Low cost and scalable manufacture of lab-on-chip devices for applications such as point-of-care testing is an urgent need. Weaving is presented as a unified, scalable and low-cost platform for the manufacture of fabric chips that can be used to perform such testing. Silk yarns with different properties are first selected, treated with the appropriate reagent solutions, dried and handloom-woven in one step into an integrated fabric chip. This platform has the unique advantage of scaling up production using existing and low cost physical infrastructure. We have demonstrated the ability to create pre-defined flow paths in fabric by using wetting and non-wetting silk yarns and a Jacquard attachment in the loom. Further, we show that yarn parameters such as the yarn twist frequency and weaving coverage area may be conveniently used to tune both the wicking rate and the absorptive capacity of the fabric. Yarns optimized for their final function were used to create an integrated fabric chip containing reagent-coated yarns. Strips of this fabric were then used to perform a proof-of-concept immunoassay with sample flow taking place by capillary action and detection being performed by a visual readout.

The demand for methods and technologies capable of rapid, inexpensive and continuous monitoring of health status or exposure to environmental pollutants persists. In this work, the development of novel surface-enhanced Raman spectroscopy (SERS) substrates from metal-coated silk fabric, known as zari, presents the potential for SERS substrates to be incorporated into clothing and other textiles for the routine monitoring of important analytes, such as disease biomarkers or environmental pollutants. Characterization of the zari fabric was completed using scanning electron microscopy, energy dispersive X-ray analysis and Raman spectroscopy. Silver nanoparticles (AgNPs) were prepared, characterized by transmission electron microscopy and UV-vis spectroscopy, and used to treat fabric samples by incubation, drop-coating and in situ synthesis. The quality of the treated fabric was evaluated by collecting the SERS signal of 4,4'-bipyridine on these substrates. When AgNPs were drop-coated on the fabric, sensitive and reproducible substrates were obtained. Adenine was selected as a second probe molecule, because it dominates the SERS signal of DNA, which is an important class of disease biomarker, particularly for pathogens such as Plasmodium spp. and Mycobacterium tuberculosis. Excellent signal enhancement could be achieved on these affordable substrates, suggesting that the developed fabric chips have the potential for expanding the use of SERS as a diagnostic and environmental monitoring tool for application in wearable sensor technologies.

We report a new image processing algorithm that extracts quantitative information about the concentration of human chorionic gonadotropin (hCG), an important early pregnancy marker, from commercially available qualitative home pregnancy kits. The algorithm could potentially be ported onto a simple camera based cell phone making it a low-cost, portable point-of-care device as opposed to costly and time consuming clinical labs for accurate quantitative determination of hCG. The algorithm takes the image of the test result as input, classifies and determines the hCG concentration based on the RGB intensities of the test line. The efficacy of the algorithm is demonstrated using control samples on commercially available strips as well as novel fabric based strips designed for this application.

Home pregnancy kits typically provide a qualitative (yes/no) result based on the concentration of human chorionic gonadotropin (hCG) present in urine samples. We present an algorithm that converts this purely qualitative test into a semiquantitative one by processing digital images of the test kit's output. The algorithm identifies the test and control lines in the image and classifies an input into one of four different hCG concentration levels based on the color of the test line. The proposed algorithm provides significant improvement over a prior method and reduces the maximum false positive rate to less than 5%. This improvement is achieved by a careful choice of the color space so as to maximize the inter-concentration separability. Also, the proposed method increases the utility of the test kits by providing useful diagnostic information. Furthermore, the algorithm could be ported to a mobile platform to make it particularly helpful in remote rural health monitoring.

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