Scanning x-ray microdiffraction of complex tissues and materials is an emerging method for the study of macromolecular structures in situ, providing information on the way molecular constituents are arranged and interact with their microenvironment. Acting as a bridge between high-resolution images of individual constituents and lower resolution microscopies that generate global views of material, scanning microdiffraction provides an approach to study the functioning of complex tissues across multiple length scales. Here, we discuss the methodology, summarize results from recent studies, and discuss the potential of the technique for future studies coordinated with other biophysical techniques.
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http://dx.doi.org/10.1016/j.sbi.2022.102421 | DOI Listing |
Mol Biol Rep
January 2025
Department of Biology, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Background: Breast carcinoma stands out as the most widespread invasive cancer and the top contributor to cancer-related mortality in women. Nanoparticles have emerged as promising tools in cancer detection, diagnosis, and prevention. In this study, the antitumor and apoptotic capability of silver nanoparticles synthesized through Scrophularia striata extract (AgNPs-SSE) was investigated toward breast cancer cells.
View Article and Find Full Text PDFR I Med J (2013)
February 2025
Department of Medicine, Division of Cardiology, Alpert Medical School of Brown University, Providence RI.
Cardiac amyloidosis (CA) is an infiltrative disease that results from the deposition of amyloid fibrils in the myocardium, resulting in restrictive cardiomyopathy. The amyloid fibrils are predominantly derived from two parent proteins, immunoglobulin light chain (AL) and transthyretin (ATTR), and ATTR is further classified into hereditary (ATTRv) and wild-type (ATTRwt) based on the presence or absence, respectively, of a mutation in the transthyretin gene. Once thought to be a rare entity, CA is increasingly recognized as a significant cause of heart failure due to improved clinical awareness and better diagnostic imaging.
View Article and Find Full Text PDFR I Med J (2013)
February 2025
Alpert Medical School of Brown University, Department of Medicine, Division of Cardiology, Rhode Island Hospital.
Cardiac Positron Emission Tomography (PET) is a power- ful imaging tool with diverse applications in the detection and diagnosis of various cardiac conditions, including inflammatory, infectious, and neoplastic processes. Using the radiotracer 18F-fluorodeoxyglucose (18F-FDG), cardiac PET enables the identification of cardiac involvement in diseases such as sarcoidosis and severe infections affecting the heart tissue. Additionally, 18F-FDG PET is valuable in the evaluation of cardiac masses, helping to assess their metabolic activity and potential malignancy.
View Article and Find Full Text PDFR I Med J (2013)
February 2025
Alpert Medical School of Brown University, Department of Medicine, Division of Cardiology, Rhode Island Hospital.
Cardiac Positron Emission Tomography (PET) can be used for the assessment of myocardial perfusion. Compared to other cardiac imaging techniques, notably Single Photon Emission Computer Tomography (SPECT), cardiac PET offers superior image resolution, higher accuracy, quantitative measures of myocardial perfusion, lower radiation exposure, and shorter image acquisition time. However, PET tends to be costlier and less widely available than SPECT due to the specialized equipment needed for generating the necessary radiotracers.
View Article and Find Full Text PDFPhysiol Rep
February 2025
Motion and Exercise Science, University of Stuttgart, Stuttgart, Germany.
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training.
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