Computational sensing of herpes simplex virus using a cost-effective on-chip microscope

Conventional and microfluidic methods for airborne virus isolation and detection

With the COVID-19 pandemic, the threat of infectious diseases to public health and safety has become much more apparent. Viral, bacterial and fungal diseases have led to the loss of millions of lives, especially in the developing world. Diseases caused by airborne viruses like SARS-CoV-2 are difficult to control, as these viruses are easily transmissible and can circulate in the air for hours. To contain outbreaks of viruses such as SARS-CoV-2 and institute targeted precautions, it is important to detect them in air and understand how they infect their targets. Point-of-care (PoC) diagnostics and point-of-need (PoN) detection methods are necessary to rapidly test patient and environmental samples, so precautions can immediately be applied. Traditional benchtop detection methods such as ELISA, PCR and culture are not suitable for PoC and PoN monitoring, because they can take hours to days and require specialized equipment. Microfluidic devices can be made at low cost to perform such assays rapidly and at the PoN. They can also be integrated with air- and liquid-based sampling technologies to capture and analyze viruses from air and body fluids. Here, conventional and microfluidic virus detection methods are reviewed and compared. The use of air sampling devices to capture and concentrate viruses is discussed first, followed by a review of analysis methods such as immunoassays, RT-PCR and isothermal amplification in conventional and microfluidic platforms. This review provides an overview of the capabilities of microfluidics in virus handling and detection, which will be useful to infectious disease researchers, biomedical engineers, and public health agencies.

Quantitative particle agglutination assay for point-of-care testing using mobile holographic imaging and deep learning

Particle agglutination assays are widely adopted immunological tests that are based on antigen-antibody interactions. Antibody-coated microscopic particles are mixed with a test sample that potentially contains the target antigen, as a result of which the particles form clusters, with a size that is a function of the antigen concentration and the reaction time. Here, we present a quantitative particle agglutination assay that combines mobile lens-free microscopy and deep learning for rapidly measuring the concentration of a target analyte; as its proof-of-concept, we demonstrate high-sensitivity C-reactive protein (hs-CRP) testing using human serum samples. A dual-channel capillary lateral flow device is designed to host the agglutination reaction using 4 μL of serum sample with a material cost of 1.79 cents per test. A mobile lens-free microscope records time-lapsed inline holograms of the lateral flow device, monitoring the agglutination process over 3 min. These captured holograms are processed, and at each frame the number and area of the particle clusters are automatically extracted and fed into shallow neural networks to predict the CRP concentration. 189 measurements using 88 unique patient serum samples were utilized to train, validate and blindly test our platform, which matched the corresponding ground truth concentrations in the hs-CRP range (0-10 μg mL^-1) with an R² value of 0.912. This computational sensing platform was also able to successfully differentiate very high CRP concentrations (e.g., >10-500 μg mL^-1) from the hs-CRP range. This mobile, cost-effective and quantitative particle agglutination assay can be useful for various point-of-care sensing needs and global health related applications.

Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity

Efforts to mitigate the COVID-19 crisis revealed that fast, accurate, and scalable testing is crucial for curbing the current impact and that of future pandemics. We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification. An ultrasensitive interferometric method was used to image four virus types with nanoscale optical path-length sensitivity. Pairing these data with fluorescence images for ground truth, we trained semantic segmentation models based on U-Net, a particular type of convolutional neural network. The trained network was applied to classify the viruses from the interferometric images only, containing simultaneously SARS-CoV-2, H1N1 (influenza-A virus), HAdV (adenovirus), and ZIKV (Zika virus). Remarkably, due to the nanoscale sensitivity in the input data, the neural network was able to identify SARS-CoV-2 vs. the other viruses with 96% accuracy. The inference time for each image is 60 ms, on a common graphic-processing unit. This approach of directly imaging unlabeled viral particles may provide an extremely fast test, of less than a minute per patient. As the imaging instrument operates on regular glass slides, we envision this method as potentially testing on patient breath condensates. The necessary high throughput can be achieved by translating concepts from digital pathology, where a microscope can scan hundreds of slides automatically.

Diagnosis of Herpes Simplex Virus: Laboratory and Point-of-Care Techniques

Herpes is a widespread viral infection caused by the herpes simplex virus (HSV) that has no permanent cure to date. There are two subtypes, HSV-1 and HSV-2, that are known to cause a variety of symptoms, ranging from acute to chronic. HSV is highly contagious and can be transmitted via any type of physical contact. Additionally, viral shedding can also happen from asymptomatic infections. Thus, early and accurate detection of HSV is needed to prevent the transmission of this infection. Herpes can be diagnosed in two ways, by either detecting the presence of the virus in lesions or the antibodies in the blood. Different detection techniques are available based on both laboratory and point of care (POC) devices. Laboratory techniques include different biochemical assays, microscopy, and nucleic acid amplification. In contrast, POC techniques include microfluidics-based tests that enable on-spot testing. Here, we aim to review the different diagnostic techniques, both laboratory-based and POC, their limits of detection, sensitivity, and specificity, as well as their advantages and disadvantages.

Measurement of serum phosphate levels using a mobile sensor

The measurement of serum phosphate concentration is crucial for patients with advanced chronic kidney disease (CKD) and those on maintenance dialysis, as abnormal phosphate levels may be associated with severe health risks. It is important to monitor serum phosphate levels on a regular basis in these patients; however, such measurements are generally limited to every 0.5-3 months, depending on the severity of CKD. This is due to the fact that serum phosphate measurements can only be performed at regular clinic visits, in addition to cost considerations. Here we present a portable and cost-effective point-of-care device capable of measuring serum phosphate levels using a single drop of blood (<60 μl). This is achieved by integrating a paper-based microfluidic platform with a custom-designed smartphone reader. This mobile sensor was tested on patients undergoing dialysis, where whole blood samples were acquired before starting the hemodialysis and during the three-hour treatment. This sampling during the hemodialysis, under patient consent, allowed us to test blood samples with a wide range of phosphate concentrations, and our results showed a strong correlation with the ground truth laboratory tests performed on the same patient samples (Pearson coefficient r = 0.95 and p < 0.001). Our 3D-printed smartphone attachment weighs about 400 g and costs less than 80 USD, whereas the material cost for the disposable test is <3.5 USD (under low volume manufacturing). This low-cost and easy-to-operate system can be used to measure serum phosphate levels at the point-of-care in about 45 min and can potentially be used on a daily basis by patients at home.

Applications of CMOS Devices for the Diagnosis and Control of Infectious Diseases

Emerging infectious diseases such as coronavirus disease of 2019 (COVID-19), Ebola, influenza A, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) in recent years have threatened the health and security of the global community as one of the greatest factors of mortality in the world. Accurate and immediate diagnosis of infectious agents and symptoms is a key to control the outbreak of these diseases. Rapid advances in complementary metal-oxide-semiconductor (CMOS) technology offers great advantages like high accuracy, high throughput and rapid measurements in biomedical research and disease diagnosis. These features as well as low cost, low power and scalability of CMOS technology can pave the way for the development of powerful devices such as point-of-care (PoC) systems, lab-on-chip (LoC) platforms and symptom screening devices for accurate and timely diagnosis of infectious diseases. This paper is an overview of different CMOS-based devices such as optical, electrochemical, magnetic and mechanical sensors developed by researchers to mitigate the problems associated with these diseases.

Detection of Bacterial and Viral Pathogens Using Photonic Point-of-Care Devices

Infectious diseases caused by bacteria and viruses are highly contagious and can easily be transmitted via air, water, body fluids, etc. Throughout human civilization, there have been several pandemic outbreaks, such as the Plague, Spanish Flu, Swine-Flu, and, recently, COVID-19, amongst many others. Early diagnosis not only increases the chance of quick recovery but also helps prevent the spread of infections. Conventional diagnostic techniques can provide reliable results but have several drawbacks, including costly devices, lengthy wait time, and requirement of trained professionals to operate the devices, making them inaccessible in low-resource settings. Thus, a significant effort has been directed towards point-of-care (POC) devices that enable rapid diagnosis of bacterial and viral infections. A majority of the POC devices are based on plasmonics and/or microfluidics-based platforms integrated with mobile readers and imaging systems. These techniques have been shown to provide rapid, sensitive detection of pathogens. The advantages of POC devices include low-cost, rapid results, and portability, which enables on-site testing anywhere across the globe. Here we aim to review the recent advances in novel POC technologies in detecting bacteria and viruses that led to a breakthrough in the modern healthcare industry.

Computational Image Analysis of Guided Acoustic Waves Enables Rheological Assessment of Sub-nanoliter Volumes

We present a method for the computational image analysis of high frequency guided sound waves based upon the measurement of optical interference fringes, produced at the air interface of a thin film of liquid. These acoustic actuations induce an affine deformation of the liquid, creating a lensing effect that can be readily observed using a simple imaging system. We exploit this effect to measure and analyze the spatiotemporal behavior of the thin liquid film as the acoustic wave interacts with it. We also show that, by investigating the dynamics of the relaxation processes of these deformations when actuation ceases, we are able to determine the liquid's viscosity using just a lens-free imaging system and a simple disposable biochip. Contrary to all other acoustic-based techniques in rheology, our measurements do not require monitoring of the wave parameters to obtain quantitative values for fluid viscosities, for sample volumes as low as 200 pL. We envisage that the proposed methods could enable high throughput, chip-based, reagent-free rheological studies within very small samples.

Optimized sensing of sparse and small targets using lens-free holographic microscopy

Lens-free holographic microscopy offers sub-micron resolution over an ultra-large field-of-view >20 mm², making it suitable for bio-sensing applications that require the detection of small targets at low concentrations. Various pixel super-resolution techniques have been shown to enhance resolution and boost signal-to-noise ratio (SNR) by combining multiple partially-redundant low-resolution frames. However, it has been unclear which technique performs best for small-target sensing. Here, we quantitatively compare SNR and resolution in experiments using no regularization, cardinal-neighbor regularization, and a novel implementation of sparsity-promoting regularization that uses analytically-calculated gradients from Bayer-pattern image sensors. We find that sparsity-promoting regularization enhances the SNR by ~8 dB compared to the other methods when imaging micron-scale beads with surface coverages up to ~4%.

Mobile Technologies for the Discovery, Analysis, and Engineering of the Global Microbiome

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.