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.

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.

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.

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.

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.

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors.

Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor's signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data.

Distance versus Capillary Flow Dynamics-Based Detection Methods on a Microfluidic Paper-Based Analytical Device (µPAD).

In recent years, there has been high interest in paper-based microfluidic sensors or microfluidic paper-based analytical devices (µPADs) towards low-cost, portable, and easy-to-use sensing for chemical and biological targets. µPAD allows spontaneous liquid flow without any external or internal pumping, as well as an innate filtration capability. Although both optical (colorimetric and fluorescent) and electrochemical detection have been demonstrated on µPADs, several limitations still remain, such as the need for additional equipment, vulnerability to ambient lighting perturbation, and inferior sensitivity. Herein, alternative detection methods on µPADs are reviewed to resolve these issues, including relatively well studied distance-based measurements and the newer capillary flow dynamics-based method. Detection principles, assay performance, strengths, and weaknesses are explained for these methods, along with their potential future applications towards point-of-care medical diagnostics and other field-based applications.

Simplified White Blood Cell Differential: An Inexpensive, Smartphone- and Paper-Based Blood Cell Count.

Sorting and measuring blood by cell type is extremely valuable clinically and provides physicians with key information for diagnosing many different disease states including: leukemia, autoimmune disorders, bacterial infections, etc. Despite the value, the present methods are unnecessarily costly and inhibitive particularly in resource poor settings, as they require multiple steps of reagent and/or dye additions and subsequent rinsing followed by manual counting using a hemocytometer, or they require a bulky, expensive equipment such as a flow cytometer. While direct on-paper imaging has been considered challenging, paper substrate offers a strong potential to simplify such reagent/dye addition and rinsing. In this work, three-layer paper-based device is developed to automate such reagent/dye addition and rinsing via capillary action, as well as separating white blood cells (WBCs) from whole blood samples. Direct onpaper imaging is demonstrated using a commercial microscope attachment to a smartphone coupled with a blue LED and 500 nm long pass optical filter. Image analysis is accomplished using an original MATLAB code, to evaluate the total WBC count, as well as differential WBC count, i.e., granulocytes (primarily neutrophils) vs. agranulocytes (primarily lymphocytes). Only a finger-prick of whole blood is required for this assay. The total assay time from finger-prick to data collection is under five minutes. Comparison with a hemocytometry-based manual counting corroborates the accuracy and effectiveness of the proposed method. This approach could be potentially used to help make blood cell counting technologies more readily available, especially in resource poor, point-of-care settings.

Flow Rate and Raspberry Pi-based Paper Microfluidic Blood Coagulation Assay Device.

Monitoring blood coagulation in response to an anticoagulant (heparin) and its reversal agent (protamine) is essential during and after surgery, especially with cardiopulmonary bypass (CPB). A current clinical standard is the use of activated clotting time (ACT), where the mechanical movement of a plunger through a whole blood-filled channel is monitored to evaluate the endpoint time of coagulation. As a rapid, simple, low-volume, and cost-effective alternative, we have developed a paper microfluidic assay and Raspberry Pi-based device with the aim of quantifying the extent of blood coagulation in response to varying doses of heparin and protamine. The flow rate of blood through the paper microfluidic channel is automatically monitored using Python-coded edge detection algorithm. For each set of assay, 8 µL of fresh human whole blood (untreated and undiluted) from human subjects is loaded onto each of 8 sample pads, which have been preloaded with varying amounts of heparin or protamine. Total assay time is 3-5 minutes including the time for sample loading and incubation.

Microfluidic Point-of-Care Ecarin-Based Clotting and Chromogenic Assays for Monitoring Direct Thrombin Inhibitors.

Direct thrombin inhibitors (DTIs), such as bivalirudin and dabigatran, have maintained steady inpatient and outpatient use as substitutes for heparin and warfarin, respectively, because of their high bioavailability and relatively safe "on-therapy" range. Current clinical methods lack the capacity to directly quantify plasma DTI concentrations across wide ranges. At present, the gold standard is the ecarin clotting time (ECT), where ecarin maximizes thrombin activity and clotting time is evaluated to assess DTIs' anticoagulation capability. This work focused on the development of a microfluidic paper analytic device (µPAD) that can quantify the extent of anticoagulation as well as DTI concentration within a patient's whole blood sample. Capillary action propels a small blood sample to flow through the nitrocellulose paper channels. Digital images of whole blood migration are then captured by our self-coded Raspberry Pi and/or the Samsung Galaxy S8 smartphone camera. Both the flow length and the blue absorbance from the plasma front on the µPAD were measured, allowing simultaneous, dual assays: ecarin clotting test (ECT) and ecarin chromogenic assay (ECA). Statistically significant (p < .05) changes in flow and absorbance were observed within our translational research study. Currently, there are no quantitative, commercially available point-of-care tests for the ECT and ECA within the United States. Both the ECT and ECA assays could be instrumental to differentiate between supratherapeutic and subtherapeutic incidents during bridging anticoagulant therapy and limit the unwarranted use of reversal agents.

A Capillary Flow Dynamics-Based Sensing Modality for Direct Environmental Pathogen Monitoring.

Toward ultra-simple and field-ready biosensors, we demonstrate a novel assay transducer mechanism based on interfacial property changes and capillary flow dynamics in antibody-conjugated submicron particle suspensions. Differential capillary flow is tunable, allowing pathogen quantification as a function of flow rate through a paper-based microfluidic device. Flow models based on interfacial and rheological properties indicate a significant relationship between the flow rate and the interfacial effects caused by target-particle aggregation. This mechanism is demonstrated for assays of Escherichia coli K12 in water samples and Zika virus (ZIKV) in blood serum. These assays achieved very low limits of detection compared with other demonstrated methods (1 log CFU/mL E. coli and 20 pg/mL ZIKV whole virus) with an operating time of 30 s, showing promise for environmental and health monitoring.

Angular photodiode array-based device to detect bacterial pathogens in a wound model.

We have developed a device that is able to rapidly and specifically diagnose bacterial pathogens in a wound model based on Mie scatter spectra from a tissue surface. The Mie scatter spectra collected is defined as the intensity of Mie scatter over the angle of detection from a tissue surface. A 650 nm LED perpendicular to the surface illuminates a tissue sample (90°) and photodiodes positioned in 10° increments from 10° to 80° of backscatter act as the detectors to collect these Mie scatter spectra. Through principal component analysis of the Mie scatter spectra collected, we have shown significant differences between Mie scatter spectra of tissues with bacterial pathogens versus those without, as well as significant differences between each species of bacteria tested. The device developed has been tested with a porcine dermis wound model, with samples inoculated with one of three bacterial species (Staphylococcus aureus, Escherichia coli, or Salmonella Typhimurium). Such a device could be critical in the monitoring of a wound for infection and rapid, specific diagnosis of a bacterial wound infection, which would significantly reduce the time and cost associated with specific diagnosis of a bacterial wound infection currently.

Sensors for blood brain barrier on a chip.

The blood-brain barrier (BBB) is a highly selective membrane that regulates the passage of substances between the bloodstream and the brain, thus safeguarding the central nervous system. This chapter provides an overview of current experimental models and detection methods utilized to study the BBB, along with the implementation of sensors and biosensors in BBB research. We discuss static and dynamic BBB models, highlighting their respective advantages and limitations. Additionally, we examine various detection methods employed in BBB research, including those specific to static and dynamic models. Furthermore, we explore the applications of physical sensors and biosensors in BBB models, focusing on their roles in monitoring barrier integrity and function. We also discuss recent advancements in sensor integration, such as robotic interrogators and integrated electrochemical and optical biosensors. Finally, we present a brief conclusion and future outlook, emphasizing the importance of continued innovation in BBB research to advance our understanding of neurological disorders and drug development.

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.

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.

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.

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.

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.

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.

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.