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Sensors and Algorithms for Biomarker Detection
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Sensors and Algorithms for Biomarker Detection

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 5288

Special Issue Editor

Dr. Ming Huang

E-Mail Website
Guest Editor
1. School of Data Science, Nagoya City University, Nagoya 467-8501, Japan
2. Division of Information Science, Nara Institute of Science and Technology, Ikoma 630-0192, Japan
Interests: health informatics for health promotion and disease management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past decade, the proliferation of medical and healthcare devices has led to an exponential increase in data generation. A pressing question now arises: can we unearth new biomarkers from this vast data pool that are pivotal for the early detection of diseases? Addressing this question requires a collaborative effort that spans sensing and information technologies. For this Special Issue, we warmly invite submissions of research papers focused on the following topics:

  1. Innovative sensors capable of capturing sensitive biomedical data or continuously monitoring physiological signals;
  2. Proof-of-concept studies on the extraction of biomarkers from data;
  3. The design and development of algorithms for identifying novel biomarkers from complex datasets.

We also welcome review papers that delve into these topics, providing insights and advancements in the field.

Dr. Ming Huang
Guest Editor

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • wearable sensors
  • medical image
  • ultrasound
  • digital health
  • personal health data

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

11 pages, 1366 KiB  
Article
A Method for Compensating Hemoglobin Interference in Total Serum Bilirubin Measurement Using a Simple Two-Wavelength Reflectance Photometer
by Lorenzo Zucchini, Carlos Daniel Coda Zabetta, Miloš Ajčević and Agostino Accardo
Sensors 2024, 24(20), 6749; https://doi.org/10.3390/s24206749 - 20 Oct 2024
Cited by 1 | Viewed by 1895
Abstract
Neonatal hyperbilirubinemia (NH) is a common condition in newborns, with elevated bilirubin levels potentially causing neurological damage or death. Accurate and timely measurements of total serum bilirubin are essential to prevent these outcomes. Direct spectrophotometry, a reliable method for measuring bilirubin, is particularly [...] Read more.
Neonatal hyperbilirubinemia (NH) is a common condition in newborns, with elevated bilirubin levels potentially causing neurological damage or death. Accurate and timely measurements of total serum bilirubin are essential to prevent these outcomes. Direct spectrophotometry, a reliable method for measuring bilirubin, is particularly useful in constrained settings due to its potential for portable low-cost instrumentation. However, this method is susceptible to interference from hemoglobin, often present due to hemolysis. Typically, this interference is reduced using complex optical filters, reagents, multiple wavelengths, or combinations thereof, which increase costs and complexity while reducing usability. This study presents a hemoglobin compensation algorithm applied to a simple, portable, two-wavelength (465 and 590 nm) reflectance photometer designed to receive 30 µL of plasma or whole blood samples and perform the measurement without any reagents. Testing across five bilirubin and hemoglobin levels (4.96 to 28 mg/dL and 0.06 to 0.99 g/dL, respectively) demonstrated that the algorithm effectively reduces hemoglobin interference and overestimation errors. The overall root mean square error was reduced from 4.86 to 1.45 mg/dL, while the measurement bias decreased from −4.46 to −0.10 mg/dL. This substantial reduction in overestimation errors supports future clinical trials with neonatal blood samples. Full article
(This article belongs to the Special Issue Sensors and Algorithms for Biomarker Detection)
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16 pages, 2509 KiB  
Article
Prediction of Vascular Access Stenosis by Lightweight Convolutional Neural Network Using Blood Flow Sound Signals
by Jia-Jung Wang, Alok Kumar Sharma, Shing-Hong Liu, Hangliang Zhang, Wenxi Chen and Thung-Lip Lee
Sensors 2024, 24(18), 5922; https://doi.org/10.3390/s24185922 - 12 Sep 2024
Cited by 2 | Viewed by 1404
Abstract
This research examines the application of non-invasive acoustic analysis for detecting obstructions in vascular access (fistulas) used by kidney dialysis patients. Obstructions in these fistulas can interrupt essential dialysis treatment. In this study, we utilized a condenser microphone to capture the blood flow [...] Read more.
This research examines the application of non-invasive acoustic analysis for detecting obstructions in vascular access (fistulas) used by kidney dialysis patients. Obstructions in these fistulas can interrupt essential dialysis treatment. In this study, we utilized a condenser microphone to capture the blood flow sounds before and after angioplasty surgery, analyzing 3819 sound samples from 119 dialysis patients. These sound signals were transformed into spectrogram images to classify obstructed and unobstructed vascular accesses, that is fistula conditions before and after the angioplasty procedure. A novel lightweight two-dimension convolutional neural network (CNN) was developed and benchmarked against pretrained CNN models such as ResNet50 and VGG16. The proposed model achieved a prediction accuracy of 100%, surpassing the ResNet50 and VGG16 models, which recorded 99% and 95% accuracy, respectively. Additionally, the study highlighted the significantly smaller memory size of the proposed model (2.37 MB) compared to ResNet50 (91.3 MB) and VGG16 (57.9 MB), suggesting its suitability for edge computing environments. This study underscores the efficacy of diverse deep-learning approaches in the obstructed detection of dialysis fistulas, presenting a scalable solution that combines high accuracy with reduced computational demands. Full article
(This article belongs to the Special Issue Sensors and Algorithms for Biomarker Detection)
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16 pages, 13107 KiB  
Article
An Ultra-Compact and Low-Cost LAMP-Based Virus Detection Device
by Dong Guo, Zhengrong Ling, Yifeng Tang, Gen Li, Tieshan Zhang, Haoxiang Zhao, Hao Ren, Yajing Shen and Xiong Yang
Sensors 2024, 24(15), 4912; https://doi.org/10.3390/s24154912 - 29 Jul 2024
Viewed by 1417
Abstract
Timely and accurate detection of viruses is crucial for infection diagnosis and treatment. However, it remains a challenge to develop a portable device that meets the requirement of being portable, powerless, user-friendly, reusable, and low-cost. This work reports a compact ∅30 × 48 [...] Read more.
Timely and accurate detection of viruses is crucial for infection diagnosis and treatment. However, it remains a challenge to develop a portable device that meets the requirement of being portable, powerless, user-friendly, reusable, and low-cost. This work reports a compact ∅30 × 48 mm portable powerless isothermal amplification detection device (material cost ∼$1 USD) relying on LAMP (Loop-Mediated Isothermal Amplification). We have proposed chromatographic-strip-based microporous permeation technology which can precisely control the water flow rate to regulate the exothermic reaction. This powerless heating combined with phase-change materials can maintain a constant temperature between 50 and 70 °C for a duration of up to 49.8 min. Compared with the conventional methods, it avoids the use of an additional insulation layer for heat preservation, greatly reducing the size and cost. We have also deployed a color card and a corresponding algorithm to facilitate color recognition, data analysis, and storage using a mobile phone. The experimental results demonstrate that our device exhibits the same limit of detection (LOD) as the ProFlex PCR for SARS-CoV-2 pseudovirus samples, with that for both being 103 copies/μL, verifying its effectiveness and reliability. This work offers a timely, low-cost, and easy way for respiratory infectious disease detection, which could provide support in curbing virus transmission and protecting the health of humans and animals, especially in remote mountainous areas without access to electricity or trained professionals. Full article
(This article belongs to the Special Issue Sensors and Algorithms for Biomarker Detection)
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