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Sensors
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AI and Neural Networks for Advanced Biomedical Sensor Applications
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AI and Neural Networks for Advanced Biomedical Sensor Applications

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 292

Special Issue Editor

Prof. Dr. Daniel Graupe

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, USA
Interests: neural engineering; neural networks

Special Issue Information

Dear Colleagues,

Coverage: Original and review papers, including clinical or research reports of preliminary results in:

  • surface-electromyographic (sEMG) signals;
  • electrocardiographic (EKG) signals;
  • electroencephalographic (EEG) signals;
  • magnetocardiographic (MCG) signals;
  • magneto-encephalographic (MEG) signals;
  • quantum dots—in vivo;
  • quantum dots—in vitro;

The papers may cover basics and applications, especially in diagnosis, drug delivery, treatment, control and brain–machine interfaces. Application in cancer research, paraplegia are welcome but may cover any other field of medicine and biology.

The papers my relate (but are not limited) to signal and image processing and spectrum analysis.

Prof. Dr. Daniel Graupe
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

  • surface-electromyographic (sEMG) signals
  • electrocardiographic (EKG) signals
  • electroencephalographic (EEG) signals
  • magnetocardiographic (MCG) signals
  • magneto-encephalographic (MEG) signals
  • quantum dots—in vivo
  • quantum dots—in vitro

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

Published Papers (1 paper)

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Research

24 pages, 5650 KiB  
Article
Preliminary Study on Sensor-Based Detection of an Adherent Cell’s Pre-Detachment Moment in a MPWM Microfluidic Extraction System
by Marius-Alexandru Dinca, Mihaita Nicolae Ardeleanu, Dan Constantin Puchianu and Gabriel Predusca
Sensors 2025, 25(9), 2726; https://doi.org/10.3390/s25092726 - 25 Apr 2025
Viewed by 199
Abstract
The extraction of adherent cells, such as B16 murine melanoma cells, from Petri dish cultures is critical in biomedical applications, including cell reprogramming, transplantation, and regenerative medicine. Traditional detachment methods—enzymatic, mechanical, or chemical—often compromise cell viability by altering membrane integrity and disrupting adhesion [...] Read more.
The extraction of adherent cells, such as B16 murine melanoma cells, from Petri dish cultures is critical in biomedical applications, including cell reprogramming, transplantation, and regenerative medicine. Traditional detachment methods—enzymatic, mechanical, or chemical—often compromise cell viability by altering membrane integrity and disrupting adhesion proteins. To address these challenges, this study investigated sensor-based detection of the pre-detachment phase in a MPWM (Microfluidic Pulse Width Modulation) extraction system. Our approach integrates a micromechatronic system with a microfluidic suction circuit, real-time CCD imaging, and computational analysis to detect and characterize the pre-detachment moment before full extraction. A precisely controlled hydrodynamic force field progressively disrupts adhesion in multiple stages, reducing mechanical stress and preserving cell integrity. Real-time video analysis enables continuous monitoring of positional dynamics and oscillatory responses. Image processing and deep learning algorithms determine object center coordinates, allowing the MPWM system to dynamically adjust suction parameters. This optimizes detachment while minimizing liquid absorption and reflux volume, ensuring efficient extraction. By combining microfluidics, sensor detection, and AI-driven image processing, this study established a non-invasive method for optimizing adherent cell detachment. These findings have significant implications for single-cell research, regenerative medicine, and high-throughput biotechnology, ensuring maximal viability and minimal perturbation. Full article
(This article belongs to the Special Issue AI and Neural Networks for Advanced Biomedical Sensor Applications)
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