scholarly journals Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 625 ◽  
Author(s):  
Elmer Ccopa Rivera ◽  
Jonathan J. Swerdlow ◽  
Rodney L. Summerscales ◽  
Padma P. Tadi Uppala ◽  
Rubens Maciel Filho ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Point Of Care ◽  
Low Cost ◽  
Data Driven ◽  
Sensor Data ◽  
Detection Range ◽  
Multimodal Data ◽  
Key Features ◽  
Predicted Values ◽  
Data Driven Modeling

Understanding relationships among multimodal data extracted from a smartphone-based electrochemiluminescence (ECL) sensor is crucial for the development of low-cost point-of-care diagnostic devices. In this work, artificial intelligence (AI) algorithms such as random forest (RF) and feedforward neural network (FNN) are used to quantitatively investigate the relationships between the concentration of   Ru ( bpy ) 3 2 + luminophore and its experimentally measured ECL and electrochemical data. A smartphone-based ECL sensor with   Ru ( bpy ) 3 2 + /TPrA was developed using disposable screen-printed carbon electrodes. ECL images and amperograms were simultaneously obtained following 1.2-V voltage application. These multimodal data were analyzed by RF and FNN algorithms, which allowed the prediction of   Ru ( bpy ) 3 2 + concentration using multiple key features. High correlation (0.99 and 0.96 for RF and FNN, respectively) between actual and predicted values was achieved in the detection range between 0.02 µM and 2.5 µM. The AI approaches using RF and FNN were capable of directly inferring the concentration of   Ru ( bpy ) 3 2 + using easily observable key features. The results demonstrate that data-driven AI algorithms are effective in analyzing the multimodal ECL sensor data. Therefore, these AI algorithms can be an essential part of the modeling arsenal with successful application in ECL sensor data modeling.

scholarly journals AnemoCheck-LRS: an optimized, color-based point-of-care test to identify severe anemia in limited-resource settings

BMC Medicine ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Marina S. Perez-Plazola ◽  
Erika A. Tyburski ◽  
Luke R. Smart ◽  
Thad A. Howard ◽  
Amanda Pfeiffer ◽  
...  
Keyword(s):  
Screening Tool ◽  
Point Of Care ◽  
Low Cost ◽  
Limited Resource ◽  
Absolute Difference ◽  
Severe Anemia ◽  
Detection Range ◽  
Point Of Care Test ◽  
Colour Scale ◽  
Life Saving

Abstract Background Severe anemia is common and frequently fatal for hospitalized patients in limited-resource settings. Lack of access to low-cost, accurate, and rapid diagnosis of anemia impedes the delivery of life-saving care and appropriate use of the limited blood supply. The WHO Haemoglobin Colour Scale (HCS) is a simple low-cost test but frequently inaccurate. AnemoCheck-LRS (limited-resource settings) is a rapid, inexpensive, color-based point-of-care (POC) test optimized to diagnose severe anemia. Methods Deidentified whole blood samples were diluted with plasma to create variable hemoglobin (Hb) concentrations, with most in the severe (≤ 7 g/dL) or profound (≤ 5 g/dL) anemia range. Each sample was tested with AnemoCheck-LRS and WHO HCS independently by three readers and compared to Hb measured by an electronic POC test (HemoCue 201+) and commercial hematology analyzer. Results For 570 evaluations within the limits of detection of AnemoCheck-LRS (Hb ≤ 8 g/dL), the average difference between AnemoCheck-LRS and measured Hb was 0.5 ± 0.4 g/dL. In contrast, the WHO HCS overestimated Hb with an absolute difference of 4.9 ± 1.3 g/dL for samples within its detection range (Hb 4–14 g/dL, n = 405). AnemoCheck-LRS was much more sensitive (92%) for the diagnosis of profound anemia than WHO HCS (22%). Conclusions AnemoCheck-LRS is a rapid, inexpensive, and accurate POC test for anemia. AnemoCheck-LRS is more accurate than WHO HCS for detection of low Hb levels, severe anemia that may require blood transfusion. AnemoCheck-LRS should be tested prospectively in limited-resource settings where severe anemia is common, to determine its utility as a screening tool to identify patients who may require transfusion.

Applications of AI and Data-Driven Modeling in Energy Production and Marketing Processes

2021 ◽  
Author(s):  
Joy Ugoyah ◽  
Anita Mary Igbine
Keyword(s):  
Artificial Intelligence ◽  
Oil And Gas ◽  
Health And Safety ◽  
Oil And Gas Industry ◽  
Data Driven ◽  
Gas Industry ◽  
Energy Needs ◽  
Safety Risks ◽  
Data Driven Modeling ◽  
Applications Of Ai

Abstract Faster and more accurate decisions are what the Oil and Gas industry needs with the world's fast-evolving energy needs and economy. The area of Artificial intelligence and Data-driven modelling is relatively new and has not found popular application in the industry. AI is an emerging technology that can be used to predict event outcomes and automate anomaly-detection processes. The various applications of AI in different industries were researched into. This paper highlighted important processes that can be improved with the application of Artificial Intelligence through data-driven modelling. It also highlights areas in the various industries where AI intelligence is already being applied and ways it can be improved. AI and data-driven modelling has the potential to improve exploration accuracy, reduce production down-time, reduce cost of maintenance, and reduce health and safety risks. This body of information can serve as a guideline for adopting AI in the oil and gas industry. A trend of industry-tailored intelligence solutions would be more effective in the evolving energy industry.

scholarly journals Design and Development of Point of Care Test and Optical Reader for Early Screening of Kidney Related Disorder

2020 ◽  
Author(s):  
Pankaj Shihvare ◽  
Satyam Mohla ◽  
Tejal Dube ◽  
Alok Verma ◽  
Rohit Srivastava
Keyword(s):  
Point Of Care ◽  
Low Cost ◽  
Early Screening ◽  
Related Disorder ◽  
Detection Range ◽  
Spot Urine ◽  
Point Of Care Test ◽  
Optical Reader ◽  
Colorimetric Assays ◽  
Urine Testing

AbstractLow-cost, paper-based colorimetric assays for early screening of albumin, creatinine and their ratio have been developed. The developed methods are noninvasive and require only 10µl of the urine sample. A reflectance-based optical reader has also been developed for the quantification of the albumin and creatinine. The developed method is based on spot urine testing which is advantageous when compared to the conventional 24-hour urine collection. The detection range of albumin and creatinine assays is 10-150 mg/dl and 25–400 mg/dl, respectively. The developed assays and optical reader were tested with the chronic kidney diseased patient’s samples at KEM Hospital, Mumbai.

scholarly journals A Low-Cost Biological Agglutination Assay for Medical Diagnostic Applications

2018 ◽  
Author(s):  
Nicolas Kylilis ◽  
Pinpunya Riangrungroj ◽  
Hung-En Lai ◽  
Valencio Salema ◽  
Luis Ángel Fernández ◽  
...  
Keyword(s):  
Diagnostic Tests ◽  
Point Of Care ◽  
Low Cost ◽  
Field Testing ◽  
Human Fibrinogen ◽  
Detection Range ◽  
Agglutination Assay ◽  
E Coli ◽  
Medical Diagnostic ◽  
Diagnostic Applications

ABSTRACTAffordable, easy-to-use diagnostic tests that can be readily deployed for point-of-care (POC) testing are key in addressing challenges in the diagnosis of medical conditions and for improving global health in general. Ideally, POC diagnostic tests should be highly selective for the biomarker, user-friendly, have a flexible design architecture and a low cost of production. Here we developed a novel agglutination assay based on wholeE. colicells surface-displaying nanobodies which bind selectively to a target protein analyte. As a proof-of-concept, we show the feasibility of this design as a new diagnostic platform by the detection of a model analyte at nanomolar concentrations. Moreover, we show that the design architecture is flexible by building assays optimized to detect a range of model analyte concentrations supported using straight-forward design rules and a mathematical model. Finally, we re-engineerE. colicells for the detection of a medically relevant biomarker by the display of two different antibodies against the human fibrinogen and demonstrate a detection limit as low as 10 pM in diluted human plasma. Overall, we demonstrate that our agglutination technology fulfills the requirement of POC testing by combining low-cost nanobody production, customizable detection range and low detection limits. This technology has the potential to produce affordable diagnostics for both field-testing in the developing world, emergency or disaster relief sites as well as routine medical testing and personalized medicine.

scholarly journals “Zhores” — Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology

2019 ◽  
Vol 9 (1) ◽  
pp. 512-520 ◽  
Author(s):  
Igor Zacharov ◽  
Rinat Arslanov ◽  
Maksim Gunin ◽  
Daniil Stefonishin ◽  
Andrey Bykov ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Predictive Analytics ◽  
Science And Technology ◽  
Data Driven ◽  
Scientific Instrument ◽  
Science And Engineering ◽  
Data Intensive ◽  
Cluster Properties ◽  
Data Driven Modeling

AbstractThe Petaflops supercomputer “Zhores” recently launched in the “Center for Computational and Data-Intensive Science and Engineering” (CDISE) of Skolkovo Institute of Science and Technology (Skoltech) opens up new exciting opportunities for scientific discoveries in the institute especially in the areas of data-driven modeling, machine learning and artificial intelligence. This supercomputer utilizes the latest generation of Intel and NVidia processors to provide resources for the most compute intensive tasks of the Skoltech scientists working in digital pharma, predictive analytics, photonics, material science, image processing, plasma physics and many more. Currently it places 7th in the Russian and CIS TOP-50 (2019) supercomputer list. In this article we summarize the cluster properties and discuss the measured performance and usage modes of this new scientific instrument in Skoltech.

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