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Biosensors
Special Issues
Biosensors for Disease Analysis
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Biosensors for Disease Analysis

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

Deadline for manuscript submissions: 5 November 2026 | Viewed by 697

Special Issue Editors

Dr. Dujuan Li

E-Mail Website
Guest Editor
Ministry of Education Engineering Research Center of Smart Microsensors and Microsystems, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China
Interests: biosensors; nanomaterials; tumor marker
Dr. Rui Wang

E-Mail Website
Guest Editor
Human Phenome Insitute, Fudan University, Shanghai, China
Interests: CRISPR; biosensors; molecular diagnostics; microfluidics; nanomaterials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, “Biosensors for Disease Analysis,” is motivated by the growing demand for rapid, accurate, and decentralized technologies that can support early diagnosis, therapy guidance, and longitudinal monitoring of disease. Recent breakthroughs in biomarker discovery, biorecognition engineering, and micro/nano-fabrication have paved the way for converting complex molecular and cellular information into actionable clinical readouts with minimal sample volumes and accelerated turnaround times.

This Special Issue covers all areas of biosensing for disease analysis, including, but not limited to, the following: enabling materials and interfaces, novel recognition elements (e.g., antibodies, aptamers, molecularly imprinted polymers, and CRISPR), and transduction strategies (electrochemical, optical, piezoelectric, thermal, and wearable formats). Topics also include sample preparation and microfluidics, multiplexed and multimodal sensing, smartphone/IoT integration, signal processing and AI-assisted interpretation, analytical validation, and studies using clinically relevant specimens. We welcome original research articles and critical reviews that report new sensor concepts, fabrication and integration methods, rigorous analytical performance, and translation toward real-world use in infectious, oncologic, metabolic, cardiovascular, and neurodegenerative diseases.

Dr. Dujuan Li
Dr. Rui Wang
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 250 words) can be sent to the Editorial Office for assessment.

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 2200 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

  • biosensors
  • disease diagnostics
  • biomarkers
  • point-of-care testing (POCT)
  • microfluidics
  • lab-on-a-chip
  • multiplex detection
  • nanomaterials and biointerfaces
  • early disease detection
  • CRISPR

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Published Papers (1 paper)

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27 pages, 3921 KB  
Article
RT-AFNet: A Hybrid ResNet-Transformer Architecture with Multi-Scale Fusion for Atrial Fibrillation Detection
by Xinyu Hu, Qingqing Duan, Yuwei Zhang, Caiyun Ma, Chang Yan and Chengyu Liu
Biosensors 2026, 16(5), 275; https://doi.org/10.3390/bios16050275 - 9 May 2026
Viewed by 280
Abstract
Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with an elevated risk of severe complications, including stroke and heart failure. Due to its paroxysmal nature and the inherent complexity of electrocardiogram (ECG) signals, developing highly accurate and robust automated detection methods remains [...] Read more.
Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with an elevated risk of severe complications, including stroke and heart failure. Due to its paroxysmal nature and the inherent complexity of electrocardiogram (ECG) signals, developing highly accurate and robust automated detection methods remains a critical challenge. To address the limitations of existing models in simultaneously capturing local morphological anomalies and long-range temporal dependencies, we proposed RT-AFNet, a novel hybrid ResNet-Transformer architecture. Specifically, RT-AFNet integrated the robust local feature extraction capabilities of a Residual Neural Network (ResNet) backbone with the global temporal modeling power of a lightweight self-attention mechanism. Furthermore, a multi-scale feature fusion strategy was introduced to optimize feature representation. The proposed RT-AFNet model was evaluated on three public AF databases: the China Physiological Signal Challenge 2018 (CPSC2018), the PhysioNet/Computing in Cardiology Challenge 2017 (CinC2017), and the MIT-BIH Atrial Fibrillation Database (MIT-BIH AF). The proposed model achieved F1 scores of 99.76%, 97.47%, and 96.20%, along with area under the curve (AUC) values of 99.97%, 98.98%, and 98.28% on the three datasets, respectively. These results demonstrate that the proposed architecture exhibits excellent generalization ability and stability across different databases, providing a robust and reliable deep learning solution for automated AF screening. Full article
(This article belongs to the Special Issue Biosensors for Disease Analysis)
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