Virus detection using nanoparticles and deep neural network-enabled smartphone system

Plasmonic coffee-ring biosensing for AI-assisted point-of-care diagnostics

A major challenge in addressing global health issues is developing simple, affordable biosensors with high sensitivity and specificity. Significant progress has been made in at-home medical detection kits, especially during the COVID-19 pandemic. Here, we demonstrated a coffee-ring biosensor with ultrahigh sensitivity, utilizing the evaporation of two sessile droplets and the formation of coffee-rings with asymmetric nanoplasmonic patterns to detect disease-relevant proteins as low as 3 pg/ml, under 12 min. Experimentally, a protein-laden droplet dries on a nanofibrous membrane, pre-concentrating biomarkers at the coffee ring. A second plasmonic droplet with functionalized gold nanoshells is then deposited at an overlapping spot and dried, forming a visible asymmetric plasmonic pattern due to distinct aggregation mechanisms. To enhance detection sensitivity, a deep neural model integrating generative and convolutional networks was used to enable quantitative biomarker diagnosis from smartphone photos. We tested four different proteins, Procalcitonin (PCT) for sepsis, SARS-CoV-2 Nucleocapsid (N) protein for COVID-19, Carcinoembryonic antigen (CEA) and Prostate-specific antigen (PSA) for cancer diagnosis, showing a working concentration range over five orders of magnitude. Sensitivities surpass equivalent lateral flow immunoassays by over two orders of magnitude using human saliva samples. The detection principle, along with the device, and materials can be further advanced for early disease diagnostics.

Self-Assembly of Nanogold Triplets on Trimeric Viral Proteins for Infectious Disease Diagnosis

Timely and accurate diagnostics for infectious diseases are essential in preventing their worldwide spread. Though rapid diagnostic tests are favored for their speed, cost-effectiveness, and ease of use, most tests compromise sensitivity, which risks false-negative results. Here, we present the self-assembly of nanogold triplets on trimeric viral surface proteins for a sensitive colorimetric assay to identify viruses. Gold triplets were self-assembled on the viral trimeric surface proteins using ultrasmall gold nanoparticles. We observed a significant wavelength shift of 70 nm, enabling straightforward naked-eye detection through gold triplets that act as catalysts for producing nanoplasmonic viruses. We established the detection limit of 3 × 105 copies/ml using an effective colorimetric assay for detecting SARS-CoV-2. The self-assembly of gold triplets on trimeric viral surface proteins provides a reliable approach to the accurate and sensitive detection of viruses.

Deep-Learning-Assisted Microfluidic Immunoassay via Smartphone-Based Imaging Transcoding System for On-Site and Multiplexed Biosensing

Point-of-care testing (POCT) with multiplexed capability, ultrahigh sensitivity, affordable smart devices, and user-friendly operation is critically needed for clinical diagnostics and food safety. This study presents a deep-learning-assisted microfluidic immunoassay platform that uses a smartphone-based imaging transcoding system, polystyrene microsphere-based encoding, and artificial-intelligence-assisted decoding. Microspheres of varying sizes act as multiprobes, with their quantities correlating to target concentrations after an immunoreaction and separation-filtration within the microfluidic chip. A smartphone with intelligent decoding software captures images of multiprobes from the chip and performs classification, counting, and concentration calculations. The "encoding-decoding" strategy and integrated microfluidic chip design allow these processes to be completed in simple steps, eliminating the need for additional immunomagnetic separation. As a proof of concept, this platform successfully detected multiple respiratory viruses and antibiotics in various real samples with high sensitivity within 30 min, demonstrating great potential as a smart, universal toolkit for next-generation POCT applications.

Machine learning in point-of-care testing: innovations, challenges, and opportunities

The landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-of-care testing (POCT). The COVID-19 pandemic has accelerated the shift from centralized laboratory testing but also catalyzed the development of next-generation POCT platforms that leverage ML to enhance the accuracy, sensitivity, and overall efficiency of point-of-care sensors. This Perspective explores how ML is being embedded into various POCT modalities, including lateral flow assays, vertical flow assays, nucleic acid amplification tests, and imaging-based sensors, illustrating their impact through different applications. We also discuss several challenges, such as regulatory hurdles, reliability, and privacy concerns, that must be overcome for the widespread adoption of ML-enhanced POCT in clinical settings and provide a comprehensive overview of the current state of ML-driven POCT technologies, highlighting their potential impact in the future of healthcare.

Reducing hepatitis C diagnostic disparities with a fully automated deep learning-enabled microfluidic system for HCV antigen detection

Viral hepatitis remains a major global health issue, with chronic hepatitis B (HBV) and hepatitis C (HCV) causing approximately 1 million deaths annually, primarily due to liver cancer and cirrhosis. More than 1.5 million people contract HCV each year, disproportionately affecting vulnerable populations, including American Indians and Alaska Natives (AI/AN). While direct-acting antivirals (DAAs) are highly effective, timely and accurate HCV diagnosis remains a challenge, particularly in resource-limited settings. The current two-step HCV testing process is costly and time-intensive, often leading to patient loss before treatment. Point-of-care (POC) HCV antigen (Ag) testing offers a promising alternative, but no FDA-approved test meets the required sensitivity and specificity. To address this, we developed a fully automated, smartphone-based POC HCV Ag assay using platinum nanoparticles, deep learning image processing, and microfluidics. With an overall accuracy of 94.59%, this cost-effective, portable device has the potential to reduce HCV-related health disparities, particularly among AI/AN populations, improving accessibility and equity in care.

Transformative laboratory medicine enabled by microfluidic automation and artificial intelligence

Laboratory medicine provides pivotal medical information through analyses of body fluids and tissues, and thus, it is essential for diagnosis of diseases as well as monitoring of disease progression. Despite its universal importance, the field is currently suffering from the limited workforce and analytical capabilities due to the increasing pressure from expanding global population and unexpected rise of noncommunicable diseases. The emerging technologies of microfluidic automation and artificial intelligence (AI) has led to the development of advanced diagnostic platforms, positioning themselves as adaptable solutions to enable highly efficient and accessible laboratory medicine. In this review, we will provide a comprehensive review of microfluidic automation, focusing on the microstructure design and automation principles, along with its intended functionalities for diagnostic purposes. Subsequently, we exemplify the integration of AI with microfluidics and illustrating how their combination benefits for the applications and what the challenges are in this rapidly evolving field. Finally, the review offers a balanced perspective on the microfluidics and AI, discussing their promising role in advancing laboratory medicine.

Rapid, portable, and sensitive detection of CaMV35S by RPA-CRISPR/Cas12a-G4 colorimetric assays with high accuracy deep learning object recognition and classification

Fast, sensitive, and portable detection of genetic modification contributes to agricultural security and food safety. Here, we developed RPA-CRISPR/Cas12a-G-quadruplex colorimetric assays that can combine with intelligent recognition by deep learning algorithms to achieve sensitive, rapid, and portable detection of the CaMV35S promoter. When the crRNA-Cas12a complex recognizes the RPA amplification product, Cas12 cleaves the G-quadruplex, causing the G4-Hemin complex to lose its peroxide mimetic enzyme function and be unable to catalyze the conversion of ABTS2- to ABTS, allowing CaMV35S concentration to be determined based on ABTS absorbance. By utilizing the RPA-CRISPR/Cas12a-G4 assay, we achieved a CaMV35S limit of detection down to 10 aM and a 0.01 % genetic modification sample in 45 min. Deep learning algorithms are designed for highly accurate classification of color results. Yolov5 objective finding and Resnet classification algorithms have been trained to identify trace (0.01 %) CaMV35S more accurately than naked eye colorimetry. We also coupled deep learning algorithms with a smartphone app to achieve portable and rapid photo identification. Overall, our findings enable low cost ($0.43), high accuracy, and intelligent detection of the CaMV35S promoter.

High-Throughput and Integrated CRISPR/Cas12a-Based Molecular Diagnosis Using a Deep Learning Enabled Microfluidic System

CRISPR/Cas-based molecular diagnosis demonstrates potent potential for sensitive and rapid pathogen detection, notably in SARS-CoV-2 diagnosis and mutation tracking. Yet, a major hurdle hindering widespread practical use is its restricted throughput, limited integration, and complex reagent preparation. Here, a system, microfluidic multiplate-based ultrahigh throughput analysis of SARS-CoV-2 variants of concern using CRISPR/Cas12a and nonextraction RT-LAMP (mutaSCAN), is proposed for rapid detection of SARS-CoV-2 and its variants with limited resource requirements. With the aid of the self-developed reagents and deep-learning enabled prototype device, our mutaSCAN system can detect SARS-CoV-2 in mock swab samples below 30 min as low as 250 copies/mL with the throughput up to 96 per round. Clinical specimens were tested with this system, the accuracy for routine and mutation testing (22 wildtype samples, 26 mutational samples) was 98% and 100%, respectively. No false-positive results were found for negative (n = 24) samples.

Intelligent sensing based on active micro/nanomotors

In the microscopic world, synthetic micro/nanomotors (MNMs) can convert a variety of energy sources into driving forces to help humans perform a number of complex tasks with greater ease and efficiency. These tiny machines have attracted tremendous attention in the field of drug delivery, minimally invasive surgery, in vivo sampling, and environmental management. By modifying their surface materials and functionalizing them with bioactive agents, these MNMs can also be transformed into dynamic micro/nano-biosensors that can detect biomolecules in real-time with high sensitivity. The extensive range of operations and uses combined with their minuscule size have opened up new avenues for tackling intricate analytical difficulties. Here, in this review, various driving methods are briefly introduced, followed by a focus on intelligent detection techniques based on MNMs. And we discuss the distinctive advantages, current issues, and challenges associated with MNM-based intelligent detection. It is believed that the future advancements of MNMs will greatly impact the diagnosis, treatment, and prevention of diseases.

Polyethylene Glycol-Mediated Directional Conjugation of Biological Molecules for Enhanced Immunoassays at the Point-of-Care

Rapid and reliable point-of-care (POC) diagnostic tests can have a significant impact on global health. One of the most common approaches for developing POC systems is the use of target-specific biomolecules. However, the conjugation of biomolecules can result in decreased activity, which may compromise the analytical performance and accuracy of the developed systems. To overcome this challenge, we present a polymer-based cross-linking protocol for controlled and directed conjugation of biological molecules. Our protocol utilizes a bifunctional thiol-polyethylene glycol (PEG)-hydrazide polymer to enable site-directed conjugation of IgG antibodies to the surface of screen-printed metal electrodes. The metal surface of the electrodes is first modified with thiolated PEG molecules, leaving the hydrazide groups available to react with the aldehyde group in the Fc fragments of the oxidized IgG antibodies. Using anti-Klebsiella pneumoniae carbapenemase-2 (KPC-2) antibody as a model antibody used for antimicrobial resistance (AMR) testing, our results demonstrate a ~10-fold increase in antibody coupling compared with the standard N-hydroxysuccinimide (NHS)-based conjugation chemistry and effective capture (>94%) of the target KPC-2 enzyme antigen on the surface of modified electrodes. This straightforward and easy-to-perform strategy of site-directed antibody conjugation can be engineered for coupling other protein- and non-protein-based biological molecules commonly used in POC testing and development, thus enhancing the potential for improved diagnostic accuracy and performance.

Rapidly adaptable automated interpretation of point-of-care COVID-19 diagnostics

BACKGROUND

Point-of-care diagnostic devices, such as lateral-flow assays, are becoming widely used by the public. However, efforts to ensure correct assay operation and result interpretation rely on hardware that cannot be easily scaled or image processing approaches requiring large training datasets, necessitating large numbers of tests and expert labeling with validated specimens for every new test kit format.

METHODS

We developed a software architecture called AutoAdapt POC that integrates automated membrane extraction, self-supervised learning, and few-shot learning to automate the interpretation of POC diagnostic tests using smartphone cameras in a scalable manner. A base model pre-trained on a single LFA kit is adapted to five different COVID-19 tests (three antigen, two antibody) using just 20 labeled images.

RESULTS

Here we show AutoAdapt POC to yield 99% to 100% accuracy over 726 tests (350 positive, 376 negative). In a COVID-19 drive-through study with 74 untrained users self-testing, 98% found image collection easy, and the rapidly adapted models achieved classification accuracies of 100% on both COVID-19 antigen and antibody test kits. Compared with traditional visual interpretation on 105 test kit results, the algorithm correctly identified 100% of images; without a false negative as interpreted by experts. Finally, compared to a traditional convolutional neural network trained on an HIV test kit, the algorithm showed high accuracy while requiring only 1/50th of the training images.

CONCLUSIONS

The study demonstrates how rapid domain adaptation in machine learning can provide quality assurance, linkage to care, and public health tracking for untrained users across diverse POC diagnostic tests.

Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay

Since many lateral flow assays (LFA) are tested daily, the improvement in accuracy can greatly impact individual patient care and public health. However, current self-testing for COVID-19 detection suffers from low accuracy, mainly due to the LFA sensitivity and reading ambiguities. Here, we present deep learning-assisted smartphone-based LFA (SMART^AI-LFA) diagnostics to provide accurate decisions with higher sensitivity. Combining clinical data learning and two-step algorithms enables a cradle-free on-site assay with higher accuracy than the untrained individuals and human experts via blind tests of clinical data (n = 1500). We acquired 98% accuracy across 135 smartphone application-based clinical tests with different users/smartphones. Furthermore, with more low-titer tests, we observed that the accuracy of SMART^AI-LFA was maintained at over 99% while there was a significant decrease in human accuracy, indicating the reliable performance of SMART^AI-LFA. We envision a smartphone-based SMART^AI-LFA that allows continuously enhanced performance by adding clinical tests and satisfies the new criterion for digitalized real-time diagnostics.

Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence

The frequent outbreak of global infectious diseases has prompted the development of rapid and effective diagnostic tools for the early screening of potential patients in point-of-care testing scenarios. With advances in mobile computing power and microfluidic technology, the smartphone-based mobile health platform has drawn significant attention from researchers developing point-of-care testing devices that integrate microfluidic optical detection with artificial intelligence analysis. In this article, we summarize recent progress in these mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms. We document the application of mobile health platforms in terms of the detection objects, including molecules, viruses, cells, and parasites. Finally, we discuss the prospects for future development of mobile health platforms.

Large-Scale Functionalized Metasurface-Based SARS-CoV-2 Detection and Quantification

Plasmonic metasurfaces consist of metal-dielectric interfaces that are excitable at background and leakage resonant modes. The sharp and plasmonic excitation profile of metal-free electrons on metasurfaces at the nanoscale can be used for practical applications in diverse fields, including optoelectronics, energy harvesting, and biosensing. Currently, Fano resonant metasurface fabrication processes for biosensor applications are costly, need clean room access, and involve limited small-scale surface areas that are not easy for accurate sample placement. Here, we leverage the large-scale active area with uniform surface patterns present on optical disc-based metasurfaces as a cost-effective method to excite asymmetric plasmonic modes, enabling tunable optical Fano resonance interfacing with a microfluidic channel for multiple target detection in the visible wavelength range. We engineered plasmonic metasurfaces for biosensing through efficient layer-by-layer surface functionalization toward real-time measurement of target binding at the molecular scale. Further, we demonstrated the quantitative detection of antibodies, proteins, and the whole viral particles of SARS-CoV-2 with a high sensitivity and specificity, even distinguishing it from similar RNA viruses such as influenza and MERS. This cost-effective plasmonic metasurface platform offers a small-scale light-manipulation system, presenting considerable potential for fast, real-time detection of SARS-CoV-2 and pathogens in resource-limited settings.

Advances in Novel Nanomaterial-Based Optical Fiber Biosensors-A Review

This article presents a concise summary of current advancements in novel nanomaterial-based optical fiber biosensors. The beneficial optical and biological properties of nanomaterials, such as nanoparticle size-dependent signal amplification, plasmon resonance, and charge-transfer capabilities, are widely used in biosensing applications. Due to the biocompatibility and bioreceptor combination, the nanomaterials enhance the sensitivity, limit of detection, specificity, and response time of sensing probes, as well as the signal-to-noise ratio of fiber optic biosensing platforms. This has established a practical method for improving the performance of fiber optic biosensors. With the aforementioned outstanding nanomaterial properties, the development of fiber optic biosensors has been efficiently promoted. This paper reviews the application of numerous novel nanomaterials in the field of optical fiber biosensing and provides a brief explanation of the fiber sensing mechanism.

Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward

Coronavirus disease 2019 (COVID-19) is a transmitted respiratory disease caused by the infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although humankind has experienced several outbreaks of infectious diseases, the COVID-19 pandemic has the highest rate of infection and has had high levels of social and economic repercussions. The current COVID-19 pandemic has highlighted the limitations of existing virological tests, which have failed to be adopted at a rate to properly slow the rapid spread of SARS-CoV-2. Pandemic preparedness has developed as a focus of many governments around the world in the event of a future outbreak. Despite the largely widespread availability of vaccines, the importance of testing has not diminished to monitor the evolution of the virus and the resulting stages of the pandemic. Therefore, developing diagnostic technology that serves as a line of defense has become imperative. In particular, that test should satisfy three criteria to be widely adopted: simplicity, economic feasibility, and accessibility. At the heart of it all, it must enable early diagnosis in the course of infection to reduce spread. However, diagnostic manufacturers need guidance on the optimal characteristics of a virological test to ensure pandemic preparedness and to aid in the effective treatment of viral infections. Nanomaterials are a decisive element in developing COVID-19 diagnostic kits as well as a key contributor to enhance the performance of existing tests. Our objective is to develop a profile of the criteria that should be available in a platform as the target product. In this work, virus detection tests were evaluated from the perspective of the COVID-19 pandemic, and then we generalized the requirements to develop a target product profile for a platform for virus detection.

Biosensors and Microfluidic Biosensors: From Fabrication to Application

Biosensors are ubiquitous in a variety of disciplines, such as biochemical, electrochemical, agricultural, and biomedical areas. They can integrate various point-of-care applications, such as in the food, healthcare, environmental monitoring, water quality, forensics, drug development, and biological domains. Multiple strategies have been employed to develop and fabricate miniaturized biosensors, including design, optimization, characterization, and testing. In view of their interactions with high-affinity biomolecules, they find application in the sensitive detection of analytes, even in small sample volumes. Among the many developed techniques, microfluidics have been widely explored; these use fluid mechanics to operate miniaturized biosensors. The currently used commercial devices are bulky, slow in operation, expensive, and require human intervention; thus, it is difficult to automate, integrate, and miniaturize the existing conventional devices for multi-faceted applications. Microfluidic biosensors have the advantages of mobility, operational transparency, controllability, and stability with a small reaction volume for sensing. This review addresses biosensor technologies, including the design, classification, advances, and challenges in microfluidic-based biosensors. The value chain for developing miniaturized microfluidic-based biosensor devices is critically discussed, including fabrication and other associated protocols for application in various point-of-care testing applications.

Nanomaterials for virus sensing and tracking

The effect of the on-going COVID-19 pandemic on global healthcare systems has underlined the importance of timely and cost-effective point-of-care diagnosis of viruses. The need for ultrasensitive easy-to-use platforms has culminated in an increased interest for rapid response equipment-free alternatives to conventional diagnostic methods such as polymerase chain reaction, western-blot assay, etc. Furthermore, the poor stability and the bleaching behavior of several contemporary fluorescent reporters is a major obstacle in understanding the mechanism of viral infection thus retarding drug screening and development. Owing to their extraordinary surface-to-volume ratio as well as their quantum confinement and charge transfer properties, nanomaterials are desirable additives to sensing and imaging systems to amplify their signal response as well as temporal resolution. Their large surface area promotes biomolecular integration as well as efficacious signal transduction. Due to their hole mobility, photostability, resistance to photobleaching, and intense brightness, nanomaterials have a considerable edge over organic dyes for single virus tracking. This paper reviews the state-of-the-art of combining carbon-allotrope, inorganic and organic-based nanomaterials with virus sensing and tracking methods, starting with the impact of human pathogenic viruses on the society. We address how different nanomaterials can be used in various virus sensing platforms (e.g. lab-on-a-chip, paper, and smartphone-based point-of-care systems) as well as in virus tracking applications. We discuss the enormous potential for the use of nanomaterials as simple, versatile, and affordable tools for detecting and tracing viruses infectious to humans, animals, plants as well as bacteria. We present latest examples in this direction by emphasizing major advantages and limitations.

Virus Detection: From State-of-the-Art Laboratories to Smartphone-Based Point-of-Care Testing

Infectious virus outbreaks pose a significant challenge to public healthcare systems. Early and accurate virus diagnosis is critical to prevent the spread of the virus, especially when no specific vaccine or effective medicine is available. In clinics, the most commonly used viral detection methods are molecular techniques that involve the measurement of nucleic acids or proteins biomarkers. However, most clinic-based methods require complex infrastructure and expensive equipment, which are not suitable for low-resource settings. Over the past years, smartphone-based point-of-care testing (POCT) has rapidly emerged as a potential alternative to laboratory-based clinical diagnosis. This review summarizes the latest development of virus detection. First, laboratory-based and POCT-based viral diagnostic techniques are compared, both of which rely on immunosensing and nucleic acid detection. Then, various smartphone-based POCT diagnostic techniques, including optical biosensors, electrochemical biosensors, and other types of biosensors are discussed. Moreover, this review covers the development of smartphone-based POCT diagnostics for various viruses including COVID-19, Ebola, influenza, Zika, HIV, et al. Finally, the prospects and challenges of smartphone-based POCT diagnostics are discussed. It is believed that this review will aid researchers better understand the current challenges and prospects for achieving the ultimate goal of containing disease-causing viruses worldwide.

Advantages of Using Extremophilic Bacteria for the Biosynthesis of Metallic Nanoparticles and Its Potential for Rare Earth Element Recovery

The exceptional potential for application that metallic nanoparticles (MeNPs) have shown, has steadily increased their demand in many different scientific and technological areas, including the biomedical and pharmaceutical industry, bioremediation, chemical synthesis, among others. To face the current challenge for transitioning toward more sustainable and ecological production methods, bacterial biosynthesis of MeNPs, especially from extremophilic microorganisms, emerges as a suitable alternative with intrinsic added benefits like improved stability and biocompatibility. Currently, biogenic nanoparticles of different relevant metals have been successfully achieved using different bacterial strains. However, information about biogenic nanoparticles from rare earth elements (REEs) is very scarce, in spite of their great importance and potential. This mini review discusses the current understanding of metallic nanoparticle biosynthesis by extremophilic bacteria, highlighting the relevance of searching for bacterial species that are able to biosynthesize RRE nanoparticles.

Noble Metal Nanoparticle Biosensors: From Fundamental Studies toward Point-of-Care Diagnostics

Noble metal nanoparticles (NMNPs) have become firmly established as effective agents to detect various biomolecules with extremely high sensitivity. This ability stems from the collective oscillation of free electrons and extremely large electric field enhancement under exposure to light, leading to various light-matter interactions such as localized surface plasmon resonance (LSPR) and surface-enhanced Raman scattering. A remarkable feature of NMNPs is their customizability by mechanisms such as particle etching, growth, and aggregation/dispersion, yielding distinct color changes and excellent opportunities for colorimetric biosensing in user-friendly assays and devices. They are readily functionalized with a large variety of capping agents and biomolecules, with resultant bioconjugates often possessing excellent biocompatibility, which can be used to quantitatively detect analytes from physiological fluids. Furthermore, they can possess excellent catalytic properties that can achieve significant signal amplification through mechanisms such as the catalytic transformation of colorless substrates to colored reporters. The various excellent attributes of NMNP biosensors have put them in the spotlight for developing high-performance in vitro diagnostic (IVD) devices that are particularly well-suited to mitigate the societal threat that infectious diseases pose. This threat continues to dominate the global health care landscape, claiming millions of lives annually. NMNP IVDs possess the potential to sensitively detect infections even at very early stages with affordable and field-deployable devices, which will be key to strengthening infectious disease management. This has been the major focal point of current research, with a view to new avenues for early multiplexed detection of infectious diseases with portable devices such as smartphones, especially in resource-limited settings.In this Account, we provide an overview of our original inspiration and efforts in NMNP-based assay development, together with some more sophisticated IVD assays by ourselves and many others. Our work in the area has led to our recent efforts in developing IVDs for high-profile infectious diseases, including Ebola and HIV. We emphasize that integration with digital platforms represents an opportunity to establish and efficiently manage widespread testing, tracking, epidemiological intelligence, and data sharing backed by community participation. We highlight how digital technologies can address the limitations of conventional diagnostic technologies at the point of care (POC) and how they may be used to abate and contain the spread of infectious diseases. Finally, we focus on more recent integrations of noble metal nanoparticles with Raman spectroscopy for accurate, noninvasive POC diagnostics with improved sensitivity and specificity.

Amplification-free smartphone-based attomolar HBV detection

Classical gold standard HBV detection relies on expensive devices and complicated procedures, thus is always restricted in large-scale hospitals and centers for disease control and prevention. To extend HBV detection to primary clinics especially in underdeveloped areas, we design amplification-free smartphone-based attomolar HBV detecting technique based on single molecule sensing. Verified by synthesized HBV target DNA, this technique reaches a detection limit at attomolar concentration (100 aM); and verified by 110 clinical samples, it also reaches a rather high sensitivity of 10⁴ copy/mL (≈2000 IU/mL) with a high accuracy of 93.64% certificated by gold standard HBV detecting devices. Besides, this technique can quantify HBV viral load in 70 min only using portable and inexpensive devices as well as simple operations. Because of its cost-effective, field-portable and operable design, highly sensitive and selective detecting capability and wireless data connectivity, this technique can be potentially used in mobile HBV diagnoses and share HBV epidemic information especially in resource limited situations.

SARS-CoV-2 RNA Detection by a Cellphone-Based Amplification-Free System with CRISPR/CAS-Dependent Enzymatic (CASCADE) Assay

CRISPR (Clustered regularly interspaced short palindromic repeats)-based diagnostic technologies have emerged as a promising alternative to accelerate delivery of SARS-CoV-2 molecular detection at the point of need. However, efficient translation of CRISPR-diagnostic technologies to field application is still hampered by dependence on target amplification and by reliance on fluorescence-based results readout. Herein, an amplification-free CRISPR/Cas12a-based diagnostic technology for SARS-CoV-2 RNA detection is presented using a smartphone camera for results readout. This method, termed Cellphone-based amplification-free system with CRISPR/CAS-dependent enzymatic (CASCADE) assay, relies on mobile phone imaging of a catalase-generated gas bubble signal within a microfluidic channel and does not require any external hardware optical attachments. Upon specific detection of a SARS-CoV-2 reverse-transcribed DNA/RNA heteroduplex target (orf1ab) by the ribonucleoprotein complex, the transcleavage collateral activity of the Cas12a protein on a Catalase:ssDNA probe triggers the bubble signal on the system. High analytical sensitivity in signal detection without previous target amplification (down to 50 copies µL-1) is observed in spiked samples, in ≈71 min from sample input to results readout. With the aid of a smartphone vision tool, high accuracy (AUC = 1.0; CI: 0.715 - 1.00) is achieved when the CASCADE system is tested with nasopharyngeal swab samples of PCR-positive COVID-19 patients.

Trapping and Detection of Single Viruses in an Optofluidic Chip

Accurate single virus detection is critical for disease diagnosis and early prevention, especially in view of current pandemics. Numerous detection methods have been proposed with the single virus sensitivity, including the optical approaches and immunoassays. However, few of them hitherto have the capability of both trapping and detection of single viruses in the microchannel. Here, we report an optofluidic potential well array to trap nanoparticles stably in the flow stream. The nanoparticle is bound with single viruses and fluorescence quantum dots through an immunolabeling protocol. Single viruses can be swiftly captured in the microchannel by optical forces and imaged by a camera. The number of viruses in solution and on each particle can be quantified via image processing. Our method can trap and detect single viruses in the 1 mL serum or water in 2 h, paving an avenue for the advanced, fast, and accurate clinical diagnosis, as well as the study of virus infectivity, mutation, drug inhibition, etc.

Glyconanomaterials for Human Virus Detection and Inhibition

Viruses are among the most infectious pathogens, responsible for the highest death toll around the world. Lack of effective clinical drugs for most viral diseases emphasizes the need for speedy and accurate diagnosis at early stages of infection to prevent rapid spread of the pathogens. Glycans are important molecules which are involved in different biological recognition processes, especially in the spread of infection by mediating virus interaction with endothelial cells. Thus, novel strategies based on nanotechnology have been developed for identifying and inhibiting viruses in a fast, selective, and precise way. The nanosized nature of nanomaterials and their exclusive optical, electronic, magnetic, and mechanical features can improve patient care through using sensors with minimal invasiveness and extreme sensitivity. This review provides an overview of the latest advances of functionalized glyconanomaterials, for rapid and selective biosensing detection of molecules as biomarkers or specific glycoproteins and as novel promising antiviral agents for different kinds of serious viruses, such as the Dengue virus, Ebola virus, influenza virus, human immunodeficiency virus (HIV), influenza virus, Zika virus, or coronavirus SARS-CoV-2 (COVID-19).