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Abstract: Diffusion of atoms or molecules in presence of magnetic field gradients not only attenuates the NMR signal but also leads to distortions close to restricting boundaries. This phenomenon is most evident in imaging with laser polarized (LP) noble gases. Diffusion of gases can be manipulated, however, by admixing inert gases of different molecular weight. In this work we analyze the effect of mixing LP-3He with SF6 on the image quality of a phantom consisting of an arrangement of capillaries with different diameters. Admixing buffer gases of higher molecular weight changes the contrast and offers a means to record images with high spatial and time resolution. Additionally we demonstrate how distortions due to edge enhancement can be reduced even for long timed MRI-sequences. [Copyright &y& Elsevier]

Purpose: This paper explores different machine learning (ML) algorithms for analyzing diffusion nuclear magnetic resonance imaging (dMRI) models when analytical fitting shows restrictions. It reviews various ML techniques for dMRI analysis and evaluates their performance on different b -values range datasets, comparing them with analytical methods., Materials and Methods: After standard fitting for reference, four sets of diffusion-weighted nuclear magnetic resonance images were used to train/test various ML algorithms for prediction of diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), and kurtosis (K). ML classification algorithms, including extra-tree classifier (ETC), logistic regression, C-support vector, extra-gradient boost, and multilayer perceptron (MLP), were used to determine the existence of diffusion parameters (D, D*, f, and K) within single voxels. Regression algorithms, including linear regression, polynomial regression, ridge, lasso, random forest (RF), elastic-net, and support-vector machines, were used to estimate the value of the diffusion parameters. Performance was evaluated using accuracy (ACC), area under the curve (AUC) tests, and cross-validation root mean square error (RMSE CV ). Computational timing was also assessed., Results: ETC and MLP were the best classifiers, with 94.1% and 91.7%, respectively, for the ACC test and 98.7% and 96.3% for the AUC test. For parameter estimation, RF algorithm yielded the most accurate results The RMSE CV percentages were: 8.39% for D, 3.57% for D*, 4.52% for f, and 3.53% for K. After the training phase, the ML methods demonstrated a substantial decrease in computational time, being approximately 232 times faster than the conventional methods., Conclusions: The findings suggest that ML algorithms can enhance the efficiency of dMRI model analysis and offer new perspectives on the microstructural and functional organization of biological tissues. This paper also discusses the limitations and future directions of ML-based dMRI analysis., Competing Interests: There are no conflicts of interest., (Copyright: © 2024 Journal of Medical Physics.)

Parvalbumins beta (β-PRVBs) are the main fish allergens. The only proven and effective treatment for this type of hypersensitivity is to consume a diet free of fish. We present the molecular characterization of B-cell epitopes by shotgun proteomics of different β-PRVBs combined with protein-based bioinformatics and IgE-reactive approaches. The final goal of this work is to identify potential peptide vaccine candidates for fish allergy. Purified β-PRVBs from the main fifteen different fish species that cause allergy were analyzed by shotgun proteomics. Identified β-PRVBs peptide sequences and ninety-eight β-PRVB protein sequences from UniProtKB were combined, aligned and analyzed to determine B-cell epitopes using the Kolaskar and Tongaonkar algorithm. The highest rated predicted B-cell peptide epitopes were evaluated by ELISA using the corresponding synthetic peptides and sera from healthy and fish allergic patients. A total of 35 peptides were identified as B-cell epitopes. The top B-cell peptide epitopes (LKLFLQV, ACAHLCK, FAVLVKQ and LFLQNFV) that may induce protective immune responses were selected as potential peptide vaccine candidates. The 3D model of these peptides were located in the surface of the protein. This study provides the global characterization of B-cell epitopes for all β-PRVBs sequences that will facilitate the design of new potential immunotherapies. SIGNIFICANCE: This work provides the global characterization of B-cell epitopes for all β-PRVBs sequences by Shotgun Proteomics combined with Protein-based Bioinformatics and IgE-reactive approaches. This study will increase our understanding of the molecular mechanisms whereby fish allergens elicit allergic reactions and will facilitate the design of new potential peptide vaccine candidates., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Purpose: To study the influence of admixing inert buffer gases to laser-polarized (3)He in terms of resulting diffusion coefficients and the consequences for image contrast and resolution., Materials and Methods: The diffusion coefficient of (3)He was altered by admixing buffer gases of various molecular weights ((4)He, N(2), and SF(6)). The influence of the pulse sequence and the diffusion coefficient on the appearance of MRI of (laser-polarized) gases was analyzed by comparison of basic theoretical concepts with demonstrative experiments., Results: Excellent agreement between theoretical description and observed signal in simple gradient echoes was observed. A maximum signal gain can be predicted and was experimentally validated. Images acquired under such conditions revealed improved resolution. The nature and concentration of the admixed gas defines a structural threshold for the observed apparent diffusion coefficient (ADC) as demonstrated with diffusion-weighted MRI on a pig's lung flooded with suitable gas mixtures., Conclusion: A novel procedure is proposed to control the diffusion coefficient of gases in MRI by admixture of inert buffer gases. Their molecular mass and concentration enter as additional parameters into the equations that describe structural contrast. This allows for setting a structural threshold up to which structures contribute to the image. For MRI of the lung this enables images of very small structural elements (alveoli) only, or in the other extreme, all airways can be displayed with minimal signal loss due to diffusion., ((c) 2006 Wiley-Liss, Inc.)