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Biosensors
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Smartphone-Based Sensors for Biomedical Applications
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Smartphone-Based Sensors for Biomedical Applications

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensor and Bioelectronic Devices".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 41976

Special Issue Editors

Dr. Yanli Lu

E-Mail Website
Guest Editor
Biosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: biosensors; smartphone based biosensors; wearable biosensors; electrochemical sensor; optical biosensors; healthcare monitoring; new electroanalytical methodology applied to environmental, food, and health fields
Special Issues, Collections and Topics in MDPI journals
Dr. Shuang Li

E-Mail Website
Guest Editor
Medical College, Tianjin University, Tianjin, China
Interests: intelligent medical sensing; smartphone-based biosensors; wearable monitoring; visual detection; POCT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smartphones have become ideal control, interaction, and analysis tools in on-site sensing systems due to their operability, connectivity, portability, and built-in sensors. With a large population of mobile Internet users, smartphone-based sensors can transform traditional professional testing into a kind of rapid and real-time detection in which everyone can participate anywhere and anytime. The ubiquity of smartphones will also greatly improve the scope of portable devices for different applications such as food analysis, environmental monitoring, and biomedical detection. How smartphone-based sensors are designed and/or developed for detecting and analyzing different targets is of great interest to researchers and can be extremely challenging. Furthermore, how to make use of the built-in functions of mobile phones, such as cameras, data processing, and physical sensors, to further simplify the sensing detection process is also worth investigating.

This Special Issue of the international journal Biosensors (2020 IF = 5.519) aims at collecting and presenting the latest advancements in smartphone-based sensors for biomedical applications, from the design of new devices to experimental verification, and up to wearable and implantable applications. Original research articles as well as review papers are welcome.

We look forward to receiving your outstanding research outcomes.

Dr. Yanli Lu
Dr. Shuang Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biosensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smartphone-based sensors
  • optical sensing
  • electrochemical sensing
  • lab-on-a-chip
  • portable or wearable device
  • integrated systems
  • multiplexed detection
  • in vitro or in vivo monitoring
  • point-of-care Testing (POCT)
  • biomedical applications

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Published Papers (7 papers)

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Research

Jump to: Review

17 pages, 1790 KiB  
Article
A Smartphone-Based Low-Cost Inverted Laser Fluorescence Microscope for Disease Diagnosis
by Omar Ormachea, Alex Villazón, Patricia Rodriguez and Mirko Zimic
Biosensors 2022, 12(11), 960; https://doi.org/10.3390/bios12110960 - 2 Nov 2022
Cited by 1 | Viewed by 3444
Abstract
Fluorescence microscopy is an important tool for disease diagnosis, often requiring costly optical components, such as fluorescence filter cubes and high-power light sources. Due to its high cost, conventional fluorescence microscopy cannot be fully exploited in low-income settings. Smartphone-based fluorescence microscopy becomes an [...] Read more.
Fluorescence microscopy is an important tool for disease diagnosis, often requiring costly optical components, such as fluorescence filter cubes and high-power light sources. Due to its high cost, conventional fluorescence microscopy cannot be fully exploited in low-income settings. Smartphone-based fluorescence microscopy becomes an interesting low-cost alternative, but raises challenges in the optical system. We present the development of a low-cost inverted laser fluorescence microscope that uses a smartphone to visualize the fluorescence image of biological samples. Our fluorescence microscope uses a laser-based simplified optical filter system that provides analog optical filtering capabilities of a fluorescence filter cube. Firstly, we validated our inverted optical filtering by visualizing microbeads labeled with three different fluorescent compounds or fluorophores commonly used for disease diagnosis. Secondly, we validated the disease diagnosis capabilities by comparing the results of our device with those of a commercial fluorescence microscope. We successfully detected and visualized Trypanosoma cruzi parasites, responsible for the Chagas infectious disease and the presence of Antineutrophil cytoplasmic antibodies of the ANCA non-communicable autoimmune disease. The samples were labeled with the fluorescein isothiocyanate (FITC) fluorophore, one of the most commonly used fluorophores for disease diagnosis. Our device provides a 400× magnification and is at least one order of magnitude cheaper than conventional commercial fluorescence microscopes. Full article
(This article belongs to the Special Issue Smartphone-Based Sensors for Biomedical Applications)
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13 pages, 4343 KiB  
Article
Compact Smartphone-Based Laser Speckle Contrast Imaging Endoscope Device for Point-of-Care Blood Flow Monitoring
by Youngkyu Kim, Woo June Choi, Jungmin Oh and Jun Ki Kim
Biosensors 2022, 12(6), 398; https://doi.org/10.3390/bios12060398 - 9 Jun 2022
Cited by 6 | Viewed by 4633
Abstract
Laser speckle contrast imaging (LSCI) is a powerful visualization tool for quantifying blood flow in tissues, providing simplicity of configuration, ease of use, and intuitive results. With recent advancements, smartphone and camera technologies are suitable for the development of smartphone-based LSCI applications for [...] Read more.
Laser speckle contrast imaging (LSCI) is a powerful visualization tool for quantifying blood flow in tissues, providing simplicity of configuration, ease of use, and intuitive results. With recent advancements, smartphone and camera technologies are suitable for the development of smartphone-based LSCI applications for point-of-care (POC) diagnosis. A smartphone-based portable LSCI endoscope system was validated for POC diagnosis of vascular disorders. The endoscope consisted of compact LED and laser illumination, imaging optics, and a flexible fiberscope assembled in a 3D-printed hand-held cartridge for access to body cavities and organs. A smartphone’s rear camera was mounted thereto, enabling endoscopy, LSCI image acquisition, and processing. Blood flow imaging was calibrated in a perfused tissue phantom consisting of a microparticle solution pumped at known rates through tissue-mimicking gel and validated in a live rat model of BBN-induced bladder cancer. Raw LSCI images successfully visualized phantom flow: speckle flow index showed linearity with the pump flow rate. In the rat model, healthy and cancerous bladders were distinguishable in structure and vasculature. The smartphone-based low-cost portable mobile endoscope for monitoring blood flow and perfusion shows promise for preclinical applications and may be suitable for primary diagnosis at home or as a cost-effective POC testing assay. Full article
(This article belongs to the Special Issue Smartphone-Based Sensors for Biomedical Applications)
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25 pages, 11713 KiB  
Article
Ensem-HAR: An Ensemble Deep Learning Model for Smartphone Sensor-Based Human Activity Recognition for Measurement of Elderly Health Monitoring
by Debarshi Bhattacharya, Deepak Sharma, Wonjoon Kim, Muhammad Fazal Ijaz and Pawan Kumar Singh
Biosensors 2022, 12(6), 393; https://doi.org/10.3390/bios12060393 - 7 Jun 2022
Cited by 73 | Viewed by 6544
Abstract
Biomedical images contain a huge number of sensor measurements that can provide disease characteristics. Computer-assisted analysis of such parameters aids in the early detection of disease, and as a result aids medical professionals in quickly selecting appropriate medications. Human Activity Recognition, abbreviated as [...] Read more.
Biomedical images contain a huge number of sensor measurements that can provide disease characteristics. Computer-assisted analysis of such parameters aids in the early detection of disease, and as a result aids medical professionals in quickly selecting appropriate medications. Human Activity Recognition, abbreviated as ‘HAR’, is the prediction of common human measurements, which consist of movements such as walking, running, drinking, cooking, etc. It is extremely advantageous for services in the sphere of medical care, such as fitness trackers, senior care, and archiving patient information for future use. The two types of data that can be fed to the HAR system as input are, first, video sequences or images of human activities, and second, time-series data of physical movements during different activities recorded through sensors such as accelerometers, gyroscopes, etc., that are present in smart gadgets. In this paper, we have decided to work with time-series kind of data as the input. Here, we propose an ensemble of four deep learning-based classification models, namely, ‘CNN-net’, ‘CNNLSTM-net’, ‘ConvLSTM-net’, and ‘StackedLSTM-net’, which is termed as ‘Ensem-HAR’. Each of the classification models used in the ensemble is based on a typical 1D Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network; however, they differ in terms of their architectural variations. Prediction through the proposed Ensem-HAR is carried out by stacking predictions from each of the four mentioned classification models, then training a Blender or Meta-learner on the stacked prediction, which provides the final prediction on test data. Our proposed model was evaluated over three benchmark datasets, WISDM, PAMAP2, and UCI-HAR; the proposed Ensem-HAR model for biomedical measurement achieved 98.70%, 97.45%, and 95.05% accuracy, respectively, on the mentioned datasets. The results from the experiments reveal that the suggested model performs better than the other multiple generated measurements to which it was compared. Full article
(This article belongs to the Special Issue Smartphone-Based Sensors for Biomedical Applications)
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19 pages, 3018 KiB  
Article
Solving Color Reproducibility between Digital Devices: A Robust Approach of Smartphones Color Management for Chemical (Bio)Sensors
by Pablo Cebrián, Leticia Pérez-Sienes, Isabel Sanz-Vicente, Ángel López-Molinero, Susana de Marcos and Javier Galbán
Biosensors 2022, 12(5), 341; https://doi.org/10.3390/bios12050341 - 17 May 2022
Cited by 5 | Viewed by 3606
Abstract
In the past twelve years, digital image colorimetry (DIC) on smartphones has acquired great importance as an alternative to the most common analytical techniques. This analysis method is based on fast, low-cost, and easily-accessible technology, which can provide quantitative information about an analyte [...] Read more.
In the past twelve years, digital image colorimetry (DIC) on smartphones has acquired great importance as an alternative to the most common analytical techniques. This analysis method is based on fast, low-cost, and easily-accessible technology, which can provide quantitative information about an analyte through the color changes of a digital image. Despite the fact that DIC is very widespread, it is not exempt from a series of problems that are not fully resolved yet, such as variability of the measurements between smartphones, image format in which color information is stored, power distribution of the illuminant used for the measurements, among others. This article proposes a methodology for the standardization and correction of these problems using self-developed software, together with the use of a 3D printed light box. This methodology is applied to three different colorimetric analyses using different types and brands of smartphones, proving that comparable measurements between devices can be achieved. As color can be related to many target analytes, establishing this measurement methodology can lead to new control analysis applicable to diverse sectors such as alimentary, industrial, agrarian, or sanitary. Full article
(This article belongs to the Special Issue Smartphone-Based Sensors for Biomedical Applications)
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Review

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18 pages, 34325 KiB  
Review
Applications of Smartphone-Based Aptasensor for Diverse Targets Detection
by Ying Lan, Baixun He, Cherie S. Tan and Dong Ming
Biosensors 2022, 12(7), 477; https://doi.org/10.3390/bios12070477 - 30 Jun 2022
Cited by 18 | Viewed by 3351
Abstract
Aptamers are a particular class of functional recognition ligands with high specificity and affinity to their targets. As the candidate recognition layer of biosensors, aptamers can be used to sense biomolecules. Aptasensors, aptamer-based biosensors, have been demonstrated to be specific, sensitive, and cost-effective. [...] Read more.
Aptamers are a particular class of functional recognition ligands with high specificity and affinity to their targets. As the candidate recognition layer of biosensors, aptamers can be used to sense biomolecules. Aptasensors, aptamer-based biosensors, have been demonstrated to be specific, sensitive, and cost-effective. Furthermore, smartphone-based devices have shown their advantages in binding to aptasensors for point-of-care testing (POCT), which offers an immediate or spontaneous responding time for biological testing. This review describes smartphone-based aptasensors to detect various targets such as metal ions, nucleic acids, proteins, and cells. Additionally, the focus is also on aptasensors-related technologies and configurations. Full article
(This article belongs to the Special Issue Smartphone-Based Sensors for Biomedical Applications)
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31 pages, 2456 KiB  
Review
Wearable Devices for Physical Monitoring of Heart: A Review
by Guillermo Prieto-Avalos, Nancy Aracely Cruz-Ramos, Giner Alor-Hernández, José Luis Sánchez-Cervantes, Lisbeth Rodríguez-Mazahua and Luis Rolando Guarneros-Nolasco
Biosensors 2022, 12(5), 292; https://doi.org/10.3390/bios12050292 - 2 May 2022
Cited by 77 | Viewed by 14474
Abstract
Cardiovascular diseases (CVDs) are the leading cause of death globally. An effective strategy to mitigate the burden of CVDs has been to monitor patients’ biomedical variables during daily activities with wearable technology. Nowadays, technological advance has contributed to wearables technology by reducing the [...] Read more.
Cardiovascular diseases (CVDs) are the leading cause of death globally. An effective strategy to mitigate the burden of CVDs has been to monitor patients’ biomedical variables during daily activities with wearable technology. Nowadays, technological advance has contributed to wearables technology by reducing the size of the devices, improving the accuracy of sensing biomedical variables to be devices with relatively low energy consumption that can manage security and privacy of the patient’s medical information, have adaptability to any data storage system, and have reasonable costs with regard to the traditional scheme where the patient must go to a hospital for an electrocardiogram, thus contributing a serious option in diagnosis and treatment of CVDs. In this work, we review commercial and noncommercial wearable devices used to monitor CVD biomedical variables. Our main findings revealed that commercial wearables usually include smart wristbands, patches, and smartwatches, and they generally monitor variables such as heart rate, blood oxygen saturation, and electrocardiogram data. Noncommercial wearables focus on monitoring electrocardiogram and photoplethysmography data, and they mostly include accelerometers and smartwatches for detecting atrial fibrillation and heart failure. However, using wearable devices without healthy personal habits will cause disappointing results in the patient’s health. Full article
(This article belongs to the Special Issue Smartphone-Based Sensors for Biomedical Applications)
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17 pages, 2687 KiB  
Review
Smartphone-Based Platforms for Clinical Detections in Lung-Cancer-Related Exhaled Breath Biomarkers: A Review
by Qiwen Yu, Jing Chen, Wei Fu, Kanhar Ghulam Muhammad, Yi Li, Wenxin Liu, Linxin Xu, Hao Dong, Di Wang, Jun Liu, Yanli Lu and Xing Chen
Biosensors 2022, 12(4), 223; https://doi.org/10.3390/bios12040223 - 8 Apr 2022
Cited by 10 | Viewed by 4065
Abstract
Lung cancer has been studied for decades because of its high morbidity and high mortality. Traditional methods involving bronchoscopy and needle biopsy are invasive and expensive, which makes patients suffer more risks and costs. Various noninvasive lung cancer markers, such as medical imaging [...] Read more.
Lung cancer has been studied for decades because of its high morbidity and high mortality. Traditional methods involving bronchoscopy and needle biopsy are invasive and expensive, which makes patients suffer more risks and costs. Various noninvasive lung cancer markers, such as medical imaging indices, volatile organic compounds (VOCs), and exhaled breath condensates (EBCs), have been discovered for application in screening, diagnosis, and prognosis. However, the detection of markers still relies on bulky and professional instruments, which are limited to training personnel or laboratories. This seriously hinders population screening for early diagnosis of lung cancer. Advanced smartphones integrated with powerful applications can provide easy operation and real-time monitoring for healthcare, which demonstrates tremendous application scenarios in the biomedical analysis region from medical institutions or laboratories to personalized medicine. In this review, we propose an overview of lung-cancer-related noninvasive markers from exhaled breath, focusing on the novel development of smartphone-based platforms for the detection of these biomarkers. Lastly, we discuss the current limitations and potential solutions. Full article
(This article belongs to the Special Issue Smartphone-Based Sensors for Biomedical Applications)
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