Interfacial Effect-Based Quantification of Droplet Isothermal Nucleic Acid Amplification for Bacterial Infection

Bacterial infection is a widespread problem in humans that can potentially lead to hospitalization and morbidity. The largest obstacle for physicians/clinicians is the time delay in accurately identifying infectious bacteria, especially their sub-species, in order to adequately treat and diagnose such infected patients. Loop-mediated amplification (LAMP) is a nucleic acid amplification method that has been widely used in diagnostic applications due to its simplicity of constant temperature, use of up to 4 to 6 primers (rendering it highly specific), and capability of amplifying low copies of target sequences. Use of interfacial effect-based monitoring is expected to dramatically shorten the time-to-results of nucleic acid amplification techniques. In this work, we developed a LAMP-based point-of-care platform for detection of bacterial infection, utilizing smartphone measurement of contact angle from oil-immersed droplet LAMP reactions. Whole bacteria (Escherichia coli O157:H7) were assayed in buffer as well as 5% diluted human whole blood. Monitoring of droplet LAMP reactions was demonstrated in a three-compartment, isothermal proportional-integrated-derived (PID)-controlled chip. Smartphone-captured images of droplet LAMP reactions, and their contact angles, were evaluated. Contact angle decreased substantially upon target amplification in both buffer and whole blood samples. In comparison, notarget control (NTC) droplets remained stable throughout the 30 min isothermal reactions. These results were explained by the pre-adsorption of plasma proteins to an oil-water interface (lowering contact angle), followed by time-dependent amplicon formation and their preferential adsorption to the plasma protein-occupied oil-water interface. Time-to-results was as fast as 5 min, allowing physicians to quickly make their decision for infected patients. The developed assay demonstrated quantification of bacteria concentration, with a limit-of-detection at 10(2) CFU/mu L for buffer samples, and binary target or no-target identification with a limit-of-detection at 10 CFU/mu L for 5% diluted whole blood samples.Cardiovascular Biomedical Engineering Training Grant from U.S. National Institutes of Health [T32HL007955]; W. L. Gore & Associates, Inc.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Signals From Out of the Blue (Light Wavelengths): Portable, Low-Cost Camera-Based Optical Chemical/Bio-Sensors Utilizing Fluorescence and Machine Learning Techniques

Optical chemical/bio-sensors have the capacity to rapidly respond to challenges in our society such as oil spills, viral diseases, and medical conditions affected by bacterial imbalance. These powerful sensors often have many advantages including ease of use, low limits of detection, inexpensive implementation, and fast results. Such features are made possible using widely available cameras (such as the Raspberry Pi camera module and smartphone cameras), fluorescent particles or inherent molecular autofluorescence, and machine learning algorithms such as support vector machine (SVM) and neural networks. Four examples of optical chemical/bio-sensor methods will be demonstrated in this work. First, we created a field-ready, Raspberry Pi-powered autofluorescence sensor for analyzing ocean oil samples using SVM, to assist cleanup efforts after a spill. This device successfully classified oil samples as light fuel (F1 score 95.7%), lubricant (F1 score 100%), or heavy fuel (F1 score 85.7%), and achieved 94% accuracy in classifying the level of asphaltene in a sample. Then, when COVID-19 arrived in the US, we developed two different smartphone biosensor methods for SARS-CoV-2 antigen detection. The first uses a custom-built device for quantifying fluorescent particle immunoagglutination from smartphone images to determine if a saline gargle sample is positive or negative for COVID-19. This device achieved a low limit of detection (LOD) of 10 ag/µL for spiked samples and high performance metrics when tested on clinical saline gargle samples, although it requires some skilled handling of the smartphone microscope attachment. The second device simply requires a smartphone video to analyze the flow rate profile of particles moving along a paper microfluidic channel that is pre-loaded with a saline gargle sample, relying on changes in surface tension during flow to determine if the sample contains SARS-CoV-2. This method has somewhat inferior performance compared to the first, with an accuracy of 89% only when turbid samples are excluded; however, an in-depth analysis of turbid samples revealed that following some simple guidelines may improve the performance of this easy-to-run assay. Finally, we designed a custom-built autofluorescence device that uses smartphone images and a convolutional neural network (CNN) to determine whether or not a bacterial sample contains Staphylococcus aureus, as is common in patients with atopic dermatitis or eczema. This novel device and method could distinguish between “healthy” and “dysbiotic” bacterial images with an F1 score of 86%. These projects highlight the adaptability and usefulness of optical chemical/bio-sensors for shedding blue or UV light on the microscopic elements affecting our daily lives.Release after 02/07/202

Smartphone-Based Microalgae Monitoring Platform Using Machine Learning

There is a growing demand for microalgae monitoring techniques since microalgae are one of the most influential underwater organisms in aquatic environments. Specifically, such a technique should be hand-held, rapid, and easily accessible in the field since current methods (benchtop microscopy, flow cytometry, or satellite imaging) require high equipment costs and well-trained personnel. This study’s main objective was to develop a field-deployable microalgae monitoring platform using only a single smartphone and inexpensive acrylic color films. It aimed to evaluate the morphological states of microalgae including stress, cell concentration, and dominant species. Using a smartphone’s white LED flash and camera, the platform detected fluorescence and reflectance intensities from microalgal samples in various excitation and emission color combinations. Multidimensional intensity data were evaluated from the smartphone images and used to train a support vector machine (SVM) based machine learning model to classify various morphological states. The SVM classification accuracies were 0.84-0.96 in classifying four- to five-tier stress types, cell concentration, and dominant species and 0.99-1.00 in classifying two-tier stress types and cell concentrations. Additional field samples were collected from the local pond and independently tested using the laboratory-collected training set, showing two-tier classification accuracies of 0.90-1.00. This platform enables accessible and on-site microalgae monitoring for nonexperts and can be potentially applied to monitoring harmful algal blooms (HABs).University of Arizona12 month embargo; first published 17 August 2023This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Smartphone-Based Microalgae Monitoring Platform Using Machine Learning

There is a growing demand for microalgae monitoring techniques since microalgae are one of the most influential underwater organisms in aquatic environments. Specifically, such a technique should be hand-held, rapid, and easily accessible in the field since current methods (benchtop microscopy, flow cytometry, or satellite imaging) require high equipment costs and well-trained personnel. This study’s main objective was to develop a field-deployable microalgae monitoring platform using only a single smartphone and inexpensive acrylic color films. It aimed to evaluate the morphological states of microalgae including stress, cell concentration, and dominant species. Using a smartphone’s white LED flash and camera, the platform detected fluorescence and reflectance intensities from microalgal samples in various excitation and emission color combinations. Multidimensional intensity data were evaluated from the smartphone images and used to train a support vector machine (SVM) based machine learning model to classify various morphological states. The SVM classification accuracies were 0.84–0.96 in classifying four- to five-tier stress types, cell concentration, and dominant species and 0.99–1.00 in classifying two-tier stress types and cell concentrations. Additional field samples were collected from the local pond and independently tested using the laboratory-collected training set, showing two-tier classification accuracies of 0.90–1.00. This platform enables accessible and on-site microalgae monitoring for nonexperts and can be potentially applied to monitoring harmful algal blooms (HABs)

Handheld UV fluorescence spectrophotometer device for the classification and analysis of petroleum oil samples

Oil spills can be environmentally devastating and result in unintended economic and social consequences. An important element of the concerted effort to respond to spills includes the ability to rapidly classify and characterize oil spill samples, preferably on-site. An easy-to-use, handheld sensor is developed and demonstrated in this work, capable of classifying oil spills rapidly on-site. Our device uses the computational power and affordability of a Raspberry Pi microcontroller and a Pi camera, coupled with three ultraviolet light emitting diodes (UV-LEDs), a diffraction grating, and collimation slit, in order to collect a large data set of UV fluorescence fingerprints from various oil samples. Based on a 160-sample (in 5x replicates each with slightly varied dilutions) database this platform is able to classify oil samples into four broad categories: crude oil, heavy fuel oil, light fuel oil, and lubricating oil. The device uses principal component analysis (PCA) to reduce spectral dimensionality (1203 features) and support vector machine (SVM) for classification with 95% accuracy. The device is also able to predict some physiochemical properties, specifically saturate, aromatic, resin, and asphaltene percentages (SARA) based off linear relationships between different principal components (PCs) and the percentages of these residues. Sample preparation for our device is also straightforward and appropriate for field deployment, requiring little more than a Pasteur pipette and not being affected by dilution factors. These properties make our device a valuable field-deployable tool for oil sample analysis.24 month embargo; published online: 10 April 2020This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

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.24 month embargo; first published 16 January 2023This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Direct capture and smartphone quantification of airborne SARS-CoV-2 on a paper microfluidic chip

SARS, a new type of respiratory disease caused by SARS-CoV, was identified in 2003 with significant levels of morbidity and mortality. The recent pandemic of COVID-19, caused by SARS-CoV-2, has generated even greater extents of morbidity and mortality across the entire world. Both SARS-CoV and SARS-CoV-2 spreads through the air in the form of droplets and potentially smaller droplets (aerosols) via exhaling, coughing, and sneezing. Direct detection from such airborne droplets would be ideal for protecting general public from potential exposure before they infect individuals. However, the number of viruses in such droplets and aerosols is too low to be detected directly. A separate air sampler and enough collection time (several hours) are necessary to capture a sufficient number of viruses. In this work, we have demonstrated the direct capture of the airborne droplets on the paper microfluidic chip without the need for any other equipment. 10% human saliva samples were spiked with the known concentration of SARS-CoV-2 and sprayed to generate liquid droplets and aerosols into the air. Antibody-conjugated submicron particle suspension is then added to the paper channel, and a smartphone-based fluorescence microscope isolated and counted the immunoagglutinated particles on the paper chip. The total capture-to-assay time was <30 min, compared to several hours with the other methods. In this manner, SARS-CoV-2 could be detected directly from the air in a handheld and low-cost manner, contributing to slowing the spread of SARS-CoV-2. We can presumably adapt this technology to a wide range of other respiratory viruses.National Institutes of HealthNo embargo COVID-19This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Smartphone-based sensitive detection of SARS-CoV-2 from saline gargle samples via flow profile analysis on a paper microfluidic chip

Respiratory viruses, especially coronaviruses, have resulted in worldwide pandemics in the past couple of decades. Saliva-based paper microfluidic assays represent an opportunity for noninvasive and rapid screening, yet both the sample matrix and test method come with unique challenges. In this work, we demonstrated the rapid and sensitive detection of SARS-CoV-2 from saliva samples, which could be simpler and more comfortable for patients than existing methods. Furthermore, we systematically investigated the components of saliva samples that affected assay performance. Using only a smartphone, an antibody-conjugated particle suspension, and a paper microfluidic chip, we made the assay user-friendly with minimal processing. Unlike the previously established flow rate assays that depended solely on the flow rate or distance, this unique assay analyzes the flow profile to determine infection status. Particle-target immunoagglutination changed the surface tension and subsequently the capillary flow velocity profile. A smartphone camera automatically measured the flow profile using a Python script, which was not affected by ambient light variations. The limit of detection (LOD) was 1 fg/μL SARS-CoV-2 from 1% saliva samples and 10 fg/μL from simulated saline gargle samples (15% saliva and 0.9% saline). This method was highly specific as demonstrated using influenza A/H1N1. The sample-to-answer assay time was <15 min, including <1-min capillary flow time. The overall accuracy was 89% with relatively clean clinical saline gargle samples. Despite some limitations with turbid clinical samples, this method presents a potential solution for rapid mass testing techniques during any infectious disease outbreak as soon as the antibodies become available.The University of ArizonaNo embargo COVID-19This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]