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

Generative adversarial networks in medical image reconstruction: A systematic literature review

PURPOSE

Recent advancements in generative adversarial networks (GANs) have demonstrated substantial potential in medical image processing. Despite this progress, reconstructing images from incomplete data remains a challenge, impacting image quality. This systematic literature review explores the use of GANs in enhancing and reconstructing medical imaging data.

METHOD

A document survey of computing literature was conducted using the ACM Digital Library to identify relevant articles from journals and conference proceedings using keyword combinations, such as "generative adversarial networks or generative adversarial network," "medical image or medical imaging," and "image reconstruction."

RESULTS

Across the reviewed articles, there were 122 datasets used in 175 instances, 89 top metrics employed 335 times, 10 different tasks with a total count of 173, 31 distinct organs featured in 119 instances, and 18 modalities utilized in 121 instances, collectively depicting significant utilization of GANs in medical imaging. The adaptability and efficacy of GANs were showcased across diverse medical tasks, organs, and modalities, utilizing top public as well as private/synthetic datasets for disease diagnosis, including the identification of conditions like cancer in different anatomical regions. The study emphasized GAN's increasing integration and adaptability in diverse radiology modalities, showcasing their transformative impact on diagnostic techniques, including cross-modality tasks. The intricate interplay between network size, batch size, and loss function refinement significantly impacts GAN's performance, although challenges in training persist.

CONCLUSIONS

The study underscores GANs as dynamic tools shaping medical imaging, contributing significantly to image quality, training methodologies, and overall medical advancements, positioning them as substantial components driving medical advancements.

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.

Latex microspheres lateral flow immunoassay with smartphone-based device for rapid detection of Cryptococcus

Cryptococcus is a pathogenic fungus that poses a threat to human health. Conventional detection methods have limited the rapid and accurate qualitative and quantitative analysis of Cryptococcus, affecting early diagnosis and treatment. In this study, we developed a Point-of-Care Testing (POCT) platform that integrates lateral flow immunoassay (LFIA) with smartphones, enabling both rapid qualitative and quantitative detection of Cryptococcus. The LFIA strip utilizes latex microspheres (LMs) as labeling probes, achieving a detection limit of 3000 CFU/mL and presenting higher sensitivity than the Colloidal Gold Nanoparticles Lateral Flow Immunoassay (AuNPs-LFIA) strip, and approximately eight times that of the AuNPs-LFIA strip. Additionally, it exhibiting no cross-reactivity with over 24 common pathogens and validated in clinical samples. For quantitative analysis, artificial intelligence algorithms were employed to convert smartphone-captured images into grayscale values. Eleven feature values were utilized as a dataset for machine learning to construct a linear regression model, with Mean Squared Error (MSE) and R2 reaching 0.45 and 0.91, respectively. Moreover, the recovery rates in the serum samples ranged from 90.0 % to 108 %, indicating a good practicability. This research presents a rapid diagnostic technology for Cryptococcus and lays the theoretical and technical groundwork for detecting other pathogens.

Advancing Microfluidic Immunity Testing Systems: New Trends for Microbial Pathogen Detection

Pathogenic microorganisms play a crucial role in the global disease burden due to their ability to cause various diseases and spread through multiple transmission routes. Immunity tests identify antigens related to these pathogens, thereby confirming past infections and monitoring the host's immune response. Traditional pathogen detection methods, including enzyme-linked immunosorbent assays (ELISAs) and chemiluminescent immunoassays (CLIAs), are often labor-intensive, slow, and reliant on sophisticated equipment and skilled personnel, which can be limiting in resource-poor settings. In contrast, the development of microfluidic technologies presents a promising alternative, offering automation, miniaturization, and cost efficiency. These advanced methods are poised to replace traditional assays by streamlining processes and enabling rapid, high-throughput immunity testing for pathogens. This review highlights the latest advancements in microfluidic systems designed for rapid and high-throughput immunity testing, incorporating immunosensors, single molecule arrays (Simoas), a lateral flow assay (LFA), and smartphone integration. It focuses on key pathogenic microorganisms such as SARS-CoV-2, influenza, and the ZIKA virus (ZIKV). Additionally, the review discusses the challenges, commercialization prospects, and future directions to advance microfluidic systems for infectious disease detection.

Generative artificial intelligence to produce high-fidelity blastocyst-stage embryo images

STUDY QUESTION

Can generative artificial intelligence (AI) models produce high-fidelity images of human blastocysts?

SUMMARY ANSWER

Generative AI models exhibit the capability to generate high-fidelity human blastocyst images, thereby providing substantial training datasets crucial for the development of robust AI models.

WHAT IS KNOWN ALREADY

The integration of AI into IVF procedures holds the potential to enhance objectivity and automate embryo selection for transfer. However, the effectiveness of AI is limited by data scarcity and ethical concerns related to patient data privacy. Generative adversarial networks (GAN) have emerged as a promising approach to alleviate data limitations by generating synthetic data that closely approximate real images.

STUDY DESIGN, SIZE, DURATION

Blastocyst images were included as training data from a public dataset of time-lapse microscopy (TLM) videos (n = 136). A style-based GAN was fine-tuned as the generative model.

PARTICIPANTS/MATERIALS, SETTING, METHODS

We curated a total of 972 blastocyst images as training data, where frames were captured within the time window of 110-120 h post-insemination at 1-h intervals from TLM videos. We configured the style-based GAN model with data augmentation (AUG) and pretrained weights (Pretrained-T: with translation equivariance; Pretrained-R: with translation and rotation equivariance) to compare their optimization on image synthesis. We then applied quantitative metrics including Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) to assess the quality and fidelity of the generated images. Subsequently, we evaluated qualitative performance by measuring the intelligence behavior of the model through the visual Turing test. To this end, 60 individuals with diverse backgrounds and expertise in clinical embryology and IVF evaluated the quality of synthetic embryo images.

MAIN RESULTS AND THE ROLE OF CHANCE

During the training process, we observed consistent improvement of image quality that was measured by FID and KID scores. Pretrained and AUG + Pretrained initiated with remarkably lower FID and KID values compared to both Baseline and AUG + Baseline models. Following 5000 training iterations, the AUG + Pretrained-R model showed the highest performance of the evaluated five configurations with FID and KID scores of 15.2 and 0.004, respectively. Subsequently, we carried out the visual Turing test, such that IVF embryologists, IVF laboratory technicians, and non-experts evaluated the synthetic blastocyst-stage embryo images and obtained similar performance in specificity with marginal differences in accuracy and sensitivity.

LIMITATIONS, REASONS FOR CAUTION

In this study, we primarily focused the training data on blastocyst images as IVF embryos are primarily assessed in blastocyst stage. However, generation of an array of images in different preimplantation stages offers further insights into the development of preimplantation embryos and IVF success. In addition, we resized training images to a resolution of 256 × 256 pixels to moderate the computational costs of training the style-based GAN models. Further research is needed to involve a more extensive and diverse dataset from the formation of the zygote to the blastocyst stage, e.g. video generation, and the use of improved image resolution to facilitate the development of comprehensive AI algorithms and to produce higher-quality images.

WIDER IMPLICATIONS OF THE FINDINGS

Generative AI models hold promising potential in generating high-fidelity human blastocyst images, which allows the development of robust AI models as it can provide sufficient training datasets while safeguarding patient data privacy. Additionally, this may help to produce sufficient embryo imaging training data with different (rare) abnormal features, such as embryonic arrest, tripolar cell division to avoid class imbalances and reach to even datasets. Thus, generative models may offer a compelling opportunity to transform embryo selection procedures and substantially enhance IVF outcomes.

STUDY FUNDING/COMPETING INTEREST(S)

This study was supported by a Horizon 2020 innovation grant (ERIN, grant no. EU952516) and a Horizon Europe grant (NESTOR, grant no. 101120075) of the European Commission to A.S. and M.Z.E., the Estonian Research Council (grant no. PRG1076) to A.S., and the EVA (Erfelijkheid Voortplanting & Aanleg) specialty program (grant no. KP111513) of Maastricht University Medical Centre (MUMC+) to M.Z.E.

TRIAL REGISTRATION NUMBER

Not applicable.

A smartphone-based centrifugal mHealth platform implementing hollow daisy-shaped quick response chip for hematocrit measurement

Due to the ever-increasing challenge of emerging and reemerging infections on global health, the development of POCT tools has been propelled. However, conventional point-of-care testing methods suffer from several limitations, including cumbersome operation, long detection times, and low accuracy, which hamper their widespread application. Compared to traditional disease diagnostic equipment, mobile health platforms offer several advantages, including portability, ease of operation, and automated analysis of detection results through recognition algorithms. Consequently, they hold great promise for the future. Here, we developed a smartphone-based centrifugal mHealth platform implementing daisy-shaped quick response chip for hematocrit measurement. The centrifugal microfluidic chip is combined with a smartphone through a back-clip-on mobile phone adapter whose control circuit is designed with low power consumption to enable the platform to operate without requiring a high-power source that is inconvenient to carry, thereby achieving the goal of portability. Concurrently, we designed a quick response chip featuring a unique hollow daisy structure that is in line with the properties of hematocrit detection. The distinctive configuration of the chip enables adequate centrifugal force to be supplied for hematocrit detection. Additionally, our customized quick response code recognition algorithm is able to recognize this chip, facilitating non-experts in performing hematocrit intelligent recognition with their smartphones.

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications

High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.

Smartphone-based point-of-care testing of the SARS-CoV-2: A systematic review

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus's worldwide pandemic has highlighted the urgent need for reliable, quick, and affordable diagnostic tests for comprehending and controlling the epidemic by tracking the world population. Given how crucial it is to monitor and manage the pandemic, researchers have recently concentrated on creating quick detection techniques. Although PCR is still the preferred clinical diagnostic test, there is a pressing need for substitutes that are sufficiently rapid and cost-effective to provide a diagnosis at the time of use. The creation of a quick and simple POC equipment is necessary for home testing. Our review's goal is to provide an overview of the many methods utilized to identify SARS-CoV 2 in various samples utilizing portable devices, as well as any potential applications for smartphones in epidemiological research and detection. The point of care (POC) employs a range of microfluidic biosensors based on smartphones, including molecular sensors, immunological biosensors, hybrid biosensors, and imaging biosensors. For example, a number of tools have been created for the diagnosis of COVID-19, based on various theories. Integrated portable devices can be created using loop-mediated isothermal amplification, which combines isothermal amplification methods with colorimetric detection. Electrochemical approaches have been regarded as a potential substitute for optical sensing techniques that utilize fluorescence for detection and as being more beneficial to the Minimizing and simplicity of the tools used for detection, together with techniques that can amplify DNA or RNA under constant temperature conditions, without the need for repeated heating and cooling cycles. Many research have used smartphones for virus detection and data visualization, making these techniques more user-friendly and broadly distributed throughout nations. Overall, our research provides a review of different novel, non-invasive, affordable, and efficient methods for identifying COVID-19 contagious infected people and halting the disease's transmission.

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.

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.

Application of microfluidic technologies on COVID-19 diagnosis and drug discovery

The ongoing coronavirus disease 2019 (COVID-19) pandemic has boosted the development of antiviral research. Microfluidic technologies offer powerful platforms for diagnosis and drug discovery for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) diagnosis and drug discovery. In this review, we introduce the structure of SARS-CoV-2 and the basic knowledge of microfluidic design. We discuss the application of microfluidic devices in SARS-CoV-2 diagnosis based on detecting viral nucleic acid, antibodies, and antigens. We highlight the contribution of lab-on-a-chip to manufacturing point-of-care equipment of accurate, sensitive, low-cost, and user-friendly virus-detection devices. We then investigate the efforts in organ-on-a-chip and lipid nanoparticles (LNPs) synthesizing chips in antiviral drug screening and mRNA vaccine preparation. Microfluidic technologies contribute to the ongoing SARS-CoV-2 research efforts and provide tools for future viral outbreaks.

A new approach to digital health? Virtual COVID-19 care: A scoping review

Aims

The use of virtual care enabled by digital technologies has increased, prompted by public health restrictions in response to COVID-19. Non-hospitalized persons in the acute phase of COVID-19 illness may have unique health needs while self-isolating in the community. This scoping review aimed to explore the nature of care, the use of digital technologies, and patient outcomes arising from virtual care among community-based self-isolating COVID-19 patients.

Methods

Literature searches for peer-reviewed articles were conducted in four bibliographic databases: CINAHL, Medline, Embase and Cochrane Database of Systematic Reviews between January and February 2022, followed by hand-searching reference lists of included articles. Two levels of screening using defined eligibility criteria among two independent reviewers were completed.

Results

Of the 773 articles retrieved, 19 were included. Results indicate that virtual care can be safe while enabling timely detection of clinical deterioration to improve the illness trajectory. COVID-19 virtual care was delivered by single health professionals or by multidisciplinary teams using a range of low-technology methods such as telephone to higher technology methods like wearable technology that transmitted physiological data to the care teams for real-time or asynchronous monitoring.

Conclusion

The review described the varied nature of virtual care including its design, implementation, and evaluation. Further research is needed for continued exploration on how to leverage digital health assets for the delivery of appropriate and safe virtual COVID-19 community care, which can support patient recovery, control transmission, and prevent intensifying the burden on the health care system, especially during surges.

Needs, Challenges and Countermeasures of SARS-CoV-2 Surveillance in Cold-Chain Foods and Packaging to Prevent Possible COVID-19 Resurgence: A Perspective from Advanced Detections

The pandemic caused by SARS-CoV-2 has a huge impact on the global economy. SARS-CoV-2 could possibly and potentially be transmitted to humans through cold-chain foods and packaging (namely good-to-human), although it mainly depends on a human-to-human route. It is imperative to develop countermeasures to cope with the spread of viruses and fulfil effective surveillance of cold-chain foods and packaging. This review outlined SARS-CoV-2-related cold-chain food incidents and current methods for detecting SARS-CoV-2. Then the needs, challenges and practicable countermeasures for SARS-CoV-2 detection, specifically for cold-chain foods and packaging, were underlined. In fact, currently established detection methods for SARS-CoV-2 are mostly used for humans; thus, these may not be ideally applied to cold-chain foods directly. Therefore, it creates a need to develop novel methods and low-cost, automatic, mini-sized devices specifically for cold-chain foods and packaging. The review intended to draw people's attention to the possible spread of SARS-CoV-2 with cold-chain foods and proposed perspectives for futuristic cold-chain foods monitoring during the pandemic.

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.

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).

How Has COVID-19 Changed the Way We Do Virtual Care? A Scoping Review Protocol

The coronavirus disease (COVID-19) pandemic created worldwide interest and use of virtual care to support public health measures and reduce the spread of infection. While some forms of virtual care have been used prior to COVID-19 such as telemedicine, little is known about other virtual modalities such as video conferencing, wearables and other digital technologies. The COVID-19 pandemic has presented an opportunity to question the efficacy and safety of virtual care, especially in terms of patient outcomes among those self-isolating. The purpose of this scoping review is to examine the safety of virtual care among active COVID-19 patients in the community and examine the types and dose of virtual care. Finally, this review will examine what patient outcomes are identified from interventions delivered virtually to treat COVID-19. We followed a systematic process guided by the PRISMA checklist for scoping reviews with a comprehensive search strategy across four bibliographic databases and handsearching reference lists. We undertook a blinded, two-stage screening process with eligibility criteria. All citations and screening were managed using the DistillerSR software. Data were extracted using a data extraction tool developed for this project. The conclusions from this review will offer greater understanding for how virtual care can be used among community-based COVID-19 patients.

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.

CRISPR-Powered Microfluidics in Diagnostics: A Review of Main Applications

In the past few years, the CRISPR (clustered regularly interspaced short palindromic repeats) applications in medicine and molecular biology have broadened. CRISPR has also been integrated with microfluidic-based biosensors to enhance the sensitivity and selectivity of medical diagnosis due to its great potentials. The CRISPR-powered microfluidics can help quantify DNAs and RNAs for different diseases such as cancer, and viral or bacterial diseases among others. Here in this review, we discussed the main applications of such tools along with their advantages and limitations.

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.

Advances in Clustered, Regularly Interspaced Short Palindromic Repeats (CRISPR)-Based Diagnostic Assays Assisted by Micro/Nanotechnologies

Clustered, regularly interspaced short palindromic repeats (CRISPR)-based diagnoses, derived from gene-editing technology, have been exploited for less than 5 years and are now reaching the stage of precommercial use. CRISPR tools have some notable features, such as recognition at physiological temperature, excellent specificity, and high-efficiency signal amplification capabilities. These characteristics are promising for the development of next-generation diagnostic technologies. In this Perspective, we present a detailed summary of which micro/nanotechnologies play roles in the advancement of CRISPR diagnosis and how they are involved. The use of nanoprobes, nanochips, and nanodevices, microfluidic technology, lateral flow strips, etc. in CRISPR detection systems has led to new opportunities for CRISPR-based diagnosis assay development, such as achieving equipment-free detection, providing more compact detection systems, and improving sensitivity and quantitative capabilities. Although tremendous progress has been made, CRISPR diagnosis has not yet reached its full potential. We discuss upcoming opportunities and improvements and how micro/nanotechnologies will continue to play key roles.