Digital filtering dissemination for optimizing impedance cytometry signal quality and counting accuracy.

Improving biosensor performance which utilize impedance cytometry is a highly interested research topic for many clinical and diagnostic settings. During development, a sensor's design and external factors are rigorously optimized, but improvements in signal quality and interpretation are usually still necessary to produce a sensitive and accurate product. A common solution involves digital signal processing after sample analysis, but these methods frequently fall short in providing meaningful signal outcome changes. This shortcoming may arise from a lack of investigative research into selecting and using signal processing functions, as many choices in current sensors are based on either theoretical results or estimated hypotheses. While a ubiquitous condition set is improbable across diverse impedance cytometry designs, there lies a need for a streamlined and rapid analytical method for discovering those conditions for unique sensors. Herein, we present a comprehensive dissemination of digital filtering parameters applied on experimental impedance cytometry data for determining the limits of signal processing on signal quality improvements. Various filter orders, cutoff frequencies, and filter types are applied after data collection for highest achievable noise reduction. After designing and fabricating a microfluidic impedance cytometer, 9 µm polystyrene particles were measured under flow and signal quality improved by 6.09 dB when implementing digital filtering. This approached was then translated to isolated human neutrophils, where similarly, signal quality improved by 7.50 dB compared to its unfiltered original data. By sweeping all filtering conditions and devising a system to evaluate filtering performance both by signal quality and object counting accuracy, this may serve as a framework for future systems to determine their appropriately optimized filtering configuration.

Time-domain signal averaging to improve microparticles detection and enumeration accuracy in a microfluidic impedance cytometer.

Microfluidic impedance cytometry is a powerful system to measure micro and nano-sized particles and is routinely used in point-of-care disease diagnostics and other biomedical applications. However, small objects near a sensor's detection limit are plagued with relatively significant background noise and are difficult to identify for every case. While many data processing techniques can be utilized to reduce noise and improve signal quality, frequently they are still inadequate to push sensor detection limits. Here, we report the first demonstration of a novel signal averaging algorithm effective in noise reduction of microfluidic impedance cytometry data, improving enumeration accuracy, and reducing detection limits. Our device uses a 22 µm tall × 100 µm wide (with 30 µm wide focused aperture) microchannel and gold coplanar microelectrodes that generate an electric field, recording bipolar pulses from polystyrene microparticles flowing through the channel. In addition to outlining a modified moving signal averaging technique theoretically and with a model data set, we also performed a compendium of characterization experiments including variations in flow rate, input voltage, and particle size. Multivariate metrics from each experiment are compared including signal amplitude, pulse width, background noise, and signal-to-noise ratio (SNR). Incorporating our technique resulted in improved SNR and counting accuracy across all experiments conducted, and the limit of detection improved from 5 to 1 µm particles without modifying microchannel dimensions. Succeeding this, we envision implementing our modified moving average technique to develop next-generation microfluidic impedance cytometry devices with an expanded dynamic range and improved enumeration accuracy. This can be exceedingly useful for many biomedical applications, such as infectious disease diagnostics where devices may enumerate larger-scale immune cells alongside sub-micron bacterium in the same sample.

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.

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.

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.

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.

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.

Frequency-Time Domain (FTD) Impedance Data Analysis to Improve Accuracy of Microparticle Enumeration in a Microfluidic Electronic Counter.

Experimental background noise present in biosensors' data hinders the ability for sensitive and accurate detection of critical biomarkers. Here, we report our digital signal processing analysis with respect to frequency and time domain (FTD) data to reduce noise in an experimental microfluidic impedance cytometer. We evaluated the effectiveness of employed noise filtering techniques independently, including baseline drift correction, high frequency noise filtering, and powerline interference mitigation. We further explored the combined effect of all filters and determine improvements in signal-to-noise (SNR) ratio and particle counting accuracy. By removing noise regimes, SNR improved with this impedance cytometer device, and our future efforts will explore filtering effects of more specific and uncommon noise spectrums to greater optimize device performance.

Point-of-critical-care diagnostics for sepsis enabled by multiplexed micro and nanosensing technologies.

Sepsis is responsible for the highest economic and mortality burden in critical care settings around the world, prompting the World Health Organization in 2018 to designate it as a global health priority. Despite its high universal prevalence and mortality rate, a disproportionately low amount of sponsored research funding is directed toward diagnosis and treatment of sepsis, when early treatment has been shown to significantly improve survival. Additionally, current technologies and methods are inadequate to provide an accurate and timely diagnosis of septic patients in multiple clinical environments. For improved patient outcomes, a comprehensive immunological evaluation is critical which is comprised of both traditional testing and quantifying recently proposed biomarkers for sepsis. There is an urgent need to develop novel point-of-care, low-cost systems which can accurately stratify patients. These point-of-critical-care sensors should adopt a multiplexed approach utilizing multimodal sensing for heterogenous biomarker detection. For effective multiplexing, the sensors must satisfy criteria including rapid sample to result delivery, low sample volumes for clinical sample sparring, and reduced costs per test. A compendium of currently developed multiplexed micro and nano (M/N)-based diagnostic technologies for potential applications toward sepsis are presented. We have also explored the various biomarkers targeted for sepsis including immune cell morphology changes, circulating proteins, small molecules, and presence of infectious pathogens. An overview of different M/N detection mechanisms are also provided, along with recent advances in related nanotechnologies which have shown improved patient outcomes and perspectives on what future successful technologies may encompass. This article is categorized under: Diagnostic Tools > Biosensing.

Investigating Cell-Particle Conjugate Orientations in a Microfluidic Channel to Ameliorate Impedance-based Signal Acquisition and Detection.

Many biomedical experimental assays rely on cell-to-microparticle conjugation and their subsequent detection to quantify disease-related biomarkers. In this report, we investigated the effect of particle attachment position on a cell's surface to a signal acquired using impedance cytometry. We also present a novel configuration of independent coplanar microelectrodes positioned at the bottom and top of the microfluidic channel. In simulation results, our configuration accurately identifies different particle positions around the cell. We implemented a channel design with focusing regions between electrodes, and considered external factors around the channel such as polydimethylsiloxane (PDMS) interacting with the electric field and physical constraints of top electrodes placed farther away from the channel which improves detection accuracy.

Design of a Multiplexed Analyte Biosensor using Digital Barcoded Particles and Impedance Spectroscopy.

Multiplexing allows quantifying multiple analytes in a single step, providing advantages over individual testing through shorter processing time, lower sample volume, and reduced cost per test. Currently, flow cytometry is the gold standard for biomedical multiplexing, but requires technical training, extensive data processing, and expensive operational and capital costs. To solve this challenge, we designed digital barcoded particles and a microfluidic architecture for multiplexed analyte quantification. In this work, we simulate and model non-fluorescence-based microfluidic impedance detection with a single excitation and detection scheme using barcoded polymer microparticles. Our barcoded particles can be designed with specific coding regions and generate numerous distinct patterns enabling digital barcoding. We found that signals based on adhered microsphere position and relative orientation were evaluated and separated based on their associated electrical signatures and had a 7 µm microsphere limit of detection. Our proposed microfluidic system can enumerate micron-sized spheres in a single assay using barcoded particles of various configurations. As representation of blood cells, the microsphere concentrations may provide useful information on disease onset and progression. Such sensors may be used for diagnostic and management of common critical care diseases like sepsis, acute kidney injury, urinary tract infections, and HIV/AIDS.

Skin-inspired, open mesh electrochemical sensors for lactate and oxygen monitoring.

Research in wearable electronics has paved the way for next-generation technology, sought to create point-of-care biosensors that combine chemical sensing on a biocompatible platform with a broad range of applications in human health monitoring. Despite significant progress, the microspatial mechanical mismatch and fluid-impermeable interface presented between skin and the electronics create adscititious problems in device lamination, conformality, and long-term monitoring. Herein, we engineered a skin-inspired, deterministically patterned, electrochemical biosensor that can be fully integrated with the curvilinear surface of the human body, while mechanically adapting to the natural stresses applied to the skin and allowing the mass transfer of gas and fluids. In particular, we developed mechanically-compliant lattice-structured biosensors for the continuous evaluation of lactate and oxygen. Systematic studies of the sensor performance were evaluated with variations in polymeric membranes and its ability to withstand commonplace harsh, multiaxial stresses.

Wearable Technology for Chronic Wound Monitoring: Current Dressings, Advancements, and Future Prospects.

Chronic non-healing wounds challenge tissue regeneration and impair infection regulation for patients afflicted with this condition. Next generation wound care technology capable of in situ physiological surveillance which can diagnose wound parameters, treat various chronic wound symptoms, and reduce infection at the wound noninvasively with the use of a closed loop therapeutic system would provide patients with an improved standard of care and an accelerated wound repair mechanism. The indicating biomarkers specific to chronic wounds include blood pressure, temperature, oxygen, pH, lactate, glucose, interleukin-6 (IL-6), and infection status. A wound monitoring device would help decrease prolonged hospitalization, multiple doctors' visits, and the expensive lab testing associated with the diagnosis and treatment of chronic wounds. A device capable of monitoring the wound status and stimulating the healing process is highly desirable. In this review, we discuss the impaired physiological states of chronic wounds and explain the current treatment methods. Specifically, we focus on improvements in materials, platforms, fabrication methods for wearable devices, and quantitative analysis of various biomarkers vital to wound healing progress.

Asymptotic tracking and disturbance rejection of the blood glucose regulation system.

Type 1 diabetes patients need external insulin to maintain blood glucose within a narrow range from 65 to 108 mg/dl (3.6 to 6.0 mmol/l). A mathematical model for the blood glucose regulation is required for integrating a glucose monitoring system into insulin pump technology to form a closed-loop insulin delivery system on the feedback of the blood glucose, the so-called "artificial pancreas". The objective of this paper is to treat the exogenous glucose from food as a glucose disturbance and then develop a closed-loop feedback and feedforward control system for the blood glucose regulation system subject to the exogenous glucose disturbance. For this, a mathematical model for the glucose disturbance is proposed on the basis of experimental data, and then incorporated into an existing blood glucose regulation model. Because all the eigenvalues of the disturbance model have zero real parts, the center manifold theory is used to establish blood glucose regulator equations. We then use their solutions to synthesize a required feedback and feedforward controller to reject the disturbance and asymptotically track a constant glucose reference of 90  mg/dl. Since the regulator equations are nonlinear partial differential equations and usually impossible to solve analytically, a linear approximation solution is obtained. Our numerical simulations show that, under the linear approximate feedback and feedforward controller, the blood glucose asymptotically tracks its desired level of 90 mg/dl approximately.