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We demonstrate multiplexed computational sensing with a point-of-care serodiagnosis assay to simultaneously quantify three biomarkers of acute cardiac injury. This point-of-care sensor includes a paper-based fluorescence vertical flow assay (fxVFA) processed by a low-cost mobile reader, which quantifies the target biomarkers through trained neural networks, all within 0.9 linearity and < 15 % coefficient of variation. The competitive performance of this multiplexed computational fxVFA along with its inexpensive paper-based design and handheld footprint make it a promising point-of-care sensor platform that could expand access to diagnostics in resource-limited settings., 17 Pages, 6 Figures

Lyme disease (also known as Lyme borreliosis) is the most common vector-borne disease in the United States with an estimated 476,000 cases per year. While historically, the long-term impact of Lyme disease on patients has been controversial, mounting evidence supports the idea that a substantial number of patients experience persistent symptoms following treatment. The research community has largely lacked the necessary funding to properly advance the scientific and clinical understanding of the disease, or to develop and evaluate innovative approaches for prevention, diagnosis, and treatment. Given the many outstanding questions raised into the diagnosis, clinical presentation and treatment of Lyme disease, and the underlying molecular mechanisms that trigger persistent disease, there is an urgent need for more support. This review article summarizes progress over the past 5 years in our understanding of Lyme and tick-borne diseases in the United States and highlights remaining challenges.

We report a deep-learning based compact spectrometer. Using a spectral encoder chip composed of unique plasmonic tiles (containing periodic nanohole-arrays), diffraction patterns created by the transmitted light through these tiles are captured by a CMOS sensor-array, without the use of any lenses or other components between the plasmonic encoder and the CMOS-chip. A neural network rapidly reconstructs the input light spectrum from the recorded lensless image data, which was blindly tested on randomly-generated new spectra to demonstrate the success of this computational on-chip spectrometer, which will find applications in various fields that demand low-cost and compact spectrum analyzers.

Conventional spectrometers are limited by trade-offs set by size, cost, signal-to-noise ratio (SNR), and spectral resolution. Here, we demonstrate a deep learning-based spectral reconstruction framework, using a compact and low-cost on-chip sensing scheme that is not constrained by the design trade-offs inherent to grating-based spectroscopy. The system employs a plasmonic spectral encoder chip containing 252 different tiles of nanohole arrays fabricated using a scalable and low-cost imprint lithography method, where each tile has a unique geometry and, thus, a unique optical transmission spectrum. The illumination spectrum of interest directly impinges upon the plasmonic encoder, and a CMOS image sensor captures the transmitted light, without any lenses, gratings, or other optical components in between, making the entire hardware highly compact, light-weight and field-portable. A trained neural network then reconstructs the unknown spectrum using the transmitted intensity information from the spectral encoder in a feed-forward and non-iterative manner. Benefiting from the parallelization of neural networks, the average inference time per spectrum is ~28 microseconds, which is orders of magnitude faster compared to other computational spectroscopy approaches. When blindly tested on unseen new spectra (N = 14,648) with varying complexity, our deep-learning based system identified 96.86% of the spectral peaks with an average peak localization error, bandwidth error, and height error of 0.19 nm, 0.18 nm, and 7.60%, respectively. This system is also highly tolerant to fabrication defects that may arise during the imprint lithography process, which further makes it ideal for applications that demand cost-effective, field-portable and sensitive high-resolution spectroscopy tools., Comment: 18 pages, 6 figures

We demonstrate a deep learning-based spectroscopy framework using a low-cost on-chip plasmonic encoder. When blindly tested on N=14,648 random spectra our system shows competitive performance, where the reconstruction of an unknown spectrum on average takes ~28µs.

We report the quantification of lysozyme in tear fluid using contact lenses and a mobile-phone well-plate reader. We observed significantly lower lysozyme levels in human participants with dry eye disease compared to healthy controls.

We present a deep learning-based framework to perform single image super-resolution of SEM images. We experimentally demonstrated that this network can enhance the resolution of SEM images by two-fold, allowing for a reduction of the scanning time and electron dosage by four-fold without any significant loss of image quality. Using blindly tested regions of a gold-on-carbon resolution test target, we quantitatively and qualitatively confirmed the image enhancement achieved by the trained network. We believe that this technique has the potential to improve the SEM imaging process, particularly in cases where imaging throughput and minimizing beam damage are of utmost importance.

We report a point-of-care (POC) assay and neural network-based diagnostic algorithm for Lyme Disease (LD). A paper-based test in a vertical flow format detects 16 different IgM and IgG LD-specific antibodies in serum using a mobile phone reader and automated image processing to quantify its colorimetric signals. The multiplexed information is then input into a trained neural-network which infers a positive or negative result for LD. The assay and diagnostic decision algorithm were validated through fully-blinded testing of human serum samples yielding an area-under-the-curve (AUC), sensitivity, and specificity of 0.950, 90.5%, and 87.0% respectively, outperforming previous Lyme POC tests.

We report a method for sensing analytes in tear-fluid using commercial contact lenses (CLs) as sample collectors for subsequent analysis with a cost-effective and field-portable reader. In this study we quantify lysozyme, the most prevalent protein in tear fluid, non-specifically bound to CLs worn by human participants. Our mobile reader uses time-lapse imaging to capture an increasing fluorescent signal in a standard well-plate, the rate-of-change of which is used to indirectly infer lysozyme concentration through the use of a standard curve. We empirically determined the best-suited CL material for our sampling procedure and assay, and subsequently monitored the lysozyme levels of nine healthy human participants over a two-week period. Of these participants who were regular CL wearers (6 out of 9), we observed an increase in lysozyme levels from 6.89 ± 2.02 μg mL-1 to 10.72 ± 3.22 μg mL-1 (mean ± SD) when inducing an instance of digital eye-strain by asking them to play a game on their mobile-phones during the CL wear-duration. We also observed a lower mean lysozyme concentration (2.43 ± 1.66 μg mL-1) in a patient cohort with dry eye disease (DED) as compared to the average monitoring level of healthy (no DED) human participants (6.89 ± 2.02 μg mL-1). Taken together, this study demonstrates tear-fluid analysis with simple and non-invasive sampling steps along with a rapid, easy-to-use, and cost-effective measurement system, ultimately indicating physiological differences in human participants. We believe this method could be used in future tear-fluid studies, even supporting multiplexed detection of a panel of tear biomarkers toward improved diagnostics and prognostics as well as personalized mobile-health applications.

We demonstrate a contact-lens (CL) based mobile sensing system which can be used to measure protein levels in human tear. By using a cost-effective mobile-phone-based well-plate reader and a fluorescent assay, we quantify lysozyme nonspecifically bound to CLs. We monitored the lysozyme levels of 9 healthy volunteers to establish individual baselines, and then compared these measurements to participants who had been diagnosed with Dry Eye Disease (N=6), observing a statistically significant difference in their means. Due to its non-invasive and simple operation, this method could be used for tear-based sensing and health monitoring applications in point-of-care settings and at home.

The microbiome has been heralded as a gauge of and contributor to both human health and environmental conditions. Current challenges in probing, engineering, and harnessing the microbiome stem from its microscopic and nanoscopic nature, diversity and complexity of interactions among its members and hosts, as well as the spatiotemporal sampling and in situ measurement limitations induced by the restricted capabilities and norm of existing technologies, leaving some of the constituents of the microbiome unknown. To facilitate significant progress in the microbiome field, deeper understanding of the constituents' individual behavior, interactions with others, and biodiversity are needed. Also crucial is the generation of multimodal data from a variety of subjects and environments over time. Mobile imaging and sensing technologies, particularly through smartphone-based platforms, can potentially meet some of these needs in field-portable, cost-effective, and massively scalable manners by circumventing the need for bulky, expensive instrumentation. In this Perspective, we outline how mobile sensing and imaging technologies could lead the way to unprecedented insight into the microbiome, potentially shedding light on various microbiome-related mysteries of today, including the composition and function of human, animal, plant, and environmental microbiomes. Finally, we conclude with a look at the future, propose a computational microbiome engineering and optimization framework, and discuss its potential impact and applications.

We demonstrate computational sensing with a multiplexed paper-based point-of-care assay for high sensitivity C-Reactive protein testing (hsCRP). Using blindly-tested human serum samples, we achieved an R2 value of 0.95 and an average coefficient-of-variation of 11.2%.

We report a multiplexed paper-based assay powered by mobile imaging and machine learning for point-of-care diagnosis of Lyme Disease. Blind testing of 96 serum samples showed a sensitivity and specificity of 85.7% and 96.3%, respectively.

Caused by the tick-borne spirochete Borrelia burgdorferi, Lyme disease (LD) is the most common vector-borne infectious disease in North America and Europe. Though timely diagnosis and treatment are effective in preventing disease progression, current tests are insensitive in early stage LD, with a sensitivity of $400/test) and extended sample-to-answer timelines (>24 h). To address these challenges, we created a cost-effective and rapid point-of-care (POC) test for early-stage LD that assays for antibodies specific to seven Borrelia antigens and a synthetic peptide in a paper-based multiplexed vertical flow assay (xVFA). We trained a deep-learning-based diagnostic algorithm to select an optimal subset of antigen/peptide targets and then blindly tested our xVFA using human samples (N(+) = 42, N(-) = 54), achieving an area-under-the-curve (AUC), sensitivity, and specificity of 0.950, 90.5%, and 87.0%, respectively, outperforming previous LD POC tests. With batch-specific standardization and threshold tuning, the specificity of our blind-testing performance improved to 96.3%, with an AUC and sensitivity of 0.963 and 85.7%, respectively.

We present a deep learning-based framework to design and quantify point-of-care sensors. As its proof-of-concept and use-case, we demonstrated a low-cost and rapid paper-based vertical flow assay (VFA) for high sensitivity C-Reactive Protein (hsCRP) testing, a common medical test used for quantifying the degree of inflammation in patients at risk of cardio-vascular disease (CVD). A machine learning-based sensor design framework was developed for two key tasks: (1) to determine an optimal configuration of immunoreaction spots and conditions, spatially-multiplexed on a paper-based sensing membrane, and (2) to accurately infer the target analyte concentration based on the signals of the optimal VFA configuration. Using a custom-designed mobile-phone based VFA reader, a clinical study was performed with 85 human serum samples to characterize the quantification accuracy around the clinically defined cutoffs for CVD risk stratification. Results from blindly-tested VFAs indicate a competitive coefficient of variation of 11.2% with a linearity of R2 = 0.95; in addition to the success in the high-sensitivity CRP range (i.e., 0-10 mg/L), our results further demonstrate a mitigation of the hook-effect at higher CRP concentrations due to the incorporation of antigen capture spots within the multiplexed sensing membrane of the VFA. This paper-based computational VFA that is powered by deep learning could expand access to CVD health screening, and the presented machine learning-enabled sensing framework can be broadly used to design cost-effective and mobile sensors for various point-of-care diagnostics applications.

A lunchbox-sized device for nucleic acid quantification that can be powered by sunlight, a flame or electricity enables the diagnosis of disease in settings with unreliable power supply.

We report resolution enhancement in scanning electron microscopy (SEM) images using a generative adversarial network. We demonstrate the veracity of this deep learning-based super-resolution technique by inferring unresolved features in low-resolution SEM images and comparing them with the accurately co-registered high-resolution SEM images of the same samples. Through spatial frequency analysis, we also report that our method generates images with frequency spectra matching higher resolution SEM images of the same fields-of-view. By using this technique, higher resolution SEM images can be taken faster, while also reducing both electron charging and damage to the samples., Comment: 8 pages, 4 figures

Plasmonic sensors have been used for a wide-range of biological and chemical sensing applications. Emerging nano-fabrication techniques have enabled these sensors to be cost-effectively mass-manufactured onto various types of substrates. To accompany these advances, major improvements in sensor read-out devices must also be achieved to fully realize the broad impact of plasmonic nano-sensors. Here, we propose a machine learning framework which can be used to design low-cost and mobile multi-spectral plasmonic readers that do not use traditionally employed bulky and expensive stabilized light-sources or high-resolution spectrometers. By training a feature selection model over a large set of fabricated plasmonic nano-sensors, we select the optimal set of illumination light-emitting-diodes needed to create a minimum-error refractive index prediction model, which statistically takes into account the varied spectral responses and fabrication-induced variability of a given sensor design. This computational sensing approach was experimentally validated using a modular mobile plasmonic reader. We tested different plasmonic sensors with hexagonal and square periodicity nano-hole arrays, and revealed that the optimal illumination bands differ from those that are ‘intuitively’ selected based on the spectral features of the sensor, e.g., transmission peaks or valleys. This framework provides a universal tool for the plasmonics community to design low-cost and mobile multi-spectral readers, helping the translation of nano-sensing technologies to various emerging applications such as wearable sensing, personalized medicine, and point-of-care diagnostics. Beyond plasmonics, other types of sensors that operate based on spectral changes can broadly benefit from this approach, including e.g., aptamer-enabled nanoparticle assays and graphene-based sensors, among others.

Mechanical flexibility and the advent of scalable, low-cost, and high-throughput fabrication techniques have enabled numerous potential applications for plasmonic sensors. Sensitive and sophisticated biochemical measurements can now be performed through the use of flexible plasmonic sensors, integrated into existing medical and industrial devices or sample collection units. More robust sensing schemes and practical techniques must be further investigated to fully realize the potentials of flexible plasmonics as a framework for designing low-cost, embedded and integrated sensors for medical, environmental, and industrial applications.

We developed a multiplexed point-of-care immunodiagnostic assay for antibody detection in human sera made through the vertical stacking of functional paper layers. In this multiplexed vertical flow immunodiagnostic assay (xVFA), a colorimetric signal is generated by gold nanoparticles captured on a spatially-multiplexed sensing membrane containing specific antigens. The assay is completed in 20 minutes, following which the sensing membrane is imaged by a cost-effective mobile-phone reader. The images are sent to a server, where the results are rapidly analyzed and relayed back to the user. The performance of the assay was evaluated by measuring Lyme-specific antibodies in human sera as model target antibodies. The presented platform is rapid, simple, inexpensive, and allows for simultaneous and quantitative measurement of multiple antibodies and/or antigens making it a suitable point-of-care platform for disease diagnostics.

Methods of detection for key biomarkers in bodily fluids that are specific, low-cost, and non-invasive are in high demand for various biomedical applications. Specifically, field-portable and cost-effective devices which can enable these measurements to be made at home or in the field are crucial for practical and widespread use of these technologies. Plasmonic sensors form an emerging bio-sensor platform that responds to the specific adsorption of bio-molecules via a spectral change in transmission or reflection mode of operation. However, to read and quantify their spectral response, expensive and bulky optics such as broad-band light sources and high resolution spectrometers are typically employed, severely limiting their potential applications in resource-limited settings. In an effort to build low-cost and compact plasmonic readers, we have developed a computational sensing framework that uses machine learning to statistically differentiate the sensor’s spectral response from fabrication related variability and other noise factors, and select the optimal illumination bands for the lowest-possible read-out error. To validate this framework we constructed a low-cost and field-portable plasmonic reader around the optimal illumination bands selected for different plasmonic nano-hole array designs. We then validated the superior performance of our computational reader by measuring a large number of independently fabricated flexible plasmonic sensors made using scalable, nano-imprint lithography methods without the use of a clean room. Additionally, these structures can subsequently be transfer-printed onto disposable, wearable platforms where they can be chemically modified to specifically and sensitively capture target biomarkers in bio-fluids e.g., tear or sweat, enabling new applications in point-of-care diagnostics.

A pocket holographic slide enables the high-throughput screening of fluid samples by embedding micro-optics directly on a micro-channel to achieve off-axis interferometric imaging. This holographic slide can be used to turn a conventional microscope into a phase-contrast imager or as a stand-alone compact imaging device by incorporating polymer lenses that are printed directly on-chip.

We introduce a computational sensing framework to select the optimal set of illumination bands for a given plasmonic sensor design and fabrication method. This framework enables optimized designs for cost-effective, sensitive, and mobile plasmonic readers.

The market for wearable sensors is predicted to grow to $5.5 billion by 2025, impacting global health in unprecedented ways. Optics and photonics will play a key role in the future of these wearable technologies, enabling highly sensitive measurements of otherwise invisible information and parameters about our health and surrounding environment. Through the implementation of optical wearable technologies, such as heart rate, blood pressure, and glucose monitors, among others, individuals are becoming more empowered to generate a wealth of rich, multifaceted physiological and environmental data, making personalized medicine a reality. Furthermore, these technologies can also be implemented in hospitals, clinics, point-of-care offices, assisted living facilities or even in patients’ homes for real-time, remote patient monitoring, creating more expeditious as well as resource-efficient systems. Several key optical technologies make such sensors possible, including e.g., optical fiber textiles, colorimetric, plasmonic, and fluorometric sensors, as well as Organic Light Emitting Diode (OLED) and Organic Photo-Diode (OPD) technologies. These emerging technologies and platforms show great promise as basic sensing elements in future wearable devices and will be reviewed in this chapter along-side currently existing fully integrated wearable optical sensors.

Understanding the organization and mechanical function of the extracellular matrix (ECM) is critical for the development of therapeutic strategies that regulate wound healing following disease or injury. However, these relationships are challenging to elucidate during remodeling following myocardial infarction (MI) due to rapid changes in cellularity and an inability to characterize both ECM microstructure and function non-destructively. In this study, we overcome those challenges through whole organ decellularization and non-linear optical microscopy to directly relate the microstructure and mechanical properties of myocardial ECM. We non-destructively quantify collagen organization, content, and cross-linking within decellularized healthy and infarcted myocardium using second harmonic generation (SHG) and two photon excited autofluorescence. Tensile mechanical testing and compositional analysis reveal that the cumulative SHG intensity within each image volume and the average collagen autofluorescence are significantly correlated with collagen content and elastic modulus of the ECM, respectively. Compared to healthy ECM, infarcted tissues demonstrate a significant increase in collagen content and fiber alignment, and a decrease in cross-linking and elastic modulus. These findings indicate that cross-linking plays a key role in stiffness at the collagen fiber level following infarction, and highlight how this non-destructive approach to assessing remodeling can be used to understand ECM structure-function relationships.

Rapid, accurate and high-throughput sizing and quantification of particulate matter (PM) in air is crucial for monitoring and improving air quality. In fact, particles in air with a diameter of ≤2.5 μm have been classified as carcinogenic by the World Health Organization. Here we present a field-portable cost-effective platform for high-throughput quantification of particulate matter using computational lens-free microscopy and machine-learning. This platform, termed c-Air, is also integrated with a smartphone application for device control and display of results. This mobile device rapidly screens 6.5 L of air in 30 s and generates microscopic images of the aerosols in air. It provides statistics of the particle size and density distribution with a sizing accuracy of ~93%. We tested this mobile platform by measuring the air quality at different indoor and outdoor environments and measurement times, and compared our results to those of an Environmental Protection Agency–approved device based on beta-attenuation monitoring, which showed strong correlation to c-Air measurements. Furthermore, we used c-Air to map the air quality around Los Angeles International Airport (LAX) over 24 h to confirm that the impact of LAX on increased PM concentration was present even at >7 km away from the airport, especially along the direction of landing flights. With its machine-learning-based computational microscopy interface, c-Air can be adaptively tailored to detect specific particles in air, for example, various types of pollen and mold and provide a cost-effective mobile solution for highly accurate and distributed sensing of air quality. Accurate on-site air-quality monitoring can be performed using lens-free microscopy on a chip coupled with machine learning. To monitor and enhance air quality, it is vital to realize rapid, accurate and high-throughput sizing of airborne particles. A portable system built by Aydogan Ozcan and co-workers from the University of California, Los Angeles, generates statistics of particle size and density from microscopic images of particulate matter in air. A sticky coverslip captures airborne particles and then light from three LEDs (red, green and blue) creates holograms of the particle distribution, captured on a CMOS image sensor and processed. The system can screen 6.5 litres of air in about 30 s and has a particle sizing accuracy of about 93%. Results obtained using this technology achieved a strong correlation with those acquired using conventional particle-sizing devices.