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

Machine learning classification of quorum sensing-induced bacterial aggregation using flow rate assays on paper chips toward bacterial species identification in potable water sources

Preventing waterborne disease caused by bacteria is especially important in low-resource settings, where skilled personnel and laboratory equipment are scarce. This work reports a straightforward method for classifying bacterial species by monitoring the capillary flow rates on a multi-channel paper microfluidic chip, where quorum sensing (QS)-induced bacterial aggregation leads to measurable changes in flow rates, enabling species differentiation. It required no fluorescent molecules, microscope, particles, covalent conjugation, or surface immobilization. Five representative QS molecules and control were added to each bacterial sample, and their different extents of bacterial aggregation resulted in varied flow rates. Flow rates were collected for the duration of the flow to build the learning database, and the XGBoost machine learning algorithm predicted the accuracy for classifying ten bacterial species, including 7 gram-negative and 3 gram-positive species. Three different algorithms were developed for high, medium, and low bacterial concentration ranges, and the classification accuracies of all the algorithms exceeded 75.0 %. Using XGBoost and the previously established database, we tested bacteria in the field water samples and successfully predicted the dominant species. The technology developed in this study, using only QS molecules and a paper microfluidic chip, offers a simple system for detecting microorganisms in drinking water to help prevent waterborne diseases.

Capillary flow velocity-based length identification of PCR and RPA products on paper microfluidic chips

This work demonstrates a novel, non-fluorescence approach to the length identification of polymerase chain reaction (PCR) and recombinase polymerase amplification (RPA) products, utilizing capillary flow velocities on paper microfluidic chips. It required only a blank paper chip, aminated microspheres, and a smartphone, with a rapid assay time and under ambient lighting. A smartphone evaluated the initial capillary flow velocities on the paper chips for the PCR and RPA products from various bacterial samples, where the pre-loaded aminated microspheres differentiated their flow velocities. Flow velocities were analyzed at different time frames and compared with the instantaneous flow velocities and interfacial tension (γLV) data. Subsequent error analysis justified the use of the initial time frames. A robust linear relationship could be established between the initial flow velocities against the square root of the product lengths, with R2 values of 0.981 for PCR and 0.993 for RPA. The assay seemed not to have a significant dependency on the cycle numbers and initial target concentrations. This novel method can be potentially used with various paper microfluidic methods of nucleic acid amplification tests towards rapid and handheld assays.

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.

Microfluidics in environmental analysis: advancements, challenges, and future prospects for rapid and efficient monitoring

Microfluidic devices have emerged as advantageous tools for detecting environmental contaminants due to their portability, ease of use, cost-effectiveness, and rapid response capabilities. These devices have wide-ranging applications in environmental monitoring of air, water, and soil matrices, and have also been applied to agricultural monitoring. Although several previous reviews have explored microfluidic devices' utility, this paper presents an up-to-date account of the latest advancements in this field for environmental monitoring, looking back at the past five years. In this review, we discuss devices for prominent contaminants such as heavy metals, pesticides, nutrients, microorganisms, per- and polyfluoroalkyl substances (PFAS), etc. We cover numerous detection methods (electrochemical, colorimetric, fluorescent, etc.) and critically assess the current state of microfluidic devices for environmental monitoring, highlighting both their successes and limitations. Moreover, we propose potential strategies to mitigate these limitations and offer valuable insights into future research and development directions.

Clarity improvement of the discoloration boundary and detection of Hg2+ ions by using a polystyrene nanoparticle-modified paper-based microdevice

Distance-based microfluidic paper-based analytical devices (μPADs) can be used to calculate the analyte content by reading the length of the discolored area in the channel. A blurred discoloration boundary is difficult to distinguish, resulting in reading errors. In this study, we constructed a μPAD modified with carboxyl-containing polystyrene nanoparticles (PS-μPAD) to improve the discoloration-boundary clarity. The filling of the pores of the fibers with the deposited polystyrene nanoparticles (PS NPs) caused a decrease in the paper porosity, resulting in a flow delay. Meanwhile, the carboxyl groups carried by PS NPs were able to form hydrogen bonds with hydroxyl-containing compounds FLPI, a Hg²⁺ probe, and the two factors acted synergistically to fix the FLPI to react in situ, raising the discoloration-boundary clarity. Compared with the unmodified μPAD, the detection of Hg²⁺ ions using the PS-μPAD still had a good linear relationship. Importantly, the color-depth difference inside and outside the discoloration boundary improved by about four times and showed excellent reproducibility in different populations. The method was simple and easy to expand, thereby providing an idea for more widespread application of distance-based μPADs.

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.

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.

Paper-based sensors for bacteria detection

The detection of pathogenic bacteria is essential to prevent and treat infections and to provide food security. Current gold-standard detection techniques, such as culture-based assays and polymerase chain reaction, are time-consuming and require centralized laboratories. Therefore, efforts have focused on developing point-of-care devices that are fast, cheap, portable and do not require specialized training. Paper-based analytical devices meet these criteria and are particularly suitable to deployment in low-resource settings. In this Review, we highlight paper-based analytical devices with substantial point-of-care applicability for bacteria detection and discuss challenges and opportunities for future development.

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.

Natural killer cell detection, quantification, and subpopulation identification on paper microfluidic cell chromatography using smartphone-based machine learning classification

Natural killer (NK) cells are immune cells that defend against viral infections and cancer and are used in cancer immunotherapies. Subpopulations of NK cells include CD56^dim and CD56^bright which either produce cytokines or cytotoxically kill cells directly. The absolute number and proportion of these cells in peripheral blood are tied to proper immune function. Current methods of cytokine detection and proportion of NK cell subpopulations require fluorescent dyes and highly specialized equipment, e.g., flow cytometry, thus rapid cell quantification and subpopulation analysis are needed in the clinical setting. Here, a smartphone-based device and a two-component paper microfluidic chip were used towards identifying NK cell subpopulation and inflammatory markers. One unit measured flow velocity via smartphone-captured video, determining cytokine (IL-2) and total NK cell concentrations in undiluted buffy coat blood samples. The other, single flow lane unit performs spatial separation of CD56^dim and CD56^bright and cells over its length using differential binding of anti-CD56 nanoparticles. A smartphone microscope combined with cloud-based machine learning predictive modeling (utilizing a random forest classification algorithm) analyzed both flow data and NK cell subpopulation differentiation. Limits of detection for cytokine and cell concentrations were 98 IU/mL and 68 cells/mL, respectively, and cell subpopulation analysis showed 89% accuracy.

Wastewater-Based Epidemiology as an Early Warning System for the Spreading of SARS-CoV-2 and Its Mutations in the Population

New methodologies based on the principle of "sewage epidemiology" have been successfully applied before in the detection of illegal drugs. The study describes the idea of early detection of a virus, e.g., SARS-CoV-2, in wastewater in order to focus on the area of virus occurrence and supplement the results obtained from clinical examination. By monitoring temporal variation in viral loads in wastewater in combination with other analysis, a virus outbreak can be detected and its spread can be suppressed early. The use of biosensors for virus detection also seems to be an interesting application. Biosensors are highly sensitive, selective, and portable and offer a way for fast analysis. This manuscript provides an overview of the current situation in the area of wastewater analysis, including genetic sequencing regarding viral detection and the technological solution of an early warning system for wastewater monitoring based on biosensors.

Human sensor-inspired supervised machine learning of smartphone-based paper microfluidic analysis for bacterial species classification

Bacteria identification has predominantly been conducted using specific bioreceptors such as antibodies or nucleic acid sequences. This approach may be inappropriate for environmental monitoring when the user does not know the target bacterial species and for screening complex water samples with many unknown bacterial species. In this work, we investigate the supervised machine learning of the bacteria-particle aggregation pattern induced by the peptide sets identified from the biofilm-bacteria interface. Each peptide is covalently conjugated to polystyrene particles and loaded together with bacterial suspensions onto paper microfluidic chips. Each peptide interacts with bacterial species to a different extent, leading to varying sizes of particle aggregation. This aggregation changes the surface tension and viscosity of the liquid flowing through the paper pores, altering the flow velocity at different extents. A smartphone camera captures this flow velocity without being affected by ambient and environmental conditions, towards a low-cost, rapid, and field-ready assay. A collection of such flow velocity data generates a unique fingerprinting profile for each bacterial species. Support vector machine is utilized to classify the species. At optimized conditions, the training model can predict the species at 93.3% accuracy out of five bacteria: Escherichia coli, Staphylococcus aureus, Salmonella Typhimurium, Enterococcus faecium, and Pseudomonas aeruginosa. Flow rates are monitored for less than 6 s and the sample-to-answer assay time is less than 10 min. The demonstrated method can open a new way of analyzing complex biological and environmental samples in a biomimetic manner with machine learning classification.

Norovirus detection in water samples at the level of single virus copies per microliter using a smartphone-based fluorescence microscope

Norovirus is a widespread public health threat and has a very low infectious dose. This protocol presents the extremely sensitive mobile detection of norovirus from water samples using a custom-built smartphone-based fluorescence microscope and a paper microfluidic chip. Antibody-conjugated fluorescent particles are immunoagglutinated and spread over the paper microfluidic chip by capillary action for individual counting using a smartphone-based fluorescence microscope. Smartphone images are analyzed using intensity- and size-based thresholding for the elimination of background noise and autofluorescence as well as for the isolation of immunoagglutinated particles. The resulting pixel counts of particles are correlated with the norovirus concentration of the tested sample. This protocol provides detailed guidelines for the construction and optimization of the smartphone- and paper-based assay. In addition, a 3D-printed enclosure is presented to incorporate all components in a dark environment. On-chip concentration and the assay of higher concentrations are presented to further broaden the assay range. This method is the first to be presented as a highly sensitive mobile platform for norovirus detection using low-cost materials. With all materials and reagents prepared, a single standard assay takes under 20 min. Although the method described is used for detection of norovirus, the same protocol could be adapted for detection of other pathogens by using different antibodies.

Microfluidic devices for detection of RNA viruses

There is a long way to go before the coronavirus disease 2019 (Covid-19) outbreak comes under control. qRT-PCR is currently used for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of Covid-19, but it is expensive, time-consuming, and not as sensitive as it should be. Finding a rapid, easy-to-use, and cheap diagnostic method is necessary to help control the current outbreak. Microfluidic systems provide a platform for many diagnostic tests, including RT-PCR, RT-LAMP, nested-PCR, nucleic acid hybridization, ELISA, fluorescence-Based Assays, rolling circle amplification, aptamers, sample preparation multiplexer (SPM), Porous Silicon Nanowire Forest, silica sol-gel coating/bonding, and CRISPR. They promise faster, cheaper, and easy-to-use methods with higher sensitivity, so microfluidic devices have a high potential to be an alternative method for the detection of viral RNA. These devices have previously been used to detect RNA viruses such as H1N1, Zika, HAV, HIV, and norovirus, with acceptable results. This paper provides an overview of microfluidic systems as diagnostic methods for RNA viruses with a focus on SARS-CoV-2.

Smartphone based on-chip fluorescence imaging and capillary flow velocity measurement for detecting ROR1+ cancer cells from buffy coat blood samples on dual-layer paper microfluidic chip

Diagnosis of hematological cancer requires complete white blood cell count, followed by flow cytometry with multiple markers, and cytology. It requires substantial time and specialized training. A dual-layer paper microfluidic chip was developed as a quicker, low-cost, and field-deployable alternative to detect ROR1+ (receptor tyrosine-like orphan receptor one) cancer cells from the undiluted and untreated buffy coat blood samples. The first capture layer consisted of a GF/D glass fiber substrate, preloaded with cancer specific anti-ROR1 conjugated fluorescent particles to its center for cancer cell capture and direct smartphone fluorescence imaging. The second flow layer was comprised of a grade 1 cellulose chromatography paper with wax-printed four channels for wicking and capillary flow-based detection. The flow velocity was used as measure of antigen concentration in the buffy coat sample. In this manner, intact cells and their antigens were separated and independently analyzed by both imaging and flow velocity analyses. A custom-made smartphone-based fluorescence microscope and automated image processing and particle counter software were developed to enumerate particles on paper, with the limit of detection of 1 cell/μL. Flow velocity analysis showed even greater sensitivity, with the limit of detection of 0.1 cells/μL in the first 6 s of assay. Comparison with capillary flow model revealed great alignment with experimental data and greater correlation to viscosity than interfacial tension. Our proposed device is able to capture and on-chip image ROR1+ cancer cells within a complex sample matrix (buffy coat) while simultaneously quantifying cell concentration in a point-of-care manner.

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.