Magnetic levitation-based miniaturized technologies for advanced diagnostics

Author:

Karakuzu Betul,Anil İnevi Muge,Tarim E. Alperay,Sarigil Oyku,Guzelgulgen Meltem,Kecili Seren,Cesmeli Selin,Koc Sadik,Baslar M. Semih,Oksel Karakus Ceyda,Ozcivici Engin,Tekin H. CumhurORCID

Abstract

AbstractTaking advantage of the magnetic gradients created using magnetic attraction and repulsion in miniaturized systems, magnetic levitation (MagLev) technology offers a unique capability to levitate, orient and spatially manipulate objects, including biological samples. MagLev systems that depend on the inherent diamagnetic properties of biological samples provide a rapid and label-free operation that can levitate objects based on their density. Density-based cellular and protein analysis based on levitation profiles holds important potential for medical diagnostics, as growing evidence categorizes density as an important variable to distinguish between healthy and disease states. The parallel processing capabilities of MagLev-based diagnostic systems and their integration with automated tools accelerates the collection of biological data. They also offer notable advantages over current diagnostic techniques that require costly and labor-intensive protocols, which may not be accessible in a low-resource setting. MagLev-based diagnostic systems are user-friendly, portable, and affordable, making remote and label-free applications possible. This review describes the recent progress in the application of MagLev principles to existing problems in the field of diagnostics and how they help discover the molecular- and cellular-level changes that accompany the disease or condition of interest. The critical parameters associated with MagLev-based diagnostic systems such as magnetic medium, magnets, sample holders, and imaging systems are discussed. The challenges and barriers that currently limit the clinical implications of MagLev-based diagnostic systems are outlined together with the potential solutions and future directions including the development of compact microfluidic systems and hybrid systems by leveraging the power of deep learning and artificial intelligence.

Funder

Yükseköğretim Kurulu

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu

Publisher

Springer Science and Business Media LLC

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