No abstract

In paper microfluidics, the development of smart and versatile switches is critical for the regulation of fluid flow across multiple channels. Past approaches in creating switches are limited by long response times, large actuation fluid volumes, and use of external control circuitry. We seek to mitigate these difficulties through the development of a unique actuator device made entirely out of chromatography paper and incorporated with folds. Selective wetting of the fold with an actuation fluid, either at the crest or trough, serves to raise or lower the actuator's tip and thus engage or break the fluidic contact between channels. Here the actuator's response time is dramatically reduced (within two seconds from wetting) and a very small volume of actuation fluid is consumed (four microliters). Using this actuation principle, we implement six switch configurations which can be grouped as single-pole single-throw (normally OFF and normally ON) and single-pole double-throw (with single and double break). By employing six actuators in parallel, an autonomous colorimetric assay is built to detect the presence of three analytes - glucose, protein, and nitrite - in artificial saliva. Finally, this work brings the concept of origami to paper microfluidics where multiple-fold geometries can be exploited for programmable switching of fluidic connections.

We developed an open microfluidic system to dispense and manipulate discrete droplets on planar plastic sheets. Here, a superhydrophobic material is spray-coated on commercially-available plastic sheets followed by the printing of hydrophilic symbols using an inkjet printer. The patterned plastic sheets are taped to a two-axis tilting platform, powered by stepper motors, that provides mechanical agitation for droplet transport. We demonstrate the following droplet operations: transport of droplets of different sizes, parallel transport of multiple droplets, merging and mixing of multiple droplets, dispensing of smaller droplets from a large droplet or a fluid reservoir, and one-directional transport of droplets. As a proof-of-concept, a colorimetric assay is implemented to measure the glucose concentration in sheep serum. Compared to silicon-based digital microfluidic devices, we believe that the presented system is appealing for various biological experiments because of the ease of altering design layouts of hydrophilic symbols, relatively faster turnaround time in printing plastic sheets, larger area to accommodate more tests, and lower operational costs by using off-the-shelf products.

Among the different types of skin cancer, melanoma is considered to be the deadliest and is difficult to treat at advanced stages. Detection of melanoma at earlier stages can lead to reduced mortality rates. Desktop-based computer-aided systems have been developed to assist dermatologists with early diagnosis. However, there is significant interest in developing portable, at-home melanoma diagnostic systems which can assess the risk of cancerous skin lesions. Here, we present a smartphone application that combines image capture capabilities with preprocessing and segmentation to extract the Asymmetry, Border irregularity, Color variegation, and Diameter (ABCD) features of a skin lesion. Using the feature sets, classification of malignancy is achieved through support vector machine classifiers. By using adaptive algorithms in the individual data-processing stages, our approach is made computationally light, user friendly, and reliable in discriminating melanoma cases from benign ones. Images of skin lesions are either captured with the smartphone camera or imported from public datasets. The entire process from image capture to classification runs on an Android smartphone equipped with a detachable 10x lens, and processes an image in less than a second. The overall performance metrics are evaluated on a public database of 200 images with Synthetic Minority Over-sampling Technique (SMOTE) (80% sensitivity, 90% specificity, 88% accuracy, and 0.85 area under curve (AUC)) and without SMOTE (55% sensitivity, 95% specificity, 90% accuracy, and 0.75 AUC). The evaluated performance metrics and computation times are comparable or better than previous methods. This all-inclusive smartphone application is designed to be easy-to-download and easy-to-navigate for the end user, which is imperative for the eventual democratization of such medical diagnostic systems.

The ability to study bacteria at the single cell level has advanced our insights into microbial physiology and genetics in ways not attainable by studying large populations using more traditional culturing methods. To improve methods to characterize bacteria at the cellular level, we developed a new microfluidic platform that enables cells to be exposed to metabolites in a gradient of concentrations. By designing lowcost, three-dimensional devices with adhesive tapes and tailoring them for bacterial imaging, we avoided the complexities of silicon and polymeric microfabrication. The incorporation of an agarose membrane as the resting substrate, along with a temperature-controlled environmental chamber, allows the culturing of bacterial cells for over 10 h under stable growth or inhibition conditions. Incorporation of an autofocusing module helped the uninterrupted, high-resolution observation of bacteria at the single-cell and at low density population levels. We used the microfluidic platform to record morphological changes in Escherichia coli during ampicillin exposure and to quantify the minimum inhibitory concentration of the antibiotic. We further demonstrated the potential of finely-tuned, incremental gene regulation in a concentration gradient utilizing CRISPR interference (CRISPRi). These low-cost engineering tools, when implemented in combination with genetic approaches such as CRISPRi, should prove useful to uncover new genetic determinants of antibiotic susceptibility and evaluate the long-term effectiveness of antibiotics in bacterial cultures.

Precision swine production can benefit from autonomous, noninvasive, and affordable devices that conduct frequent checks on the well-being status of pigs. Here, we present a remote monitoring tool for the objective measurement of some behavioral indicators that may help in assessing the health and welfare status—namely, posture, gait, vocalization, and external temperature. The multiparameter electronic sensor board is characterized by laboratory measurements and by animal tests. Relevant behavioral health indicators are discussed for implementing machine learning algorithms and decision support tools to detect animal lameness, lethargy, pain, injury, and distress. The roadmap for technology adoption is also discussed, along with challenges and the path forward. The presented technology can potentially lead to efficient management of farm animals, targeted focus on sick animals, medical cost savings, and less use of antibiotics.

The soybean cyst nematode (SCN), Heterodera glycines, is the most damaging pathogen of soybeans in the United States. To assess the severity of nematode infestations in the field, SCN egg population densities are determined. Cysts (dead females) of the nematode must be extracted from soil samples and then ground to extract the eggs within. Sucrose centrifugation commonly is used to separate debris from suspensions of extracted nematode eggs. We present a method using OptiPrep as a density gradient medium with improved separation and recovery of extracted eggs compared to the sucrose centrifugation technique. Also, computerized methods were developed to automate the identification and counting of nematode eggs from the processed samples. In one approach, a high-resolution scanner was used to take static images of extracted eggs and debris on filter papers, and a deep learning network was trained to identify and count the eggs among the debris. In the second approach, a lensless imaging setup was developed using off-the-shelf components, and the processed egg samples were passed through a microfluidic flow chip made from double-sided adhesive tape. Holographic videos were recorded of the passing eggs and debris, and the videos were reconstructed and processed by custom software program to obtain egg counts. The performance of the software programs for egg counting was characterized with SCN-infested soil collected from two farms, and the results using these methods were compared with those obtained through manual counting.

In nature, several organisms possess a magnetic compass to navigate or migrate them to desired locations. It is thought that these organisms may use biogenic magnetic matter or light-sensitive photoreceptors to sense and orient themselves in magnetic fields. To unravel the underlying principles of magnetosensitivity and magnetoreception, previous experiments have been conducted on bacteria, vertebrates, crustaceans, and insects. In this study, the model organism, C. elegans, is used to test their response and sensitivity to static magnetic fields in the range of 5 milli Tesla to 120 milli Tesla. Single wild-type C. elegans are put in microfluidic channels and exposed to permanent magnets for five cycles of thirty-second time intervals. The worm movement is recorded and analyzed with custom software to calculate the average velocity and the percentage of turning and curling. Contrary to some published studies, our results did not show a significant difference compared to control experiments. This suggests that C. elegans may not sense static magnetic fields in the range of field strengths that we tested.