Multi-Channel Signals in Dynamic Force-Clamp Mode of Microcantilever Sensors for Detecting Cellular Peripheral Brush - read online

Sep 21, 2026

Development of renal cysts, cancers, and viral infections is tied to pathological changes and defects in the cellular peripheral brush, motivating detection of lesions in this structure. A piecewise linear viscoelastic constitutive model is constructed for the cell, accounting for combined contributions from the peripheral brush and intracellular architecture. Laplace-domain and inverse Laplace techniques, together with temporal differential methods and the differential quadrature method in space, yield signal interpretation models for microcantilever quasi-static and dynamic responses.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Abstract

Introduction

Cellular peripheral brush and AFM signals

Quasi-static and dynamic AFM detection modes

Need for viscoelastic constitutive modeling

sensors Article Multi-Channel Signals in Dynamic Force-Clamp Mode of Microcantilever Sensors for Detecting Cellular Peripheral Brush Qiang Lyu 1,2, Fan Pei 2, Ying-Long Zhao 2, Jia-Wei Ling 1 and Neng-Hui Zhang 1, * Citation: Lyu, Q.; Pei, F.; Zhao, Y.-L.; Ling, J.-W.; Zhang, N.-H. Multi-Channel Signals in Dynamic Force-Clamp Mode of Microcantilever Sensors for Detecting Cellular Peripheral Brush. Sensors 2024, 24, 6312. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)s24196312 Academic Editor: Nicole Jaffrezic-Renault Received: 5 August 2024 Revised: 13 September 2024 Accepted: 26 September 2024 Published: 29 September 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Shanghai Key Laboratory of Mechanics in Energy Engineering, Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science, Shanghai University, Shanghai 200072, China; [lvqiang@xynu.edu.cn](lvqiang@xynu.edu.cn) (Q.L.); [ljw0910@shu.edu.cn](ljw0910@shu.edu.cn) (J.-W.L.) 2 College of Architecture and Civil Engineering, Henan New Environmentally-Friendly Civil Engineering Materials Engineering Research Center, Xinyang Normal University, Xinyang 464000, China; [peifan2769@163.com](peifan2769@163.com) (F.P.); [yinglongzhao@yeah.net](yinglongzhao@yeah.net) (Y.-L.Z.) * [Correspondence: nhzhang@shu.edu.cn](Correspondence: nhzhang@shu.edu.cn) Abstract: The development of numerous diseases, such as renal cyst, cancer, and viral infection, is closely associated with the pathological changes and defects in the cellular peripheral brush. Therefore, it is necessary to develop a potential new method to detect lesions of cellular peripheral brush. Here, a piecewise linear viscoelastic constitutive model of cell is established considering the joint contribution of the peripheral brush and intra-cellular structure. By combining the Laplace transformation and its inverse transformation, and the differential method in the temporal domain and differential quadrature method (DQM) in the spatial domain, the signal interpretation models for quasi-static and dynamic signals of microcantilever are solved. The influence mechanisms of the peripheral brush on the viscoelastic properties of cells and quasi-static/dynamic signals of microcantilever are clarified. The results not only reveal that the peripheral brush has significant effects on the complex modulus of the cell and multi-channel signals of the microcantilever, but also suggest that an alternative mapping method by collecting multi-channel signals including quasistatic and higher frequency signals with more brush indexes could be potentially used to identify cancerous cells. Keywords: peripheral brush; cellular viscoelasticity; microcantilever; detection mechanism; multi-channel signals 1. Introduction The cellular brush is one of the important peripheral microstructures in a living cell, which consist mainly of microvilli, microridges, and cilia [1], and plays an important role in physiological processes such as cell–cell interaction, cell migration, differentiation and proliferation, embryonic development, inflammation, and tumors [2,3] . Relevant experiments based on atomic force microscopy (AFM) have shown that the cellular peripheral brush can affect the mechanical responses of cells, thus changing the static/dynamic detection signals of AFM microscale sensors [4–6] . Thus, it is imperative to develop a reasonable constitutive model to accurately reflect the cellular peripheral brush and to take the mechanical properties of the peripheral brush as crucial indicators to distinguish between normal and cancerou

FAQs

Why is detecting the cellular peripheral brush important?

Disease development such as renal cysts, cancer, and viral infection is closely associated with pathological changes and defects in the cellular peripheral brush. Detecting lesions in this region is therefore necessary.

What model is proposed to interpret microcantilever signals?

The work establishes a piecewise linear viscoelastic constitutive model of the cell that jointly considers the peripheral brush and intra-cellular structure.

How are quasi-static and dynamic signals for the microcantilever handled?

Signal interpretation models are solved for both quasi-static and dynamic cases by combining Laplace transformation and its inverse with temporal-domain differential methods and the differential quadrature method in the spatial domain.

Sep 21, 2026

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