Dataset Information

A smartphone based device for the detection of sulfane sulfurs in biological systems.

ABSTRACT: Sulfane sulfur species are newly recognized signaling molecules that play physiological roles in many biological events. The development of new technologies for the detection of sulfane sulfurs is important. Point-of-care (POC) devices are in-field rapid and low-cost detectors that are more convenient to use than bulky and expensive standard instruments. In this report, a new fluorescent probe (SSP5) was designed to detect sulfane sulfurs using a POC sulfane sulfur smartphone spectrum apparatus (S4A). This probe proved to be sensitive and selective for sulfane sulfur species over other biologically relevant sulfur species such as cysteine and H2S. The low-cost and compact S4A has achieved comparable performance to standard laboratory equipment in both a standard buffer system and a synthetic urine system. The proposed system (SSP5 + S4A) has the potential for high accuracy and rapid detection of sulfane sulfur species in remote and low resource settings.

SUBMITTER: Neill DL

PROVIDER: S-EPMC6625781 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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A smartphone based device for the detection of sulfane sulfurs in biological systems.

Neill Deshka L DL Chang Yu-Chung YC Chen Wei W Li Lei L Xian Ming M

Sensors and actuators. B, Chemical 20190424

Sulfane sulfur species are newly recognized signaling molecules that play physiological roles in many biological events. The development of new technologies for the detection of sulfane sulfurs is important. Point-of-care (POC) devices are in-field rapid and low-cost detectors that are more convenient to use than bulky and expensive standard instruments. In this report, a new fluorescent probe (SSP5) was designed to detect sulfane sulfurs using a POC sulfane sulfur smartphone spectrum apparatus ...[more]

PMID: 31303694

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Project description:Diabetic Retinopathy (DR) may result in various degrees of vision loss and even blindness if not diagnosed in a timely manner. Therefore, having an annual eye exam helps early detection to prevent vision loss in earlier stages, especially for diabetic patients. Recent technological advances made smartphone-based retinal imaging systems available on the market to perform small-sized, low-powered, and affordable DR screening in diverse environments. However, the accuracy of DR detection depends on the field of view and image quality. Since smartphone-based retinal imaging systems have much more compact designs than a traditional fundus camera, captured images are likely to be the low quality with a smaller field of view. Our motivation in this paper is to develop an automatic DR detection model for smartphone-based retinal images using the deep learning approach with the ResNet50 network. This study first utilized the well-known AlexNet, GoogLeNet, and ResNet50 architectures, using the transfer learning approach. Second, these frameworks were retrained with retina images from several datasets including EyePACS, Messidor, IDRiD, and Messidor-2 to investigate the effect of using images from the single, cross, and multiple datasets. Third, the proposed ResNet50 model is applied to smartphone-based synthetic images to explore the DR detection accuracy of smartphone-based retinal imaging systems. Based on the vision-threatening diabetic retinopathy detection results, the proposed approach achieved a high classification accuracy of 98.6%, with a 98.2% sensitivity and a 99.1% specificity while its AUC was 0.9978 on the independent test dataset. As the main contributions, DR detection accuracy was improved using the deep transfer learning approach for the ResNet50 network with publicly available datasets and the effect of the field of view in smartphone-based retinal imaging was studied. Although a smaller number of images were used in the training set compared with the existing studies, considerably acceptable high accuracies for validation and testing data were obtained.

Dataset Information

A smartphone based device for the detection of sulfane sulfurs in biological systems.

Publications

A smartphone based device for the detection of sulfane sulfurs in biological systems.

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