Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
3.4
Journals
Micromachines
Special Issues
Intelligent Approaches in Biosensing
share announcement

Intelligent Approaches in Biosensing

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 1755

Special Issue Editors

Dr. Tatiana M. Zimina

E-Mail Website
Guest Editor
Department of Micro- and Nanoelectronics, Saint Petersburg Electrotechnical University “LETI”, 197022 Saint Petersburg, Russia
Interests: biosensors; microfluidics; lab-on-a-chip; peptide aptamers; magnetic nanoparticles; antibiotic resistance; microsystem engineering
Dr. Dmitrii Kaplun

E-Mail Website
Guest Editor
Department of Automation and Control Processes, Saint Petersburg Electrotechnical University “LETI”, 197022 Saint Petersburg, Russia
Interests: digital signal processing; machine learning; embedded computing

Special Issue Information

Dear Colleagues,

Currently, biosensors are becoming an increasingly multidisciplinary field of science. Modern biosensor systems should provide high sensitivity and selectivity, a high detection rate, small sample volume, compact size, and low cost. Increasing the efficiency of their performance requires cooperative solutions for specialists in the field of physics, chemistry, engineering, information technology, medicine, etc. Information technologies such as machine learning, intelligent data processing, embedded and mobile computing can significantly improve the quality of identification, characterization, processing of samples, as well as to carry out an intelligent analysis of the results. Automation of analysis based on integrated microfluidic biosensor systems can provide extremely high-quality screening in healthcare, ecology, chemical and food industries.

Dr. Tatiana M. Zimina
Dr. Dmitrii Kaplun
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. Micromachines 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 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

  • biosensors
  • machine learning
  • lab-on-a-chip
  • automated analysis
  • express detection

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Review

51 pages, 19470 KiB  
Review
Hybrid Impedimetric Biosensors for Express Protein Markers Detection
by Nikita Sitkov, Andrey Ryabko, Vyacheslav Moshnikov, Andrey Aleshin, Dmitry Kaplun and Tatiana Zimina
Micromachines 2024, 15(2), 181; https://doi.org/10.3390/mi15020181 - 25 Jan 2024
Viewed by 1135
Abstract
Impedimetric biosensors represent a powerful and promising tool for studying and monitoring biological processes associated with proteins and can contribute to the development of new approaches in the diagnosis and treatment of diseases. The basic principles, analytical methods, and applications of hybrid impedimetric [...] Read more.
Impedimetric biosensors represent a powerful and promising tool for studying and monitoring biological processes associated with proteins and can contribute to the development of new approaches in the diagnosis and treatment of diseases. The basic principles, analytical methods, and applications of hybrid impedimetric biosensors for express protein detection in biological fluids are described. The advantages of this type of biosensors, such as simplicity and speed of operation, sensitivity and selectivity of analysis, cost-effectiveness, and an ability to be integrated into hybrid microfluidic systems, are demonstrated. Current challenges and development prospects in this area are analyzed. They include (a) the selection of materials for electrodes and formation of nanostructures on their surface; (b) the development of efficient methods for biorecognition elements’ deposition on the electrodes’ surface, providing the specificity and sensitivity of biosensing; (c) the reducing of nonspecific binding and interference, which could affect specificity; (d) adapting biosensors to real samples and conditions of operation; (e) expanding the range of detected proteins; and, finally, (f) the development of biosensor integration into large microanalytical system technologies. This review could be useful for researchers working in the field of impedimetric biosensors for protein detection, as well as for those interested in the application of this type of biosensor in biomedical diagnostics. Full article
(This article belongs to the Special Issue Intelligent Approaches in Biosensing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop