Quantitation of Femtomolar-Level Protein Biomarkers Using a Simple Microbubbling Digital Assay and Bright-Field Smartphone Imaging

Nanomedicines for Improved Management of Ectopic Pregnancy: A Narrative Review

Ectopic pregnancy (EP) - the implantation of an embryo outside of the endometrial cavity, often in the fallopian tube - is a significant contributor to maternal morbidity and leading cause of maternal death due to hemorrhage in first trimester. Current diagnostic modalities including human chorionic gonadotropin (hCG) quantification and ultrasonography are effective, but may still misdiagnose EP at initial examination in many cases. Depending on the patient's hemodynamic stability and gestational duration of the pregnancy, as assessed by history, hCG measurement and ultrasonography, management strategies may include expectant management, chemotherapeutic treatment using methotrexate (MTX), or surgical intervention. While these strategies are largely successful, expectant management may result in tubal rupture if the pregnancy does not resolve spontaneously; MTX administration is not always successful and may induce significant side effects; and surgical intervention may result in loss of the already-damaged fallopian tube, further hampering the patient's subsequent attempts to conceive. Nanomaterial-based technologies offer the potential to enhance delivery of diagnostic imaging contrast and therapeutic agents to more effectively and safely manage EP. The purpose of this narrative review is to summarize the current state of nanomedicine technology dedicated to its potential to improve both the diagnosis and treatment of EP.

Portable Near-Infrared to Near-Infrared Platform for Homogeneous Quantification of Biomarkers in Complex Biological Samples

Förster resonance energy transfer (FRET)-based homogeneous immunoassay obviates tedious washing steps and thus is a promising approach for immunoassays. However, a conventional FRET-based homogeneous immunoassay operating in the visible region is not able to overcome the interference of complex biological samples, thus resulting in insufficient detection sensitivity and poor accuracy. Here, we develop a near-infrared (NIR)-to-NIR FRET platform (Ex = 808 nm, Em = 980 nm) that enables background-free high-throughput homogeneous quantification of various biomarkers in complex biological samples. This NIR-to-NIR FRET platform is portable and easy to operate and is mainly composed of a high-performance NIR-to-NIR FRET pair based on lanthanide-doped nanoparticles (LnNPs) and a custom-made microplate reader for readout of NIR luminescence signals. We demonstrate that this NIR-to-NIR FRET platform is versatile and robust, capable of realizing highly sensitive and accurate detection of various critical biomarkers, including small molecules (morphine and 1,25-dihydroxyvitamin D), proteins (human chorionic gonadotropin), and viral particles (adenovirus) in unprocessed complex biological samples (urine, whole blood, and feces) within 5-10 min. We expect this NIR-to-NIR FRET platform to provide low-cost healthcare for populations living in resource-limited areas and be widely used in many other fields, such as food safety and environmental monitoring.

Shifting the paradigm in RNA virus detection: integrating nucleic acid testing and immunoassays through single-molecule digital ELISA

In this review article, we explore the characteristics of RNA viruses and their potential threats to humanity. We also provide a brief overview of the primary contemporary techniques used for the early detection of such viruses. After thoroughly analyzing the strengths and limitations of these methods, we highlight the importance of integrating nucleic acid testing with immunological assays in RNA virus detection. Although notable methodological differences between nucleic acid testing and immune assays pose challenges, the emerging single-molecule immunoassay-digital ELISA may be applied to technically integrate these techniques. We emphasize that the greatest value of digital ELISA is its extensive compatibility, which creates numerous opportunities for real-time, large-scale testing of RNA viruses. Furthermore, we describe the possible developmental trends of digital ELISA in various aspects, such as reaction carriers, identification elements, signal amplification, and data reading, thus revealing the remarkable potential of single-molecule digital ELISA in future RNA virus detection.

Enhanced NIR-II Fluorescent Lateral Flow Biosensing Platform Based on Supramolecular Host-Guest Self-Assembly for Point-of-Care Testing of Tumor Biomarkers

Point-of-care detection of tumor biomarkers with high sensitivity remains an enormous challenge in the early diagnosis and mass screening of cancer. Fluorescent lateral flow immunoassay (LFA) is an attractive platform for point-of-care testing due to its inherent advantages. Particularly, a fluorescent probe is crucial to improving the analytical performance of the LFA platform. Herein, we developed an enhanced second near-infrared (NIR-II) LFA (ENIR-II LFA) platform based on supramolecular host-guest self-assembly for detection of the prostate-specific antigen (PSA) as a model analyte. In this platform, depending on the effective supramolecular surface modification strategy, cucurbit[7]uril (CB[7])-covered rare-earth nanoparticles (RENPs) emitting in the NIR-II (1000-1700 nm) window were prepared and employed as an efficient fluorescent probe (RENPs-CB[7]). Benefiting from its superior optical properties, such as low autofluorescence, excellent photostability, enhanced fluorescence intensity, and increased antibody-conjugation efficiency, the ENIR-II LFA platform displayed a wide linear detection range from 0.65 to 120 ng mL-1, and the limit of detection was down to 0.22 ng mL-1 for PSA, which was 18.2 times lower than the clinical cutoff value. Moreover, the testing time was also shortened to 6 min. Compared with the commercial visible fluorescence LFA kit (VIS LFA) and the previously reported NIR-II LFA based on a RENPs-PAA probe, this ENIR-II LFA demonstrated more competitive advantages in analytical sensitivity, detection range, testing time, and production cost. Overall, the ENIR-II LFA platform offers great potential for the highly sensitive, rapid, and convenient detection of tumor biomarkers and is expected to serve as a useful technique in the general population screening of the high-incidence cancer region.

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.

Digital detection of proteins

This paper reviews methods for detecting proteins based on molecular digitization, i.e., the isolation and detection of single protein molecules or singulated ensembles of protein molecules. The single molecule resolution of these methods has resulted in significant improvements in the sensitivity of immunoassays beyond what was possible using traditional "analog" methods: the sensitivity of some digital immunoassays approach those of methods for measuring nucleic acids, such as the polymerase chain reaction (PCR). The greater sensitivity of digital protein detection has resulted in immuno-diagnostics with high potential societal impact, e.g., the early diagnosis and therapeutic intervention of Alzheimer's Disease. In this review, we will first provide the motivation for developing digital protein detection methods given the limitations in the sensitivity of analog methods. We will describe the paradigm shift catalyzed by single molecule detection, and will describe in detail one digital approach - which we call digital bead assays (DBA) - based on the capture and labeling of proteins on beads, identifying "on" and "off" beads, and quantification using Poisson statistics. DBA based on the single molecule array (Simoa) technology have sensitivities down to attomolar concentrations, equating to ∼10 proteins in a 200 μL sample. We will describe the concept behind DBA, the different single molecule labels used, the ways of analyzing beads (imaging of arrays and flow), the binding reagents and substrates used, and integration of these technologies into fully automated and miniaturized systems. We provide an overview of emerging approaches to digital protein detection, including those based on digital detection of nucleic acids labels, single nanoparticle detection, measurements using nanopores, and methods that exploit the kinetics of single molecule binding. We outline the initial impact of digital protein detection on clinical measurements, highlighting the importance of customized assay development and translational clinical research. We highlight the use of DBA in the measurement of neurological protein biomarkers in blood, and how these higher sensitivity methods are changing the diagnosis and treatment of neurological diseases. We conclude by summarizing the status of digital protein detection and suggest how the lab-on-a-chip community might drive future innovations in this field.

Activatable G-quadruplex based catalases for signal transduction in biosensing

Discovery of oxidative catalysis with G-quadruplex•hemin constructs prompted a range of exciting developments in the field of biosensor design. Thus, G-quadruplex based DNAzymes with peroxidase activity found a niche as signal transduction modules in a wide range of analytical applications. The ability of nucleic acid scaffolds to recognise a variety of practically meaningful markers and to translate the recognition events into conformational changes powers numerous sensor design possibilities. In this work, we establish a catalase activity of G-quadruplex•hemin scaffolds. Catalase activated hydrogen peroxide decomposition generates molecular oxygen that forms bubbles. Observation of bubbles is a truly equipment free signal readout platform that is highly desirable in limited resources or do-it-yourself environments. We take a preliminary insight into a G-quadruplex structure-folding topology-catalase activity correlation and establish efficient operating conditions. Further, we demonstrate the platform's potential as a signal transduction modality for reporting on biomolecular recognition using an oligonucleotide as a proof-of-concept target. Ultimately, activatable catalases based on G-quadruplex•hemin scaffolds promise to become valuable contributors towards accessible molecular diagnostics applications.

Merging microfluidics with luminescence immunoassays for urgent point-of-care diagnostics of COVID-19

The Coronavirus disease 2019 (COVID-19) outbreak has urged the establishment of a global-wide rapid diagnostic system. Current widely-used tests for COVID-19 include nucleic acid assays, immunoassays, and radiological imaging. Immunoassays play an irreplaceable role in rapidly diagnosing COVID-19 and monitoring the patients for the assessment of their severity, risks of the immune storm, and prediction of treatment outcomes. Despite of the enormous needs for immunoassays, the widespread use of traditional immunoassay platforms is still limited by high cost and low automation, which are currently not suitable for point-of-care tests (POCTs). Microfluidic chips with the features of low consumption, high throughput, and integration, provide the potential to enable immunoassays for POCTs, especially in remote areas. Meanwhile, luminescence detection can be merged with immunoassays on microfluidic platforms for their good performance in quantification, sensitivity, and specificity. This review introduces both homogenous and heterogenous luminescence immunoassays with various microfluidic platforms. We also summarize the strengths and weaknesses of the categorized methods, highlighting their recent typical progress. Additionally, different microfluidic platforms are described for comparison. The latest advances in combining luminescence immunoassays with microfluidic platforms for POCTs of COVID-19 are further explained with antigens, antibodies, and related cytokines. Finally, challenges and future perspectives were discussed.

Flash Characterization of Smartphones Used in Point-of-Care Diagnostics

Rapidly growing interest in smartphone cameras as the basis of point-of-need diagnostic and bioanalytical technologies increases the importance of quantitative characterization of phone optical performance under real-world operating conditions. In the context of our development of lateral-flow immunoassays based on phosphorescent nanoparticles, we have developed a suite of tools for characterizing the temporal and spectral profiles of smartphone torch and flash emissions, and their dependence on phone power state. In this work, these tools are described and documented to make them easily available to others, and demonstrated by application to characterization of Apple iPhone 5s, iPhone 6s, iPhone 8, iPhone XR, and Samsung Note8 flash performance as a function of time and wavelength, at a variety of power settings. Flash and torch intensity and duration vary with phone state and among phone models. Flash has high variability when the battery charge is below 10%, thus, smartphone-based Point-of-Care (POC) tests should only be performed at a battery level of at least 15%. Some output variations could substantially affect the results of assays that rely on the smartphone flash.

Deep learning-assisted sensitive detection of fentanyl using a bubbling-microchip

Deep learning-enabled smartphone-based image processing has significant advantages in the development of point-of-care diagnostics. Conventionally, most deep-learning applications require task specific large scale expertly annotated datasets. Therefore, these algorithms are oftentimes limited only to applications that have large retrospective datasets available for network development. Here, we report the possibility of utilizing adversarial neural networks to overcome this challenge by expanding the utility of non-specific data for the development of deep learning models. As a clinical model, we report the detection of fentanyl, a small molecular weight drug that is a type of opioid, at the point-of-care using a deep-learning empowered smartphone assay. We used the catalytic property of platinum nanoparticles (PtNPs) in a smartphone-enabled microchip bubbling assay to achieve high analytical sensitivity (detecting fentanyl at concentrations as low as 0.23 ng mL-1 in phosphate buffered saline (PBS), 0.43 ng mL-1 in human serum and 0.64 ng mL-1 in artificial human urine). Image-based inferences were made by our adversarial-based SPyDERMAN network that was developed using a limited dataset of 104 smartphone images of microchips with bubble signals from tests performed with known fentanyl concentrations and using our retrospective library of 17 573 non-specific bubbling-microchip images. The accuracy (± standard error of mean) of the developed system in determining the presence of fentanyl, when using a cutoff concentration of 1 ng mL-1, was 93 ± 0% in human serum (n = 100) and 95.3 ± 1.5% in artificial human urine (n = 100).

Pretreatment Methods for Human Nasopharyngeal Swabs to Increase the Signal to Noise Ratio of High Sensitivity Immunoassays

Mucous samples collected through nasopharyngeal (NP) swabs are considered gold standard specimens for the detection of respiratory pathogens. Matrices of these highly viscous samples often cause significant background noises in immunoassays, especially immunoassays with high sensitivity. We demonstrated such nonspecific background signals in both a chemiluminescence enzyme-linked immunosorbent assay (ELISA) and a novel highly sensitive immunoassay called Microbubbling SARS-CoV-2 Antigen Assay (MSAA). We developed and demonstrated the effectiveness of two quick sample pretreatment methods, filtration and preadsorption, to decrease nonspecific signals and increase the signal-to-noise ratio (SNR). Using these pretreatment methods, the SNR (at 3.6 × 10⁴ copies/mL of inactivated SARS-CoV-2) was increased by 42.4-fold (95% CI 41.0-43.8) and 67.1-fold (95% CI 57.9-76.3) in the MSAA, and 1.3-fold (95% CI 0.9-1.7) and 1.8-fold (95% CI 1.6-2.0) in the chemiluminescence ELISA assay. Sample pretreatment methods developed in this study are broadly adaptable for the development of immunoassays for highly viscous samples.

Debulking different Corona (SARS-CoV-2 delta, omicron, OC43) and Influenza (H1N1, H3N2) virus strains by plant viral trap proteins in chewing gums to decrease infection and transmission

Because oral transmission of SARS-CoV-2 is 3-5 orders of magnitude higher than nasal transmission, we investigated debulking of oral viruses using viral trap proteins (CTB-ACE2, FRIL) expressed in plant cells, delivered through the chewing gum. In omicron nasopharyngeal (NP) samples, the microbubble count (based on N-antigen) was significantly reduced by 20 μg of FRIL (p < 0.0001) and 0.925 μg of CTB-ACE2 (p = 0.0001). Among 20 delta or omicron NP samples, 17 had virus load reduced below the detection level of spike protein in the RAPID assay, after incubation with the CTB-ACE2 gum powder. A dose-dependent 50% plaque reduction with 50-100 ng FRIL or 600-800 μg FRIL gum against Influenza strains H1N1, H3N2, and Coronavirus HCoV-OC43 was observed with both purified FRIL, lablab bean powder or gum. In electron micrographs, large/densely packed clumps of overlapping influenza particles and FRIL protein were observed. Chewing simulator studies revealed that CTB-ACE2 release was time/dose-dependent and release was linear up to 20 min chewing. Phase I/II placebo-controlled, double-blinded clinical trial (IND 154897) is in progress to evaluate viral load in saliva before or after chewing CTB-ACE2/placebo gum. Collectively, this study advances the concept of chewing gum to deliver proteins to debulk oral viruses and decrease infection/transmission.

Recent Advances in Digital Biosensing Technology

Digital biosensing assays demonstrate remarkable advantages over conventional biosensing systems because of their ability to achieve single-molecule detection and absolute quantification. Unlike traditional low-abundance biomarking screening, digital-based biosensing systems reduce sample volumes significantly to the fL-nL level, which vastly reduces overall reagent consumption, improves reaction time and throughput, and enables high sensitivity and single target detection. This review presents the current technology for compartmentalizing reactions and their applications in detecting proteins and nucleic acids. We also analyze existing challenges and future opportunities associated with digital biosensing and research opportunities for developing integrated digital biosensing systems.

Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus

Fluorescence-based microarray offers great potential in clinical diagnostics due to its high-throughput capability, multiplex capabilities, and requirement for a minimal volume of precious clinical samples. However, the technique relies on expensive and complex imaging systems for the analysis of signals. In the present study, we developed a smartphone-based application to analyze signals from protein microarrays to quantify disease biomarkers. The application adopted Android Studio open platform for its wide access to smartphones, and Python was used to design a graphical user interface with fast data processing. The application provides multiple user functions such as "Read", "Analyze", "Calculate" and "Report". For rapid and accurate results, we used ImageJ, Otsu thresholding, and local thresholding to quantify the fluorescent intensity of spots on the microarray. To verify the efficacy of the application, three antigens each with over 110 fluorescent spots were tested. Particularly, a positive correlation of over 0.97 was achieved when using this analytical tool compared to a standard test for detecting a potential biomarker in lupus nephritis. Collectively, this smartphone application tool shows promise for cheap, efficient, and portable on-site detection in point-of-care diagnostics.

DNA Walkers for Biosensing Development

The ability to design nanostructures with arbitrary shapes and controllable motions has made DNA nanomaterials used widely to construct diverse nanomachines with various structures and functions. The DNA nanostructures exhibit excellent properties, including programmability, stability, biocompatibility, and can be modified with different functional groups. Among these nanoscale architectures, DNA walker is one of the most popular nanodevices with ingenious design and flexible function. In the past several years, DNA walkers have made amazing progress ranging from structural design to biological applications including constructing biosensors for the detection of cancer-associated biomarkers. In this review, the key driving forces of DNA walkers are first summarized. Then, the DNA walkers with different numbers of legs are introduced. Furthermore, the biosensing applications of DNA walkers including the detection- of nucleic acids, proteins, ions, and bacteria are summarized. Finally, the new frontiers and opportunities for developing DNA walker-based biosensors are discussed.

Debulking SARS-CoV-2 in saliva using angiotensin converting enzyme 2 in chewing gum to decrease oral virus transmission and infection

To advance a novel concept of debulking virus in the oral cavity, the primary site of viral replication, virus-trapping proteins CTB-ACE2 were expressed in chloroplasts and clinical-grade plant material was developed to meet FDA requirements. Chewing gum (2 g) containing plant cells expressed CTB-ACE2 up to 17.2 mg ACE2/g dry weight (11.7% leaf protein), have physical characteristics and taste/flavor like conventional gums, and no protein was lost during gum compression. CTB-ACE2 gum efficiently (>95%) inhibited entry of lentivirus spike or VSV-spike pseudovirus into Vero/CHO cells when quantified by luciferase or red fluorescence. Incubation of CTB-ACE2 microparticles reduced SARS-CoV-2 virus count in COVID-19 swab/saliva samples by >95% when evaluated by microbubbles (femtomolar concentration) or qPCR, demonstrating both virus trapping and blocking of cellular entry. COVID-19 saliva samples showed low or undetectable ACE2 activity when compared with healthy individuals (2,582 versus 50,126 ΔRFU; 27 versus 225 enzyme units), confirming greater susceptibility of infected patients for viral entry. CTB-ACE2 activity was completely inhibited by pre-incubation with SARS-CoV-2 receptor-binding domain, offering an explanation for reduced saliva ACE2 activity among COVID-19 patients. Chewing gum with virus-trapping proteins offers a general affordable strategy to protect patients from most oral virus re-infections through debulking or minimizing transmission to others.

Digital plasmonic nanobubble detection for rapid and ultrasensitive virus diagnostics

Rapid and sensitive diagnostics of infectious diseases is an urgent and unmet need as evidenced by the COVID-19 pandemic. Here, we report a strategy, based on DIgitAl plasMONic nanobubble Detection (DIAMOND), to address this need. Plasmonic nanobubbles are transient vapor bubbles generated by laser heating of plasmonic nanoparticles (NPs) and allow single-NP detection. Using gold NPs as labels and an optofluidic setup, we demonstrate that DIAMOND achieves compartment-free digital counting and works on homogeneous immunoassays without separation and amplification steps. DIAMOND allows specific detection of respiratory syncytial virus spiked in nasal swab samples and achieves a detection limit of ~100 PFU/mL (equivalent to 1 RNA copy/µL), which is competitive with digital isothermal amplification for virus detection. Therefore, DIAMOND has the advantages including one-step and single-NP detection, direct sensing of intact viruses at room temperature, and no complex liquid handling, and is a platform technology for rapid and ultrasensitive diagnostics.

Femtomolar SARS-CoV-2 Antigen Detection Using the Microbubbling Digital Assay with Smartphone Readout Enables Antigen Burden Quantitation and Tracking

BACKGROUND

High-sensitivity severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen assays are desirable to mitigate false negative results. Limited data are available to quantify and track SARS-CoV-2 antigen burden in respiratory samples from different populations.

METHODS

We developed the Microbubbling SARS-CoV-2 Antigen Assay (MSAA) with smartphone readout, with a limit of detection of 0.5 pg/mL (10.6 fmol/L) nucleocapsid antigen or 4000 copies/mL inactivated SARS-CoV-2 virus in nasopharyngeal (NP) swabs. We developed a computer vision and machine learning-based automatic microbubble image classifier to accurately identify positives and negatives and quantified and tracked antigen dynamics in intensive care unit coronavirus disease 2019 (COVID-19) inpatients and immunocompromised COVID-19 patients.

RESULTS

Compared to qualitative reverse transcription-polymerase chain reaction methods, the MSAA demonstrated a positive percentage agreement of 97% (95% CI 92%-99%) and a negative percentage agreement of 97% (95% CI 94%-100%) in a clinical validation study with 372 residual clinical NP swabs. In immunocompetent individuals, the antigen positivity rate in swabs decreased as days-after-symptom-onset increased, despite persistent nucleic acid positivity. Antigen was detected for longer and variable periods of time in immunocompromised patients with hematologic malignancies. Total microbubble volume, a quantitative marker of antigen burden, correlated inversely with cycle threshold values and days-after-symptom-onset. Viral sequence variations were detected in patients with long duration of high antigen burden.

CONCLUSIONS

The MSAA enables sensitive and specific detection of acute infections and quantification and tracking of antigen burden and may serve as a screening method in longitudinal studies to identify patients who are likely experiencing active rounds of ongoing replication and warrant close viral sequence monitoring.

A photoelectrochemical biosensor based on methylene blue sensitized Bi5O7I for sensitive detection of PSA

Herein, bismuth oxyiodide (Bi₅O₇I) was used as a signal probe to construct an effective sensitization structure with methylene blue (MB), combined with protein conversion strategy, and a photoelectrochemical (PEC) biosensor was constructed for sensitive detection of prostate-specific antigen (PSA). The designed biosensor had a high sensitivity and a low detection limit (LOD) of 0.047 fg mL^-1, which opened up a simple way for the detection of PSA and showed a good application prospect in clinical and medical fields.

Recent innovations in cost-effective polymer and paper hybrid microfluidic devices

Hybrid microfluidic systems that are composed of multiple different types of substrates have been recognized as a versatile and superior platform, which can draw benefits from different substrates while avoiding their limitations. This review article introduces the recent innovations of different types of low-cost hybrid microfluidic devices, particularly focusing on cost-effective polymer- and paper-based hybrid microfluidic devices. In this article, the fabrication of these hybrid microfluidic devices is briefly described and summarized. We then highlight various hybrid microfluidic systems, including polydimethylsiloxane (PDMS)-based, thermoplastic-based, paper/polymer hybrid systems, as well as other emerging hybrid systems (such as thread-based). The special benefits of using these hybrid systems have been summarized accordingly. A broad range of biological and biomedical applications using these hybrid microfluidic devices are discussed in detail, including nucleic acid analysis, protein analysis, cellular analysis, 3D cell culture, organ-on-a-chip, and tissue engineering. The perspective trends of hybrid microfluidic systems involving the improvement of fabrication techniques and broader applications are also discussed at the end of the review.

Development and Application of Mobile Apps for Molecular Sensing: A Review

Modern smartphone-based sensing devices are generally standalone detection platforms that can transduce signals (via the built-in USB port, audio jack, or camera), perform analysis through mobile applications (apps), and display results on the screen/user interface. The advancement toward this ultimate form of on-site chemical analysis and point-of-care diagnosis is tied closely with the evolution of mobile technology. Previous reviews in the field mainly focused on the physical platforms while overlooking the role of mobile apps in such devices. There exist three general stages throughout the development: (1) early generation telemedicine, (2) mobile phone-assisted clinical diagnosis (without apps), and (3) mobile app-based sensing devices for various analytes. This review presents the key breakthroughs during each stage, recent development, remaining challenges, and future perspectives of the field. Representative examples, spanning from the pioneering point-of-care testing to the latest devices with integrated mobile apps, are classified by their sensing mechanisms. The review also discusses the scarcity of open-source apps dedicated to molecular sensing. With the introduction of more open-source and commercial apps, the mobile app-based detection system is anticipated to dominate point-of-care diagnosis and on-site molecular sensing in our opinion.

Femtomolar SARS-CoV-2 Antigen Detection Using the Microbubbling Digital Assay with Smartphone Readout Enables Antigen Burden Quantitation and Dynamics Tracking

Background

Little is known about the dynamics of SARS-CoV-2 antigen burden in respiratory samples in different patient populations at different stages of infection. Current rapid antigen tests cannot quantitate and track antigen dynamics with high sensitivity and specificity in respiratory samples.

Methods

We developed and validated an ultra-sensitive SARS-CoV-2 antigen assay with smartphone readout using the Microbubbling Digital Assay previously developed by our group, which is a platform that enables highly sensitive detection and quantitation of protein biomarkers. A computer vision-based algorithm was developed for microbubble smartphone image recognition and quantitation. A machine learning-based classifier was developed to classify the smartphone images based on detected microbubbles. Using this assay, we tracked antigen dynamics in serial swab samples from COVID patients hospitalized in ICU and immunocompromised COVID patients.

Results

The limit of detection (LOD) of the Microbubbling SARS-CoV-2 Antigen Assay was 0.5 pg/mL (10.6 fM) recombinant nucleocapsid (N) antigen or 4000 copies/mL inactivated SARS-CoV-2 virus in nasopharyngeal (NP) swabs, comparable to many rRT-PCR methods. The assay had high analytical specificity towards SARS-CoV-2. Compared to EUA-approved rRT-PCR methods, the Microbubbling Antigen Assay demonstrated a positive percent agreement (PPA) of 97% (95% confidence interval (CI), 92-99%) in symptomatic individuals within 7 days of symptom onset and positive SARS-CoV-2 nucleic acid results, and a negative percent agreement (NPA) of 97% (95% CI, 94-100%) in symptomatic and asymptomatic individuals with negative nucleic acid results. Antigen positivity rate in NP swabs gradually decreased as days-after-symptom-onset increased, despite persistent nucleic acid positivity of the same samples. The computer vision and machine learning-based automatic microbubble image classifier could accurately identify positives and negatives, based on microbubble counts and sizes. Total microbubble volume, a potential marker of antigen burden, correlated inversely with Ct values and days-after-symptom-onset. Antigen was detected for longer periods of time in immunocompromised patients with hematologic malignancies, compared to immunocompetent individuals. Simultaneous detectable antigens and nucleic acids may indicate the presence of replicating viruses in patients with persistent infections.

Conclusions

The Microbubbling SARS-CoV-2 Antigen Assay enables sensitive and specific detection of acute infections, and quantitation and tracking of antigen dynamics in different patient populations at various stages of infection. With smartphone compatibility and automated image processing, the assay is well-positioned to be adapted for point-of-care diagnosis and to explore the clinical implications of antigen dynamics in future studies.

Single-Particle Counting Based on Digital Plasmonic Nanobubble Detection for Rapid and Ultrasensitive Diagnostics

Rapid and sensitive diagnostics of infectious diseases is an urgent and unmet need as evidenced by the COVID-19 pandemic. Here we report a novel strategy, based on DIgitAl plasMONic nanobubble Detection (DIAMOND), to address these gaps. Plasmonic nanobubbles are transient vapor bubbles generated by laser heating of plasmonic nanoparticles and allow single-particle detection. Using gold nanoparticles labels and an optofluidic setup, we demonstrate that DIAMOND achieves a compartment-free digital counting and works on homogeneous assays without separation and amplification steps. When applied to the respiratory syncytial virus diagnostics, DIAMOND is 150 times more sensitive than commercial lateral flow assays and completes measurements within 2 minutes. Our method opens new possibilities to develop single-particle digital detection methods and facilitate rapid and ultrasensitive diagnostics.

One Sentence Summary

Single-particle digital plasmonic nanobubble detection allows rapid and ultrasensitive detection of viruses in a one-step homogeneous assay.

Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning

Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for large amounts of specialist-annotated images for traditional DL model training may preclude generalizability of smartphone-based diagnostics. Here, we employed adversarial neural networks with conditioning to develop an easily reconfigurable virus diagnostic platform that leverages a dataset of smartphone-taken microfluidic chip photos to rapidly generate image classifiers for different target pathogens on-demand. Adversarial learning was also used to augment this real image dataset by generating 16,000 realistic synthetic microchip images, through style generative adversarial networks (StyleGAN). We used this platform, termed smartphone-based pathogen detection resource multiplier using adversarial networks (SPyDERMAN), to accurately detect different intact viruses in clinical samples and to detect viral nucleic acids through integration with CRISPR diagnostics. We evaluated the performance of the system in detecting five different virus targets using 179 patient samples. The generalizability of the system was confirmed by rapid reconfiguration to detect SARS-CoV-2 antigens in nasal swab samples (n = 62) with 100% accuracy. Overall, the SPyDERMAN system may contribute to epidemic preparedness strategies by providing a platform for smartphone-based diagnostics that can be adapted to a given emerging viral agent within days of work.

Point-of-Care Diagnoses and Assays Based on Lateral Flow Test

Analytical devices for point-of-care diagnoses are highly desired and would improve quality of life when first diagnoses are made early and pathologies are recognized soon. Lateral flow tests (LFTs) are such tools that can be easily performed without specific equipment, skills, or experiences. This review is focused on the use of LFT in point-of-care diagnoses. The principle of the assay is explained, and new materials like nanoparticles for labeling, new recognition molecules for interaction with an analyte, and new additional instrumentation like signal scaling by a smartphone camera are described and discussed. Advantages of the LFT devices as well as their limitations are described and discussed here considering actual papers that are properly cited.

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

Emerging and reemerging infections present an ever-increasing challenge to global health. Here, we report a nanoparticle-enabled smartphone (NES) system for rapid and sensitive virus detection. The virus is captured on a microchip and labeled with specifically designed platinum nanoprobes to induce gas bubble formation in the presence of hydrogen peroxide. The formed bubbles are controlled to make distinct visual patterns, allowing simple and sensitive virus detection using a convolutional neural network (CNN)-enabled smartphone system and without using any optical hardware smartphone attachment. We evaluated the developed CNN-NES for testing viruses such as hepatitis B virus (HBV), HCV, and Zika virus (ZIKV). The CNN-NES was tested with 134 ZIKV- and HBV-spiked and ZIKV- and HCV-infected patient plasma/serum samples. The sensitivity of the system in qualitatively detecting viral-infected samples with a clinically relevant virus concentration threshold of 250 copies/ml was 98.97% with a confidence interval of 94.39 to 99.97%.

Naked-eye based point-of-care detection of E.coli O157: H7 by a signal-amplified microfluidic aptasensor

Fast and sensitive detection of E.coli O157: H7 is significantly essential for clinical management as well as for transmission prevention during disease outbreaks. Though many types of detection strategies have been implemented for measuring E.coli O157: H7, most of them still rely on complex instruments or tedious/laborious setups, which restrict their applications in resource-limited scenarios. Herein, we introduce an eye-based microfluidic aptasensor (EA-Sensor) for fast detection of E.coli O157: H7 without the assist of any instruments. We demonstrate the perfect coupling of aptamer sensing, hybridization chain reaction (HCR)-amplification and a distance-based visualized readout to quantitatively determine the pathogen concentration. We first used gel-electrophoresis assay to evaluate the system and the results proved that E.coli O157: H7 was well recognized by the aptamer and HCR could increase the signal by about 100 folds. In addition, the Aptamer specificity and signal-amplification ability were verified on the EA-Sensor for sensing E.coli O157: H7 by naked eyes. Furthermore, we demonstrated that E.coli O157: H7 in milk could be accurately and conveniently measured with good performance. With the benefits of operation integration and strategy integration, our EA-Sensor shows advantages of high specificity, easy operation, efficient amplification and visualized readout, which offers a favorable point-of-care tool for E.coli O157: H7 or other pathogen detection in resource-constrained settings.

Short Keynote Paper: Single Molecule Detection of Protein Biomarkers to Define the Continuum From Health to Disease

This paper describes the need for technologies that improve analytical sensitivity to proteins to better define and monitor the progression from heath to disease over the course of an individual's life. These technologies have the potential to allow the early diagnosis of disease, and trigger treatments at the time when they have the greatest opportunity to be effective. We will describe a technology that we have developed for high sensitivity protein detection, namely, single molecule arrays (Simoa). Simoa is based on the capture of protein molecules on magnetic beads, labeling each protein with an enzyme, and counting of single enzyme labels on beads that are isolated in arrays of femtoliter wells. Simoa has enabled the detection of proteins at subfemtomolar concentrations in a variety of biological fluids. We describe the impact of higher sensitivity of proteins using Simoa on: less invasive testing; earlier detection of disease; providing biomarker baseline profiles for healthy individuals; testing of small sample volumes; monitoring of therapeutic efficacy; faster tests; and detection of proteins in complex samples. We also provide a perspective of how new technologies that allow the low-cost manufacture and miniaturization of Simoa could drive the next wave of analytical devices, including wearables.