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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.