Two-dimensional (2D) strong-coupling point-resolved spectroscopy (S-PRESS) is introduced as a novel approach to (1)H MR spectroscopy (MRS) in the prostate. The technique provides full spectral information and allows for an accurate characterization of the citrate (Cit) signal. The method is based on acquiring a series of PRESS spectra with constant total echo time (TE). The indirect dimension is encoded by varying the relative lengths of the first and second TEs (TE(1) + TE(2) = TE). In the resulting 2D spectra, only the signal of strongly coupled spin systems is spread into the second dimension, which leads to more clearly arranged spectra. Furthermore, the spectral parameters of Cit (coupling constant J and chemical shift difference delta of the AB spin system) can be determined with high accuracy in vivo. The sequence is analytically optimized for maximal "strong coupling peaks" of Cit at 3T. 2D S-PRESS spectra are compared with JPRESS spectra in vitro as well as in vivo.
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http://dx.doi.org/10.1002/mrm.21082 | DOI Listing |
Cell Div
January 2025
Babak Myeloma Group, Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Background: Multiple myeloma (MM) represents the second most common hematological malignancy characterized by the infiltration of the bone marrow by plasma cells that produce monoclonal immunoglobulin. While the quality and length of life of MM patients have significantly increased, MM remains a hard-to-treat disease; almost all patients relapse. As MM is highly heterogenous, patients relapse at different times.
View Article and Find Full Text PDFJ Biomol NMR
January 2025
Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
The NMR signals from protein sidechains are rich in information about intra- and inter-molecular interactions, but their detection can be complicated due to spectral overlap as well as conformational and hydrogen exchange. In this work, we demonstrate a protocol for multi-dimensional solid-state NMR spectral editing of signals from basic sidechains based on Hadamard matrix encoding. The Hadamard method acquires multi-dimensional experiments in such a way that both the backbone and under-sampled sidechain signals can be decoded for unambiguous editing in the N spectral frequency dimension.
View Article and Find Full Text PDFNature
January 2025
Lunar and Planetary Laboratory, University of Arizona, Tucson, AZ, USA.
Evaporation or freezing of water-rich fluids with dilute concentrations of dissolved salts can produce brines, as observed in closed basins on Earth and detected by remote sensing on icy bodies in the outer Solar System. The mineralogical evolution of these brines is well understood in regard to terrestrial environments, but poorly constrained for extraterrestrial systems owing to a lack of direct sampling. Here we report the occurrence of salt minerals in samples of the asteroid (101955) Bennu returned by the OSIRIS-REx mission.
View Article and Find Full Text PDFJ Oleo Sci
January 2025
Regeneration and Stem Cell Biology Lab, Centre for Molecular and Nanomedical Sciences, International Research Centre, Sathyabama Institute of Science and Technology.
Coelomic fluid of earthworms is a valuable source of novel bioactive compounds with therapeutic applications. To gain insight into the bioactive compounds in the coelomic fluid, this study used Perionyx excavatus, a tropical earthworm distinguished for its remarkable ability for regeneration. This study aimed to identify fluorescent bioactive compounds in the coelomic fluid of P.
View Article and Find Full Text PDFAnal Chim Acta
March 2025
Artificial Intelligence Research Center, Chang Gung University, Taoyuan, 333323, Taiwan; Department of Artificial Intelligence, College of Intelligent Computing, Chang Gung University, Taoyuan, 333323, Taiwan. Electronic address:
Background: In recent years, employing deep learning methods in the biosensing area has significantly reduced data analysis time and enhanced data interpretation and prediction accuracy. In some SPR fields, research teams have further enhanced detection capabilities using deep learning techniques. However, the application of deep learning to spectroscopic surface plasmon resonance (SPR) biosensors has not been reported.
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