Metabolic disorder and functional disturbance in the central executive network in minimal hepatic encephalopathy.

The aim of this paper is to investigate dynamical functional disturbance in central executive network in minimal hepatic encephalopathy and determine its association with metabolic disorder and cognitive impairment. Data of magnetic resonance spectroscopy and resting-state functional magnetic resonance imaging were obtained from 27 cirrhotic patients without minimal hepatic encephalopathy, 20 minimal hepatic encephalopathy patients, and 24 healthy controls. Central executive network was identified utilizing seed-based correlation approach. Dynamic functional connectivity across central executive network was calculated using sliding-window approach. Functional states were estimated by K-means clustering. Right dorsolateral prefrontal cortex metabolite ratios (i.e. glutamate and glutamine complex/total creatine, myo-inositol / total creatine, and choline / total creatine) were determined. Neurocognitive performance was determined by psychometric hepatic encephalopathy scores. Minimal hepatic encephalopathy patients had decreased myo-inositol / total creatine and choline / total creatine and increased glutamate and glutamine complex / total creatine in right dorsolateral prefrontal cortex (all P ≤ 0.020); decreased static functional connectivity between bilateral dorsolateral prefrontal cortex and between right dorsolateral prefrontal cortex and lateral-inferior temporal cortex (P ≤ 0.001); increased frequency and mean dwell time in state-1 (P ≤ 0.001), which exhibited weakest functional connectivity. Central executive network dynamic functional indices were significantly correlated with right dorsolateral prefrontal cortex metabolic indices and psychometric hepatic encephalopathy scores. Right dorsolateral prefrontal cortex myo-inositol / total creatine and mean dwell time in state-1 yielded best potential for diagnosing minimal hepatic encephalopathy. Dynamic functional disturbance in central executive network may contribute to neurocognitive impairment and could be correlated with metabolic disorder.

Arsenic trioxide-induced apoptosis contributes to suppression of viral reservoir in SIV-infected rhesus macaques.

Latent viral reservoir is recognized as the major obstacle to achieving a functional cure for HIV infection. We previously reported that arsenic trioxide (As2O3) combined with antiretroviral therapy (ART) can reactivate the viral reservoir and delay viral rebound after ART interruption in chronically simian immunodeficiency virus (SIV)-infected macaques. In this study, we further investigated the effect of As2O3 independent of ART in chronically SIV-infected macaques. We found that As2O3-only treatment significantly increased the CD4/CD8 ratio, improved SIV-specific T cell responses, and reactivated viral latency in chronically SIVmac239-infected macaques. RNA-sequencing analysis revealed that As2O3 treatment downregulated the expression levels of genes related to HIV entry and infection, while the expression levels of genes related to transcription initiation, cell apoptosis, and host restriction factors were significantly upregulated. Importantly, we found that As2O3 treatment specifically induced apoptosis of SIV-infected CD4+ T cells. These findings revealed that As2O3 might not only impact viral latency, but also induce the apoptosis of HIV-infected cells and thus block the secondary infection of bystanders. Moreover, we investigated the therapeutic potential of this regimen in acutely SIVmac239-infected macaques and found that As2O3 + ART treatment effectively restored the CD4+ T cell count, delayed disease progression, and improved survival in acutely SIV-infected macaques. In sum, this work provides new insights to develop As2O3 as a component of the "shock-and-kill" strategy toward HIV functional cure. IMPORTANCE Although antiretroviral therapy (ART) can effectively suppress the viral load of AIDS patients, it cannot functionally cure HIV infection due to the existence of HIV reservoir. Strategies toward HIV functional cure are still highly anticipated to ultimately end the pandemic of AIDS. Herein, we investigated the direct role of As2O3 independent of ART in chronically SIV-infected macaques and explored the underlying mechanisms of the potential of As2O3 in the treatment of HIV/SIV infection. Meanwhile, we investigated the therapeutic effects of ART+As2O3 in acutely SIVmac239-infected macaques. This study showed that As2O3 has the potential to be launched into the "shock-and-kill" strategy to suppress HIV/SIV reservoir due to its latency-reversing and apoptosis-inducing properties.

Deep Learning Methodology for Obtaining Ultraclean Pure Shift Proton Nuclear Magnetic Resonance Spectra.

Nuclear magnetic resonance (NMR) is one of the most powerful analytical techniques. In order to obtain high-quality NMR spectra, a real-time Zangger-Sterk (ZS) pulse sequence is employed to collect low-quality pure shift NMR data with high efficiency. Then, a neural network named AC-ResNet and a loss function named SM-CDMANE are developed to train a network model. The model with excellent abilities of suppressing noise, reducing line widths, discerning peaks, and removing artifacts is utilized to process the acquired NMR data. The processed spectra with noise and artifact suppression and small line widths are ultraclean and high-resolution. Peaks overlapped heavily can be resolved. Weak peaks, even hidden in the noise, can be discerned from noise. Artifacts, even as high as spectral peaks, can be removed completely while not suppressing peaks. Eliminating perfectly noise and artifacts and smoothing baseline make spectra ultraclean. The proposed methodology would greatly promote various NMR applications.

Simultaneous acquirement of pure shift 2D homonuclear correlation spectra.

Two dimensional homonuclear correlation spectra like COSY, TOCSY and NOESY are classic two-dimensional spectra for analyzing coupling networks and delivering structural information of molecules. Two main challenges of the homonuclear correlation spectra are resolution and efficiency. Because of the complexity of the molecular structure and the effect of scalar coupling, spectral resolution is still difficult to meet the demand, and a higher resolution is needed to improve the quality of the homonuclear correlation spectrum. On the other hand, although some homonuclear correlation spectra are often used together (such as COSY and TOCSY), they are generally sampled separately, and as the number of sampling points in the indirect dimension increases, experiment time increases dramatically. Here, we propose a scheme that can be used to simultaneously obtain indirect dimension pure shift COSY and TOCSY or indirect dimension pure shift COSY and NOESY to improve the resolution of them, while reducing the sampling time and improving the efficiency.

Fast Acquisition of High-Quality Nuclear Magnetic Resonance Pure Shift Spectroscopy via a Deep Neural Network.

Pure shift methods improve the resolution of proton nuclear magnetic resonance spectra at the cost of time. The pure shift yielded by chirp excitation (PSYCHE) method is a promising pure shift method. We propose a method of reconstructing the undersampled PSYCHE spectra based on deep learning to accelerate the spectra acquisition. It only takes 17 s to obtain a high-quality pure shift spectrum. The network can completely remove undersampling artifacts and chunking sidebands and improve the signal-to-noise ratio, obtaining completely clean pure shift spectra. The reconstruction quality is better than the iterative soft thresholding method. In addition, the network can differentiate low-level signals and chunking sidebands with similar intensities in the mixture, remove sidebands, and retain signals, promoting correct mixture analysis.

Evaluation of brown adipose tissue with intermolecular double-quantum coherence magnetic resonance spectroscopy at 3.0 T.

In the current study, we propose a single-voxel (SV) magnetic resonance spectroscopy (MRS) pulse sequence, based on intermolecular double-quantum coherence (iDQC), for in vivo specific assessment of brown adipose tissue (BAT) at 3 T. The multilocular adipocyte, present in BAT, typically contains a large number of small lipid droplets surrounded by abundant intracellular water, while the monolocular adipocyte, present in white adipose tissue (WAT), accommodates only a single large lipid droplet with much less water content. The SV-iDQC sequence probes the spatial correlation between water and fat spins at a distance of about the size of an adipocyte, thus can be used for assessment of BAT, even when mixed with WAT and/or muscle tissues. This sequence for measurement of water-to-fat (water-fat) iDQC signals was tested on phantoms and mouse BAT and WAT tissues. It was then used to differentiate adipose tissues in the supraclavicular and subcutaneous regions of healthy youth human volunteers (n = 6). Phantom results with water-fat emulsions demonstrated enhanced water-fat iDQC signal with increased voxel size, increased energy level of emulsification, or increased distribution balance of water and fat spins. The animal tissue experiments resulted in obvious water-fat iDQC signal in mouse BAT, while this signal was almost absent in the WAT spectrum. The optimal choice of the dipolar coupling distance for the observation was approximately 100 µm, as tested on both emulsion phantom and animal tissue. The water-fat iDQC signals observed in the supraclavicular adipose tissues were higher than in the subcutaneous adipose tissues in healthy young volunteers (0.43 ± 0.36 vs. 0.10 ± 0.06, p = 0.06). It was concluded that the iDQC-based sequence has potential for assessment of mouse and human BAT at 3 T, which is of interest for clinical research and the diagnosis of obesity and associated diseases.

Unambiguous and accurate measurement of scalar coupling constants through a selective refocusing NMR experiment.

Scalar coupling plays an important role in the analysis of molecular structure and dynamics. A great number of nuclear magnetic resonance (NMR) selective refocusing experiments, such as 2D G-SERF and PSYCHEDELIC, were developed to extract scalar coupling constants involving a selected proton from overlapped spectra. However, intense axial peaks occur in this type of experiments, leading to possible ambiguity in the assignment of spectral peaks and subsequent accurate measurement of 1H-1H scalar coupling constants. Here, a method based on selective coherence transfer and PSYCHEDELIC module is designed to acquire absorption-mode selective refocusing spectrum while suppressing intense axial peaks. Therefore, unambiguous and accurate measurement of scalar coupling constants involving the selectively excited proton can be achieved. The performances of the proposed method are demonstrated on several samples.

A simple data post-processing method for axial peaks free 2D PSYCHEDELIC NMR spectra.

Homonuclear scalar coupling plays an important role in the elucidation of molecular structure and dynamics. However, complex multiplets due to 1H-1H scalar coupling splittings complicate the assignment of peaks in overcrowded spectral regions. Although many methods focusing on disentangling couplings have been proposed in recent years, some defects like intense axial peaks and dispersive components still exist. Herein, a simple data post-processing method based on the interleaved acquisition mode PSYCHEDELIC (Pure Shift Yielded by CHirp Excitation to DELiver Individual Couplings) is designed to acquire absorption-mode 2D J spectrum while eradicating axial peaks. This approach provides a high resolution and pure absorptive spectrum, permitting unambiguous and accurate measurement of scalar coupling constants involving a given proton.

High-resolution 2-D NMR spectroscopy based on the Radon transform and pure shift technique for studying chemical shifts perturbations.

Chemical shift plays an important role in molecular analysis. However, chemical shifts are influenced by temperature, solvent concentration, pressure, and so forth. Therefore, measuring chemical shift perturbations caused by these factors is helpful to molecular studies. A new form of 2-D spectroscopy (projection spectroscopy) has been introduced whose indirect dimension is derived by implementing the Radon transform on a series of conventional 1-D proton spectra and indicates such perturbations. However, signal overlap may exist in the conventional 1-D spectra and hence in the resulting projection spectra, hampering clear multiplet analysis and accurate extraction of perturbations. Here, the pure shift decoupling technique is employed to obtain clearer projection spectrum with higher spectral resolution. The combination of pure shift technique and the Radon transform is helpful to accurately extract chemical shift perturbations. It is believed that this application will open up a vast prospect for molecular analysis.

Improvement in Signal-to-Noise Ratio of Liquid-State NMR Spectroscopy via a Deep Neural Network DN-Unet.

Nuclear magnetic resonance (NMR) is one of the most powerful analytical tools and is extensively applied in many fields. However, compared to other spectroscopic techniques, NMR has lower sensitivity, impeding its wider applications. Using data postprocessing techniques to increase the NMR spectral signal-to-noise ratio (SNR) is a relatively simple and cost-effective method. In this work, a deep neural network, termed as DN-Unet, is devised to suppress noise in liquid-state NMR spectra to enhance SNR. It combines structures of encoder-decoder and convolutional neural network. Different from traditional deep learning training strategy, M-to-S strategy is developed to enhance DN-Unet capability that multiple noisy spectra (inputs) correspond to a same single noiseless spectrum (label) in the training stage. The trained 1D model can be used for denoising not only 1D but also high dimension spectra, further improving DN-Unet's performance. 1D, 2D, and 3D NMR spectra were utilized to evaluate DN-Unet performance. The results suggest that DN-Unet provides larger than 200-fold increase in SNR with weak peaks hidden in noise perfectly recovered and spurious peaks suppressed well. Since DN-Unet developed here to increase SNR is based on data postprocessing, it is universal for a variety of samples and NMR platforms. The great SNR enhancement and extreme excellence in differentiating signal and noise would greatly promote various liquid-state NMR applications.

Disturbance of thalamic metabolism and its association with regional neural dysfunction and cognitive impairment in minimal hepatic encephalopathy.

PURPOSE: To conduct the first investigation on thalamic metabolic alterations in minimal hepatic encephalopathy (MHE) and elucidate their association with intrinsic neural activity change and cognitive dysfunction. METHODS: Thirty-eight cirrhotic patients [18 with MHE, 20 without MHE (NHE)] and 21 healthy controls (HC) were included, all of whom underwent 1H-magnetic resonance spectroscopy, resting-state functional magnetic resonance imaging (fMRI), as well as cognitive assessment based on the Psychometric Hepatic Encephalopathy Score (PHES). Metabolite ratios in the thalamus were measured, including N-acetyl aspartate (NAA)/creatine (Cr), glutamate plus glutamine (Glx)/Cr, choline (Cho)/Cr, and myo-inositol (mI)/Cr. Intrinsic neural activity was evaluated based on frequency-specific amplitude of low-frequency fluctuations (ALFF) using fMRI signals. RESULTS: MHE patients showed an increase in Glx/Cr and a decrease in Cho/Cr and mI/Cr, compared with HC. These changes were aggravated from NHE to MHE. Cho/Cr and mI/Cr were positively correlated with regional ALFF derived from the frequency-specific band (0.01-0.027 Hz) and PHES. Receiver operating characteristic curve analysis showed that Cho/Cr and mI/Cr measurements exhibited moderate discrimination ability between NHE and MHE. CONCLUSION: Our findings provide evidence that MHE is associated with disturbed metabolism in the thalamus, which may contribute to the altered neural activity and underlie the mechanisms of cognitive impairments. MRS measurements in the thalamus could serve as the potential biomarker for diagnosing MHE among cirrhotic patients.

Fast reconstruction of non-uniform sampling multidimensional NMR spectroscopy via a deep neural network.

Multidimensional nuclear magnetic resonance (NMR) spectroscopy is used to examine the chemical structures of the studied systems. Unfortunately, the application of NMR spectra is limited by their long acquisition time, especially for 3D, 4D, and higher dimensional spectra. Non-uniform sampling (NUS) has been widely recognized as a powerful tool to reduce the NMR experimental time. But the quality of NUS spectra depends on appropriate reconstruction algorithms. As an effective data processing method, deep learning has been widely used in many fields in recent years. In this work, a deep learning-based strategy for fast reconstruction of non-uniform sampling NMR spectra is proposed. In our experiments, the proposed deep neural network has better performance in removing artifacts and preserving weak peaks than typical convolutional neural networks of U-Net and DenseNet. Besides, a novel approach of generating training data is utilized to reduce the computational burden of neural networks, and thus training our network can be easier and faster than previous deep learning-based works. Compared with the two currently available methods, SMILE and hmsIST, our strategy can provide comparable reconstruction quality in terms of peak intensities and the fidelity of peak shape. The reconstruction time of our methods is also comparable to or faster than the two methods, especially for 3D spectra.

Fully Exploiting the Power of 2D NMR J-Resolved Spectroscopy.

Nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical tool that enables one to study molecular properties and interactions. Homonuclear couplings provide valuable structural information but are often difficult to disentangle in crowded 1H NMR spectra where complex multiplets and signal overlap commonly exist. Multidimensional NMR experiments push the power of NMR to a new level by providing better signal dispersion. Among them, 2D J-resolved spectroscopy is widely used for multiplet analysis and the measurement of scalar coupling constants. Here, we present a new 2D J-resolved method, CASCADE, through which easier multiplet analysis and unambiguous measurement of specific coupling constants can be achieved at the same time, fully exploiting the power of 2D J-resolved spectroscopy. It is expected that this method may replace a conventional 2D J experiment in many cases, facilitating structural and configurational studies as well as chemical and biological analyses.

Boosting resolution in NMR spectroscopy by chemical shift upscaling.

Resolution is an essential challenge in NMR spectroscopy. Narrow chemical shift range and extensive signal splittings due to scalar couplings often give rise to spectral congestion and even overlap in NMR spectra. Magnetic field strength is directly responsible for spectral resolution as higher magnetic field strength offers better signal dispersion. However, the process of further increasing magnetic field strength of NMR instruments is slow and expensive. Methodology aimed at resolution issue has long been developing. Here, we present a chemical shift upscaling method, in which chemical shifts are upscaled by a given factor while scalar couplings are unchanged. As a result, signal dispersion and hence the resolution are improved. Therefore, it is possible to separate multiplets which originally overlap with each other and to extract their integrals for quantitative analysis. Improved signal dispersion and the preservation of scalar couplings also facilitate multiplet analysis and signal assignment. Chemical shift upscaling offers a method for enhancing resolution limited by magnetic field strength.

PE-SERF: A sensitivity-improved experiment to measure JHH in crowded spectra.

Aiming at facilitating the analysis of molecular structure, the gradient-encoded selective refocusing methods (G-SERF) and a great number of its variants for measuring proton-proton coupling constants have been proposed. However, the sensitivity is an issue in the 2D gradient-encoded experiments, because the signal intensity is determined by the slice thickness of the sample that depends on encoding gradient and the bandwidth of selective pulses which is limited by the smallest chemical shift difference of any two coupled protons. Here, we present a method dubbed PE-SERF (perfect echo selective refocusing) which can determine all JHH values involving a selected proton with improved sensitivity compared to original G-SERF experiment. The modules of perfect echo involving selective pulses and gradient-encoded selective refocusing are combined in the method, so that the unwanted J couplings arising from coupled spin pairs in the same sample slice would be nullified. In this way, instead of single proton, a pair of coupled protons is allowed to share a sample slice, and thus the slice thickness can be increased and the spectral sensitivity can be improved. The performance of the method is demonstrated by experiments on quinine and strychnine.

Pushing resolution limits for extracting 1H-1H scalar coupling constants by a resolution-enhanced selective refocusing method.

Nuclear magnetic resonance (NMR) spectroscopy enables one to study molecular structure and dynamics in a noninvasive manner and has long served as a versatile and indispensable analytical tool in physics, chemistry, and biology. Scalar coupling, an essential feature in NMR spectroscopy, provides rich information regarding molecular structure and conformation. The measurement of scalar coupling constants, therefore, constitutes an important issue in NMR spectroscopy. Homonuclear 2D J-resolved spectroscopy is a powerful tool for multiplet analysis and coupling measurement. Recently, a number of phase-sensitive J-resolved methods and selective measuring methods have been developed to facilitate the extraction of coupling constants. However, resolution remains a crucial challenge when extracting small coupling constants or under inhomogeneous fields. In this paper, we present a resolution-enhanced selective refocusing (RESERF) method for the extraction of coupling constants. The effect of magnetic field inhomogeneity can be eliminated, resulting in very narrow linewidths. Therefore, samples with small coupling constants or under inhomogeneous fields can be well analyzed. The RESERF method may be of great value for structural and conformational studies in chemistry and biology.

High Resolution Nuclear Magnetic Resonance Spectroscopy on Biological Tissue and Metabolomics.

High-resolution nuclear magnetic resonance (NMR) spectroscopy is a universal analytical tool. It can provide detailed information on chemical shifts, J coupling constants, multiplet patterns, and relative peak areas. It plays an important role in the fields of chemistry, biology, medicine, and pharmacy. A highly homogeneous magnetic field is a prerequisite for excellent spectral resolution. However, in some cases, such as in vivo and ex vivo biological tissues, the magnetic field inhomogeneity due to magnetic susceptibility variation in samples is unavoidable and hard to eliminate by conventional methods. The techniques based on intermolecular multiple quantum coherences and conventional single quantum coherence can remove the influence of the field inhomogeneity effects and be applied to obtain highresolution NMR spectra of biological tissues, including in vivo animal and human tissues. Broadband 1H homo-decoupled NMR spectroscopy displays J coupled resonances as collapsed singlets, resulting in highly resolved spectra. It can be used to acquire high-resolution spectra of some pharmaceuticals. The J-difference edited spectra can be used to detect J coupled metabolites, such as γ-aminobutyric acid, the detection of which is interfered by intense neighboring peaks. High-resolution 1H NMR spectroscopy has been widely utilized for the identification and characterization of biological fluids, constituting an important tool in drug discovery, drug development, and disease diagnosis.

High-resolution methods for the measurement of scalar coupling constants.

Scalar couplings provide important information regarding molecular structure and dynamics. The measurement of scalar coupling constants constitutes a topic of interest and significance in NMR spectroscopy. However, the measurement of J values is often not straightforward because of complex signal splitting patterns and signal overlap. Many methods have been proposed for the measurement of scalar coupling constants, both for homonuclear and heteronuclear cases. Different approaches to the measurement of scalar coupling constants are reviewed here with several applications presented. The accurate measurement of scalar coupling constants can greatly facilitate molecular structure elucidation and the study of molecule dynamics.

A simultaneous multi-slice selective J-resolved experiment for fully resolved scalar coupling information.

Proton-proton scalar coupling plays an important role in molecular structure elucidation. Many methods have been proposed for revealing scalar coupling networks involving chosen protons. However, determining all JHH values within a fully coupled network remains as a tedious process. Here, we propose a method termed as simultaneous multi-slice selective J-resolved spectroscopy (SMS-SEJRES) for simultaneously measuring JHH values out of all coupling networks in a sample within one experiment. In this work, gradient-encoded selective refocusing, PSYCHE decoupling and echo planar spectroscopic imaging (EPSI) detection module are adopted, resulting in different selective J-edited spectra extracted from different spatial positions. The proposed pulse sequence can facilitate the analysis of molecular structures. Therefore, it will interest scientists who would like to efficiently address the structural analysis of molecules.