstrate imaging and length quantification of single molecule DNA strands using a compact, lightweight and cost-effective fluorescence microscope installed on a mobile phone. In addition to an optomechanical attachment that creates a high contrast dark-field imaging setup using an external lens, thin-film interference filters, a miniature dovetail stage and a laser-diode for oblique-angle excitation, we also created a computational framework and a mobile phone application connected to a server back-end for measurement of the lengths of individual DNA molecules that are labeled and stretched using disposable chips. Using this mobile phone platform, we imaged single DNA molecules of various lengths to demonstrate a sizing accuracy of <1 kilobase-pairs (kbp) for 10 kbp and longer DNA samples imaged over a field-of-view of ∼2 mm².

This paper introduces a wearable Tele-ECG and heart rate (HR) monitoring system which has a novel architecture including a stretchable singlet redesigned with textile electrodes (TEs), textile threads, snap fasteners, Velcro, sponges, and an ECG circuit. In addition, a Bluetooth low energy (BLE), a smartphone, a server, and a web page have been added to the system for remote monitoring. The TE can be attached to and removed from the singlet by a Velcro, which allows the user to dry-clean the TE easily for long-term use. A new holter-based ECG system has been designed to evaluate the TE-based ECG system and the average correlation between the recorded ECG signals is obtained as 99.23%. A filtered digital signal, with a high signal-to-noise ratio of 45.62 dB, is transmitted to the smartphone via BLE. The ECG signal is plotted, the HR is calculated with 1.83% mean absolute percentage error, and displayed. The data are sent to the server, allowing the patient's physician to analyze the signals in real time through the web page or the smartphone. If HR reaches beyond the normal range or user presses the “HELP” button on the smartphone screen, the physician is informed automatically by an short message service (SMS) with a location pin on the map. The battery lasts approximately 14 days and when it needs replacement, the system automatically alerts the users by an SMS and a flashing LED. This fast and uninterrupted telemonitoring system has the potential to improve the patient's life quality by providing a psychological reassurance.

Monitoring yeast cell viability and concentration is important in brewing, baking and biofuel production. However, existing methods of measuring viability and concentration are relatively bulky, tedious and expensive. Here we demonstrate a compact and cost-effective automatic yeast analysis platform (AYAP), which can rapidly measure cell concentration and viability. AYAP is based on digital in-line holography and on-chip microscopy and rapidly images a large field-of-view of 22.5 mm². This lens-free microscope weighs 70 g and utilizes a partially-coherent illumination source and an opto-electronic image sensor chip. A touch-screen user interface based on a tablet-PC is developed to reconstruct the holographic shadows captured by the image sensor chip and use a support vector machine (SVM) model to automatically classify live and dead cells in a yeast sample stained with methylene blue. In order to quantify its accuracy, we varied the viability and concentration of the cells and compared AYAP's performance with a fluorescence exclusion staining based gold-standard using regression analysis. The results agree very well with this gold-standard method and no significant difference was observed between the two methods within a concentration range of 1.4 × 10⁵ to 1.4 × 10⁶ cells per mL, providing a dynamic range suitable for various applications. This lensfree computational imaging technology that is coupled with machine learning algorithms would be useful for cost-effective and rapid quantification of cell viability and density even in field and resource-poor settings.