The paramagnetic spin relaxation filter is described for the rapid NMR screening of intermolecular interactions between ligands and macromolecular anionic receptors with large transverse relaxation enhancements ( ). The addition of micromolar concentrations of Gd to the mixture produces the immediate broadening/suppression of the NMR signals of interacting species while leaving unaffected those of noncompetitive binders (one-dimensional and two-dimensional experiments). The method is highly sensitive, unveiling interactions that are too weak to generate changes in chemical shifts or relaxation times. It is operationally very simple and hence, it is amenable to ready implementation by nonspecialists. Examples of application such as detecting the formation of interpolymer complexes, cyclodextrin host-guest interactions, and the screening of DNA ligands are included that demonstrate the reliability and broad applicability of the method.
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http://dx.doi.org/10.1021/acsomega.7b02074 | DOI Listing |
Surg Radiol Anat
January 2025
Department of Ophthalmology & Visual Sciences, University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.
Purpose: To report the normative dimensions of the frontal nerve (FN) on fat-suppressed suppressed gadolinium (fs-gad) enhanced magnetic resonance imaging (MRI).
Method: A retrospective cohort study of patients who underwent coronal fs-gad T1-weighted MRI. Orbits were excluded if there was unilateral or bilateral pathology of the FN or optic nerve sheath (ONS), incomplete MRI sequences, poor image quality or indiscernible FN on radiological assessment.
World J Microbiol Biotechnol
January 2025
Graduate School of Science and Technology, Shizuoka University, Shizuoka, Japan.
Marine resources are attractive for screening new useful bacteria. From a marine sediment sample, we performed isolation and screening of bacterial strains in search of new bioactive compounds. HPLC and ESI-MS analysis indicated that the new bacterium, Lysinibacillus sp.
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
Brown University Health Cardiovascular Institute; Rhode Island, the Miriam and Newport Hospitals; Warren Alpert Medical School, Brown University.
Cardiac magnetic resonance imaging (CMR) is an exciting noninvasive imaging modality with increasing utilization in the field of cardiovascular medicine. In conjunction with echocardiogram, computed tomography, and invasive therapies, CMR has provided exceptional capability to further evaluate complex clinical cardiac conditions. CMR provides both anatomical and physiological information of a variety of tissue types, without the need for ionizing radiation.
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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