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Fluorine MRI ((19) F MRI) is receiving an increasing attention as a viable alternative to proton-based MRI ((1) H MRI) for dedicated application in molecular imaging. The (19) F nucleus has a high gyromagnetic ratio, a 100% natural abundance and is furthermore hardly present in human tissues allowing for hot spot MR imaging. The applicability of (19) F MRI as a molecular and cellular imaging technique has been exploited, ranging from cell tracking to detection and imaging of tumors in preclinical studies. In addition to applications, developing new contrast materials with improved relaxation properties has also been a core research topic in the field, since the inherently low longitudinal relaxation rates of perfluorocarbon compounds result in relatively low imaging efficiency. Borrowed from (1) H MRI, the incorporation of lanthanides, specifically Gd(III) complexes, as signal modulating ingredients in the nanoparticle formulation has emerged as a promising approach to improvement of the fluorine signal. Three different perfluorocarbon emulsions were investigated at five different magnetic field strengths. Perfluoro-15-crown-5-ether was used as the core material and Gd(III)DOTA-DSPE, Gd(III)DOTA-C6-DSPE and Gd(III)DTPA-BSA as the relaxation altering components. While Gd(III)DOTA-DSPE and Gd(III)DOTA-C6-DSPE were favorable constructs for (1) H NMR, Gd(III)DTPA-BSA showed the strongest increase in (19F) R(1). These results show the potential of the use of paramagnetic lipids to increase (19F) R(1) at clinical field strengths (1.5-3 T). At higher field strengths (6.3-14 T), gadolinium does not lead to an increase in (19F) R(1) compared with emulsions without gadolinium, but leads to an significant increase in (19F) R(2). Our data therefore suggest that the most favorable situation for fluorine measurements is at high magnetic fields without the inclusion of gadolinium constructs.

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Leukocytes, also known as white blood cells, are a group of cells that protect the body against infections, which is an important part of the immune system. The classification of white blood cells is widely used to diagnose various diseases, such as AIDS, leukemia, myeloma and anemia. However, the conventional methods to classify white blood cells are time consuming and prone to errors. In this paper, one of the most popular neural networks, convolutional neural network (CNN) is selected to differentiate between different types of white blood cells, namely, eosinophil, lymphocyte, monocyte and neutrophil. The CNN was coupled with Alexnet, Resnet50, Densenet201 and GoogleNet in turn, and trained with the Kaggle Dataset. Then, Gaussian and median filters were applied separately to the images in the database. The new images were classified again by the CNN with each of the four networks. The results obtained after applying the two filters to the images were better than the results obtained with the original data. The research results make it easier to diagnose blood related diseases.

in Wuhan, China. This disease has spread to almost all countries in a short time. Countries take a series of stringent measures, including the prohibition of going out to prevent the virus that spreads COVID-19 disease. In this paper, we aimed to diagnose COVID-19 disease from X_RAY images by using deep learning architectures. In addition, 96.30% accuracy rate has been achieved with the hybrid architecture we have improved. While developing the hybrid model, the last 5 layers of Resnet 50 architecture were ejected. 10 layers were added in place of the 5 layers that were removed. The count of layers, which is 177 in the Resnet50 architecture, has been increased to 182 in the hybrid model. Thanks to these layer changes made in Resnet50, the accuracy rate has been increased more. Classification was performed with AlexNet, Resnet50, GoogLeNet, VGG16 and developed hybrid architectures using COVID-19 Chest X-Ray dataset and Chest X-Ray images (Pneumonia) datasets. As a result, when other scientific works in the literature are examined, it is finalized that the improved hybrid method offers better results than other deep learning architectures and can be used in computer-aided systems to diagnose COVID-19 disease.

2-hydroxyglutarate (2-HG) has emerged as a biomarker of tumor cell isocitrate dehydrogenase mutations that may enable the differential diagnosis of patients with glioma. At 3 T, detection of 2-HG with magnetic resonance spectroscopy is challenging because of metabolite signal overlap and spectral pattern modulation by slice selection and chemical shift displacement. Using density matrix simulations and phantom experiments, an optimized semi-LASER scheme (echo time = 110 milliseconds) considerably improves localization of the 2-HG spin system compared with that of an existing point-resolved spectroscopy sequence. This results in a visible 2-HG peak in the in vivo spectra at 1.9 ppm in the majority of isocitrate dehydrogenase-mutated tumors. Detected concentrations of 2-HG were similar using both sequences, although the use of semi-LASER generated narrower confidence intervals. Signal overlap with glutamate and glutamine, as measured by pairwise fitting correlation, was reduced. Lactate was readily detectable across patients with glioma using the method presented here (mean Cramér–Rao lower bound: 10% ± 2%). Together with more robust 2-HG detection, long-echo time semi-LASER offers the potential to investigate tumor metabolism and stratify patients in vivo at 3 T.