Development of a cloud-based flow rate tool for eNAMPT biomarker detection.

Increased levels of extracellular nicotinamide phosphoribosyltransferase (eNAMPT) are increasingly recognized as a highly useful biomarker of inflammatory disease and disease severity. In preclinical animal studies, a monoclonal antibody that neutralizes eNAMPT has been generated to successfully reduce the extent of inflammatory cascade activation. Thus, the rapid detection of eNAMPT concentration in plasma samples at the point of care (POC) would be of great utility in assessing the benefit of administering an anti-eNAMPT therapeutic. To determine the feasibility of this POC test, we conducted a particle immunoagglutination assay on a paper microfluidic platform and quantified its extent with a flow rate measurement in less than 1 min. A smartphone and cloud-based Google Colab were used to analyze the flow rates automatically. A horizontal flow model and an immunoagglutination binding model were evaluated to optimize the detection time, sample dilution, and particle concentration. This assay successfully detected eNAMPT in both human whole blood and plasma samples (diluted to 10 and 1%), with the limit of detection of 1-20 pg/mL (equivalent to 0.1-0.2 ng/mL in undiluted blood and plasma) and a linear range of 5-40 pg/mL. Furthermore, the smartphone POC assay distinguished clinical samples with low, mid, and high eNAMPT concentrations. Together, these results indicate this POC assay, which utilizes low-cost materials, time-effective methods, and a straightforward immunoassay (without surface immobilization), may reliably allow rapid determination of eNAMPT blood/plasma levels to advantage patient stratification in clinical trials and guide ALT-100 mAb therapeutic decision-making.

Recent Uses of Paper Microfluidics in Isothermal Nucleic Acid Amplification Tests.

Isothermal nucleic acid amplification tests have recently gained popularity over polymerase chain reaction (PCR), as they only require a constant temperature and significantly simplify nucleic acid amplification. Recently, numerous attempts have been made to incorporate paper microfluidics into these isothermal amplification tests. Paper microfluidics (including lateral flow strips) have been used to extract nucleic acids, amplify the target gene, and detect amplified products, all toward automating the process. We investigated the literature from 2020 to the present, i.e., since the onset of the COVID-19 pandemic, during which a significant surge in isothermal amplification tests has been observed. Paper microfluidic detection has been used extensively for recombinase polymerase amplification (RPA) and its related methods, along with loop-mediated isothermal amplification (LAMP) and rolling circle amplification (RCA). Detection was conducted primarily with colorimetric and fluorometric methods, although a few publications demonstrated flow distance- and surface-enhanced Raman spectroscopic (SERS)-based detection. A good number of publications could be found that demonstrated both amplification and detection on paper microfluidic platforms. A small number of publications could be found that showed extraction or all three procedures (i.e., fully integrated systems) on paper microfluidic platforms, necessitating the need for future work.

Microparticle-Based Detection of Viruses.

Surveillance of viral pathogens in both point-of-care and clinical settings is imperative to preventing the widespread propagation of disease-undetected viral outbreaks can pose dire health risks on a large scale. Thus, portable, accessible, and reliable biosensors are necessary for proactive measures. Polymeric microparticles have recently gained popularity for their size, surface area, and versatility, which make them ideal biosensing tools. This review cataloged recent investigations on polymeric microparticle-based detection platforms across eight virus families. These microparticles were used as labels for detection (often with fluorescent microparticles) and for capturing viruses for isolation or purification (often with magnetic microparticles). We also categorized all methods by the characteristics, materials, conjugated receptors, and size of microparticles. Current approaches were compared, addressing strengths and weaknesses in the context of virus detection. In-depth analyses were conducted for each virus family, categorizing whether the polymeric microparticles were used as labels, for capturing, or both. We also summarized the types of receptors conjugated to polymeric microparticles for each virus family.

Rapid and sensitive detection of miRNA via light scatter-aided emulsion-based isothermal amplification using a custom low-cost device.

MicroRNAs are likely to be a next-generation clinical biomarker for many diseases. While gold-standard technologies, e.g., reverse transcription-quantitative polymerase chain reaction (RT-qPCR), exist for microRNA detection, there is a need for rapid and low-cost testing. Here, an emulsion loop-mediated isothermal amplification (eLAMP) assay was developed for miRNA that compartmentalizes a LAMP reaction and shortens the time-to-detection. The miRNA was a primer to facilitate the overall amplification rate of template DNA. Light scatter intensity decreased when the emulsion droplet got smaller during the ongoing amplification, which was utilized to moitor the amplification non-invasively. A custom low-cost device was designed and fabricated using a computer cooling fan, a Peltier heater, an LED, a photoresistor, and a temperature controller. It allowed more stable vortexing and accurate light scatter detection. Three miRNAs, miR-21, miR-16, and miR-192, were successfully detected using the custom device. Specifically, new template and primer sequences were developed for miR-16 and miR-192. Zeta potential measurements and microscopic observations confirmed emulsion size reduction and amplicon adsorption. The detection limit was 0.01 fM, corresponding to 2.4 copies per reaction, and the detection could be made in 5 min. Since the assays were rapid and both template and miRNA + template could eventually be amplified, we introduced the success rate (compared to the 95% confidence interval of the template result) as a new measure, which worked well with lower concentrations and inefficient amplifications. This assay brings us one step closer to allowing circulating miRNA biomarker detection to become commonplace in the clinical world.

A portable immunosensor provides sensitive and rapid detection of Borrelia burgdorferi antigen in spiked blood.

There are no assays for detecting B. burgdorferi antigen in blood of infected Lyme disease individuals. Here, we provide proof-of-principle evidence that we can quantify B. burgdorferi antigen in spiked blood using a portable smartphone-based fluorescence microscope that measures immunoagglutination on a paper microfluidic chip. We targeted B. burgdorferi OspA to develop a working prototype and added examples of two antigens (OspC and VlsE) that have diagnostic value for discrimination of Lyme disease stage. Using an extensively validated monoclonal antibody to OspA (LA-2), detection of OspA antigen had a broad linear range up to 100 pg/mL in 1% blood and the limit of detection (LOD) was 100 fg/mL (= 10 pg/mL in undiluted blood), which was 1000 times lower than our target of 10 ng/mL. Analysis of the two other targets was done using polyclonal and monoclonal antibodies. OspC antigen was detected at LOD 100 pg/mL (= 10 ng/mL of undiluted blood) and VlsE antigen was detected at LOD 1-10 pg/mL (= 0.1-1 ng/mL of undiluted blood). The method is accurate and was performed in 20 min from sample to answer. When optimized for detecting several B. burgdorferi antigens, this assay may differentiate active from past infections and facilitate diagnosis of Lyme disease in the initial weeks of infection, when antibody presence is typically below the threshold to be detected by serologic methods.

Receptor-based detection of microplastics and nanoplastics: Current and future.

Plastic pollution is an emerging environmental concern, gaining significant attention worldwide. They are classified into microplastics (MP; defined from 1 µm to 5 mm) and smaller nanoplastics (NP; <1 µm). NPs may pose higher ecological risks than MPs. Various microscopic and spectroscopic techniques have been used to detect MPs, and the same methods have occasionally been used for NPs. However, they are not based on receptors, which provide high specificity in most biosensing applications. Receptor-based micro/nanoplastics (MNP) detection can provide high specificity, distinguishing MNPs from the environmental samples and, more importantly, identifying the plastic types. It can also offer a low limit of detection (LOD) required for environmental screening. Such receptors are expected to detect NPs specifically at the molecular level. This review categorizes the receptors into cells, proteins, peptides, fluorescent dyes, polymers, and micro/nanostructures. Detection techniques used with these receptors are also summarized and categorized. There is plenty of room for future research to test for broader classes of environmental samples and many plastic types, to lower the LOD, and to apply the current techniques for NPs. Portable and handheld MNP detection should also be demonstrated for field use since the current demonstrations primarily utilized laboratory instruments. Detection on microfluidic platforms will also be crucial in miniaturizing and automating the assay and, eventually, collecting an extensive database to support machine learning-based classification of MNP types.

Loop-Mediated Isothermal Amplification on Paper Microfluidic Chips for Highly Sensitive and Specific Zika Virus Detection Using Smartphone.

Zika virus (ZIKV) infection may cause serious birth defects and is a critical concern for women of child-bearing age in affected regions. A simple, portable, and easy-to-use ZIKV detection method would enable point-of-care testing, which may aid in prevention of the spread of the virus. Herein, we describe a reverse transcription isothermal loop-mediated amplification (RT-LAMP) method that detects the presence of ZIKV RNA in complex samples (e.g., blood, urine, and tap water). Phenol red is the colorimetric indicator of successful amplification. Color changes based on the amplified RT-LAMP product from the presence of viral target are monitored using a smartphone camera under ambient light conditions. A single viral RNA molecule per µL can be detected in as quickly as 15 min using this method with 100% sensitivity and 100% specificity in blood and tap water, while 100% sensitivity and 67% specificity in urine. This platform can also be used to identify other viruses including SARS-CoV-2 and improve the current state of field-based diagnostics.

Sensitive SARS-CoV-2 salivary antibody assays for clinical saline gargle samples using smartphone-based competitive particle immunoassay platforms.

Antibody assay for SARS-CoV-2 has become increasingly important to track latent and asymptomatic infections, check the individual's immune status, and confirm vaccine efficacy and durability. However, current SARS-CoV-2 antibody assays require invasive blood collection, requiring a remote laboratory and a trained phlebotomist. Direct detection of SARS-CoV-2 antibodies from clinical saline gargle samples has been considered challenging due to the smaller number of antibodies in such specimens and the high limit of detection of currently available rapid tests. This work demonstrates simple and non-invasive methods for detecting SARS-CoV-2 salivary antibodies. Competitive particle immunoassays were developed on a paper microfluidic chip using the receptor-binding domain (RBD) antigens on spike proteins. Using a smartphone, they were monitored by counting the captured fluorescent particles or evaluating the capillary flow velocities. The limit of detection (LOD), cross-binding between alpha- and omicron-strains, and the effect of angiotensin-converting enzyme 2 (ACE2) presence were investigated. LODs were 1-5 ng/mL in both 10% and 1% saliva. Clinical saline gargle samples were assayed using both methods, showing a statistical difference between virus-negative and virus-positive samples, although the assays targeted antibodies. Only a small number of virus-positive samples were antibody-negative. The high assay sensitivity detected a small number of antibodies developed even during the early phase of infections. Overall, this work demonstrates the ability to detect SARS-CoV-2 salivary IgG antibodies on simple, cost-effective, portable platforms towards mitigating SARS-CoV-2 and potentially other respiratory viruses.

eXtreme gradient boosting-based classification of bacterial mixtures in water and milk using wireless microscopic imaging of quorum sensing peptide-conjugated particles.

Numerous bacteria can cause water- and foodborne diseases and are often found in bacterial mixtures, making their detection challenging. Specific bioreceptors or selective growth media are necessary for most bacterial detection methods. In this work, we collectively used five quorum sensing-based peptides identified from bacterial biofilms to identify 10 different bacterial species (Bacillus subtilis, Campylobacter jejuni, Enterococcus faecium, Escherichia coli, Legionella pneumophila, Listeria monocytogenes, Pseudomonas aeruginosa, Salmonella Typhimurium, Staphylococcus aureus, Vibrio parahaemolyticus) and their mixtures in water and milk. Four different machine learning classification methods were used: k-nearest neighbors (k-NN), decision tree (DT), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost). Peptides were crosslinked to submicron particles, and peptide-bacteria interactions on paper microfluidic chips caused the particle aggregation. A wireless, pocket fluorescence microscope (interfaced with a smartphone) counted such particle aggregations. XGBoost showed the best accuracy of 83.75% in identifying bacterial species from water samples using 320 different datasets and 91.67% from milk samples using 140 different datasets (5 peptide features per dataset). Each peptide's contribution to correct classification was evaluated. The results were concentration-dependent, allowing the identification of a dominant species from bacterial mixtures. Using XGBoost and the previous milk database, we tested 14 blind samples of various bacterial mixtures in milk samples, with an accuracy of 81.55% to predict the dominant species. The entire process could be completed within a half hour. The demonstrated system can provide a handheld, low-cost, easy-to-operate tool for potential hygiene spot-checks, public health, or personal healthcare.

Capillary flow velocity profile analysis on paper-based microfluidic chips for screening oil types using machine learning.

We conceived a novel approach to screen oil types on a wax-printed paper-based microfluidic platform. Various oil samples spontaneously flowed through a micrometer-scale channel via capillary action while their components were filtered and partitioned. The resulting capillary flow velocity profile fluctuated during the flow, which was used to screen oil types. Raspberry Pi camera captured the video clips, and a custom Python code analyzed them to obtain the capillary flow velocity profiles. 106 velocity profiles (each with 125 frames for 5 s) were recorded from various oil samples to build a training database. Principal component analysis (PCA), support vector machine (SVM), and linear discriminant analysis (LDA) were used to classify the oil types into heavy-to-medium crude, light crude, marine fuel, lubricant, and diesel oils. The second-order polynomial SVM model with PCA as a pre-processing step showed the highest accuracy: 90% in classifying crude oils and 81% in classifying non-crude oils. The assay took less than 30 s from the sample to answer, with 5 s of the capillary action-driven flow. This simple and effective assay will allow rapid preliminary screening of oil types, enable early tracking, and reduce the number of suspect samples to be analyzed by laboratory fingerprinting analysis.

Rapid, sensitive detection of PFOA with smartphone-based flow rate analysis utilizing competitive molecular interactions during capillary action.

Perfluorinated-alkyl substances (PFAS) pose an unmet threat to the public because they are not strictly monitored and regulated. Perfluorinated-carbon alkyl chains (PFOA), a type of PFAS, at 70 fg/µL is the current health and safety recommendation. Current testing methods for PFOA and PFAS chemicals include HPLC-MS/MS and molecularly imprinted polymers, which are expensive, time-consuming, and require training. In this work, PFOA and PFOS detection was performed on a paper microfluidic chip using competitive interactions between PFOA/PFOS, cellulose fibers, and various reagents (L-lysine, casein, and albumin). Such interactions altered the surface tension at the wetting front and, subsequently, the capillary flow rate. A smartphone captured the videos of this capillary action. The samples flowed through the channel in less than 2 min. Albumin worked the best in detecting PFOA, followed by casein. The detection limit was 10 ag/µL in DI water and 1 fg/µL in effluent (processed) wastewater. Specificity to other non-fluorocarbon surfactants was also tested, using anionic sodium dodecyl sulfate (SDS), non-ionic Tween 20, and cationic cetrimonium bromide (CTAB). A combination of the reagents successfully distinguished PFOA from all three surfactants at 100% accuracy. This low-cost, handheld assay can be an accessible alternative for rapid in situ estimation of PFOA concentration.

Machine Learning-Based Quantification of (-)-trans-Δ-Tetrahydrocannabinol from Human Saliva Samples on a Smartphone-Based Paper Microfluidic Platform.

(-)-trans-Δ-Tetrahydrocannabinol (THC) is a major psychoactive component in cannabis. Despite the recent trends of THC legalization for medical or recreational use in some areas, many THC-driven impairments have been verified. Therefore, convenient, sensitive, quantitative detection of THC is highly needed to improve its regulation and legalization. We demonstrated a biosensor platform to detect and quantify THC with a paper microfluidic chip and a handheld smartphone-based fluorescence microscope. Microfluidic competitive immunoassay was applied with anti-THC-conjugated fluorescent nanoparticles. The smartphone-based fluorescence microscope counted the fluorescent nanoparticles in the test zone, achieving a 1 pg/mL limit of detection from human saliva samples. Specificity experiments were conducted with cannabidiol (CBD) and various mixtures of THC and CBD. No cross-reactivity to CBD was found. Machine learning techniques were also used to quantify the THC concentrations from multiple saliva samples. Multidimensional data were collected by diluting the saliva samples with saline at four different dilutions. A training database was established to estimate the THC concentration from multiple saliva samples, eliminating the sample-to-sample variations. The classification algorithms included k-nearest neighbor (k-NN), decision tree, and support vector machine (SVM), and the SVM showed the best accuracy of 88% in estimating six different THC concentrations. Additional validation experiments were conducted using independent validation sample sets, successfully identifying positive samples at 100% accuracy and quantifying the THC concentration at 80% accuracy. The platform provided a quick, low-cost, sensitive, and quantitative point-of-care saliva test for cannabis.

COVID-19 variants' cross-reactivity on the paper microfluidic particle counting immunoassay.

SARS-CoV-2 has mutated many times since the onset of the COVID-19 pandemic, and the omicron is currently the most dominant variant. Determining the specific strain of the virus is beneficial in providing proper care and containment of the disease. We have previously reported a novel method of counting the number of particle immunoagglutination on a paper microfluidic chip using a smartphone-based fluorescence microscope. A single-copy-level detection was demonstrated from clinical saline gargle samples. In this work, we further evaluated two different SARS-CoV-2 monoclonal antibodies to spike vs. nucleocapsid antigens for detecting omicron vs. delta and spike vs. nucleocapsid proteins. The SARS-CoV-2 monoclonal antibody to nucleocapsid proteins could distinguish omicron from delta variants and nucleocapsid from spike proteins. However, such distinction could not be found with the monoclonal antibody to spike proteins, despite the numerous mutations found in spike proteins among variants. This result may suggest a clue to the role of nucleocapsid proteins in recognizing different variants.

Smartphone-based paper microfluidic competitive immunoassay for the detection of α-amanitin from mushrooms.

α-Amanitin is often considered the most poisonous mushroom toxin produced by various mushroom species, which are hard to identify from edible, non-toxic mushrooms. Conventional detection methods require expensive and bulky equipment or fail to meet high analytical sensitivity. We developed a smartphone-based fluorescence microscope platform to detect α-amanitin from dry mushroom tissues. Antibody-nanoparticle conjugates were captured by immobilized antigen-hapten conjugates while competing with the free analytes in the sample. Captured fluorescent nanoparticles were excited at 460 nm and imaged at 500 nm. The pixel numbers of such nanoparticles in the test zone were counted, showing a decreasing trend with increasing analyte concentration. The detection method exhibited a low detection limit (1 pg/mL), high specificity, and selectivity, allowing us to utilize a simple rinsing for toxin extraction and avoiding the need for high-speed centrifugation. In addition, this assay's short response time and portable features enable field detection of α-amanitin from amanitin-producing mushrooms.

Progression of LAMP as a Result of the COVID-19 Pandemic: Is PCR Finally Rivaled?

Reflecting on the past three years and the coronavirus disease 19 (COVID-19) pandemic, varying global tactics offer insights into the most effective public-health responses. In the US, specifically, rapid and widespread testing was quickly prioritized to lower restrictions sooner. Essentially, only two types of COVID-19 diagnostic tests were publicly employed during the peak pandemic: the rapid antigen test and reverse transcription polymerase chain reaction (RT-PCR). However, neither test ideally suited the situation, as rapid antigen tests are far too inaccurate, and RT-PCR tests require skilled personnel and sophisticated equipment, leading to long wait times. Loop-mediated isothermal amplification (LAMP) is another exceptionally accurate nucleic acid amplification test (NAAT) that offers far quicker time to results. However, RT-LAMP COVID-19 tests have not been embraced as extensively as rapid antigen tests or RT-PCR. This review will investigate the performance of current RT-LAMP-based COVID-19 tests and summarize the reasons behind the hesitancy to embrace RT-LAMP instead of RT-PCR. We will also look at other LAMP platforms to explore possible improvements in the accuracy and portability of LAMP, which could be applied to COVID-19 diagnostics and future public-health outbreaks.

Smartphone-based autofluorescence imaging to detect bacterial species on laboratory surfaces.

The potential of bacterial contamination is commonly seen in biological and clinical laboratory surfaces, creating a need to detect the presence of bacteria on a surface. Various bacterial species have been found to naturally exist on surfaces, including Escherichia coli, Salmonella Typhimurium, and Staphylococcus aureus that were investigated in this study. Bacterial presence was identified from laboratory surfaces using a smartphone and low-cost components without culturing or staining. Autofluorescence from bacteria was quantified using a 405 nm LED as an excitation light source. A low-cost acrylic film could isolate the autofluorescence emission. ImageJ was used to process and analyze the images and quantify the emitted autofluorescence signal. This imaging platform successfully detected the presence of all three bacterial species from the heavily used laboratory surfaces. A trend of decreasing fluorescence signal was observed with decreasing bacterial concentration, and the limit of detection was 104 CFU cm-2. It could also distinguish from tap water, protein (bovine serum albumin), and NaCl solutions. This preliminary work emphasizes the ability to detect autofluorescence signals of bacteria and non-microbial surface contaminants using a cost-effective and straightforward imaging platform.

Bioanalytical approaches for the detection, characterization, and risk assessment of micro/nanoplastics in agriculture and food systems.

This review discusses the most recent literature (mostly since 2019) on the presence and impact of microplastics (MPs, particle size of 1 µm to 5 mm) and nanoplastics (NPs, particle size of 1 to 1000 nm) throughout the agricultural and food supply chain, focusing on the methods and technologies for the detection and characterization of these materials at key entry points. Methods for the detection of M/NPs include electron and atomic force microscopy, vibrational spectroscopy (FTIR and Raman), hyperspectral (bright field and dark field) and fluorescence imaging, and pyrolysis-gas chromatography coupled to mass spectrometry. Microfluidic biosensors and risk assessment assays of MP/NP for in vitro, in vivo, and in silico models have also been used. Advantages and limitations of each method or approach in specific application scenarios are discussed to highlight the scientific and technological obstacles to be overcome in future research. Although progress in recent years has increased our understanding of the mechanisms and the extent to which MP/NP affects health and the environment, many challenges remain largely due to the lack of standardized and reliable detection and characterization methods. Most of the methods available today are low-throughput, which limits their practical application to food and agricultural samples. Development of rapid and high-throughput field-deployable methods for onsite screening of MP/NPs is therefore a high priority. Based on the current literature, we conclude that detecting the presence and understanding the impact of MP/NP throughout the agricultural and food supply chain require the development of novel deployable analytical methods and sensors, the combination of high-precision lab analysis with rapid onsite screening, and a data hub(s) that hosts and curates data for future analysis.

Sensitive, smartphone-based SARS-CoV-2 detection from clinical saline gargle samples.

Saliva specimens have drawn interest for diagnosing respiratory viral infections due to their ease of collection and decreased risk to healthcare providers. However, rapid and sensitive immunoassays have not yet been satisfactorily demonstrated for such specimens due to their viscosity and low viral loads. Using paper microfluidic chips and a smartphone-based fluorescence microscope, we developed a highly sensitive, low-cost immunofluorescence particulometric SARS-CoV-2 assay from clinical saline gargle samples. We demonstrated the limit of detection of 10 ag/µL. With easy-to-collect saline gargle samples, our clinical sensitivity, specificity, and accuracy were 100%, 86%, and 93%, respectively, for n = 27 human subjects with n = 13 RT-qPCR positives.

Machine learning classification of bacterial species using mix-and-match reagents on paper microfluidic chips and smartphone-based capillary flow analysis.

Traditionally, specific bioreceptors such as antibodies have rapidly identified bacterial species in environmental water samples. However, this method has the disadvantages of requiring an additional process to conjugate or immobilize bioreceptors on the assay platform, which becomes unstable at room temperature. Here, we demonstrate a novel mix-and-match method to identify bacteria species by loading the bacterial samples with simple bacteria interacting components (not bioreceptors), such as lipopolysaccharides, peptidoglycan, and bovine serum albumin, and carboxylated particles, all separately on multiple channels. Neither covalent conjugation nor surface immobilization was necessary. Interactions between bacteria and the above bacteria interacting components resulted in varied surface tension and viscosity, leading to various flow velocities of capillary action through the paper fibers. The smartphone camera and a custom Python code recorded multiple channel flow velocity, each loaded with different bacteria interacting components. A multi-dimensional data set was obtained for a given bacterial species and concentration and used as a machine learning training model. A support vector machine was applied to classify the six bacterial species: Escherichia coli, Salmonella Typhimurium, Pseudomonas aeruginosa, Staphylococcus aureus, Enterococcus faecium, and Bacillus subtilis. Under optimized conditions, the training model predicts the bacterial species with an accuracy of > 85% of the six bacteria species.