Guido Buonincontri – författare
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Dielectrophoresis microfluidic chips have been widely used in various biological applications due to their advantages of convenient operation, high throughput, and low cost.
However, most of the DEP microfluidic chips are based on 2D planar electrodes which have some limitations, such as electric field attenuation, small effective working regions, and weak DEP forces. In order to overcome the limitations of 2D planar electrodes, two kinds of thick-electrode DEP chips were designed to realize manipulation and multi-parameter measurement of single cells.
Based on the multi-electrode structure of thick-electrode DEP, a single-cell 3D electro-rotation chip of "Armillary Sphere" was designed. The chip uses four thick electrodes and a bottom planar electrode to form an electric field chamber, which can control 3D rotation of single cells under different electric signal configurations. Electrical property measurement and 3D image reconstruction of single cells are achieved based on single-cell 3D rotation. This work overcomes the limitations of 2D planar electrodes and effectively solves the problem of unstable spatial position of single-cell samples, and provides a new platform for single-cell analysis.
Based on multi-electrode structure of thick-electrode DEP, a microfluidic chip with optoelectronic integration was presented. A dual-fiber optical stretcher embedded in thick electrodes can trap and stretch a single cell while the thick electrodes are used for single-cell rotation. Stretching and rotation manipulation gives the chip the ability to simultaneously measure mechanical and electrical properties of single cells, providing a versatile platform for single-cell analysis, further extending the application of thick-electrode DEP in biological manipulation and analysis.
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Among medical imaging modalities, magnetic resonance imaging (MRI) stands out for its excellent soft-tissue contrast, anatomical detail, and high sensitivity for disease detection. However, as proven by the continuous and vast effort to develop new MRI techniques, limitations and open challenges remain. The primary source of contrast in MRI images are the various relaxation parameters associated with the nuclear magnetic resonance (NMR) phenomena upon which MRI is based. Although it is possible to quantify these relaxation parameters (qMRI) they are rarely used in the clinic, and radiological interpretation of images is primarily based upon images that are relaxation time weighted. The clinical adoption of qMRI is mainly limited by the long acquisition times required to quantify each relaxation parameter as well as questions around their accuracy and reliability. More specifically, the main limitations of qMRI methods have been the difficulty in dealing with the high inter-parameter correlations and a high sensitivity to MRI system imperfections.
Recently, new methods for rapid qMRI have been proposed. The multi-parametric models at the heart of these techniques have the main advantage of accounting for the correlations between the parameters of interest as well as system imperfections. This holistic view on the MR signal makes it possible to regress many individual parameters at once, potentially with a higher accuracy. Novel, accurate techniques promise a fast estimation of relevant MRI quantities, including but not limited to longitudinal (T1) and transverse (T2) relaxation times. Among these emerging methods, MR Fingerprinting (MRF), synthetic MR (syMRI or MAGIC), and T1‒T2 Shuffling are making their way into the clinical world at a very fast pace. However, the main underlying assumptions and algorithms used are sometimes different from those found in the conventional MRI literature, and can be elusive at times. In this book, we take the opportunity to study and describe the main assumptions, theoretical background, and methods that are the basis of these emerging techniques.
Quantitative transient state imaging provides an incredible, transformative opportunity for MRI. There is huge potential to further extend the physics, in conjunction with the underlying physiology, toward a better theoretical description of the underlying models, their application, and evaluation to improve the assessment of disease and treatment efficacy.