Applied Magnetic Resonance最新文献

Diclofenac is a non-steroidal anti-inflammatory drug (NSAID). Here, we use double electron–electron resonance (DEER, also known as PELDOR) to study the interaction of spin-labeled diclofenac (diclofenac-SL) with three types of model membranes consisting of palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), an equimolar mixture of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), and this mixture with the addition of 20 mol% cholesterol. The results suggest that lipid-mediated lateral clustering of diclofenac-SL molecules occurs in all cases. For the POPC bilayer, alternative clustering takes place in two opposite leaflets, with random distribution of the molecules within the clusters. For DOPC/DPPC and DOPC/DPPC/cholesterol bilayers, diclofenac-SL molecules are separated by a distance of at least 1.4 nm. DOPC/DPPC/cholesterol bilayers are known to form nanoscale liquid disordered and liquid ordered lateral structures, the latter called lipid rafts. For this case, diclofenac-SL molecules were found to be captured by lipid rafts, forming a quasi-regular two-dimensional substructure in them with a “superlattice” parameter of ~ 3.0 nm.

We study 25–120 GHz electron spin resonance in a quasi-two-dimensional (S=) 3/2 antiferromagnet on an isosceles triangular lattice Cs(_2)CoCl(_4). Due to the frustration of the exchange interaction along the lateral sides of the triangles, the exchange network may be viewed as a quasi-one-dimensional system of weakly interacting chains. The strong single-ion anisotropy of Co(^{2+}) allows a pseudospin (s=) 1/2 formulation of the problem. We observe in experiments a well-pronounced temperature crossover from the ESR of individual pseudospins with a g-factor of 3.3 (corresponding well to individual pseudospins s=1/2) to ESR spectrum shifted strongly down in frequency at the temperature range below 1 K. This shifted ESR spectrum corresponds well to the singularity at the lower boundary of the quasi-spinon continuum of an XXZ spin chain in a transverse field, calculated in theory by Bruognolo et al., in Phys Rev B 94:085136, 2016 and by Laurell et al., in Phys Rev Lett 127:037201, 2021.

Gallium alloys are widespread materials in microelectronics and are promising as components of nanocomposites for using in soft robotics, wearable electronics, and sensors. Here, we present NMR studies of the impact of a particular nanoconfinement on the phase diagram for the GaInSn eutectic alloy in a porous Al₂O₃ ceramic template with the middle pore size 11 nm. Measurements of the NMR spectra and Knight shifts were carried out for ⁷¹Ga, ⁶⁹Ga, and ¹¹⁵In isotopes from 180 to 310 K. The precipitation of gallium-rich segregates with crystalline structures of α- and β-Ga was found at cooling. The evolution of the NMR lines during cooling and warming evidenced the occurrence of the liquid–liquid-phase transition in the melt fraction with pronounced amount of indium. The results obtained for the GaInSn alloy embedded into the porous aluminum oxide ceramic were found to differ remarkably from the phase diagrams of the alloy confined within silica opal and glass porous matrices.

The gradient coils represent an indispensable constituent within magnetic resonance imaging systems. Their performance significantly impacts the quality of images, particularly the nonlinearity of the gradient magnetic field. Due to the presence of ferromagnetic materials surrounding the gradient coil in the permanent magnet system, the magnetic field of the gradient coil experiences influence. Consideration must be given to ferromagnetic materials during the design phase. The objective of this study is to design gradient coils that mitigates the impact of ferromagnetic materials on gradient field linearity. In this paper, the original coil structure is formulated utilizing the discrete trajectory method, while introducing mirrored current to elucidate the effects of ferromagnetic material. Through the integration of these two methods, gradient coil structures with excellent linearity are achieved. Ultimately, the optimal gradient coils are fabricated, and computational as well as experimental findings demonstrate concordance between measured nonlinear degree and efficiency of the gradient coils with theoretical calculations in the presence of ferromagnetic materials.

Brain hemorrhage is a critical medical condition that is likely to cause long-term disabilities and death. Timely and precise emergency care, incorporating the accurate interpretation of computed tomography (CT) images, plays a crucial role in the effective management of a hemorrhagic stroke. However, conventional artificial intelligence methods are capable enough to detect the presence or absence of hemorrhage but fail to detect multiple types of hemorrhage with high accuracy. To address this, the paper introduces an innovative Deep Learning based approach that automatically detects, segments, and classifies subtypes of intracranial hemorrhages. The proposed model is trained and evaluated on two different datasets. It is initially trained on a dataset of CT images from the Radiological Society of North America (RSNA) brain CT hemorrhage database, which contained 752,803 head non-contrast computer tomography images obtained from 2,200 patients. Furthermore, the model's performance is validated using a real-time CT dataset collected from a diagnostic lab, comprising 15,000 CT scan images from 176 patients. The proposed model surpasses standard benchmarks for detection and classification, achieving exceptional metrics. It showcases overall segmentation accuracy with a Dice score and Jaccard Index of 0.99 and 0.88 respectively, while the classification metrics include an accuracy of 0.99, precision, recall, and F1 score of 0.97, 0.98, and 0.97 respectively. When two expert radiologists independently assessed the predicted hemorrhage locations and subtypes, ensuring uniform specificity levels, they determined the observed rate of false positives per patient was less. These results validate its applicability as a dependable clinical decision support tool.

The local specific absorption rate (SAR) is a key safety indicator in high-field MRI. Constructing a specific model for each patient is important for accurate estimation of local SAR. The aim of this study is to construct subject-specific knee models based on low-field images for realizing accurate local SAR estimation in high-field MRI systems (3T and 1.5T). The proposed method used two U-Net networks for tissue segmentation of knee joint and the classification results of the two networks were merged to generate the final models. Muscle has high dielectric properties and large volume, which have an important influence on the electromagnetic field distribution. To improve the accuracy of muscle segmentation, a U-Net making use of boundary information was used to segment muscle alone to overcome the problem of inhomogeneous intensity at the edge of the muscle region. Other tissues were segmented by another U-Net, which used a weighted loss function to mitigate the adverse influence of class imbalances between tissues. The proposed method was compared with other methods using manual delineation as the standard. Its muscle segmentation performance was better than that of the comparison methods. On the other hand, local SAR in 3T using models constructed by these methods was also evaluated through electromagnetic simulation separately. It was shown that the maximum SAR_10g of the models constructed by the proposed method was much closer to that of manual delineation on the whole. These results validated the availability of the proposed method.

Eighty years ago, the advent of electron paramagnetic resonance (EPR) revolutionized our ability to observe the physical world of unpaired electron spins. The inception of EPR spawned multiple scientific areas with a focus on discerning the roles of paramagnetic metals and organic radicals in an array of processes and materials. More recently, the emergence of site-directed spin labeling combined with distance measurement technology and molecular modeling has harnessed the power of EPR, to ‘watch proteins move’. Spin labels have enabled the measurement of distance constraints and site-specific dynamics in biomolecules to provide rich details of structure and structural changes that are tightly linked to biological function. Historically, nitroxide radicals are the most common spin labels. However, decades of method development and technological innovation have created a plethora of spin label types to extend the reach of EPR throughout the realm of biophysics. In this review we overview recent developments that improve the sensitivity of distance measurements using Cu(II) labels. These achievements over the last three years promise advancements in the ability of EPR to measure structural and dynamical constraints beyond what is possible using common spin labels. First, we briefly discuss pulsed and continuous-wave EPR techniques that discern the coordination of Cu(II) to monitor spin-labeling efficiency and binding in biological environments. Next, we outline the bottlenecks that impact sensitivity in pulsed dipolar spectroscopy and the strategic steps taken to remove these bottlenecks to collect distance measurements in hours. More precisely, we focus on the fast-spin phase memory relaxation time, the broad EPR spectrum due to anisotropy, and orientational selectivity effects inherent to Cu(II). Finally, we showcase the versatile application of Cu(II) spin labels in biological systems and the advantages of Cu(II) in pulsed dipolar spectroscopy to access nanomolar protein concentrations.