Machine learning enabled flexible and low-cost plasmonic sensors for point-of-care sensing (Conference Presentation)

2018 ◽  
Author(s):  
Zachary Ballard ◽  
Daniel Shir ◽  
Shyama Sathianathan ◽  
Sarah Bazargan ◽  
Aashish Bhardwaj ◽  
...  
Keyword(s):  
Machine Learning ◽  
Point Of Care ◽  
Low Cost ◽  
Plasmonic Sensors

scholarly journals Deep Learning-Enabled Point-of-Care Sensing Using Multiplexed Paper-Based Sensors

2019 ◽  
Author(s):  
Zachary Ballard ◽  
Hyou-Arm Joung ◽  
Artem Goncharov ◽  
Jesse Liang ◽  
Karina Nugroho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Point Of Care ◽  
Low Cost ◽  
High Sensitivity ◽  
Cost Effective ◽  
Serum Samples ◽  
Cvd Risk ◽  
Medical Test ◽  
Sensing Membrane

ABSTRACTWe present a deep learning-based framework to design and quantify point-of-care sensors. As its proof-of-concept and use-case, we demonstrated a low-cost and rapid paper-based vertical flow assay (VFA) for high sensitivity C-Reactive Protein (hsCRP) testing, a common medical test used for quantifying the degree of inflammation in patients at risk of cardio-vascular disease (CVD). A machine learning-based sensor design framework was developed for two key tasks: (1) to determine an optimal configuration of immunoreaction spots and conditions, spatially-multiplexed on a paper-based sensing membrane, and (2) to accurately infer the target analyte concentration based on the signals of the optimal VFA configuration. Using a custom-designed mobile-phone based VFA reader, a clinical study was performed with 85 human serum samples to characterize the quantification accuracy around the clinically defined cutoffs for CVD risk stratification. Results from blindly-tested VFAs indicate a competitive coefficient of variation of 11.2% with a linearity of R2 = 0.95; in addition to the success in the high-sensitivity CRP range (i.e., 0-10 mg/L), our results further demonstrate a mitigation of the hook-effect at higher CRP concentrations due to the incorporation of antigen capture spots within the multiplexed sensing membrane of the VFA. This paper-based computational VFA that is powered by deep learning could expand access to CVD health screening, and the presented machine learning-enabled sensing framework can be broadly used to design cost-effective and mobile sensors for various point-of-care diagnostics applications.

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

2019 ◽  
Author(s):  
Qiannan Duan ◽  
Jianchao Lee ◽  
Jinhong Gao ◽  
Jiayuan Chen ◽  
Yachao Lian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Toxicity ◽  
Model Building ◽  
Low Cost ◽  
Enzyme Reaction ◽  
Chemical Effect ◽  
Technological Innovations ◽  
Acute Toxicity Test ◽  
Toxicity Prediction ◽  
Traditional Understanding

<p>Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.</p>

scholarly journals Validation of a point-of-care rapid diagnostic test for hepatitis C for use in resource-limited settings

2019 ◽  
Vol 11 (4) ◽  
pp. 314-315
Author(s):  
James S Leathers ◽  
Maria Belen Pisano ◽  
Viviana Re ◽  
Gertine van Oord ◽  
Amir Sultan ◽  
...  
Keyword(s):  
Point Of Care ◽  
Low Cost ◽  
Rapid Diagnosis ◽  
Direct Acting Antivirals ◽  
Resource Limited Settings ◽  
Rapid Diagnosis Test ◽  
Resource Limited ◽  
Specificity And Sensitivity ◽  
Hcv Screening ◽  
Direct Acting

Abstract Background Treatment of HCV with direct-acting antivirals has enabled the discussion of HCV eradication worldwide. Envisioning this aim requires implementation of mass screening in resource-limited areas, usually constrained by testing costs. Methods We validated a low-cost, rapid diagnosis test (RDT) for HCV in three different continents in 141 individuals. Results The HCV RDT showed 100% specificity and sensitivity across different samples regardless of genotype or viral load (in samples with such information, 90%). Conclusions The HCV test validated in this study can allow for HCV screening in areas of need when properly used.

scholarly journals Rapid and Sensitive Detection of miRNA Based on AC Electrokinetic Capacitive Sensing for Point-of-Care Applications

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3985
Author(s):  
Nan Wan ◽  
Yu Jiang ◽  
Jiamei Huang ◽  
Rania Oueslati ◽  
Shigetoshi Eda ◽  
...  
Keyword(s):  
Limit Of Detection ◽  
Point Of Care ◽  
Low Cost ◽  
Clinical Diagnostics ◽  
Detection Methods ◽  
Capacitive Sensing ◽  
Application Potential ◽  
Mirna Detection ◽  
Mirna 25 ◽  
Low Sensitivity

A sensitive and efficient method for microRNAs (miRNAs) detection is strongly desired by clinicians and, in recent years, the search for such a method has drawn much attention. There has been significant interest in using miRNA as biomarkers for multiple diseases and conditions in clinical diagnostics. Presently, most miRNA detection methods suffer from drawbacks, e.g., low sensitivity, long assay time, expensive equipment, trained personnel, or unsuitability for point-of-care. New methodologies are needed to overcome these limitations to allow rapid, sensitive, low-cost, easy-to-use, and portable methods for miRNA detection at the point of care. In this work, to overcome these shortcomings, we integrated capacitive sensing and alternating current electrokinetic effects to detect specific miRNA-16b molecules, as a model, with the limit of detection reaching 1.0 femto molar (fM) levels. The specificity of the sensor was verified by testing miRNA-25, which has the same length as miRNA-16b. The sensor we developed demonstrated significant improvements in sensitivity, response time and cost over other miRNA detection methods, and has application potential at point-of-care.

scholarly journals Thoracic Point-of-Care Ultrasound: A SARS-CoV-2 Data Repository for Future Artificial Intelligence and Machine Learning

2021 ◽  
pp. 155335062110186
Author(s):  
Abdel-Moneim Mohamed Ali ◽  
Emran El-Alali ◽  
Adam S. Weltz ◽  
Scott T. Rehrig
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Point Of Care ◽  
Prognostic Indicators ◽  
Data Repository ◽  
Point Of Care Ultrasound ◽  
Ultrasound Images ◽  
Clinical Protocol ◽  
Improved Outcomes ◽  
Current Experience

Current experience suggests that artificial intelligence (AI) and machine learning (ML) may be useful in the management of hospitalized patients, including those with COVID-19. In light of the challenges faced with diagnostic and prognostic indicators in SARS-CoV-2 infection, our center has developed an international clinical protocol to collect standardized thoracic point of care ultrasound data in these patients for later AI/ML modeling. We surmise that in the future AI/ML may assist in the management of SARS-CoV-2 patients potentially leading to improved outcomes, and to that end, a corpus of curated ultrasound images and linked patient clinical metadata is an invaluable research resource.

scholarly journals A glucose meter interface for point-of-care gene circuit-based diagnostics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Evan Amalfitano ◽  
Margot Karlikow ◽  
Masoud Norouzi ◽  
Katariina Jaenes ◽  
Seray Cicek ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
Synthetic Biology ◽  
Molecular Diagnostics ◽  
Point Of Care ◽  
Low Cost ◽  
Gene Circuit ◽  
Reporter Systems ◽  
Glucose Meter ◽  
Pandemic Response ◽  
Glucose Output

AbstractRecent advances in cell-free synthetic biology have given rise to gene circuit-based sensors with the potential to provide decentralized and low-cost molecular diagnostics. However, it remains a challenge to deliver this sensing capacity into the hands of users in a practical manner. Here, we leverage the glucose meter, one of the most widely available point-of-care sensing devices, to serve as a universal reader for these decentralized diagnostics. We describe a molecular translator that can convert the activation of conventional gene circuit-based sensors into a glucose output that can be read by off-the-shelf glucose meters. We show the development of new glucogenic reporter systems, multiplexed reporter outputs and detection of nucleic acid targets down to the low attomolar range. Using this glucose-meter interface, we demonstrate the detection of a small-molecule analyte; sample-to-result diagnostics for typhoid, paratyphoid A/B; and show the potential for pandemic response with nucleic acid sensors for SARS-CoV-2.

scholarly journals Demonstration of a Label-Free and Low-Cost Optical Cavity-Based Biosensor Using Streptavidin and C-Reactive Protein

Biosensors ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 4
Author(s):  
Donggee Rho ◽  
Seunghyun Kim
Keyword(s):  
Limit Of Detection ◽  
Point Of Care ◽  
Low Cost ◽  
Optical Cavity ◽  
Sample Volume ◽  
Small Sample ◽  
Label Free ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Cavity Structure

An optical cavity-based biosensor (OCB) has been developed for point-of-care (POC) applications. This label-free biosensor employs low-cost components and simple fabrication processes to lower the overall cost while achieving high sensitivity using a differential detection method. To experimentally demonstrate its limit of detection (LOD), we conducted biosensing experiments with streptavidin and C-reactive protein (CRP). The optical cavity structure was optimized further for better sensitivity and easier fluid control. We utilized the polymer swelling property to fine-tune the optical cavity width, which significantly improved the success rate to produce measurable samples. Four different concentrations of streptavidin were tested in triplicate, and the LOD of the OCB was determined to be 1.35 nM. The OCB also successfully detected three different concentrations of human CRP using biotinylated CRP antibody. The LOD for CRP detection was 377 pM. All measurements were done using a small sample volume of 15 µL within 30 min. By reducing the sensing area, improving the functionalization and passivation processes, and increasing the sample volume, the LOD of the OCB are estimated to be reduced further to the femto-molar range. Overall, the demonstrated capability of the OCB in the present work shows great potential to be used as a promising POC biosensor.

Sign in / Sign up

Export Citation Format

Share Document