A computer vision enhanced smart phone platform for microfluidic urine glucometry

Glucose is an important biomarker for diagnosing and prognosing various diseases, including diabetes and hypoglycemia, which can have severe side effects, symptoms, and even lead to death in patients. As a result, there is a need for quick and economical glucose level measurements to help identify those at potential risk. With the increase in smartphone users, portable smartphone glucose sensors are becoming popular. In this paper, we present a disposable microfluidic glucose sensor that accurately and rapidly quantifies glucose levels in human urine using a combination of colorimetric analysis and computer vision. This glucose sensor implements a disposable microfluidic device based on medical-grade tapes and glucose analysis strips on a glass slide integrated with a custom-made polydimethylsiloxane (PDMS) micropump that accelerates capillary flow, making it economical, convenient, rapid, and equipment-free. After absorbing the target solution, the disposable device is slid into the 3D-printed main chassis and illuminated exclusively with Light Emitting Diode (LED) illumination, which is pivotal to color-sensitive experiments. After collecting images, the images are imported into the algorithm to measure the glucose levels using computer vision and average RGB values measurements. This article illustrates the impressive accuracy and consistency of the glucose sensor in quantifying glucose in sucrose water. This is evidenced by the close agreement between the computer vision method used by the sensor and the traditional method of measuring in the biology field, as well as the small variation observed between different sensor performances. The exponential regression curve used in the study further confirms the strong relationship between glucose concentrations and average RGB values, with an R-square value of 0.997 indicating a high degree of correlation between these variables. The article also emphasizes the potential transferability of the solution described to other types of assays and smartphone-based sensors.

High Sensitivity and High Throughput Magnetic Flow CMOS Cytometers with 2D Oscillator Array and Inter-Sensor Spectrogram Cross-correlation

In the paper, we present an integrated flow cytometer with a 2D array of magnetic sensors based on dual-frequency oscillators in a 65-nm CMOS process, with the chip packaged with microfluidic controls. The sensor architecture and the presented array signal processing allows uninhibited flow of the sample for high throughput without the need for hydrodynamic focusing to a single sensor. To overcome the challenge of sensitivity and specificity that comes as a trade off with high throughout, we perform two levels of signal processing. First, utilizing the fact that a magnetically tagged cell is expected to excite sequentially an array of sensors in a time-delayed fashion, we perform inter-site cross-correlation of the sensor spectrograms that allows us to suppress the probability of false detection drastically, allowing theoretical sensitivity reaching towards sub-ppM levels that is needed for rare cell or circulating tumor cell detection. In addition, we implement two distinct methods to suppress correlated low frequency drifts of singular sensors-one with an on-chip sensor reference and one that utilizes the frequency dependence of the susceptibility of super-paramagnetic magnetic beads that we deploy as tags. We demonstrate these techniques on a 7×7 sensor array in 65 nm CMOS technology packaged with microfluidics with magnetically tagged dielectric particles and cultu lymphoma cancer cells.

Clinical evaluation of a fully electronic microfluidic white blood cell analyzer

The White Blood Cell (WBC) count is one of the key parameters signaling the health of the immune system. Abnormal WBC counts often signal a systemic insult to the body such as an underlying infection or an adverse side effect to medication. Typically, the blood collected is sent to a central lab for testing, and results come back within hours, which is often inconvenient and may delay time-sensitive diagnosis or treatment. Here, we present the CytoTracker, a fully electronic, microfluidic based instant WBC analyzer with the potential to be used at point-of-care. The CytoTracker is a lightweight, portable, affordable platform capable of quantifying WBCs within minutes using only 50 μl of blood (approximately one drop of blood). In this study, we clinically evaluated the accuracy and performance of CytoTracker in measuring WBC and granulocyte counts. A total of 210 adult patients were recruited in the study. We validated the CytoTracker against a standard benchtop analyzer (Horiba Point of Care Hematology Analyzer, ABX Micros 60). Linear dynamic ranges of 2.5 k/μl- 35 k/μl and 0.6 k/μl- 26 k/μl were achieved for total WBC count and granulocyte count with correlation coefficients of 0.97 and 0.98. In addition, we verified CytoTracker's capability of identifying abnormal blood counts with above 90% sensitivity and specificity. The promising results of this clinical validation study demonstrate the potential for the use of the CytoTracker as a reliable and accurate point-of-care WBC analyzer.

Multi-modal sensing with integrated machine learning to differentiate specific leukocytes targeted by electrically sensitive hybrid particles

The growing need for personalized, accurate, and non-invasive diagnostic technology has resulted in significant advancements, from pushing current mechanistic limitations to innovative modality developments across various disease-related biomarkers. However, there still lacks clinical solutions for analyzing multiple biomarkers simultaneously, limiting prognosis for patients suffering with complicated diseases or comorbidities. Here, we conceived, fabricated, and validated a multifrequency impedance cytometry apparatus with novel frequency-sensitive barcoded metal oxide Janus particles (MOJPs) as cell-receptor targeting agents. These microparticles are modulated by a metal oxide semi-coating which exhibit electrical property changes in a multifrequency electric field and are functionalized to target CD11b and CD66b membrane proteins on neutrophils. A multi-modal system utilizing supervised machine learning and simultaneous high-speed video microscopy classifies immune-specific surface receptors targeted by MOJPs as they form neutrophil-MOJP conjugates, based on multivariate multifrequency electrical recordings. High precision and sensitivity were determined based on the type of MOJPs conjugated with cells (>90% accuracy between neutrophil-MOJP conjugates versus cells alone). Remarkably, the design could differentiate the number of MOJPs conjugated per cell within the same MOJP class (>80% accuracy); which also improved comparing electrical responses across different MOJP types (>75% accuracy) as well. Such trends were consistent in individual blood samples and comparing consolidated data across multiple samples, demonstrating design robustness. The configuration may further expand to include more MOJP types targeting critical biomarker receptors in one sample and increase the modality's multiplexing potential.

Cell phone microscopy enabled low-cost manufacturable colorimetric urine glucose test

Glucose serves as a pivotal biomarker crucial for the monitoring and diagnosis of a spectrum of medical conditions, encompassing hypoglycemia, hyperglycemia, and diabetes, all of which may precipitate severe clinical manifestations in individuals. As a result, there is a growing demand within the medical domain for the development of rapid, cost-effective, and user-friendly diagnostic tools. In this research article, we introduce an innovative glucose sensor that relies on microfluidic devices meticulously crafted from disposable, medical-grade tapes. These devices incorporate glucose urine analysis strips securely affixed to microscope glass slides. The microfluidic channels are intricately created through laser cutting, representing a departure from traditional cleanroom techniques. This approach streamlines production processes, enhances cost-efficiency, and obviates the need for specialized equipment. Subsequent to the absorption of the target solution, the disposable device is enclosed within a 3D-printed housing. Image capture is seamlessly facilitated through the use of a smartphone camera for subsequent colorimetric analysis. Our study adeptly demonstrates the glucose sensor's capability to accurately quantify glucose concentrations within sucrose solutions. This is achieved by employing an exponential regression model, elucidating the intricate relationship between glucose concentrations and average RGB (Red-Green-Blue) values. Furthermore, our comprehensive analysis reveals minimal variation in sensor performance across different instances. Significantly, this study underscores the potential adaptability and versatility of our solution for a wide array of assay types and smartphone-based sensor systems, making it particularly promising for deployment in resource-constrained settings and undeveloped countries. The robust correlation established between glucose concentrations and average RGB values, substantiated by an impressive R-square value of 0.98709, underscores the effectiveness and reliability of our pioneering approach within the medical field.

Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis

Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.

Digital assay for rapid electronic quantification of clinical pathogens using DNA nanoballs

Fast and accurate detection of nucleic acids is key for pathogen identification. Methods for DNA detection generally rely on fluorescent or colorimetric readout. The development of label-free assays decreases costs and test complexity. We present a novel method combining a one-pot isothermal generation of DNA nanoballs with their detection by electrical impedance. We modified loop-mediated isothermal amplification by using compaction oligonucleotides that self-assemble the amplified target into nanoballs. Next, we use capillary-driven flow to passively pass these nanoballs through a microfluidic impedance cytometer, thus enabling a fully compact system with no moving parts. The movement of individual nanoballs is detected by a change in impedance providing a quantized readout. This approach is flexible for the detection of DNA/RNA of numerous targets (severe acute respiratory syndrome coronavirus 2, HIV, β-lactamase gene, etc.), and we anticipate that its integration into a standalone device would provide an inexpensive (<$5), sensitive (10 target copies), and rapid test (<1 hour).

A two-minute assay for electronic quantification of antibodies in saliva enabled through a reusable microfluidic multi-frequency impedance cytometer and machine learning analysis

The use of saliva as a diagnostic fluid has always been appealing due to the ability for rapid and non-invasive sampling for monitoring health status and the onset and progression of disease and treatment progress. Saliva is rich in protein biomarkers and provides a wealth of information for diagnosis and prognosis of various disease conditions. Portable electronic tools which rapidly monitor protein biomarkers would facilitate point-of-care diagnosis and monitoring of various health conditions. For example, the detection of antibodies in saliva can enable rapid diagnosis and tracking disease pathogenesis of various auto-immune diseases like sepsis. Here, we present a novel method involving immuno-capture of proteins on antibody coated beads and electrical detection of dielectric properties of the beads. The changes in electrical properties of a bead when capturing proteins are extremely complex and difficult to model physically in an accurate manner. The ability to measure impedance of thousands of beads at multiple frequencies, however, allows for a data-driven approach for protein quantification. By moving from a physics driven approach to a data driven approach, we have developed, for the first time ever to the best of our knowledge, an electronic assay using a reusable microfluidic impedance cytometer chip in conjunction with supervised machine learning to quantifying immunoglobulins G (IgG) and immunoglobulins A (IgA) in saliva within two minutes.

Nucleic Acid Quantification by Multi-Frequency Impedance Cytometry and Machine Learning

Determining nucleic acid concentrations in a sample is an important step prior to proceeding with downstream analysis in molecular diagnostics. Given the need for testing DNA amounts and its purity in many samples, including in samples with very small input DNA, there is utility of novel machine learning approaches for accurate and high-throughput DNA quantification. Here, we demonstrated the ability of a neural network to predict DNA amounts coupled to paramagnetic beads. To this end, a custom-made microfluidic chip is applied to detect DNA molecules bound to beads by measuring the impedance peak response (IPR) at multiple frequencies. We leveraged electrical measurements including the frequency and imaginary and real parts of the peak intensity within a microfluidic channel as the input of deep learning models to predict DNA concentration. Specifically, 10 different deep learning architectures are examined. The results of the proposed regression model indicate that an R_Squared of 97% with a slope of 0.68 is achievable. Consequently, machine learning models can be a suitable, fast, and accurate method to measure nucleic acid concentration in a sample. The results presented in this study demonstrate the ability of the proposed neural network to use the information embedded in raw impedance data to predict the amount of DNA concentration.

A Smartphone-Based Disposable Hemoglobin Sensor Based on Colorimetric Analysis

Hemoglobin is a biomarker of interest for the diagnosis and prognosis of various diseases such as anemia, sickle cell disease, and thalassemia. In this paper, we present a disposable device that has the potential of being used in a setting for accurately quantifying hemoglobin levels in whole blood based on colorimetric analysis using a smartphone camera. Our biosensor employs a disposable microfluidic chip which is made using medical-grade tapes and filter paper on a glass slide in conjunction with a custom-made PolyDimethylSiloaxane (PDMS) micropump for enhancing capillary flow. Once the blood flows through the device, the glass slide is imaged using a smartphone equipped with a custom 3D printed attachment. The attachment has a Light Emitting Diode (LED) that functions as an independent light source to reduce the noise caused by background illumination and external light sources. We then use the RGB values obtained from the image to quantify the hemoglobin levels. We demonstrated the capability of our device for quantifying hemoglobin in Bovine Hemoglobin Powder, Frozen Beef Blood, and human blood. We present a logarithmic model that specifies the relationship between the Red channel of the RGB values and Hemoglobin concentration.

Detection of respiratory inflammation biomarkers in non-processed exhaled breath condensate samples using reduced graphene oxide

In this work, we studied several important parameters regarding the standardization of a portable sensor of nitrite, a key biomarker of inflammation in the respiratory tract in untreated EBC samples. The storage of the EBC samples and electrical properties of both EBC samples and the sensor as main standardization parameters were investigated. The sensor performance was performed using differential pulse voltammetry (DPV) in a standard nitrite solution and untreated EBC samples. The storage effect was monitored by comparing sensor data of fresh and stored samples for one month at -80 °C. Results show, on average, a 20 percent reduction of peak current for stored solutions. The sensor's performance was compared with a previous EBC nitrite sensor and chemiluminescence method. The results demonstrate a good correlation between the present sensor and chemiluminescence for low nitrite concentrations in untreated EBC samples. The electrical behavior of the sensor and electrical variation between EBC samples were characterized using methods such as noise analysis, electrochemical impedance spectroscopy (EIS), electrical impedance (EI), and voltage shift. Data show that reduced graphene oxide (rGO) has lower electrical noise and a higher electron transfer rate regarding nitrite detection. Also, a voltage shift can be applied to calibrate the data based on the electrical variation between different EBC samples. This result makes it easy to calibrate the electrical difference between EBC samples and have a more reproducible portable chip design without using bulky EI instruments. This work helps detect nitrite in untreated and pure EBC samples and evaluates critical analytical EBC properties essential for developing portable and on-site point-of-care sensors.

A portable analog front-end system for label-free sensing of proteins using nanowell array impedance sensors

Proteins are useful biomarkers for a wide range of applications such as cancer detection, discovery of vaccines, and determining exposure to viruses and pathogens. Here, we present a low-noise front-end analog circuit interface towards development of a portable readout system for the label-free sensing of proteins using Nanowell array impedance sensing with a form factor of approximately 35cm². The electronic interface consists of a low-noise lock-in amplifier enabling reliable detection of changes in impedance as low as 0.1% and thus detection of proteins down to the picoMolar level. The sensitivity of our system is comparable to that of a commercial bench-top impedance spectroscope when using the same sensors. The aim of this work is to demonstrate the potential of using impedance sensing as a portable, low-cost, and reliable method of detecting proteins, thus inching us closer to a Point-of-Care (POC) personalized health monitoring system. We have demonstrated the utility of our system to detect antibodies at various concentrations and protein (45 pM IL-6) in PBS, however, our system has the capability to be used for assaying various biomarkers including proteins, cytokines, virus molecules and antibodies in a portable setting.

MACHINE LEARNING ENABLES QUANTIFYING CELL-JANUS PARTICLE CONJUGATES THROUGH MICROFLOWING IMPEDANCE SIGNALS

In this work, we demonstrate the differentiation of demodulated multifrequency signals from impedance sensitive microparticles when targeting surface receptors on neutrophils in a microfluidic impedance cytometer. These scheme uses a single signal input and detection configuration, and machine learning can differentiate particle types with up to 82% accuracy.

Development of a portable toolkit to diagnose coral thermal stress

Coral bleaching, precipitated by the expulsion of the algal symbionts that provide colonies with fixed carbon is a global threat to reef survival. To protect corals from anthropogenic stress, portable tools are needed to detect and diagnose stress syndromes and assess population health prior to extensive bleaching. Here, medical grade Urinalysis strips, used to detect an array of disease markers in humans, were tested on the lab stressed Hawaiian coral species, Montipora capitata (stress resistant) and Pocillopora acuta (stress sensitive), as well as samples from nature that also included Porites compressa. Of the 10 diagnostic reagent tests on these strips, two appear most applicable to corals: ketone and leukocytes. The test strip results from M. capitata were explored using existing transcriptomic data from the same samples and provided evidence of the stress syndromes detected by the strips. We designed a 3D printed smartphone holder and image processing software for field analysis of test strips (TestStripDX) and devised a simple strategy to generate color scores for corals (reflecting extent of bleaching) using a smartphone camera (CoralDX). Our approaches provide field deployable methods, that can be improved in the future (e.g., coral-specific stress test strips) to assess reef health using inexpensive tools and freely available software.

Biosensors and machine learning for enhanced detection, stratification, and classification of cells: a review

Biological cells, by definition, are the basic units which contain the fundamental molecules of life of which all living things are composed. Understanding how they function and differentiating cells from one another, therefore, is of paramount importance for disease diagnostics as well as therapeutics. Sensors focusing on the detection and stratification of cells have gained popularity as technological advancements have allowed for the miniaturization of various components inching us closer to Point-of-Care (POC) solutions with each passing day. Furthermore, Machine Learning has allowed for enhancement in the analytical capabilities of these various biosensing modalities, especially the challenging task of classification of cells into various categories using a data-driven approach rather than physics-driven. In this review, we provide an account of how Machine Learning has been applied explicitly to sensors that detect and classify cells. We also provide a comparison of how different sensing modalities and algorithms affect the classifier accuracy and the dataset size required.

Antibody-functionalized aluminum oxide-coated particles targeting neutrophil receptors in a multifrequency microfluidic impedance cytometer

Personalized diagnostics of infectious diseases require monitoring disease progression due to their ever-changing physiological conditions and the multi-faceted organ system mechanisms involved in disease pathogenesis. In such instances, the recommended clinical strategies involve multiplexing data collection from critical biomarkers related to a patient's conditions along with longitudinal frequent patient monitoring. Numerous detection technologies exist both in research and commercial settings to monitor these conditions, however, they fail to provide biomarker multiplexing ability with design and data processing simplicity. For a recently conceived multiplexing biomarker modality, this work demonstrates the use of electrically sensitive microparticles targeting and identifying membrane receptors on leukocytes using a single detection source, with a high potential for multiplexing greater than any existing impedance-based single-detection scheme. Here, polystyrene microparticles are coated with varying thicknesses of metal oxides, which generate quantifiable impedance shifts when exposed to multifrequency electric fields depending on the metal oxide thickness. Using multifrequency impedance cytometry, these particles can be measured and differentiated rapidly across one coplanar electrode scheme. After surface-functionalizing particles with antibodies targeting CD11b and CD66b receptors, the particles are combined with isolated neutrophils to measure receptor expression. A combination of data analysis techniques including multivariate analysis, supervised machine learning, and unsupervised machine learning was able to accurately differentiate samples with up to 91% accuracy. This proof-of-concept study demonstrates the potential for these oxide-coated particles for enumerating specific leukocytes enabling multiplexing. Further, additional coating thicknesses or different metal oxide materials can enable a compendium of multiplexing targeting resource to be used to develop a high-multiplexing sensor for targeting membrane receptor expression.

Aluminum Oxide-Coated Particle Differentiation Employing Supervised Machine Learning and Impedance Cytometry

This article uses a supervised machine learning (ML) system for identifying groups of nanoparticles coated with metal oxides of varying thicknesses using a microfluidic impedance cytometer. These particles generate unique impedance signatures when probed with a multifrequency electric field and finds applications in enabling many multiplexed biosensing technologies. However, current experimental and data processing techniques are unable to sensitively differentiate different metal oxide coated particle types. Here, we employ various machine learning models and collect multiple particle metrics measured. In reported experiments, a 75% accuracy was determined to separate aluminum oxide coated (10nm and 30nm), which is significantly greater than observing only univariate data between different microparticle types. This approach will enable ML models to differentiate such particles with greater accuracies.

A microwell-based impedance sensor on an insertable microneedle for real-time in vivo cytokine detection

Impedance-based protein detection sensors for point-of-care diagnostics require quantitative specificity, as well as rapid or real-time operation. Furthermore, microfabrication of these sensors can lead to the formation of factors suitable for in vivo operation. Herein, we present microfabricated needle-shaped microwell impedance sensors for rapid-sample-to-answer, label-free detection of cytokines, and other biomarkers. The microneedle form factor allows sensors to be utilized in transcutaneous or transvascular sensing applications. In vitro, experimental characterization confirmed sensor specificity and sensitivity to multiple proteins of interest. Mechanical characterization demonstrated sufficient microneedle robustness for transcutaneous insertion, as well as preserved sensor function postinsertion. We further utilized these sensors to carry out real-time in vivo quantification of human interleukin 8 (hIL8) concentration levels in the blood of transgenic mice that endogenously express hIL8. To assess sensor functionality, hIL8 concentration levels in serum samples from the same mice were quantified by ELISA. Excellent agreement between real-time in vivo sensor readings in blood and subsequent ELISA serum assays was observed over multiple transgenic mice expressing hIL8 concentrations from 62 pg/mL to 539 ng/mL.

Single-step label-free nanowell immunoassay accurately quantifies serum stress hormones within minutes

A non-faradaic label-free cortisol sensing platform is presented using a nanowell array design, in which the two probe electrodes are integrated within the nanowell structure. Rapid and low volume (≤5 μl) sensing was realized through functionalizing nanoscale volume wells with antibodies and monitoring the real-time binding events. A 28-well plate biochip was built on a glass substrate by sequential deposition, patterning, and etching steps to create a stack nanowell array sensor with an electrode gap of 40 nm. Sensor response for cortisol concentrations between 1 and 15 μg/dl in buffer solution was recorded, and a limit of detection of 0.5 μg/dl was achieved. Last, 65 human serum samples were collected to compare the response from human serum samples with results from the standard enzyme-linked immunosorbent assay (ELISA). These results confirm that nanowell array sensors could be a promising platform for point-of-care testing, where real-time, laboratory-quality diagnostic results are essential.

Potential Microfluidic Devices for COVID-19 Antibody Detection at Point-of-Care (POC): A Review

COVID-19 has been declared a global pandemic which has brought the world economy and the society to a standstill. The current emphasis of testing is on detection of genetic material of SARS-CoV-2. Such tests are useful for assessing the current state of a subject: Infected or not infected. In addition to such tests, antibody testing is necessary to stratify the population into three groups: never exposed, infected, and immune. Such a stratification is necessary for safely reopening the society and remobilizing the economy. The aim of this review article is to inform the audience of the current diagnostic and surveillance technologies that are being employed for the detection of SARS-CoV-2 antibodies along with their shortcomings, and to highlight microfluidic sensors and devices that show promise of being commercialized for detection and quantification of SARS-CoV-2 antibodies in low-resource and Point-of-Care (POC) settings.

Functionalization of hybrid surface microparticles for in vitro cellular antigen classification

Hybrid material surfaces on microparticles are emerging as vehicles for many biomedical multiplexing applications. Functionalization of these hybrid surface microparticles to biomolecules presents unique challenges related to optimization of surface chemistries including uniformity, repeatability, and sample sparring. Hybrid interfaces between microlevel surfaces and individual biomolecules will provide different microenvironments impacting the surface functionalization optimization and efficiency. Here, we propose and validate the first demonstration of streptavidin adsorption-based antibody functionalization on unmodified, hybrid surface microparticles for in vitro analysis. We test this analytical technique and fabricate hybrid surface microparticles with a polystyrene core and aluminum oxide semi-coating. Additionally, we optimize the streptavidin-biotin functionalization chemistry in both assay implementation and sample sparring via analytical mass balances for these microparticles and subsequently conjugate anti-human CD11b antibodies. Result confirmation and characterization occurs from ultraviolet protein absorbance and ImageJ processing of fluorescence microscopy images. Additionally, we design and implement the multi-sectional imaging (MSI) approach to support functionalization uniformity on the hybrid surface microparticles. Finally, as a proof-of-concept performance, we validate anti-CD11b antibodies functionalization by visualizing hybrid surface microparticles conjugate to human neutrophils isolated from blood samples collected from potentially septic patients. Our study introduces and defines a category of functionalization for hybrid surface microparticles with the intent of minuscule sample volumes, low cost, and low environmental impact to be used for many cellular or proteomic in vitro multiplexing applications in the future. Graphical abstract.

Electronic classification of barcoded particles for multiplexed detection using supervised machine learning analysis

Wearable biosensors are of great interest in recent years due to their potential in health related applications. Multiplex biomarker analysis is needed in wearable devices to improve the sensitivity and reliability. Electronic barcoding of micro-particles has the possibility to enable multiplexed biomarker analysis. Compared with traditional optical and plasmonic methods for barcoding, electronically barcoded particles can be classified using ultra-compact electronic readout platforms. Nano-electronic barcoding works by depositing a thin layer of oxide on the top half of a micro-particle. The thickness and dielectric property of the oxide layer can be tuned to modulate the frequency dependent impedance signature of the particles. A one to one correspondence between a target biomarker and each barcoded particle can potentially be established using this technique. The barcoded particles could be tested with wearable devices to enable multiplex analysis for portable point-of-care diagnostics and real-time monitoring. In this work, we fabricated nine barcoded particles by forming oxide layers of different thicknesses and different dielectric materials using atomic layer deposition and assessed the ability to accurately classify particle barcodes using multi-frequency impedance cytometry in conjunction with supervised machine learning.

Toward point-of-care assessment of patient response: a portable tool for rapidly assessing cancer drug efficacy using multifrequency impedance cytometry and supervised machine learning

We present a novel method to rapidly assess drug efficacy in targeted cancer therapy, where antineoplastic agents are conjugated to antibodies targeting surface markers on tumor cells. We have fabricated and characterized a device capable of rapidly assessing tumor cell sensitivity to drugs using multifrequency impedance spectroscopy in combination with supervised machine learning for enhanced classification accuracy. Currently commercially available devices for the automated analysis of cell viability are based on staining, which fundamentally limits the subsequent characterization of these cells as well as downstream molecular analysis. Our approach requires as little as 20 μL of volume and avoids staining allowing for further downstream molecular analysis. To the best of our knowledge, this manuscript presents the first comprehensive attempt to using high-dimensional data and supervised machine learning, particularly phase change spectra obtained from multi-frequency impedance cytometry as features for the support vector machine classifier, to assess viability of cells without staining or labelling.

On-chip Multiwell Plate Impedance Analysis of Microwell Array Sensor for Label-free Detection of Cytokines in Rat Serum

We present a novel method for label-free detection of cytokines in serum after ten minutes of stabilization at nanomolar concentrations. Detection of proteins in blood using label-free impedance-based techniques is difficult due to high salt concentration of the matrix, which results in screening of the charge of the target proteins. The microwell array design provides enhanced electrochemical sensitivity by electric field focusing in the wells. We describe an on-chip multiwell plate sensing configuration where sensitivity benefits from the high salt concentration of the matrix and demonstrate robust performance through testing in rat serum.

Tissue Paper as a Substrate for Electronic Biosensing

In the presented work, we combine a commonplace commodity, tissue paper, with biosensing capabilities to provide on-the-go detection of biomarkers. We developed a simple light-weight sensor using single walled carbon nanotubes (SWCNT) on commercially available tissue paper. As a proof of concept, we show that these sensors can be used to quantify Interleukin-6 protein concentration. Detection is carried out by examining the modulation in electrical properties of the substrate induced by addition of protein on these sensors. Our results show that these sensors can provide the foundation for development of tissue paper based portable sensors for biomarkers relevant to on-the-go testing.

Fully integrated wearable impedance cytometry platform on flexible circuit board with online smartphone readout

We present a wearable microfluidic impedance cytometer implemented on a flexible circuit wristband with on-line smartphone readout for portable biomarker counting and analysis. The platform contains a standard polydimethylsiloxane (PDMS) microfluidic channel integrated on a wristband, and the circuitry on the wristband is composed  of a custom analog lock-in amplification system, a microcontroller with an 8-bit analog-to-digital converter (ADC), and a Bluetooth module wirelessly paired with a smartphone. The lock-in amplification (LIA) system is implemented with a novel architecture which consists of the lock-in amplifier followed by a high-pass filter stage with DC offset subtraction, and a post-subtraction high gain stage enabling detection of particles as small as 2.8  μm using the 8-bit ADC. The Android smartphone application was used to initiate the system and for offline data-plotting and peak counting, and supports online data readout, analysis, and file management. The data is exportable to researchers and medical professionals for in-depth analysis and remote health monitoring. The system, including the microfluidic sensor, microcontroller, and Bluetooth module all fit on the wristband with a footprint of less than 80 cm2. We demonstrate the ability of the system to obtain generalized blood cell counts; however the system can be applied to a wide variety of biomarkers by interchanging the standard microfluidic channel with microfluidic channels designed for biomarker isolation.

Toward Wireless Health Monitoring via an Analog Signal Compression-Based Biosensing Platform

Wireless all-analog biosensor design for the concurrent microfluidic and physiological signal monitoring is presented in this paper. The key component is an all-analog circuit capable of compressing two analog sources into one analog signal by the analog joint source-channel coding (AJSCC). Two circuit designs are discussed, including the stacked-voltage-controlled voltage source (VCVS) design with the fixed number of levels, and an improved design, which supports a flexible number of AJSCC levels. Experimental results are presented on the wireless biosensor prototype, composed of printed circuit board realizations of the stacked-VCVS design. Furthermore, circuit simulation and wireless link simulation results are presented on the improved design. Results indicate that the proposed wireless biosensor is well suited for sensing two biological signals simultaneously with high accuracy, and can be applied to a wide variety of low-power and low-cost wireless continuous health monitoring applications.

Top-down fabrication meets bottom-up synthesis for nanoelectronic barcoding of microparticles

Traditional optical and plasmonic techniques for barcoding of micro-particles for multiplexed bioassays are generally high in throughput, however bulky instrumentation is often required for performing readout. Electrical impedance based detection allows for ultra-compact instrumentation footprint necessary for wearable devices, however to date, the lack of ability to electronically barcode micro-particles has been a long standing bottleneck towards enabling multiplexed electronic biomarker assays. Nanoelectronic barcoding, which to the best of our knowledge is the first impedance based solution for micro-particle barcoding, works by forming tunable nano-capacitors on the surface of micro-spheres, effectively modulating the frequency dependent dielectric properties of the spheres allowing one bead barcode to be distinguished from another. Nanoelectronic barcoding uses a well-known, but unexplored electromagnetic phenomenon of micro-particles: the Clausius-Mossotti (CM) factor spectrum of a Janus particle (JP) shifts depending on the zeta (wall) potential of the metallic half of the microsphere, and the fact that the complex impedance spectrum of a particle directly corresponds to the CM factor spectrum. A one-to-one correspondence will be established between each biomarker and the corresponding engineered microsphere. This transformative new method for barcoding will enable a new class of handheld and wearable biosensors capable of multiplexed continuous temporal bio-monitoring. The proposed nano-electronically barcoded particles utilize both bottom-up synthesis and top-down fabrication to enable precisely engineered frequency dependent dielectric signatures. Multi-frequency lock-in measurements of the complex impedance, in conjunction with multi-variate analysis of impedance data, allows for particle differentiation using a fully functional ultra-compact electronic detector.

BioMEMS-based coding for secure medical diagnostic devices

Trustworthy and usable point-of-care solutions require not only effective disease diagnostic procedures to ensure delivery of rapid and accurate outcomes, but also lightweight privacy-preserving capabilities. In this paper, we present a Biomedical Microelectromachanical System (BioMEMS)-based sensor for portable, inexpensive smartphone-based biomarker detection. The biosensor presented here provides the ability for signal encryption at the physical sensor level to ensure patient's diagnostic confidentiality. Our results show that this design allow us to protect the samples measurements while accurately distinguish different test samples.

Toward point-of-care management of chronic respiratory conditions: Electrochemical sensing of nitrite content in exhaled breath condensate using reduced graphene oxide

We present a portable non-invasive approach for measuring indicators of inflammation and oxidative stress in the respiratory tract by quantifying a biomarker in exhaled breath condensate (EBC). We discuss the fabrication and characterization of a miniaturized electrochemical sensor for detecting nitrite content in EBC using reduced graphene oxide. The nitrite content in EBC has been demonstrated to be a promising biomarker of inflammation in the respiratory tract, particularly in asthma. We utilized the unique properties of reduced graphene oxide (rGO); specifically, the material is resilient to corrosion while exhibiting rapid electron transfer with electrolytes, thus allowing for highly sensitive electrochemical detection with minimal fouling. Our rGO sensor was housed in an electrochemical cell fabricated from polydimethyl siloxane (PDMS), which was necessary to analyze small EBC sample volumes. The sensor is capable of detecting nitrite at a low over-potential of 0.7 V with respect to an Ag/AgCl reference electrode. We characterized the performance of the sensors using standard nitrite/buffer solutions, nitrite spiked into EBC, and clinical EBC samples. The sensor demonstrated a sensitivity of 0.21 μA μM^-1 cm^-2 in the range of 20-100 μM and of 0.1 μA μM^-1 cm^-2 in the range of 100-1000 μM nitrite concentration and exhibited a low detection limit of 830 nM in the EBC matrix. To benchmark our platform, we tested our sensors using seven pre-characterized clinical EBC samples with concentrations ranging between 0.14 and 6.5 μM. This enzyme-free and label-free method of detecting biomarkers in EBC can pave the way for the development of portable breath analyzers for diagnosing and managing changes in respiratory inflammation and disease.

Robust dipstick urinalysis using a low-cost, micro-volume slipping manifold and mobile phone platform

We introduce a novel manifold and companion software for dipstick urinalysis that eliminate many of the aspects that are traditionally plagued by user error: precise sample delivery, accurate readout timing, and controlled lighting conditions. The proposed all-acrylic slipping manifold is reusable, reliable, and low in cost. A simple timing mechanism ensures results are read out at the appropriate time. Results are obtained by capturing videos using a mobile phone and by analyzing them using custom-designed software. We show that the results obtained with the proposed device are as accurate and consistent as a properly executed dip-and-wipe method, the industry gold-standard, suggesting the potential for this strategy to enable confident urinalysis testing in home environments.

Portable Cytometry Using Microscale Electronic Sensing

In this manuscript, we present three different micro-impedance sensing architectures for electronic counting of cells and beads. The first method of sensing is based on using an open circuit sensing electrode integrated in a micro-pore, which measures the shift in potential as a micron-sized particle passes through. Our micro-pore, based on a funnel shaped microchannel, was fabricated in PDMS and was bound covalently to a glass substrate patterned with a gold open circuit electrode. The amplification circuitry was integrated onto a battery-powered custom printed circuit board. The second method is based on a three electrode differential measurement, which opens up the potential of using signal processing techniques to increase signal to noise ratio post measurement. The third architecture uses a contactless sensing approach, which significantly minimizes the cost of the consumable component of the impedance cytometer. We demonstrated proof of concept for the three sensing architectures by measuring the detected signal due to the passage of micron sized beads through the pore.

Multiplexed actuation using ultra dielectrophoresis for proteomics applications: a comprehensive electrical and electrothermal design methodology

In this work, we present a methodological approach to analyze an enhanced dielectrophoresis (DEP) system from both a circuit analysis and electrothermal view points. In our developed model, we have taken into account various phenomena and constraints such as voltage degradation (due to the presence of the protecting oxide layer), oxide breakdown, instrumentation limitations, and thermal effects. The results from this analysis are applicable generally to a wide variety of geometries and high voltage microsystems. Here, these design guidelines were applied to develop a robust electronic actuation system to perform a multiplexed bead-based protein assay. To carry out the multiplexed functionality, along a single microfluidic channel, an array of proteins is patterned, where each element is targeting a specific secondary protein coated on micron-sized beads in the subsequently introduced sample solution. Below each element of the array, we have a pair of addressable interdigitated electrodes. By selectively applying voltage at the terminals of each interdigitated electrode pair, the enhanced DEP, or equivalently 'ultra'-DEP (uDEP) force detaches protein-bound beads from each element of the array, one by one, without disturbing the bound beads in the neighboring regions. The detached beads can be quantified optically or electrically downstream. For proof of concept, we illustrated 16-plex actuation capability of our device to elute micron-sized beads that are bound to the surface through anti-IgG and IgG interaction which is on the same order of magnitude in strength as typical antibody-antigen interactions. In addition to its application in multiplexed protein analysis, our platform can be potentially utilized to statistically characterize the strength profile of biological bonds, since the multiplexed format allows for high throughput force spectroscopy using the array of uDEP devices, under the same buffer and assay preparation conditions.

Depletion of cells and abundant proteins from biological samples by enhanced dielectrophoresis

Platforms that are sensitive and specific enough to assay low-abundance protein biomarkers, in a high throughput multiplex format, within a complex biological fluid specimen, are necessary to enable protein biomarker based diagnostics for diseases such as cancer. The signal from an assay for a low-abundance protein biomarker in a biological fluid sample like blood is typically buried in a background that arises from the presence of blood cells and from high-abundance proteins that make up 90% of the assayed protein mass. We present an automated on-chip platform for the depletion of cells and highly abundant serum proteins in blood. Our platform consists of two components, the first of which is a microfluidic mixer that mixes beads containing antibodies against the highly abundant proteins in the whole blood. This complex mixture (consisting of beads, cells, and serum proteins) is then injected into the second component of our microfluidic platform, which comprises a filter trench to capture all the cells and the beads. The size-based trapping of the cells and beads into the filter trench is significantly enhanced by leveraging additional negative dielectrophoretic forces to push the micron sized particles (cells and beads which have captured the highly abundant proteins) down into the trench, allowing the serum proteins of lower abundance to flow through. In general, dielectrophoresis using bare electrodes is incapable of producing forces beyond the low piconewton range that tend to be insufficient for separation applications. However, by using electrodes passivated with atomic layer deposition, we demonstrate the application of enhanced negative DEP electrodes together with size-based flltration induced by the filter trench, to deplete 100% of the micron sized particles in the mixture.

Nanoelectronic impedance detection of target cells

Detection of cells is typically performed using optical fluorescence based techniques such as flow cytometry. Here we present the impedance detection of target cells using a nanoelectronic probe we have developed, which we refer to as the nanoneedle biosensor. The nanoneedle consists of a thin film conducting electrode layer at the bottom, an insulative oxide layer above, another conductive electrode layer above, and a protective oxide above. The electrical impedance is measured between the two electrode layers. Cells captured on the surface of the nanoneedle tip results in a decrease in the impedance across the sensing electrodes. The basic mechanisms behind the electrical response of cells in solution under an applied alternating electrical field stems from modulation of the relative permittivity at the interface. In this paper we discuss, the circuit model, the nanofabrication, and the testing and characterization of the sensor. We demonstrate proof of concept for detection of yeast cells with specificity. We envision the sensor presented in this paper to be combined with microfluidic pre-concentration technologies to develop low cost point-of-care diagnostic assays for the clinical setting.

Microneedle Biosensor: A Method for Direct Label-free Real Time Protein Detection

Here we present the development of an array of electrical micro-biosensors in a microfluidic channel, called microneedle biosensors. A microneedle biosensor is a real-time, label-free, direct electrical detection platform, which is capable of high sensitivity detection, measuring the change in ionic current and impedance modulation, due to the presence or reaction of biomolecules such as proteins and nucleic acids. In this study, we successfully fabricated and electrically characterized the sensors and demonstrated successful detection of target protein. In this study, we used biotinylated bovine serum albumin as the receptor and streptavidin as the target analyte.

Erratum: Publisher's Note: "Label-free electronic probing of nucleic acids and proteins at the nanoscale using the nanoneedle biosensor" [Biomicrofluidics 7, 044114 (2013)]

[This corrects the article on p. 044114 in vol. 7.].

Label-free electronic probing of nucleic acids and proteins at the nanoscale using the nanoneedle biosensor

Detection of proteins and nucleic acids is dominantly performed using optical fluorescence based techniques, which are more costly and timely than electrical detection due to the need for expensive and bulky optical equipment and the process of fluorescent tagging. In this paper, we discuss our study of the electrical properties of nucleic acids and proteins at the nanoscale using a nanoelectronic probe we have developed, which we refer to as the Nanoneedle biosensor. The nanoneedle consists of four thin film layers: a conductive layer at the bottom acting as an electrode, an oxide layer on top, and another conductive layer on top of that, with a protective oxide above. The presence of proteins and nucleic acids near the tip results in a decrease in impedance across the sensing electrodes. There are three basic mechanisms behind the electrical response of DNA and protein molecules in solution under an applied alternating electrical field. The first change stems from modulation of the relative permittivity at the interface. The second mechanism is the formation and relaxation of the induced dipole moment. The third mechanism is the tunneling of electrons through the biomolecules. The results presented in this paper can be extended to develop low cost point-of-care diagnostic assays for the clinical setting.

Smart surface for elution of protein-protein bound particles: nanonewton dielectrophoretic forces using atomic layer deposited oxides

By increasing the strength of the negative dielectrophoresis force, we demonstrated a significantly improved electrokinetic actuation and switching microsystem that can be used to elute specifically bound beads from the surface. In this work using atomic layer deposition we deposited a pinhole free nanometer-scale thin film oxide as a protective layer to prevent electrodes from corrosion, when applying high voltages (>20 V(pp)) at the electrodes. Then, by exciting the electrodes at high frequency, we capacitively coupled the electrodes to the buffer in order to avoid electric field degradation and, hence, reduction in dielectrophoresis force due to the presence of the insulating oxide layer. To illustrate the functionality of our system, we demonstrated 100% detachment of anti-IgG and IgG bound beads (which is on the same order of magnitude in strength as typical antibody-antigen interactions) from the surface, upon applying the improved negative dielectrophoresis force. The significantly enhanced switching performance presented in this work shows orders of magnitude of improvement in on-to-off ratio and switching response time, without any need for chemical eluting agents, as compared to the previous work. The promising results from this work vindicates that the functionality of this singleplexed platform can be extended to perform a multiplexed bead-based assay where in a single channel an array of proteins are patterned each targeting a different antigen or protein.

Microfluidic diagnostic tool for the developing world: contactless impedance flow cytometry

In this work, we demonstrate a novel and cost-effective approach to implement a disposable microfluidic contactless impedance cytometer. Conventional methods for single cell impedance cytometry use microfabricated electrodes in direct contact with the buffer to measure changes of its electrical impedance when cells pass through the applied electric field. However, this approach requires expensive microfabrication of electrodes, and also, the fabricated electrodes cannot be reused without thorough and time-consuming cleaning process. Here, we introduce a novel approach to allow for single cell impedance cytometry using electrodes that can be reused, without the need for microfabrication of the electrodes. This disposable device can be potentially inserted onto a printed circuit board (PCB) which has a non-disposable, yet inexpensive, electronic reading apparatus. This significantly reduces the manufacturing costs, making it suitable for low resource settings, such as point-of-care testing in the developing countries.

Improvement in cell capture throughput using parallel bioactivated microfluidic channels

Optimization of targeted cell capture with microfluidic devices continues to be a challenge. On the one hand, microfluidics allow working with microliter volumes of liquids, whereas various applications in the real world require detection of target analyte in large volumes, such as capture of rare cell types in several ml of blood. This contrast of volumes (microliter vs. ml) has prevented the emergence of microfluidic cell capture sensors in the clinical setting. Here, we study the improvement in cell capture and throughput achieved using parallel bioactivated microfluidic channels. The device consists of channels in parallel with each other tied to a single channel. We discuss fabrication and testing of our devices, and show the ability for an improvement in throughput detection of target cells.