Chemical shift-encoded multishot EPI for navigator-free prostate DWI.

PURPOSE: DWI is an important contrast for prostate MRI to enable early and accurate detection of cancer. This study introduces a Dixon 3-shot-EPI protocol with structured low-rank reconstruction for navigator-free DWI. The aim is to overcome the limitations of single-shot EPI (ssh-EPI), such as geometric distortions and fat signal interference, while addressing the motion-induced phase variations of multishot EPI and simultaneously allowing water/fat separation. METHODS: DWI data were acquired from 7 healthy volunteers using both Dixon 3-shot EPI and standard fat-suppressed ssh-EPI with similar scan times for comparison. Two readers evaluated image quality using a 5-point Likert scale regarding different aspects. The ADC values were quantitatively compared between protocols. To show feasibility in a clinical setting, the protocol was applied to two patients. RESULTS: From the reader scores, Dixon 3-shot EPI significantly reduced geometric distortion compared with ssh-EPI (p < 0.01), with no significant differences in edge definition, SNR, or overall image quality. There was no significant difference in ADC values between the two protocols. However, the Dixon multishot-EPI protocol offered advantages such as self-referenced B0 map-driven distortion correction, greater flexibility in imaging parameters, and superior fat suppression. In the patient data, the lesion could be clearly identified in both protocols and on the associated ADC maps. CONCLUSION: The proposed Dixon 3-shot-EPI protocol shows promise as an alternative to ssh-EPI for prostate DWI, providing reduced geometric distortions and improved fat suppression. It addresses common DWI issues based on EPI and enhances scanning flexibility, indicating potential for optimized imaging.

Quelling the Geometry Factor Effect in Quantum Chemical Calculations of 13C NMR Chemical Shifts with the Aid of the pecG-n (n = 1, 2) Basis Sets.

A root factor for the accuracy of all quantum chemical calculations of nuclear magnetic resonance (NMR) chemical shifts is the quality of the molecular equilibrium geometry used. In turn, this quality depends largely on the basis set employed at the geometry optimization stage. This parameter represents the main subject of the present study, which is a continuation of our recent work, where new pecG-n (n = 1, 2) basis sets for the geometry optimization were introduced. A goal of this study was to compare the performance of our geometry-oriented pecG-n (n = 1, 2) basis sets against the other basis sets in massive calculations of 13C NMR shielding constants/chemical shifts in terms of their efficacy in reducing geometry factor errors. The testing was carried out with both large-sized biologically active natural products and medium-sized compounds with complicated electronic structures. The former were treated using the computation protocol based on the density functional theory (DFT) and considered in the theoretical benchmarking, while the latter were treated using the computational scheme based on the upper-hierarchy coupled cluster (CC) methods and were used in the practical benchmarking involving the comparison with experimental NMR data. Both the theoretical and practical analyses showed that the pecG-1 and pecG-2 basis sets resulted in substantially reduced geometry factor errors in the calculated 13C NMR chemical shifts/shielding constants compared to their commensurate analogs, with the pecG-2 basis set being the best of all the considered basis sets.

Use of two-point and six-point Dixon MRI for fat fraction analysis in the lumbar vertebral bodies and paraspinal muscles in healthy dogs: comparison with magnetic resonance spectroscopy.

Introduction: Fatty degeneration of the vertebral bodies and paravertebral muscles is associated with the presence, severity, and prognosis of spinal disease such as intervertebral disc degeneration. Therefore, the fat fraction (FF) of the vertebral bodies and paraspinal muscles has been considered a potential biomarker for assessing the pathophysiology, progression, and treatment response of spinal disease. Magnetic resonance spectroscopy (MRS) is considered the reference standard for fat quantification; however, it has limitations of a long acquisition time and is technically demanding. Chemical shift-encoding water-fat imaging, called the Dixon method, has recently been applied for rapid fat quantification with high spatial resolution. However, the Dixon method has not been validated in veterinary medicine, and we hypothesized that the Dixon method would provide a comparable assessment of the FF to MRS but would be faster and easier to implement in dogs. Methods: In this prospective study, we assessed the FF of the lumbar vertebral bodies and paravertebral muscles from the first to sixth lumbar vertebrae using MRS, the two-point Dixon method (LAVA-FLEX), and the six-point Dixon method (IDEAL-IQ) and compared these techniques. Results and discussion: The FFs of vertebral bodies and paravertebral muscles derived from LAVA-FLEX and IDEAL-IQ showed significant correlations and agreement with those obtained with MRS. In particular, the FFs obtained with IDEAL-IQ showed higher correlations and better agreement with those obtained with MRS than those derived by LAVA-FLEX. Both Dixon methods showed excellent intra- and interobserver reproducibility for FF analysis of the vertebral bodies and paraspinal muscles. However, the test-retest repeatability of vertebral body and paraspinal muscle FF analysis was low for all three sequences, especially for the paraspinal muscles. The results of this study showed that LAVA-FLEX and IDEAL-IQ have high reproducibility and that their findings were highly correlated with the FFs of the lumber vertebral bodies and paraspinal muscles determined by MRS in dogs. The FF analysis could be performed much more easily and quickly using LAVA-FLEX and IDEAL-IQ than using MRS. In conclusion, LAVA-FLEX and IDEAL-IQ can be used as routine procedures in spinal magnetic resonance imaging in dogs for FF analysis of the vertebral bodies and paraspinal muscles.

Assessment of chemical-shift and diffusion-weighted magnetic resonance imaging in differentiating malignant and benign vertebral lesions in oncologic patients. A single institution experience.

BACKGROUND: To analyze the contribution of two non-standard magnetic resonance imaging (MRI) techniques the chemical-shift image (CSI), and diffusion-weighted imaging (DWI) in distinguishing malignant and benign vertebral bone marrow lesions (VBMLs). PATIENTS AND METHODS: Conventional spine MRI protocol, followed by CSI and DWI was performed with a 1.5 T system on 102 oncologic patients between January 2020 and December 2023. From the identified 325 VBMLs, 102 representative lesions (one per patient) were selected. VBMLs were divided into malignant (n = 74) and benign (n = 28) based on histopathology, or imaging follow-up. The quantitative parameters for VBMLs assessment were signal intensity ratio (SIR) derived from CSI and apparent diffusion coefficient (ADC) derived from DWI. RESULTS: The malignant VBMLs had significantly higher SIR values (p < 0.05) and lower ADC values compared to benign VBMLs (p < 0.05). The area under the curve (AUC) was 0.953 (p < 0.001) for SIR, and 0.894 for ADC (p < 0.001) (cut-off at > 0.82, and ≤ 1.57x10-3 mm2/s, respectively). The sensitivity and specificity for SIR were 93.6%, and 88.5%, while for ADC were 88.2% and 92.3% (respectively). The combined use of SIR and ADC improved the diagnostic accuracy to AUC of 0.988 (p < 0.001, cut-off at > 0.19), sensitivity, and specificity of 100.0% and 90.9% (respectively). CONCLUSIONS: Quantitative parameters, SIR and ADC, derived from two non-standard MRI techniques, CSI, and DWI, showed diagnostic strength in differentiating malignant and benign VBMLs. Combining both methods can further enhance the diagnostic performance and accuracy of spine MRI in clinical practice.

Unraveling the Origin of Elemental Chemical Shift and the Role of Atomic Hydrogen in a Surface Ullmann Coupling System.

The Ullmann coupling of aryl halides is a powerful method in the on-surface synthesis of functional materials. Understanding its basic aspects and influencing factors can aid in the use of this tool for the fabrication of intriguing structures. In this study, we unveil (1) the origin of the shift in the elemental binding energy (BE) and (2) the functions of atomic hydrogen (AH) in a typical Ullmann coupling system using combined spectroscopy and microscopy techniques. During debromination of the aryl halide precursor, the work function (WF) alteration is correlated with the surface Br amount. The WF change instead of C-Ag formation is proposed to play a dominant role in the shift of the molecular C 1s BE. AH dosing onto organometallic chains leads to chain decomposition and surface Br removal. In contrast, AH dosing onto covalent poly(para-phenylene) (PPP) chains results in superhydrogenation in addition to Br removal. The C 1s BE shift is attributed to both WF change and superhydrogenation effects. Thermal annealing restores the PPP chains by eliminating superhydrogenation, which causes the C 1s BE to shift to a high BE. This study provides deep insights into the mechanisms of Ullmann coupling on surfaces, highlighting the significant role of WF alterations and AH treatments in these processes.

Imino chemical shift assignments of tRNAAsp, tRNAVal and tRNAPhe from Escherichia coli.

Transfer RNAs (tRNAs) are an essential component of the protein synthesis machinery. In order to accomplish their cellular functions, tRNAs go through a highly controlled biogenesis process leading to the production of correctly folded tRNAs. tRNAs in solution adopt the characteristic L-shape form, a stable tertiary conformation imperative for the cellular stability of tRNAs, their thermotolerance, their interaction with protein and RNA complexes and their activity in the translation process. The introduction of post-transcriptional modifications by modification enzymes, the global conformation of tRNAs, and their cellular stability are highly interconnected. We aim to further investigate this existing link by monitoring the maturation of bacterial tRNAs in E. coli extracts using NMR. Here, we report on the 1H, 15N chemical shift assignment of the imino groups and some amino groups of unmodified and modified E. coli tRNAAsp, tRNAVal and tRNAPhe, which are essential for characterizing their maturation process using NMR spectroscopy.

Comparative analysis of hepatic fat quantification across 5 T, 3 T and 1.5 T: A study on consistency and feasibility.

OBJECTIVES: Magnetic resonance imaging (MRI) is a critical noninvasive technique for evaluating liver steatosis, with efficient and precise fat quantification being essential for diagnosing liver diseases. This study leverages 5 T ultra-high-field MRI to demonstrate the clinical significance of liver fat quantification, and explores the consistency and accuracy of the Proton Density Fat Fraction (PDFF) in the liver across different magnetic field strengths and measurement methodologies. METHODS: The study involved phantoms with lipid contents ranging from 0 % to 30 % and 35 participants (21 females, 14 males; average age 30.17 ± 13.98 years, body mass index 25.84 ± 4.76, waist-hip ratio 0.84 ± 0.09). PDFF measurements were conducted using chemical shift encoded (CSE) MRI at 5 T, 3 T, and 1.5 T, alongside magnetic resonance spectroscopy (MRS) at 5 T and 1.5 T for both liver and phantoms, analyzed using jMRUI software. The MRS-derived PDFF values served as the reference standard. Repeatability of 5 T MRI measurements was assessed through correlation analysis, while accuracy was evaluated using linear regression analysis against the reference standards. RESULTS: The CSE-PDFF measurements at 5 T demonstrated strong consistency with those at 3 T and 1.5 T, showing high intraclass correlation coefficients (ICC) of 0.988 and 0.980, respectively (all p < 0.001). There was also significant consistency across ROIs within liver lobes, with ICC values ranging from 0.975 to 0.986 (all p < 0.001). MRS-PDFF measurements for both phantoms and liver at 5 T and 1.5 T exhibited substantial agreement, with ICC values of 0.996 and 0.980, respectively (all p < 0.001). Particularly, ICC values for ROIs in the liver ranged from 0.963 to 0.990 (all p < 0.001). Despite overall agreement, statistically significant differences were noted in specific ROIs within the liver lobes (p = 0.004 and 0.012). The CSE and MRS PDFF measurements at 5 T displayed strong consistency, with an ICC of 0.988 (p < 0.001), and significant agreement was also found between 5 T CSE and 1.5 T MRS PDFF measurements, with an ICC of 0.978 (p < 0.001). Agreement was significant within the ROIs of the liver lobes on the same platform at 5 T, with ICC values ranging from 0.986 to 0.991 (all p < 0.001). CONCLUSION: PDFF measurements at 5 T MR imaging exhibited both accuracy and repeatability, indicating that 5 T imaging provides reliable quantification of liver fat content and shows substantial potential for clinical diagnostic applications.

Accelerated Chemical Shift Encoded Cardiac MRI with Use of Resolution Enhancement Network.

BACKGROUND: Cardiovascular magnetic resonance (CMR) chemical shift encoding (CSE) enables myocardial fat imaging. We sought to develop a deep learning network (FastCSE) to accelerate CSE. METHODS: FastCSE was built on a super-resolution generative adversarial network extended to enhance complex-valued image sharpness. FastCSE enhances each echo image independently before water-fat separation. FastCSE was trained with retrospectively identified cines from 1519 patients (56 ± 16 years; 866 men) referred for clinical 3T CMR. In a prospective study of 16 participants (58 ± 19 years; 7 females) and 5 healthy individuals (32 ± 17 years; 5 females), dual-echo CSE images were collected with 1.5 × 1.5mm2, 2.5 × 1.5 mm2, and 3.8 × 1.9mm2 resolution using generalized autocalibrating partially parallel acquisition (GRAPPA). FastCSE was applied to images collected with resolution of 2.5 × 1.5mm2 and 3.8 × 1.9 mm2 to restore sharpness. Fat images obtained from two-point Dixon reconstruction were evaluated using a quantitative blur metric and analyzed with 5-way analysis of variance. RESULTS: FastCSE successfully reconstructed CSE images inline. FastCSE acquisition, with a resolution of 2.5 × 1.5mm² and 3.8 × 1.9 mm², reduced the number of breath-holds without impacting visualization of fat by approximately 1.5-fold and 3-fold compared to GRAPPA acquisition with a resolution of 1.5 × 1.5 mm², from 3.0 ± 0.8 breath-holds to 2.0 ± 0.2 and 1.1 ± 0.4 breath-holds, respectively. FastCSE improved image sharpness and removed ringing artifacts in GRAPPA fat images acquired with a resolution of 2.5 × 1.5 mm2 (0.31 ± 0.03 vs. 0.35 ± 0.04, P < 0.001) and 3.8 × 1.9 mm2 (0.31 ± 0.03 vs. 0.42 ± 0.06, P < 0.001). Blurring in FastCSE images was similar to blurring in images with 1.5 × 1.5 mm² resolution (0.32 ±0.03 vs. 0.31 ± 0.03, P = 0.78; 0.32 ± 0.03 vs. 0.31 ± 0.03, P = 0.90). CONCLUSION: We showed that a deep learning-accelerated CSE technique based on complex-valued resolution enhancement can reduce the number of breath-holds in CSE imaging without impacting the visualization of fat. FastCSE showed similar image sharpness compared to a standardized parallel imaging method.

Deep network and multi-atlas segmentation fusion for delineation of thigh muscle groups in three-dimensional water-fat separated MRI.

Purpose: Segmentation is essential for tissue quantification and characterization in studies of aging and age-related and metabolic diseases and the development of imaging biomarkers. We propose a multi-method and multi-atlas methodology for automated segmentation of functional muscle groups in three-dimensional (3D) thigh magnetic resonance images. These groups lie anatomically adjacent to each other, rendering their manual delineation a challenging and time-consuming task. Approach: We introduce a framework for automated segmentation of the four main functional muscle groups of the thigh, gracilis, hamstring, quadriceps femoris, and sartorius, using chemical shift encoded water-fat magnetic resonance imaging (CSE-MRI). We propose fusing anatomical mappings from multiple deformable models with 3D deep learning model-based segmentation. This approach leverages the generalizability of multi-atlas segmentation (MAS) and accuracy of deep networks, hence enabling accurate assessment of volume and fat content of muscle groups. Results: For segmentation performance evaluation, we calculated the Dice similarity coefficient (DSC) and Hausdorff distance 95th percentile (HD-95). We evaluated the proposed framework, its variants, and baseline methods on 15 healthy subjects by threefold cross-validation and tested on four patients. Fusion of multiple atlases, deformable registration models, and deep learning segmentation produced the top performance with an average DSC of 0.859 and HD-95 of 8.34 over all muscles. Conclusions: Fusion of multiple anatomical mappings from multiple MAS techniques enriches the template set and improves the segmentation accuracy. Additional fusion with deep network decisions applied to the subject space offers complementary information. The proposed approach can produce accurate segmentation of individual muscle groups in 3D thigh MRI scans.

Aromaticity and Antiaromaticity Reversals between the Electronic Ground State and the Two Lowest Triplet States of Thiophene.

It is shown, by examining the variations in off-nucleus isotropic magnetic shielding around a molecule, that thiophene which is aromatic in its electronic ground state (S0) becomes antiaromatic in its lowest triplet state (T1) and then reverts to being aromatic in T2. Geometry relaxation has an opposite effect on the aromaticities of the ππ* vertical T1 and T2: The antiaromaticity of T1 is reduced whereas the aromaticity of T2 is enhanced. The shielding picture around T2 is found to closely resemble those around certain second singlet ππ* excited states (S2), for example, those of benzene and cyclooctatetraene, thought to be "strongly aromatic" because of their very negative nucleus-independent chemical shift (NICS) values. It is argued that while NICS values correctly follow the changes in aromaticity along the potential energy surface of a single electronic state, the use of NICS values for the purpose of quantitative comparisons between the aromaticities of different electronic states cannot be justified theoretically and should be avoided. "Strongly aromatic" S2 and T2 states should be referred to simply as "aromatic" because detailed comparisons between the properties of these states and those of the corresponding S0 states do not suggest higher levels of aromaticity.

High-Precision Determination of NMR Interaction Parameters by Measurement of Single Crystals: A Review of Classical and Advanced Methods.

In this review, the process of extracting precise values for NMR interaction tensors from single crystal samples is systematically explored. Starting with a description of the orientation dependence of the considered interactions, i.e., chemical shift, dipolar, and quadrupole interaction, the techniques for acquiring and analysing single-crystal spectra are outlined. This includes the 'classical' approach, which requires the acquisition of three rotation patterns around three rotation axes that are orthogonal to each other, as well as more recent strategies aimed at reducing the number of required NMR spectra. One such strategy is the 'single-rotation method', which exploits the symmetry relations between tensors in the crystal structure to reduce the necessary amount of orientation-dependent data. This concept may be extended to additionally include the orientation of the goniometer axis itself in the data fit, which may be termed the 'minimal-rotation method'. Other, more exotic schemes, such as the use of specialised probe designs or the investigation of single crystals under magic-angle-spinning, are also briefly discussed. Actual values of NMR interaction tensors as determined from the various single-crystal methods have been collected and are provided in tables for spin I=1/2, I=1, and half-integer spins with I>1/2.

Spatiospectral image processing workflow considerations for advanced MR spectroscopy of the brain.

Magnetic resonance spectroscopy (MRS) is one of the few non-invasive imaging modalities capable of making neurochemical and metabolic measurements in vivo. Traditionally, the clinical utility of MRS has been narrow. The most common use has been the "single-voxel spectroscopy" variant to discern the presence of a lactate peak in the spectra in one location in the brain, typically to evaluate for ischemia in neonates. Thus, the reduction of rich spectral data to a binary variable has not classically necessitated much signal processing. However, scanners have become more powerful and MRS sequences more advanced, increasing data complexity and adding 2 to 3 spatial dimensions in addition to the spectral one. The result is a spatially- and spectrally-variant MRS image ripe for image processing innovation. Despite this potential, the logistics for robustly accessing and manipulating MRS data across different scanners, data formats, and software standards remain unclear. Thus, as research into MRS advances, there is a clear need to better characterize its image processing considerations to facilitate innovation from scientists and engineers. Building on established neuroimaging standards, we describe a framework for manipulating these images that generalizes to the voxel, spectral, and metabolite level across space and multiple imaging sites while integrating with LCModel, a widely used quantitative MRS peak-fitting platform. In doing so, we provide examples to demonstrate the advantages of such a workflow in relation to recent publications and with new data. Overall, we hope our characterizations will lower the barrier of entry to MRS processing for neuroimaging researchers.

Sensitivity Enhancement of Ultra-Wideline NMR by Progressive Saturation of the Proton Reservoir Under Magic-Angle Spinning.

Solid-state NMR of low-γ nuclides is often characterized by low sensitivity and by significant spectral broadenings induced by the quadrupolar and the chemical-shift anisotropy interactions. Herein, we introduce an indirect acquisition method, termed PROgressive Saturation of the Proton Reservoir Under Spinning (PROSPRUS), which could facilitate the acquisition of ultra-wideline NMR spectra under magic-angle spinning (MAS), in systems with a sufficiently long dipolar relaxation time, T1D. PROSPRUS NMR relies on the generation of so-called second-order dipolar order among abundant protons undergoing MAS, and on the subsequent depletion of this dipolar order by a series of looped cross-polarization events, transferring the proton order into polarization of the low-γ I-nuclei as a function of the latter's offsets. While the spin dynamics of the ensuing experiment is complex, particularly when dealing with narrow I spectral lines, it is shown that PROSPRUS can lead to faithful lineshapes for ultra-wideline spin-1/2 and spin-1 species, providing high sensitivity with extremely low RF power requirements. It is also shown that the ensuing 1H-detected PROSPRUS experiments can efficiently characterize I-spin lineshapes in excess of 1 MHz without having to retune electronics, while providing improvements in sensitivity per unit time over current broadband direct-detection methods by up to a factor of four.

Tetrakis(trimethylsilyl)silane as a standard compound for fast spinning Solid-State NMR experiments.

The development of magic angle spinning (MAS) at rates ranging from 30 kHz to greater than 100 kHz has substantially advanced solid-state nuclear magnetic resonance (SSNMR) spectroscopy 1H-detection methods. The small rotors required for such MAS rates have a limited sample volume and low 13C-detection sensitivity, rendering the traditional set of standard compounds for SSNMR insufficient or highly inconvenient for shimming and magic-angle calibration. Additionally, the reproducibility of magic angle setting, chemical shift referencing, and probe position can be especially critical for SSNMR experiments at high fields. These conditions suggest the need for a high signal-to-noise ratio (SNR) 1H-detection standard compound, which is preferably multi-purpose, to simplify instrument set up for ultra-fast MAS SSNMR instruments at high magnetic fields. In this study, we present the results for setting magic angle and shimming using tetrakis(trimethylsilyl)silane (TTMSS, or TKS), a tetramethylsilane (TMS) analogue, at near 40 kHz and demonstrate that we can achieve favorable results in less time but with equal or superior precision as traditional KBr and adamantane standards. The high SNR and TMS-like chemical shift of TKS also opens the possibilities for using TKS as an internal standard with biological samples. A single rotor containing a four-component mixture of TKS, adamantane, uniformly 13C, 15N-labeled N-acetyl valine and KBr was used to perform a complete configuration and calibration of a SSNMR probe without sample changes. We anticipate TKS as a standard compound to be especially effective at very high MAS conditions and to greatly simplify the instrument set up for high and ultra-high field SSNMR instruments.

T1 relaxation: Chemo-physical fundamentals of magnetic resonance imaging and clinical applications.

A knowledge of the complex phenomena that regulate T1 signal on Magnetic Resonance Imaging is essential in clinical practice for a more effective characterization of pathological processes. The authors review the physical basis of T1 Relaxation Time and the fundamental aspects of physics and chemistry that can influence this parameter. The main substances (water, fat, macromolecules, methemoglobin, melanin, Gadolinium, calcium) that influence T1 and the different MRI acquisition techniques that can be applied to enhance their presence in diagnostic images are then evaluated. An extensive case illustration of the different phenomena and techniques in the areas of CNS, abdomino-pelvic, and osteoarticular pathology is also proposed. CRITICAL RELEVANCE STATEMENT: T1 relaxation time is strongly influenced by numerous factors related to tissue characteristics and the presence in the context of the lesions of some specific substances. An examination of these phenomena with extensive MRI exemplification is reported. KEY POINTS: The purpose of the paper is to illustrate the chemical-physical basis of T1 Relaxation Time. MRI methods in accordance with the various clinical indications are listed. Several examples of clinical application in abdominopelvic and CNS pathology are reported.

Assessment of lumbar paraspinal muscle morphology using mDixon Quant magnetic resonance imaging (MRI): a cross-sectional study in healthy subjects.

Background: Lumbar paraspinal muscles (LPM) are a part of the deep spinal stabilisation system and play an important role in stabilising the lumbar spine and trunk. Inadequate function of these muscles is thought to be an essential aetiological factor in low back pain, and several neuromuscular diseases are characterised by dysfunction of LPM. The main aims of our study were to develop a methodology for LPM assessment using advanced magnetic resonance imaging (MRI) methods, including a manual segmentation process, to confirm the measurement reliability, to evaluate the LPM morphological parameters [fat fraction (FF), total muscle volume (TMV) and functional muscle volume (FMV)] in a healthy population, to study the influence of physiological factors on muscle morphology, and to build equations to predict LPM morphological parameters in a healthy population. Methods: This prospective cross-sectional observational comparative single-centre study was conducted at the University Hospital in Brno, enrolling healthy volunteers from April 2021 to March 2023. MRI of the lumbar spine and LPM (erector spinae muscle and multifidus muscle) were performed using a 6-point Dixon gradient echo sequence. The segmentation of the LPM and the control muscle (psoas muscle) was done manually to obtain FF and TMV in a range from Th12/L1 to L5/S1. Intra-rater and inter-rater reliability were evaluated. Linear regression models were constructed to assess the effect of physiological factors on muscle FF, TMV and FMV. Results: We enrolled 90 healthy volunteers (median age 38 years, 45 men). The creation of segmentation masks and the assessment of FF and TMV proved reliable (Dice coefficient 84% to 99%, intraclass correlation coefficient ≥0.97). The univariable models showed that FF of LPM was influenced the most by age (39.6% to 44.8% of variability, P<0.001); TMV and FMV by subject weight (34.9% to 67.6% of variability, P<0.001) and sex (24.7% to 64.1% of variability, P<0.001). Multivariable linear regression models for FF of LPM included age, body mass index and sex, with R-squared values ranging from 45.4% to 51.1%. Models for volumes of LPM included weight, age and sex, with R-squared values ranged from 37.4% to 76.8%. Equations were developed to calculate predicted FF, TMV and FMV for each muscle. Conclusions: A reliable methodology has been developed to assess the morphological parameters (biomarkers) of the LPM. The morphological parameters of the LPM are significantly influenced by physiological factors. Equations were constructed to calculate the predicted FF, TMV and FMV of individual muscles in relation to anthropometric parameters, age, and sex. This study, which presented LPM assessment methodology and predicted values of LPM morphological parameters in a healthy population, could improve our understanding of diseases involving LPM (low back pain and some neuromuscular diseases).

Solution NMR backbone assignment of the N-terminal tandem Zα1-Zα2 domains of Z-DNA binding protein 1.

The detection of nucleic acids that are present in atypical conformations is a crucial trigger of the innate immune response. Human Z-DNA binding protein 1 (ZBP1) is a pattern recognition receptor that harbors two Zα domains that recognize Z-DNA and Z-RNA. ZBP1 detects this alternate nucleic acid conformation as foreign, and upon stabilization of these substrates, it triggers activation of an immune response. Here, we present the backbone chemical shift assignment of a construct encompassing the Zα1 and Zα2 domains as well as the interconnecting linker of ZBP1. These assignments can be directly transferred to the isolated Zα1 and Zα2 domains, thereby demonstrating that these domains maintain virtually identical structures in the tandem context.

Can Graph Machines Accurately Estimate 13C NMR Chemical Shifts of Benzenic Compounds?

In the organic laboratory, the 13C nuclear magnetic resonance (NMR) spectrum of a newly synthesized compound remains an essential step in elucidating its structure. For the chemist, the interpretation of such a spectrum, which is a set of chemical-shift values, is made easier if he/she has a tool capable of predicting with sufficient accuracy the carbon-shift values from the structure he/she intends to prepare. As there are few open-source methods for accurately estimating this property, we applied our graph-machine approach to build models capable of predicting the chemical shifts of carbons. For this study, we focused on benzene compounds, building an optimized model derived from training a database of 10,577 chemical shifts originating from 2026 structures that contain up to ten types of non-carbon atoms, namely H, O, N, S, P, Si, and halogens. It provides a training root-mean-squared relative error (RMSRE) of 0.5%, i.e., a root-mean-squared error (RMSE) of 0.6 ppm, and a mean absolute error (MAE) of 0.4 ppm for estimating the chemical shifts of the 10k carbons. The predictive capability of the graph-machine model is also compared with that of three commercial packages on a dataset of 171 original benzenic structures (1012 chemical shifts). The graph-machine model proves to be very efficient in predicting chemical shifts, with an RMSE of 0.9 ppm, and compares favorably with the RMSEs of 3.4, 1.8, and 1.9 ppm computed with the ChemDraw v. 23.1.1.3, ACD v. 11.01, and MestReNova v. 15.0.1-35756 packages respectively. Finally, a Docker-based tool is proposed to predict the carbon chemical shifts of benzenic compounds solely from their SMILES codes.

Non-contrast spectral CT vs chemical-shift MRI in discriminating lipid-poor adrenal lesions.

OBJECTIVES: To compare the diagnostic performance of conventional non-contrast CT, dual-energy spectral CT, and chemical-shift MRI (CS-MRI) in discriminating lipid-poor adenomas (> 10-HU on non-contrast CT) from non-adenomas. METHODS: A total of 110 patients (69 men; 41 women; mean age 66.5 ± 13.4 years) with 80 lipid-poor adenomas and 30 non-adenomas who underwent non-contrast dual-layer spectral CT and CS-MRI were retrospectively identified. For each lesion, non-contrast attenuation on conventional 120-kVp images, ΔHU-index ([attenuation difference between virtual monoenergetic 140-keV and 40-keV images]/conventional attenuation × 100), and signal intensity index (SI-index) were quantified. Each parameter was compared between adenomas and non-adenomas using the Mann-Whitney U-test. The area under the receiver operating characteristic curve (AUC) and sensitivity to achieve > 95% specificity for adenoma diagnosis were determined. RESULTS: Conventional non-contrast attenuation was lower in adenomas than in non-adenomas (22.4 ± 8.6 HU vs 32.8 ± 48.5 HU), whereas ΔHU-index (148.0 ± 103.2 vs 19.4 ± 25.8) and SI-index (41.6 ± 19.6 vs 4.2 ± 10.2) were higher in adenomas (all, p < 0.001). ΔHU-index showed superior performance to conventional non-contrast attenuation (AUC: 0.919 [95% CI: 0.852-0.963] vs 0.791 [95% CI: 0.703-0.863]; sensitivity: 75.0% [60/80] vs 27.5% [22/80], both p < 0.001), and near equivalent to SI-index (AUC: 0.952 [95% CI: 0.894-0.984], sensitivity 85.0% [68/80], both p > 0.05). Both the ΔHU-index and SI-index provided a sensitivity of 96.0% (48/50) for hypoattenuating adenomas (≤ 25 HU). For hyperattenuating (> 25 HU) adenomas, SI-index showed higher sensitivity than ΔHU-index (66.7% [20/30] vs 40.0% [12/30], p = 0.022). CONCLUSIONS: Non-contrast spectral CT and CS-MRI outperformed conventional non-contrast CT in distinguishing lipid-poor adenomas from non-adenomas. While CS-MRI demonstrated superior sensitivity for adenomas measuring > 25 HU, non-contrast spectral CT provided high discriminative values for adenomas measuring ≤ 25 HU. CLINICAL RELEVANCE STATEMENT: Spectral attenuation analysis improves the diagnostic performance of non-contrast CT in discriminating lipid-poor adrenal adenomas, potentially serving as an alternative to CS-MRI and obviating the necessity for additional diagnostic workup in indeterminate adrenal incidentalomas, particularly for lesions measuring ≤ 25 HU. KEY POINTS: Incidental adrenal lesion detection has increased as abdominal CT use has become more frequent. Non-contrast spectral CT and CS-MRI differentiated lipid-poor adenomas from non-adenomas better than conventional non-contrast CT. For lesions measuring ≤ 25 HU, spectral CT may obviate the need for additional evaluation.

EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models.

Nuclear magnetic resonance (NMR) crystallography is one of the main methods in structural biology for analyzing protein stereochemistry and structure. The chemical shift of the resonance frequency reflects the effect of the protons in a molecule producing distinct NMR signals in different chemical environments. Apprehending chemical shifts from NMR signals can be challenging since having an NMR structure does not necessarily provide all the required chemical shift information, making predictive models essential for accurately deducing chemical shifts, either from protein structures or, more ideally, directly from amino acid sequences. Here, we present EFG-CS, a web server that specializes in chemical shift prediction. EFG-CS employs a machine learning-based transfer prediction model for backbone atom chemical shift prediction, using ESMFold-predicted protein structures. Additionally, ESG-CS incorporates a graph neural network-based model to provide comprehensive side-chain atom chemical shift predictions. Our method demonstrated reliable performance in backbone atom prediction, achieving comparable accuracy levels with root mean square errors (RMSE) of 0.30 ppm for H, 0.22 ppm for Hα, 0.89 ppm for C, 0.89 ppm for Cα, 0.84 ppm for Cß, and 1.69 ppm for N. Moreover, our approach also showed predictive capabilities in side-chain atom chemical shift prediction achieving RMSE values of 0.71 ppm for Hß, 0.74-1.15 ppm for Hδ, and 0.58-0.94 ppm for Hγ, solely utilizing amino acid sequences without homology or feature curation. This work shows for the first time that generative AI protein models can predict NMR shifts nearly comparable to experimental models. This web server is freely available at https://biosig.lab.uq.edu.au/efg_cs, and the chemical shift prediction results can be downloaded in tabular format and visualized in 3D format.