Machine-Learning-Assisted Microfluidic Nanoplasmonic Digital Immunoassay for Cytokine Storm Profiling in COVID-19 Patients

A nanoplasmonic cell-on-a-chip for in situ monitoring of PD-L1+ exosome-mediated immune modulation

Despite the transformative impact of immune checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 pathway in cancer therapy, up to 80% of patients fail to respond, necessitating reliable predictive biomarkers to guide treatment decisions. Recent studies highlight the critical role of tumor-derived exosomal PD-L1 in immune evasion, and its potential as a diagnostic and prognostic biomarker in cancer immunotherapy. However, significant challenges remain in elucidating the functional roles of PD-L1+ exosomes in immune suppression, as current methods lack the ability to precisely and simultaneously characterize and monitor exosome secretion and the corresponding immune modulation on site. To address this, we developed an integrated microfluidic platform that combines a digital nanoplasmonic immunoassay with a cell-on-a-chip system, enabling in situ monitoring of PD-L1+ exosome secretion and exosome-mediated T cell immune responses. This nanoplasmonic immunoassay integrated cell-on-a-chip (NIIC) creates a localized co-cultured microenvironment that facilitates exosome-mediated cellular interactions without direct contact. The NIIC employs machine-learning assisted signal processing for highly sensitive detection of both exosomes and cytokines, providing spatial and quantitative analysis of immune modulation in situ. Using this system, we demonstrated that PD-L1+ exosomes from cancer cells significantly suppressed IFN-γ and IL-2 secretion in neighboring T cells, offering direct insights into exosome-mediated immune suppression. The NIIC platform represents a powerful tool for advancing the understanding of exosome-driven immune modulation and holds potential for predicting clinical responses to anti-PD-1/PD-L1 therapies, paving the way for more personalized cancer immunotherapy strategies.

Lab-in-a-Tip: a multiplex immunoassay platform based on a self-assembled barcoded protein array

High throughput immunoassay is increasingly crucial for both scientific and clinical applications. Here we propose a "Lab-in-a-Tip" (LIT) concept to fabricate a pipette tip containing a high-density protein array and other essential reagents. The protein array is made by self-assembling digitally encoded microparticles inside the modified tip. Mounted on a robotic workstation, it automates liquid-handling steps. Notably, compared with Luminex, the current gold standard in multiplex immunoassays, such a design enables LIT to demonstrate multiple advantages in terms of analytical sensitivity, speed, and throughput. It detects analyte concentrations as low as fg/ml, representing a sensitivity improvement of two orders of magnitude over Luminex. Incubation time is reduced to 15 minutes from Luminex's 210 minutes. Furthermore, LIT requires only 10 µl of sample, one-fifth of what Luminex needs. This makes LIT ideal for rapid diagnostics and studies with limited biological samples, greatly expanding its application scope.

Weight Differences-Based Multi-level Signal Profiling for Homogeneous and Ultrasensitive Intelligent Bioassays

Current high-sensitivity immunoassay protocols often involve complex signal generation designs or rely on sophisticated signal-loading and readout devices, making it challenging to strike a balance between sensitivity and ease of use. In this study, we propose a homogeneous-based intelligent analysis strategy called Mata, which uses weight analysis to quantify basic immune signals through signal subunits. We perform nanomagnetic labeling of target capture events on micrometer-scale polystyrene subunits, enabling magnetically regulated kinetic signal expression. Signal subunits are classified through the multi-level signal classifier in synergy with the developed signal weight analysis and deep learning recognition models. Subsequently, the basic immune signals are quantified to achieve ultra-high sensitivity. Mata achieves a detection of 0.61 pg/mL in 20 min for interleukin-6 detection, demonstrating sensitivity comparable to conventional digital immunoassays and over 22-fold that of chemiluminescence immunoassay and reducing detection time by more than 70%. The entire process relies on a homogeneous reaction and can be performed using standard bright-field optical imaging. This intelligent analysis strategy balances high sensitivity and convenient operation and has few hardware requirements, presenting a promising high-sensitivity analysis solution with wide accessibility.

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.

Plasmonic Biosensors for Health Monitoring: Inflammation Biomarker Detection

Surface plasmon resonance (SPR) and localized SPR (LSPR) biosensors have emerged as viable technologies in the clinical detection of biomarkers for a wide array of health conditions. The success of SPR biosensors lies in their ability to monitor in real-time label-free biomarkers in complex biofluids. Recent breakthroughs in nanotechnology and surface chemistry have significantly improved this feature, notably from the incorporation of advanced nanomaterials including gold nanoparticles, graphene, and carbon nanotubes providing better SPR sensor performance in terms of detection limits, stability, and specificity. Recent progress in microfluidic integration has enabled SPR biosensors to detect multiple biomarkers simultaneously in complex biological samples. Taken together, these advances are closing the gap for their use in clinical diagnostics and point-of-care (POC) applications. While broadly applicable, the latest advancements in plasmonic biosensing are overviewed using inflammation biomarkers C-reactive protein (CRP), interleukins (ILs), tumor necrosis factor-α (TNF-α), procalcitonin (PCT), ferritin, and fibrinogen for a series of conditions, including cardiovascular diseases, autoimmune disorders, infections, and sepsis, as a key example of plasmonic biosensors for clinical applications. We highlight developments in sensor design, nanomaterial integration, surface functionalization, and multiplexing and provide a look forward to clinical applications by assessing the current limitations and exploring future directions for translating SPR biosensors for diagnostics and health monitoring. By enhancement of diagnostic accuracy, reproducibility, and accessibility, particularly in POC settings, SPR biosensors have the potential to significantly contribute to personalized healthcare and bring real-time, high-precision diagnostics to the forefront of clinical practice.

The Latest Advances in Microfluidic DLD Cell Sorting Technology: The Optimization of Channel Design

Cell sorting plays a crucial role in both medical and biological research. As a key passive sorting technique in the field of microfluidics, deterministic lateral displacement (DLD) has been widely applied to cell separation and sorting. This review aims to summarize the latest advances in the optimization of channel design for microfluidic DLD cell sorting. First, we provide an overview of the design elements of microfluidic DLD cell sorting channels, focusing on key factors that affect separation efficiency and accuracy, including channel geometry, fluid dynamics, and the interaction between cells and channel surfaces. Subsequently, we review recent innovations and progress in channel design for microfluidic DLD technology, exploring its applications in biomedical fields and its integration with machine learning. Additionally, we discuss the challenges currently faced in optimizing channel design for microfluidic DLD cell sorting. Finally, based on existing research, we make a summary and put forward prospective views on the further development of this field.

Deep-Learning-Assisted Digital Fluorescence Immunoassay on Magnetic Beads for Ultrasensitive Determination of Protein Biomarkers

Digital fluorescence immunoassay (DFI) based on random dispersion magnetic beads (MBs) is one of the powerful methods for ultrasensitive determination of protein biomarkers. However, in the DFI, improving the limit of detection (LOD) is challenging since the ratio of signal-to-background and the speed of manual counting beads are low. Herein, we developed a deep-learning network (ATTBeadNet) by utilizing a new hybrid attention mechanism within a UNet3+ framework for accurately and fast counting the MBs and proposed a DFI using CdS quantum dots (QDs) with narrow peak and optical stability as reported at first time. The developed ATTBeadNet was applied to counting the MBs, resulting in the F1 score (95.91%) being higher than those of other methods (ImageJ, 68.33%; computer vision-based, 92.99%; fully convolutional network, 75.00%; mask region-based convolutional neural network, 70.34%). On principle-on-proof, a sandwich MB-based DFI was proposed, in which human interleukin-6 (IL-6) was taken as a model protein biomarker, while antibody-bound streptavidin-coated MBs were used as capture MBs and antibody-HRP-tyramide-functionalized CdS QDs were used as the binding reporter. When the developed ATTBeadNet was applied to the MB-based DFI of IL-6 (20 μL), the linear range from 5 to 100 fM and an LOD of 3.1 fM were achieved, which are better than those using the ImageJ method (linear range from 30 to 100 fM and LOD of 20 fM). This work demonstrates that the integration of the deep-learning network with DFI is a promising strategy for the highly sensitive and accurate determination of protein biomarkers.

Localized surface plasmon resonance sensing based on monometallic gold nanoparticles: from material preparation to detection of bioanalytes

The tunable geometrical properties of gold nanoparticles (AuNPs) endow them with the capacity to exhibit distinct behaviors with respect to both macroscopic (color) and microscopic (resonance wavelength) aspects, which has been extensively utilized in localized surface plasmon resonance (LSPR) sensing platforms. Additionally, functionalizing AuNP surfaces enhances the platforms' capabilities, allowing for the detection of a wide range of molecules related to various aspects of human health. In this review, we comprehensively elucidate the fundamental principles of LSPR biosensing and provide an in-depth survey of the preparation processes for metal nanoparticles, encompassing deposition technology for large-scale particle production as well as ion reduction methods that afford superior control over the particles' physical and chemical attributes. The sensing strategies based on adjustment of the dielectric environment and particle dispersion-aggregation levels are thoroughly reviewed and discussed. The discussion focused on a specific class of nanoparticles, characterized by their uniform shape and size, with each section bifurcated into two parts: a summary of the salient features and recent discoveries pertaining to the sensing strategy, as well as illustrations of representative, cutting-edge applications employing the strategy. We specifically aim to scrutinize analytes commonly encountered in the biomedical realm, encompassing biomarkers that serve as indicators of a wide range of diseases and microbial pathogens, while also prognosticating the future development trends of LSPR optical biosensor platforms within the biomedical field.

Embracing Poisson Encapsulation Statistics for Improved Droplet Digital Immunoassay

Digital immunoassays enable the detection of protein biomarkers with very low concentrations, but the analysis stringently requires single-bead encapsulation. Low bead density has been adopted to minimize multiple-bead encapsulations, but the trade-off is the low droplet effectiveness (∼10%) in droplet-based assays. Here we report the method of inclusive droplet digital ELISA (iddELISA) that embraces all types of encapsulations by factoring in their varied "on-off" probabilities in the statistical inference. We derived the statistical model, optimized the bead encapsulation and immunoreaction, and developed an image analysis pipeline for accurate droplet and bead recognition, showing that approximately 40% of the droplets could be used in the analysis. Using the detection of SARS-CoV-2 nucleocapsid protein as a demonstration, the iddELISA achieved a limit of detection of 0.71 fg/mL, which was much lower than conventional ELISA as well as droplet digital ELISA. By effectively incorporating multiple bead encapsulations, the iddELISA simplified the digital immunoassay while improving the counting efficiency and sensitivity, representing a unique concept in digital immunoassays.

Fully Automated and AI-Assisted Optical Fiber Sensing System for Multiplexed and Continuous Brain Monitoring

Continuous and comprehensive brain monitoring is crucial for timely identification of changes or deterioration in brain function, enabling prompt intervention and personalized treatments. However, existing brain monitoring systems struggle to offer continuous and accurate monitoring of multiple brain biomarkers simultaneously. This study introduces a multiplexed optical fiber sensing system for continuous and simultaneous monitoring of six cerebrospinal fluid (CSF) biomarkers using tip-functionalized optical fibers and computational algorithms. Optimized machine learning models are developed and integrated for real-time spectra analysis, allowing for precise and continuous readout of biomarker concentrations. The developed machine learning-assisted fiber optic sensing system exhibits high sensitivity (0.04, 0.38, 0.67, 2.62, 0.0064, 0.33 I/I0 change per units of temperature, dissolved oxygen, glucose, pH, Na+, Ca2+, respectively), reversibility, and selectivity toward target biomarkers with a total diameter less than 2.5 mm. By monitoring brain metabolic and ionic dynamics, this system accurately identified brain physiology deterioration and recovery using ex vivo traumatic brain injury models. Additionally, the system successfully tracked biomarker fluctuations in clinical CSF samples with high accuracy (R2 > 0.93), demonstrating excellent sensitivity and selectivity in reflecting disease progression in real time. These findings underscore the enormous potential of automated and multiplexed optical fiber sensing systems for intraoperative and postoperative monitoring of brain physiologies.

Harnessing nanoengineered CAR-T cell strategies to advance solid tumor immunotherapy

The efficacy and safety of chimeric antigen receptor (CAR) T cell therapy is still inconclusive in solid tumor treatment. Recently, nanotechnology has emerged as a potent strategy to reshape CAR-T cell therapy with promising outcomes. This review aims to discuss the significant potential of nano-engineered CAR-T cell therapy in addressing existing challenges, including CAR-T cell engineering evolution, tumor microenvironment (TME) modulation, and precise CAR-T cell therapy (precise targeting, monitoring, and activation), under the main consideration of clinical translation. It also focuses on the growing trend of technological convergence within this domain, such as mRNA therapeutics, organoids, neoantigen, and artificial intelligence. Moreover, safety management of nanomedicine is seriously emphasized to facilitate clinical translation.

Machine Learning-Driven Innovations in Microfluidics

Microfluidic devices have revolutionized biosensing by enabling precise manipulation of minute fluid volumes across diverse applications. This review investigates the incorporation of machine learning (ML) into the design, fabrication, and application of microfluidic biosensors, emphasizing how ML algorithms enhance performance by improving design accuracy, operational efficiency, and the management of complex diagnostic datasets. Integrating microfluidics with ML has fostered intelligent systems capable of automating experimental workflows, enabling real-time data analysis, and supporting informed decision-making. Recent advances in health diagnostics, environmental monitoring, and synthetic biology driven by ML are critically examined. This review highlights the transformative potential of ML-enhanced microfluidic systems, offering insights into the future trajectory of this rapidly evolving field.

Machine learning-assisted laccase-like activity nanozyme for intelligently onsite real-time and dynamic analysis of pyrethroid pesticides

The intelligently efficient, reliable, economical and portable onsite assay toward pyrethroid pesticides (PPs) residues is critical for food safety analysis and environmental pollution traceability. Here, a fluorescent nanozyme Cu-ATP@ [Ru(bpy)3]2+ with laccase-like activity was designed to develop a versatile machine learning-assisted colorimetric and fluorescence dual-modal assay for efficient onsite intelligent decision recognition and quantification of PPs residues. In the presence of alkaline phosphatase (ALP), the laccase-like activity of Cu-ATP@ [Ru(bpy)3]2+ was enhanced to oxidize colorless o-phenylenediamine (OPD) into dark-yellow 2,3-diaminophenazine (DAP) via electron transfer, appearing a new yellow fluorescence at 550 nm. Meanwhile, the red fluorescence of Cu-ATP@ [Ru(bpy)3]2+ at 600 nm was quenched due to the internal filter effect (IFE) of DAP towards Cu-ATP@ [Ru(bpy)3]2+. However, the selective inhibition of PPs toward ALP activity enabled to observe a dual-modal response of PPs concentration-dependent decrease in colorimetric signal and enhancement in the fluorescence intensity ratio of F600 nm/F550 nm. On this basis, both the colorimetric and fluorescence images were captured and processed with a home-made WeChat applet-installed smartphone to extract the corresponding image color information, thus achieving machine learning-assisted onsite real-time and dynamic intelligent decision recognition and quantification of PPs residues in real samples, which shows a promising potential in safeguarding food safety and environmental health.

Designing nanotheranostics with machine learning

The inherent limits of traditional diagnoses and therapies have driven the development and application of emerging nanotechnologies for more effective and safer management of diseases, herein referred to as 'nanotheranostics'. Although many important technological successes have been achieved in this field, widespread adoption of nanotheranostics as a new paradigm is hindered by specific obstacles, including time-consuming synthesis of nanoparticles, incomplete understanding of nano-bio interactions, and challenges regarding chemistry, manufacturing and the controls required for clinical translation and commercialization. As a key branch of artificial intelligence, machine learning (ML) provides a set of tools capable of performing time-consuming and result-perception tasks, thus offering unique opportunities for nanotheranostics. This Review summarizes the progress and challenges in this emerging field of ML-aided nanotheranostics, and discusses the opportunities in developing next-generation nanotheranostics with reliable datasets and advanced ML models to offer better clinical benefits to patients.

An evaluation of recent advancements in biological sensory organ-inspired neuromorphically tuned biomimetic devices

In the field of neuroscience, significant progress has been made regarding how the brain processes information. Unlike computer processors, the brain comprises neurons and synapses instead of memory blocks and transistors. Despite advancements in artificial neural networks, a complete understanding concerning brain functions remains elusive. For example, to achieve more accurate neuron replication, we must better understand signal transmission during synaptic processes, neural network tunability, and the creation of nanodevices featuring neurons and synapses. This study discusses the latest algorithms utilized in neuromorphic systems, the production of synaptic devices, differences between single and multisensory gadgets, recent advances in multisensory devices, and the promising research opportunities available in this field. We also explored the ability of an artificial synaptic device to mimic biological neural systems across diverse applications. Despite existing challenges, neuroscience-based computing technology holds promise for attracting scientists seeking to enhance solutions and augment the capabilities of neuromorphic devices, thereby fostering future breakthroughs in algorithms and the widespread application of cutting-edge technologies.

Nanoplasmonic biosensors for environmental sustainability and human health

Monitoring the health conditions of the environment and humans is essential for ensuring human well-being, promoting global health, and achieving sustainability. Innovative biosensors are crucial in accurately monitoring health conditions, uncovering the hidden connections between the environment and human well-being, and understanding how environmental factors trigger autoimmune diseases, neurodegenerative diseases, and infectious diseases. This review evaluates the use of nanoplasmonic biosensors that can monitor environmental health and human diseases according to target analytes of different sizes and scales, providing valuable insights for preventive medicine. We begin by explaining the fundamental principles and mechanisms of nanoplasmonic biosensors. We investigate the potential of nanoplasmonic techniques for detecting various biological molecules, extracellular vesicles (EVs), pathogens, and cells. We also explore the possibility of wearable nanoplasmonic biosensors to monitor the physiological network and healthy connectivity of humans, animals, plants, and organisms. This review will guide the design of next-generation nanoplasmonic biosensors to advance sustainable global healthcare for humans, the environment, and the planet.

Optical dark-field spectroscopy of single plasmonic nanoparticles for molecular biosciences

An ideal sensor capable of quantifying analytes in minuscule sample volumes represents a significant technological advancement. Plasmonic nanoparticles integrated with optical dark-field spectroscopy have reached this capability, demonstrating versatility and expanding applicability across in vitro and in vivo subjects. This review underscores the applicability of optical dark-field spectroscopy with single plasmonic nanoparticles to elucidate a wide range of biomolecular characteristics, including binding constants, molecular dynamics, distances, and forces, as well as recording cell communication signals. Perspectives highlight the potential for the development of implantable nanosensors for metabolite detection in animal models, illustrating the technique's efficacy without the need for labeling molecules. In summary, this review aims to consolidate knowledge of this adaptable and robust technique for decoding molecular biological phenomena within the nano- and bio-scientific community.

Nanotechnology in Advancing Chimeric Antigen Receptor T Cell Therapy for Cancer Treatment

Chimeric antigen receptor (CAR) T cell therapy has emerged as a groundbreaking treatment for hematological cancers, yet it faces significant hurdles, particularly regarding its efficacy in solid tumors and concerning associated adverse effects. This review provides a comprehensive analysis of the advancements and ongoing challenges in CAR-T therapy. We highlight the transformative potential of nanotechnology in enhancing CAR-T therapy by improving targeting precision, modulating the immune-suppressive tumor microenvironment, and overcoming physical barriers. Nanotechnology facilitates efficient CAR gene delivery into T cells, boosting transfection efficiency and potentially reducing therapy costs. Moreover, nanotechnology offers innovative solutions to mitigate cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS). Cutting-edge nanotechnology platforms for real-time monitoring of CAR-T cell activity and cytokine release are also discussed. By integrating these advancements, we aim to provide valuable insights and pave the way for the next generation of CAR-T cell therapies to overcome current limitations and enhance therapeutic outcomes.

Deep learning-assisted monitoring of trastuzumab efficacy in HER2-Overexpressing breast cancer via SERS immunoassays of tumor-derived urinary exosomal biomarkers

Monitoring drug efficacy is significant in the current concept of companion diagnostics in metastatic breast cancer. Trastuzumab, a drug targeting human epidermal growth factor receptor 2 (HER2), is an effective treatment for metastatic breast cancer. However, some patients develop resistance to this therapy; therefore, monitoring its efficacy is essential. Here, we describe a deep learning-assisted monitoring of trastuzumab efficacy based on a surface-enhanced Raman spectroscopy (SERS) immunoassay against HER2-overexpressing mouse urinary exosomes. Individual Raman reporters bearing the desired SERS tag and exosome capture substrate were prepared for the SERS immunoassay; SERS tag signals were collected to prepare deep learning training data. Using this deep learning algorithm, various complicated mixtures of SERS tags were successfully quantified and classified. Exosomal antigen levels of five types of cell-derived exosomes were determined using SERS-deep learning analysis and compared with those obtained via quantitative reverse transcription polymerase chain reaction and western blot analysis. Finally, drug efficacy was monitored via SERS-deep learning analysis using urinary exosomes from trastuzumab-treated mice. Use of this monitoring system should allow proactive responses to any treatment-resistant issues.

Selenomethionine Inhibited HADV-Induced Apoptosis Mediated by ROS through the JAK-STAT3 Signaling Pathway

Adenovirus (HAdV) can cause severe respiratory infections in children and immunocompromised patients. There is a lack of specific therapeutic drugs for HAdV infection, and the study of anti-adenoviral drugs has far-reaching clinical implications. Elemental selenium can play a specific role as an antioxidant in the human immune cycle by non-specifically binding to the amino acid methionine in body proteins. Methods: The antiviral mechanism of selenomethionine was explored by measuring cell membrane status, intracellular DNA status, cytokine secretion, mitochondrial membrane potential, and ROS production. Conclusions: Selenomethionine improved the regulation of ROS-mediated apoptosis by modulating the expression of Jak1/2, STAT3, and BCL-XL, which led to the inhibition of apoptosis. It is anticipated that selenomethionine will offer a new anti-adenoviral therapeutic alternative.

Nucleic acid aptamer based thermally oxidized porous silicon/zinc oxide microarray chip for detection of ochratoxin A in cereals

A nucleic acid aptamer based thermally oxidized porous silicon/zinc oxide microarray chip was constructed for the detection of ochratoxin A. The hybrid chains formed by aptamer and complementary chains labeled with fluorescent groups and fluorescent burst groups were used as recognition molecules, and the detection of toxins was accomplished on the chip by the principle of fluorescence signal burst and recovery. The modified QuEChERS method was used for sample pretreatment and the performance of the method was evaluated. The results showed that the linear range was 0.02 ∼ 200 ng/kg with the detection limit of 0.0196 ng/kg under the optimal detection conditions. The method was applied to different cereals with the recoveries of 90.30 ∼ 111.69 %. The developed microarray chip has the advantages of being cost-effective, easy to prepare, sensitive and specific, and can provide a new method for the detection of other toxins.

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.

Recent advances in point-of-care testing of COVID-19

Advances in microfluidic device miniaturization and system integration contribute to the development of portable, handheld, and smartphone-compatible devices. These advancements in diagnostics have the potential to revolutionize the approach to detect and respond to future pandemics. Accordingly, herein, recent advances in point-of-care testing (POCT) of coronavirus disease 2019 (COVID-19) using various microdevices, including lateral flow assay strips, vertical flow assay strips, microfluidic channels, and paper-based microfluidic devices, are reviewed. However, visual determination of the diagnostic results using only microdevices leads to many false-negative results due to the limited detection sensitivities of these devices. Several POCT systems comprising microdevices integrated with portable optical readers have been developed to address this issue. Since the outbreak of COVID-19, effective POCT strategies for COVID-19 based on optical detection methods have been established. They can be categorized into fluorescence, surface-enhanced Raman scattering, surface plasmon resonance spectroscopy, and wearable sensing. We introduced next-generation pandemic sensing methods incorporating artificial intelligence that can be used to meet global health needs in the future. Additionally, we have discussed appropriate responses of various testing devices to emerging infectious diseases and prospective preventive measures for the post-pandemic era. We believe that this review will be helpful for preparing for future infectious disease outbreaks.

Methods gold standard in clinic millifluidics multiplexed extended gate field-effect transistor biosensor with gold nanoantennae as signal amplifiers

We present a portable multiplexed biosensor platform based on the extended gate field-effect transistor and demonstrate its amplified response thanks to gold nanoparticle-based bioconjugates introduced as a part of the immunoassay. The platform comprises a disposable chip hosting an array of 32 extended gate electrodes, a readout module based on a single transistor operating in constant charge mode, and a multiplexer to scan sensing electrodes one-by-one. Although employing only off-the-shelf electronic components, our platform achieves sensitivities comparable to fully customized nanofabricated potentiometric sensors. In particular, it reaches a detection limit of 0.2 fM for the pure molecular assay when sensing horseradish peroxidase-linked secondary antibody (∼0.4 nM reached by standard microplate methods). Furthermore, with the gold nanoparticle bioconjugation format, we demonstrate ca. 5-fold amplification of the potentiometric response compared to a pure molecular assay, at the detection limit of 13.3 fM. Finally, we elaborate on the mechanism of this amplification and propose that nanoparticle-mediated disruption of the diffusion barrier layer is the main contributor to the potentiometric signal enhancement. These results show the great potential of our portable, sensitive, and cost-efficient biosensor for multidimensional diagnostics in the clinical and laboratory settings, including e.g., serological tests or pathogen screening.

Ultrasensitive, Multiplexed Buoyant Sensor for Monitoring Cytokines in Biofluids

Multiplexed quantification of low-abundance protein biomarkers in complex biofluids is important for biomedical research and clinical diagnostics. However, in situ sampling without perturbing biological systems remains challenging. In this work, we report a buoyant biosensor that enables in situ monitoring of protein analytes at attomolar concentrations with a 15 min temporal resolution. The buoyant biosensor implemented with fluorescent nanolabels enabled the ultrasensitive and multiplexed detection and quantification of cytokines. Implementing the biosensor in a digital manner (i.e., counting the individual nanolabels) further improves the low detection limit. We demonstrate that the biosensor enables the detection and quantification of the time-varying concentrations of cytokines (e.g., IL-6 and TNF-α) in macrophage culture media without perturbing the live cells. The easy-to-apply biosensor with attomolar sensitivity and multiplexing capability can enable an in situ analysis of protein biomarkers in various biofluids and tissues to aid in understanding biological processes and diagnosing and treating diverse diseases.

Calligraphy of Nanoplasmonic Bioink-Based Multiplex Immunosensor for Precision Immune Monitoring and Modulation

Immunomodulation therapies have attracted immense interest recently for the treatment of immune-related diseases, such as cancer and viral infections. This new wave of enthusiasm for immunomodulators, predominantly revolving around cytokines, has spurred emerging needs and opportunities for novel immune monitoring and diagnostic tools. Considering the highly dynamic immune status and limited window for therapeutic intervention, precise real-time detection of cytokines is critical to effectively monitor and manage the immune system and optimize the therapeutic outcome. The clinical success of such a rapid, sensitive, multiplex immunoanalytical platform further requires the system to have ease of integration and fabrication for sample sparing and large-scale production toward massive parallel analysis. In this article, we developed a nanoplasmonic bioink-based, label-free, multiplex immunosensor that can be readily "written" onto a glass substrate via one-step calligraphy patterning. This facile nanolithography technique allows programmable patterning of a minimum of 3 μL of nanoplasmonic bioink in 1 min and thus enables fabrication of a nanoplasmonic microarray immunosensor with 2 h simple incubation. The developed immunosensor was successfully applied for real-time, parallel detection of multiple cytokines (e.g., interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and transforming growth factor-beta (TGF-β)) in immunomodulated macrophage samples. This integrated platform synergistically incorporates the concepts of nanosynthesis, nanofabrication, and nanobiosensing, showing great potential in the scalable production of label-free multiplex immunosensing devices with superior analytical performance for clinical applications in immunodiagnostics and immunotherapy.

CRISPR/Cas12a-based fluorescence aptasensor integrated with two-dimensional cobalt oxyhydroxide nanosheets for IFN-γ detection

Cytokine storm (CS) is a risky immune overreaction accompanied by significant elevations of pro-inflammatory cytokines including interferon-γ (IFN-γ), interleukin and tumor necrosis factor. Sensitive detection of cytokine is conducive to studying CS progress and diagnosing infectious diseases. In this study, we developed a tandem system combining aptamer, strand displacement amplification (SDA), CRISPR/Cas12a, and cobalt oxyhydroxide nanosheets (termed Apt-SCN tandem system) as a signal-amplified platform for IFN-γ detection. Owing to the stronger affinity, target IFN-γ bound specifically to the aptamer from aptamer-complementary DNA (Apt-cDNA) duplex. The cDNA released from the Apt-cDNA duplex initiated SDA, resulting in the generation of double-stranded DNA products that could activate the trans-cleavage activity of CRISPR/Cas12a. The activated CRISPR/Cas12a further cleaved FAM-labeled single-stranded DNA probe, preventing it from adhering to the cobalt oxyhydroxide nanosheets and recovering the fluorescence signal. Sensitive fluorometric analysis of IFN-γ was successfully performed with detection limit as low as 0.37 nM. Unlike traditional protein analysis methods, Apt-SCN tandem system incorporates multiple signal amplification techniques and may also be applicable for other cytokines assay. This study was the initial study to utilize SDA and CRISPR/Cas12a to detect IFN-γ, showing great potential for cytokines clinical assay and CS prevention.

A digital nanoplasmonic microarray immunosensor for multiplexed cytokine monitoring during CAR T-cell therapy from a leukemia tumor microenvironment model

The release of cytokines by chimeric antigen receptor (CAR) T-cells and tumor resident immune cells defines a significant part of CAR T-cell functional activity and patient immune responses during CAR T-cell therapy. However, few studies have so far precisely characterized the cytokine secretion dynamics in the tumor niche during CAR T-cell therapy, which requires multiplexed, and timely biosensing platforms and integration with biomimetic tumor microenvironment. Herein, we implemented a digital nanoplasmonic microarray immunosensor with a microfluidic biomimetic Leukemia-on-a-Chip model to monitor cytokine secretion dynamics during CD19 CAR T-cell therapy against precursor B-cell acute lymphocytic leukemia (B-ALL). The integrated nanoplasmonic biosensors achieved precise multiplexed cytokine measurements with low operating sample volume, short assay time, heightened sensitivity, and negligible sensor crosstalk. Using the digital nanoplasmonic biosensing approach, we measured the concentrations of six cytokines (TNF-α, IFN-γ, MCP-1, GM-CSF, IL-1β, and IL-6) during first 5 days of CAR T-cell treatment in the microfluidic Leukemia-on-a-Chip model. Our results revealed a heterogeneous secretion profile of various cytokines during CAR T-cell therapy and confirmed a correlation between the cytokine secretion profile and the CAR T-cell cytotoxic activity. The capability to monitor immune cell cytokine secretion dynamics in a biomimetic tumor microenvironment could further help in study of cytokine release syndrome during CAR T-cell therapy and in development of more efficient and safer immunotherapies.

Microfluidic Organ-Chips and Stem Cell Models in the Fight Against COVID-19

SARS-CoV-2, the virus underlying COVID-19, has now been recognized to cause multiorgan disease with a systemic effect on the host. To effectively combat SARS-CoV-2 and the subsequent development of COVID-19, it is critical to detect, monitor, and model viral pathogenesis. In this review, we discuss recent advancements in microfluidics, organ-on-a-chip, and human stem cell-derived models to study SARS-CoV-2 infection in the physiological organ microenvironment, together with their limitations. Microfluidic-based detection methods have greatly enhanced the rapidity, accessibility, and sensitivity of viral detection from patient samples. Engineered organ-on-a-chip models that recapitulate in vivo physiology have been developed for many organ systems to study viral pathology. Human stem cell-derived models have been utilized not only to model viral tropism and pathogenesis in a physiologically relevant context but also to screen for effective therapeutic compounds. The combination of all these platforms, along with future advancements, may aid to identify potential targets and develop novel strategies to counteract COVID-19 pathogenesis.

The application of nanoparticles in point-of-care testing (POCT) immunoassays

The Covid-19 pandemic has led to greater recognition of the importance of the fast and timely detection of pathogens. Recent advances in point-of-care testing (POCT) technology have shown promising results for rapid diagnosis. Immunoassays are among the most extensive POCT assays, in which specific labels are used to indicate and amplify the immune signal. Nanoparticles (NPs) are above the rest because of their versatile properties. Much work has been devoted to NPs to find more efficient immunoassays. Herein, we comprehensively describe NP-based immunoassays with a focus on particle species and their specific applications. This review describes immunoassays along with key concepts surrounding their preparation and bioconjugation to show their defining role in immunosensors. The specific mechanisms, microfluidic immunoassays, electrochemical immunoassays (ELCAs), immunochromatographic assays (ICAs), enzyme-linked immunosorbent assays (ELISA), and microarrays are covered herein. For each mechanism, a working explanation of the appropriate background theory and formalism is articulated before examining the biosensing and related point-of-care (POC) utility. Given their maturity, some specific applications using different nanomaterials are discussed in more detail. Finally, we outline future challenges and perspectives to give a brief guideline for the development of appropriate platforms.

Recent Advances in Molecular and Immunological Diagnostic Platform for Virus Detection: A Review

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused an ongoing coronavirus disease (COVID-19) outbreak and a rising demand for the development of accurate, timely, and cost-effective diagnostic tests for SARS-CoV-2 as well as other viral infections in general. Currently, traditional virus screening methods such as plate culturing and real-time PCR are considered the gold standard with accurate and sensitive results. However, these methods still require sophisticated equipment, trained personnel, and a long analysis time. Alternatively, with the integration of microfluidic and biosensor technologies, microfluidic-based biosensors offer the ability to perform sample preparation and simultaneous detection of many analyses in one platform. High sensitivity, accuracy, portability, low cost, high throughput, and real-time detection can be achieved using a single platform. This review presents recent advances in microfluidic-based biosensors from many works to demonstrate the advantages of merging the two technologies for sensing viruses. Different platforms for virus detection are classified into two main sections: immunoassays and molecular assays. Moreover, available commercial sensing tests are analyzed.

Microsystem Advances through Integration with Artificial Intelligence

Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.

Explanatory predictive model for COVID-19 severity risk employing machine learning, shapley addition, and LIME

The rapid spread of SARS-CoV-2 threatens global public health and impedes the operation of healthcare systems. Several studies have been conducted to confirm SARS-CoV-2 infection and examine its risk factors. To produce more effective treatment options and vaccines, it is still necessary to investigate biomarkers and immune responses in order to gain a deeper understanding of disease pathophysiology. This study aims to determine how cytokines influence the severity of SARS-CoV-2 infection. We measured the plasma levels of 48 cytokines in the blood of 87 participants in the COVID-19 study. Several Classifiers were trained and evaluated using Machine Learning and Deep Learning to complete missing data, generate synthetic data, and fill in any gaps. To examine the relationship between cytokine storm and COVID-19 severity in patients, the Shapley additive explanation (SHAP) and the LIME (Local Interpretable Model-agnostic Explanations) model were applied. Individuals with severe SARS-CoV-2 infection had elevated plasma levels of VEGF-A, MIP-1b, and IL-17. RANTES and TNF were associated with healthy individuals, whereas IL-27, IL-9, IL-12p40, and MCP-3 were associated with non-Severity. These findings suggest that these cytokines may promote the development of novel preventive and therapeutic pathways for disease management. In this study, the use of artificial intelligence is intended to support clinical diagnoses of patients to determine how each cytokine may be responsible for the severity of COVID-19, which could lead to the identification of several cytokines that could aid in treatment decision-making and vaccine development.

Spatiotemporal dissection of tumor microenvironment via in situ sensing and monitoring in tumor-on-a-chip

Real-time monitoring in the tumor microenvironment provides critical insights of cancer progression and mechanistic understanding of responses to cancer treatments. However, clinical challenges and significant questions remain regarding assessment of limited clinical tissue samples, establishment of validated, controllable pre-clinical cancer models, monitoring of static versus dynamic markers, and the translation of insights gained from in vitro tumor microenvironments to systematic investigation and understanding in clinical practice. State-of-art tumor-on-a-chip strategies will be reviewed herein, and emerging real-time sensing and monitoring platforms for on-chip analysis of tumor microenvironment will also be examined. The integration of the sensors with tumor-on-a-chip platforms to provide spatiotemporal information of the tumor microenvironment and the associated challenges will be further evaluated. Though optimal integrated systems for in situ monitoring are still in evolution, great promises lie ahead that will open new paradigm for rapid, comprehensive analysis of cancer development and assist clinicians with powerful tools to guide the diagnosis, prognosis and treatment course in cancer.

Microfluidic-based technologies for diagnosis, prevention, and treatment of COVID-19: recent advances and future directions

The COVID-19 pandemic has posed significant challenges to existing healthcare systems around the world. The urgent need for the development of diagnostic and therapeutic strategies for COVID-19 has boomed the demand for new technologies that can improve current healthcare approaches, moving towards more advanced, digitalized, personalized, and patient-oriented systems. Microfluidic-based technologies involve the miniaturization of large-scale devices and laboratory-based procedures, enabling complex chemical and biological operations that are conventionally performed at the macro-scale to be carried out on the microscale or less. The advantages microfluidic systems offer such as rapid, low-cost, accurate, and on-site solutions make these tools extremely useful and effective in the fight against COVID-19. In particular, microfluidic-assisted systems are of great interest in different COVID-19-related domains, varying from direct and indirect detection of COVID-19 infections to drug and vaccine discovery and their targeted delivery. Here, we review recent advances in the use of microfluidic platforms to diagnose, treat or prevent COVID-19. We start by summarizing recent microfluidic-based diagnostic solutions applicable to COVID-19. We then highlight the key roles microfluidics play in developing COVID-19 vaccines and testing how vaccine candidates perform, with a focus on RNA-delivery technologies and nano-carriers. Next, microfluidic-based efforts devoted to assessing the efficacy of potential COVID-19 drugs, either repurposed or new, and their targeted delivery to infected sites are summarized. We conclude by providing future perspectives and research directions that are critical to effectively prevent or respond to future pandemics.

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.

Affinity Exploration of SARS-CoV-2 RBD Variants to mAb-Functionalized Plasmonic Metasurfaces for Label-Free Immunoassay Boosting

Plasmonic metasurfaces (PMs) functionalized with the monoclonal antibody (mAb) are promising biophotonic sensors for biomolecular interaction analysis and convenient immunoassay of various biomarkers, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Previous PM biosensing suffers from the slow affinity detection rate and lack of sufficient immunoassay studies on various SARS-CoV-2 variants. Here, we develop a high-efficiency affinity testing method based on label-free PM sensors with mAbs and demonstrate their binding characteristics to 12 spike receptor binding domain (RBD) variants of SARS-CoV-2. In addition to the research of plasmonic near-field influence on surface biomolecule sensing, we provide a comprehensive report about the Langmuir binding equilibrium of molecular kinetics between 12 SARS-CoV-2 RBD variants and mAb-functionalized PMs, which plays a crucial role in label-free immunosensing. A high-affinity mAb can be combined with the highly sensitive propagating plasmonic mode to boost the detection of SARS-CoV-2 variants. Owing to a better understanding of molecular dynamics on PMs, we develop an ultrasensitive biosensor of the SARS-CoV-2 Omicron variant. The experiments show great distinguishment of P < 0.0001 from respiratory diseases induced by other viruses, and the limit of detection is 2 orders smaller than the commercial colloidal gold immunoassay. Our study shows the label-free biosensing by low-cost wafer-scale PMs, which will provide essential information on biomolecular interaction and facilitate high-precision point-of-care testing for emerging SARS-CoV-2 variants in the future.

Artificial Intelligence-Assisted Digital Immunoassay Based on a Programmable-Particle-Decoding Technique for Multitarget Ultrasensitive Detection

The development of a multitarget ultrasensitive immunoassay is significant to fields such as medical research, clinical diagnosis, and food safety inspection. In this study, an artificial intelligence (AI)-assisted programmable-particle-decoding technique (APT)-based digital immunoassay system was developed to perform multitarget ultrasensitive detection. Multitarget was encoded by programmable polystyrene (PS) microspheres with different characteristics (particle size and number), and subsequent visible signals were recorded under an optical microscope after the immune reaction. The resultant images were further analyzed using a customized, AI-based computer vision technique to decode the intrinsic properties of polystyrene microspheres and to reveal the types and concentrations of targets. Our strategy has successfully detected multiple inflammatory markers in clinical serum and antibiotics with a broad detection range from pg/mL to μg/mL without extra signal amplification and conversion. An AI-based digital immunoassay system exhibits great potential to be used for the next generation of multitarget detection in disease screening for candidate patients.

An electrochemical paper-based hydrogel immunosensor to monitor serum cytokine for predicting the severity of COVID-19 patients

Analysis of cytokines levels in human serum is critical as it can be a "symptom diagnostic biomarker" in COVID-19, giving real-time information about human health status. Here, we present the construction and performance of a low-price immunosensor (∼US$0.428 per test) based on microfluidic paper-based system to detect cytokine for predicting the health status of COVID-19 patients. Interleukin-6 (IL-6) was selected as the detection model for the close relationship between IL-6 and COVID-19. The assay, which we integrated into foldable paper system, leverages the magnetic immunoassay, the streptavidin-horseradish peroxidase (HRP) associated with tetramethyl benzidine/hydrogen peroxide (TMB/H₂O₂) to amplify the signal for electrochemical readout. To improve the sensitivity of cytokine detection, a hybrid of gold nanoparticles (AuNPs) and polypyrrole (PPy) hydrogel was modified on the working electrode to increase the conductivity and improve the electron transfer rate. With our prototypic origami paper-based immunosensor operated in differential pulse voltammetry (DPV) mode, we achieved excellent results with a dynamic range from 5 to 1000 pg/mL and a lower detection limit (LOD) of 0.654 pg/mL. Furthermore, we evaluated the capability of the clinical application of the proposed immunosensor using human serum samples from a hospital. The results indicate that our proposed immunosensor has great potential in early diagnosing high-risk COVID-19 patients.

Trends of Biosensing: Plasmonics through Miniaturization and Quantum Sensing

Despite being extremely old concepts, plasmonics and surface plasmon resonance-based biosensors have been increasingly popular in the recent two decades due to the growing interest in nanooptics and are now of relevant significance in regards to applications associated with human health. Plasmonics integration into point-of-care devices for health surveillance has enabled significant levels of sensitivity and limit of detection to be achieved and has encouraged the expansion of the fields of study and market niches devoted to the creation of quick and incredibly sensitive label-free detection. The trend reflects in wearable plasmonic sensor development as well as point-of-care applications for widespread applications, demonstrating the potential impact of the new generation of plasmonic biosensors on human well-being through the concepts of personalized medicine and global health. In this context, the aim here is to discuss the potential, limitations, and opportunities for improvement that have arisen as a result of the integration of plasmonics into microsystems and lab-on-chip over the past five years. Recent applications of plasmonic biosensors in microsystems and sensor performance are analyzed. The final analysis focuses on the integration of microfluidics and lab-on-a-chip with quantum plasmonics technology prospecting it as a promising solution for chemical and biological sensing. Here it is underlined how the research in the field of quantum plasmonic sensing for biological applications has flourished over the past decade with the aim to overcome the limits given by quantum fluctuations and noise. The significant advances in nanophotonics, plasmonics and microsystems used to create increasingly effective biosensors would continue to benefit this field if harnessed properly.

Evaluation of SARS-CoV-2-Specific T-Cell Activation with a Rapid On-Chip IGRA

Interferon-gamma release assays (IGRAs) that measure pathogen-specific T-cell response rates can provide a more reliable estimate of protection than specific antibody levels but have limited potential for widespread use due to their workflow, personnel, and instrumentation demands. The major vaccines for SARS-CoV-2 have demonstrated substantial efficacy against all of its current variants, but approaches are needed to determine how these vaccines will perform against future variants, as they arise, to inform vaccine and public health policies. Here we describe a rapid, sensitive, nanolayer polylysine-integrated microfluidic chip IGRA read by a fluorescent microscope that has a 5 h sample-to-answer time and uses ∼25 μL of a fingerstick whole blood sample. Results from this assay correlated with those of a comparable clinical IGRA when used to evaluate the T-cell response to SARS-CoV-2 peptides in a population of vaccinated and/or infected individuals. Notably, this streamlined and inexpensive assay is suitable for high-throughput analyses in resource-limited settings for other infectious diseases.

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.

Noble Metal Nanoparticles for Point-of-Care Testing: Recent Advancements and Social Impacts

Point-of-care (POC) tests for the diagnosis of diseases are critical to the improvement of the standard of living, especially for resource-limited areas or countries. In recent years, nanobiosensors based on noble metal nanoparticles (NM NPs) have emerged as a class of effective and versatile POC testing technology. The unique features of NM NPs ensure great performance of associated POC nanobiosensors. In particular, NM NPs offer various signal transduction principles, such as plasmonics, catalysis, photothermal effect, and so on. Significantly, the detectable signal from NM NPs can be tuned and optimized by controlling the physicochemical parameters (e.g., size, shape, and elemental composition) of NPs. In this article, we introduce the inherent merits of NM NPs that make them attractive for POC testing, discuss recent advancement of NM NPs-based POC tests, highlight their social impacts, and provide perspectives on challenges and opportunities in the field. We hope the review and insights provided in this article can inspire new fundamental and applied research in this emerging field.

Pd@Pt Nanodendrites as Peroxidase Nanomimics for Enhanced Colorimetric ELISA of Cytokines with Femtomolar Sensitivity

Colorimetric enzyme-linked immunosorbent assay (ELISA) has been widely applied as the gold-standard method for cytokine detection over decades. However, it has become a critical challenge to further improve the detection sensitivity of ELISA as limited by the catalytic activity of enzymes. Herein, we report an enhanced colorimetric ELISA for ultrasensitive detection of interleukin-6 (IL-6, as a model cytokine for demonstration) using Pd@Pt core@shell nanodendrites (Pd@Pt NDs) as peroxidase nanomimics (named "Pd@Pt ND ELISA"), pushing the sensitivity up to femtomolar level. Specifically, the Pd@Pt NDs are rationally engineered by depositing Pt atoms on Pd nanocubes (NCs) to generate rough dendrite-like Pt skins on the Pd surfaces via Volmer-Weber growth mode. They can be produced on a large scale with highly uniform size, shape, composition, and structure. They exhibit significantly enhanced peroxidase-like catalytic activity with catalytic constants (K cat ) more than 2000-fold higher than those of horseradish peroxidase (HRP, an enzyme commonly used in ELISA). Using Pd@Pt NDs as the signal labels, the Pd@Pt ND ELISA presents strong colorimetric signals for the quantitative determination of IL-6 with a wide dynamic range of 0.05-100 pg mL-1 and an ultralow detection limit of 0.044 pg mL-1 (1.7 fM). This detection limit is 21-fold lower than that of conventional HRP-based ELISA. The reproducibility and specificity of the Pd@Pt ND ELISA are excellent. More significantly, the Pd@Pt ND ELISA was validated for analyzing IL-6 in human serum samples with high accuracy and reliability through recovery tests. Our results demonstrate that the colorimetric Pd@Pt ND ELISA is a promising biosensing tool for ultrasensitive determination of cytokines and thus is expected to be applied in a variety of clinical diagnoses and fundamental biomedical studies.

Microfluidics Technology in SARS-CoV-2 Diagnosis and Beyond: A Systematic Review

With the progression of the COVID-19 pandemic, new technologies are being implemented for more rapid, scalable, and sensitive diagnostics. The implementation of microfluidic techniques and their amalgamation with different detection techniques has led to innovative diagnostics kits to detect SARS-CoV-2 antibodies, antigens, and nucleic acids. In this review, we explore the different microfluidic-based diagnostics kits and how their amalgamation with the various detection techniques has spearheaded their availability throughout the world. Three other online databases, PubMed, ScienceDirect, and Google Scholar, were referred for articles. One thousand one hundred sixty-four articles were determined with the search algorithm of microfluidics followed by diagnostics and SARS-CoV-2. We found that most of the materials used to produce microfluidics devices were the polymer materials such as PDMS, PMMA, and others. Centrifugal force is the most commonly used fluid manipulation technique, followed by electrochemical pumping, capillary action, and isotachophoresis. The implementation of the detection technique varied. In the case of antibody detection, spectrometer-based detection was most common, followed by fluorescence-based as well as colorimetry-based. In contrast, antigen detection implemented electrochemical-based detection followed by fluorescence-based detection, and spectrometer-based detection were most common. Finally, nucleic acid detection exclusively implements fluorescence-based detection with a few colorimetry-based detections. It has been further observed that the sensitivity and specificity of most devices varied with implementing the detection-based technique alongside the fluid manipulation technique. Most microfluidics devices are simple and incorporate the detection-based system within the device. This simplifies the deployment of such devices in a wide range of environments. They can play a significant role in increasing the rate of infection detection and facilitating better health services.