A Portable Smartphone-Based Sensing System Using a 3D-Printed Chip for On-Site Biochemical Assays

Recently, smartphone-based chromogenic sensing with paper-based microfluidic technology has played an increasingly important role in biochemical assays. However, generally there were three defects: (i) the paper-based chips still required complicated fabrication, and the hydrophobic boundaries on the chips were not clear enough; (ii) the chromogenic signals could not be steadily captured; (iii) the smartphone apps were restricted to the detection of specific target analytes and could not be extended for different assays unless reprogrammed. To solve these problems, in this study, a portable smartphone-based sensing system with a 3D-printed chip was developed. A 3D-printed imaging platform was designed to significantly reduce sensing errors generated during signal capture, and a brand-new strategy for signal processing in downloadable apps was established. As a proof-of-concept, the system was applied for detection of organophosphorus pesticides and multi-assay of fruit juice, showing excellent sensing performance. For different target analytes, the most efficient color channel could be selected for signal analysis, and the calibration equation could be directly set in user interface rather than programming environment, thus the developed system could be flexibly extended for other biochemical assays. Consequently, this study provides a novel methodology for smartphone-based biochemical sensing.

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3D Printed Smart Silk Wearable Sensors

The Analyst ◽

10.1039/d0an02292f ◽

2021 ◽

Author(s):

Tianshu Chu ◽

Huili Wang ◽

Yumeng Qiu ◽

Haoxi Luo ◽

Bingfang He ◽

...

Keyword(s):

Point Of Care ◽

Wearable Sensors ◽

Point Of Care Testing ◽

Biochemical Sensing ◽

3D Printed ◽

Physiological And Biochemical

Wearable sensors play a key role in point-of-care testing (POCT) for its flexible and integration capability on sensitive physiological and biochemical sensing. Here, we present a multifunction wearable silk patch...

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Development of a sensing system based on coupling magnetic solid phase extraction and colorimetric detection for determination of organophosphorus pesticides in fruit extract and environmental sample

Sensors and Actuators B Chemical ◽

10.1016/j.snb.2021.130157 ◽

2021 ◽

pp. 130157

Author(s):

Faranak Faghiri ◽

Maryam Hajjami ◽

Farshid Ghorbani

Keyword(s):

Solid Phase Extraction ◽

Solid Phase ◽

Organophosphorus Pesticides ◽

Colorimetric Detection ◽

Magnetic Solid Phase Extraction ◽

Phase Extraction ◽

Fruit Extract ◽

Sensing System ◽

Magnetic Solid

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A quantitative sensing system based on a 3D-printed ion-selective electrode for rapid and sensitive detection of bacteria in biological fluid

Talanta ◽

10.1016/j.talanta.2021.123040 ◽

2021 ◽

pp. 123040

Author(s):

Tao Zhang ◽

Giraso Keza Monia Kabandana ◽

Adam Michael Ratajczak ◽

Chengpeng Chen

Keyword(s):

Biological Fluid ◽

Ion Selective Electrode ◽

Sensitive Detection ◽

Selective Electrode ◽

Sensing System ◽

3D Printed

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Classification Models for Skin Tumor Detection Using Texture Analysis in Medical Images

Journal of Imaging ◽

10.3390/jimaging6060051 ◽

2020 ◽

Vol 6 (6) ◽

pp. 51

Author(s):

Marcos A. M. Almeida ◽

Iury A. X. Santos

Keyword(s):

Early Diagnosis ◽

Medical Images ◽

Skin Tumor ◽

Classification Models ◽

Color Channel ◽

Second Order Statistics ◽

New Strategy ◽

Channel Information ◽

Occurrence Matrix

Medical images have made a great contribution to early diagnosis. In this study, a new strategy is presented for analyzing medical images of skin with melanoma and nevus to model, classify and identify lesions on the skin. Machine learning applied to the data generated by first and second order statistics features, Gray Level Co-occurrence Matrix (GLCM), keypoints and color channel information—Red, Green, Blue and grayscale images of the skin were used to characterize decisive information for the classification of the images. This work proposes a strategy for the analysis of skin images, aiming to choose the best mathematical classifier model, for the identification of melanoma, with the objective of assisting the dermatologist in the identification of melanomas, especially towards an early diagnosis.

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A biochemical sensing system using an 11-MUA/calix[6]arene bilayer to sense amine vapors

Journal of Micromechanics and Microengineering ◽

10.1088/0960-1317/17/8/004 ◽

2007 ◽

Vol 17 (8) ◽

pp. 1435-1441 ◽

Cited By ~ 7

Author(s):

Da-Jeng Yao ◽

Yong-Ruei Yang ◽

Chu-Chun Tai ◽

Wen-Hsiu Hsiao ◽

Yong-Chien Ling

Keyword(s):

Sensing System ◽

Biochemical Sensing

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A lanthanide-based ratiometric fluorescent biosensor for the enzyme-free detection of organophosphorus pesticides

Analytical Methods ◽

10.1039/d1ay00345c ◽

2021 ◽

Author(s):

Tianlin Wang ◽

Yaqing Liu

Keyword(s):

Organophosphorus Pesticides ◽

Simple Operation ◽

Fluorescent Sensing ◽

Fluorescent Biosensor ◽

Sensing System ◽

Short Time

A terbium-based ratiometric fluorescent sensing system was developed for OPs detection with the merits of enzyme-free, simple operation, short-time and sensitive.

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Determination of Organophosphorus Pesticides Using Solid-Phase Extraction Followed by Gas Chromatography–Mass Spectrometry

Journal of Chromatographic Science ◽

10.1093/chromsci/bmab033 ◽

2021 ◽

Author(s):

Peng Sun ◽

Shusheng Zheng ◽

Rui Yan ◽

Yongfu Lian

Keyword(s):

Mass Spectrometry ◽

Gas Chromatography ◽

Solid Phase Extraction ◽

Solid Phase ◽

Organophosphorus Pesticides ◽

Gas Chromatography Mass Spectrometry ◽

Phase Extraction ◽

Chromatography Mass Spectrometry ◽

Target Analytes

Abstract In this study, a method was developed for determination of three organophosphorus pesticides (OPPs) using graphene aerogel solid-phase extraction (SPE) combined with gas chromatography–mass spectrometry (GC–MS). The experimental results showed that the target analytes could be extracted on packed SPE cartridges and then eluted with tetrahydrofuran. The final sample solution was analyzed by GC–MS, which demonstrated good linearity between 0.5 and 500 μg L−1 with a correlation coefficient (r) of 0.9991–0.9998. The limits of detection (S/N = 3) and the limits of quantification (S/N = 10) for the three OPPs ranged from 0.38 to 0.82 μg L−1 and 1.32 to 2.75 μg L−1, respectively. The accuracy of the proposed method was evaluated by measuring the recovery of the spiked samples, which ranged from 91.2 to 103.7%, with relative standard deviations of 2.1–7.2%. This method was successfully applied for the determination of the target analytes in water samples taken from tap, wetlands and canal water.

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Machine learning classification can reduce false positives in structure-based virtual screening

10.1101/2020.01.10.902411 ◽

2020 ◽

Cited By ~ 1

Author(s):

Yusuf Adeshina ◽

Eric Deeds ◽

John Karanicolas

Keyword(s):

Virtual Screening ◽

Acetylcholinesterase Inhibitors ◽

General Purpose ◽

Training Dataset ◽

Screening Methods ◽

Scoring Functions ◽

Machine Learning Classification ◽

Biochemical Assays ◽

New Strategy ◽

Boosted Decision Trees

AbstractWith the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discovery’s search for active chemical matter. Modern virtual screening methods are still, however, plagued with high false positive rates: typically, only about 12% of the top-scoring compounds actually show activity when tested in biochemical assays. We argue that most scoring functions used for this task have been developed with insufficient thoughtfulness into the datasets on which they are trained and tested, leading to overly simplistic models and/or overtraining. These problems are compounded in the literature because none of the studies reporting new scoring methods have validated their model prospectively within the same study. Here, we report a new strategy for building a training dataset (D-COID) that aims to generate highly-compelling decoy complexes that are individually matched to available active complexes. Using this dataset, we train a general-purpose classifier for virtual screening (vScreenML) that is built on the XGBoost framework of gradient-boosted decision trees. In retrospective benchmarks, our new classifier shows outstanding performance relative to other scoring functions. We additionally evaluate the classifier in a prospective context, by screening for new acetylcholinesterase inhibitors. Remarkably, we find that nearly all compounds selected by vScreenML show detectable activity at 50 µM, with 10 of 23 providing greater than 50% inhibition at this concentration. Without any medicinal chemistry optimization, the most potent hit from this initial screen has an IC50 of 280 nM, corresponding to a Ki value of 173 nM. These results support using the D-COID strategy for training classifiers in other computational biology tasks, and for vScreenML in virtual screening campaigns against other protein targets. Both D-COID and vScreenML are freely distributed to facilitate such efforts.

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