The geometric configuration of cis-trans isomers critically influences their biological activities, making the separation and configurational identification essential for efficacy evaluation. In this study, cis/trans-1,2,4-oxadiazole derivatives were synthesized and comprehensively characterized by nuclear magnetic resonance (NMR), Fourier transform infrared spectroscopy (FT-IR), high-resolution mass spectrometry (HRMS), and theoretical calculations. This approach enabled unambiguous identification and distinction of a pair of isomers, including cis-4-[2-(3-((1H-indol-3-yl)methyl)-1,2,4-oxadiazol-5-yl)vinyl]-N,N-dimethylaniline and its trans-configuration analogue. Anti-Toxoplasma gondii activity assessment revealed that the cis-isomer (compound 2, selectivity index SI = 1.50) exhibited higher activity than the trans-isomer (compound 1, SI = 0.92) and the positive control spiramycin (SI = 0.98), highlighting the value of configurational separation and identification in pharmaceutical research. This study deepens the understanding of configuration-activity relationships and provides new insights for developing highly selective anti-Toxoplasma agents.
Ammonium fluoride(NH4F)etching is an effective method for constructing hierarchical zeolites,yet its precise impact on the detailed structure and acidity remains unclear.This study employs probe molecule-assisted solid-state NMR technique to systematically investigate the evolution of acid site structures and properties during NH4F etching of ZSM-5 zeolites.The results reveal that the degree of NH₄F etching significantly manipulates the amount,strength,type,and spatial accessibility of acid sites,and it is also directly associated with the development of hierarchical pore structures.This work elucidates the influence mechanism of NH4F etching on zeolite structure and acidic property at the atomic level,providing a theoretical foundation for the rational design and optimization of efficient hierarchical zeolites.
This study aims to explore the structural changes in white matter and the physiological mechanisms in type 2 diabetes mellitus (T2DM) rats by magnetic resonance imaging (MRI) technology. A T2DM model was established by 8 weeks of high-fat diet combined with intraperitoneal injection of a dose of 30 mg/kg streptozotocin (STZ). 4 weeks after modeling, visual analysis of white matter in rats was performed by MRI, and the physiological mechanism of MRI index changes was further explained by immunohistology. The results showed that the striatal white matter volume decreased, the fractional anisotropy (FA), mean diffusivity (MD) and axial diffusivity (AD) decreased, and radial diffusivity (RD) increased in T2DM rats. Immunostain of phosphorylated neurofilament (SMI-31) and myelin basic protein (MBP) indicated axonal damage and demyelination of striatum in T2DM rats. In conclusion, the striatal injury in T2DM rats was observed by diffusion tensor imaging (DTI), and abnormal DTI index may be a manifestation of striatal axonal damage and demyelination, which can potentially be used as surrogates for evaluating diabetic brain injuries.
The tautomeric distribution of monosaccharides could influence reaction pathways and product selectivity during their conversion and utilization. Using DMSO-d6 as cosolvent, we investigated the tautomeric distribution of monosaccharides in imidazolyl ionic liquids at 25 ℃ by quantitative 1H NMR. In acidic ionic liquids, the results indicate that the proportion of β-pyranose after equilibrium decreases in the following order: D-glucose > D-glucosamine hydrochloride > N-acetyl-D-glucosamine > D-mannose. D-fructose is almost completely converted to 5-hydroxymethylfurfural (formed from the furanose) in [HSO3-BMIM]HSO4. In other ionic liquids, the proportion of furanose even exceeds that of pyranose after equilibrium. The introduction of amphoteric metal chloride and fluorine may lead to a higher proportion of furanose. The addition of [BMIM]BF4 and other reagents may inhibit the tautomeric conversion of monosaccharides. The fundamental data on monosaccharide tautomer distribution can guide the selection and design of ionic liquids for biomass conversion.
Diffusion magnetic resonance imaging (dMRI) is extensively employed to investigate the microstructure and fiber tract orientation of white matter in the brain. However, high angular and multi-shell sampling with high spatial resolution usually requires a prolonged scan time. In recent years, deep learning techniques have been widely adopted for dMRI super-resolution reconstruction, which aims to reconstruct high-resolution imaging signals from rapidly acquired images under sparse sampling conditions, thereby enabling more accurate fitting of brain microstructure imaging parameters. This paper surveys and analyzes the latest research progress in deep learning-based reconstruction of brain dMRI. According to different reconstruction targets, the methods are classified into three categories: reconstruction of basic diffusion metrics, reconstruction of high-order microstructure metrics, and reconstruction of the fiber orientation distribution function (fODF). The implementation techniques, evaluation metrics, and commonly used public datasets for each category are discussed in detail. Finally, the main challenges and research trends in dMRI super-resolution reconstruction are summarized.
Magnetic resonance imaging (MRI) and positron emission tomography (PET) are commonly used imaging techniques for the early diagnosis of Alzheimer's disease (AD). The combination of these two modalities enables a more comprehensive assessment of brain status by utilizing both anatomical and metabolic information. However, traditional multimodal fusion, which relies primarily on simple channel splicing, fails to fully exploit the complementary information across modalities and limits the model's effectiveness. To address this, this paper proposes a multi-task classification model for AD based on adversarial learning and cross-attention. The model reduces inter-modal feature discrepancies through adversarial learning, followed by feature fusion via cross-attention, and introduces a brain age prediction task as an auxiliary task to improve classification performance. Experimental results demonstrate that the proposed method achieves an accuracy of 91.10% and an F1 score of 91.01% in classifying AD, mild cognitive impairment (MCI), and normal controls (NC). This not only enhances the accuracy of early diagnosis but also strengthens the ability to monitor disease progression, thereby providing strong support for clinical interventions in AD.
Mild traumatic brain injury (mTBI) is a common neurological disorder in clinical practice, yet its diagnosis and management remain challenging due to the hidden nature of symptoms and the underlying pathological complexity. While imaging techniques such as computed tomography (CT) and conventional magnetic resonance imaging (MRI) are the mainstay for acute-phase assessment, they have limitations in detecting subtle injuries and evaluating long-term prognosis. In recent years, multimodal MRI technology has been developed in the research of mTBI, offering novel approaches for revealing its potential pathological mechanisms and exploring the key objective imaging indicators. Specifically, susceptibility weighted imaging (SWI) is sensitive for detecting microbleeds and iron deposition in the brain; amide proton transfer (APT) imaging reflects changes in molecular and metabolic levels; diffusion and functional imaging techniques help depict abnormalities in white matter microstructure and brain networks. The integration of multimodal MRI and the construction of imaging databases will be important directions for advancing early diagnosis, precise assessment, and AI-assisted intervention. This article systematically reviews research progress of related MRI techniques, analyzes their advantages and limitations, and discusses their prospects in clinical translation.
One-dimensional proton nuclear magnetic resonance (NMR) spectroscopy is a high-resolution, non-invasive technique widely used for structure elucidation and composition analysis. However, when applied to complex systems, its effectiveness is often hampered by overlapping peaks from similar chemical shifts and J-coupling, along with concentration variations obscuring weak signals from low-abundance compounds. To enhance the detection sensitivity for weak signals in crowded spectral regions, this work proposes Hadamard-DQF-LMO, integrating longitudinal multi-spin orders (LMOs), Hadamard encoding, and double quantum filtering (DQF). The approach utilizes DQF to selectively detect LMO signals while suppressing strong interference. The incorporation of polychromatic transition pulses and Hadamard-encoded 180° pulses enables parallel acquisition of multiple frequencies, which significantly improves both sensitivity and detection efficiency. Experiments on orange juice and mixed amino acid samples demonstrate peak separation with enhanced selectivity and signal-to-noise ratio (SNR), offering a novel and effective strategy for the NMR analysis of complex systems.
Portable nuclear magnetic resonance (NMR) spectrometers are susceptible to external electromagnetic interference (EMI), leading to low signal-to-noise ratio and reduced analytical accuracy. This paper proposes a method based on multiple reference coils and deep learning: multi-channel coils are used to acquire ambient electromagnetic noise in parallel, which is fed into a multi-channel Transformer reconstruction (MCTR) network. The network captures long-range dependencies of ambient electromagnetic signals, predicts and removes the environmental noise in the main receiving coil in real time. Experiments show that this method effectively suppresses noise in both simulated and actual NMR data, improves detection performance, outperforms traditional methods, maintains high signal quality, and has strong robustness. It provides effective support for the application of portable NMR in complex electromagnetic environments, and is expected to promote the development of on-site detection.
This study designed and constructed a pH-responsive intelligent theranostic nanoprobe.The nanoprobe utilizes perfluorocarbon nanoparticles with 19F magnetic resonance imaging(MRI)contrast capability as the core,with bovine serum albumin(BSA)adsorbed on the surface to form a"protein corona".Furthermore,tannic acid-iron(TA-FeⅢ)is complexed to create the core-shell structured PP@BSA-TAFeⅢ nanoparticles(NPs)probe.Owing to the unsaturated coordination state of Fe3+in TA-FeⅢ,the nanoprobe binds to transferrin upon entering the bloodstream,forming a"hybrid"protein corona and thereby achieving active targeting of transferrin receptors(TfR)hi ghly expressed on the tumor cell membrane.The nanoprobe efficiently accumulates in tumor tissue and generates a strong 19F-MRI signal,enabling highly sensitive tumor imaging.Upon entry into the acidic tumor microenvironment,Fe3+is released to induce ferroptosis,while TA-FeⅢ exhibits excellent photothermal effects,thereby achieving synergistic therapy.This in situ regulation strategy of protein corona offers a new approach for precise tumor diagnosis and therapy.
To investigate the dynamic properties of resting-state functional connectivity associated with antiretroviral therapy (ART), functional magnetic resonance imaging data from 45 treated people with HIV (PWH), 56 untreated PWH, and 68 healthy controls were collected. Group independent component analysis and sliding window analysis were conducted to obtain window-functional connectivity matrices, and their dynamic properties were quantified. The results showed that the baseline state and the weakly activated state reflect HIV-related abnormal dynamics and ART-related recovery. The weakly activated state reflected the recovery of cerebellum-related connections and putamen-related functional compensation. The baseline state reflected the recovery of extensive connections except for the visual network. Visual-related connections reflected ART-related adverse reactions in both states. These findings suggest that the cerebellum and putamen may be sensitive biomarkers for ART-related recovery, and the visual network can serve as a target for adjuvant therapy.
Magnetic resonance fingerprint (MRF) is an efficient multi-parameter quantitative imaging technology. However, traditional methods relying on signal dictionaries for parameter quantization are plagued by significant discretization errors and low matching efficiency. To overcome the limitations of existing supervised learning approaches that depend on pseudo-labels and lack physical interpretability, this study proposes a self-supervised parameter quantization method that integrates imaging physical models and manifold structure modeling. This method establishes reliable unlabeled constraints through Bloch equation-driven self-supervised physical consistency learning. By incorporating manifold structure-driven knowledge distillation, it transfers features of long frames to short frame models, realizing joint optimization of physical constraints and structural priors, thereby improving both accuracy and efficiency under unlabeled conditions. Experiments have verified this method’s superior accuracy and robustness, providing a novel approach for efficient and reliable MRF parameter estimation.
To explore the correlation between the imaging anatomical characteristics based on magnetic resonance diffusion-weighted imaging (MR-DWI) and recurrence in patients with acute cerebral infarction, this study retrospectively analyzed clinical and MR data of 211 patients clinically confirmed with acute cerebral infarction and meeting the inclusion and exclusion criteria. The number of lesions and involved cerebral blood supply areas were counted, and the total area of infarction and the area of subcutaneous fat were measured. The correlation between the recurrence and non-recurrence of patients within one year and the above data was analyzed. The results showed that there was a statistically significant difference in the number of lesions and the number of involved blood supply areas between the recurrence and non-recurrence groups of acute cerebral infarction (p<0.05), there was no statistically significant difference in the total area of lesions on axial images and the area of subcutaneous fat between the two groups (p > 0.05). Therefore, it is concluded that the number of lesions and the number of involved cerebral blood supply areas on MR-DWI images of acute cerebral infarction patients are the main influencing factors for recurrence within one year. This study provides imaging evidence for clinicians to conduct individualized treatment and prognostic evaluation for patients.
In nuclear magnetic resonance (NMR) systems, the radio frequency (RF) pulse generator critically influences imaging quality. Traditional direct digital synthesis (DDS) techniques rely on large-capacity lookup tables to achieve high precision, which results in excessive consumption of on-chip block random access memory (BRAM) resources and limits flexibility. This study presents a novel RF pulse generator design based on the coordinate rotation digital computer (CORDIC) algorithm. By integrating a custom CORDIC core within a field-programmable gate array (FPGA), the proposed system achieves digital modulation of RF signal’s frequency, phase, and amplitude. Combined with a Zynq-7000 system-on-chip (SoC), this design delivers a highly integrated and low-power hardware architecture. Experimental results demonstrate that the generator can output RF pulses with a frequency resolution of 0.046 Hz and a phase resolution of 0.005 5˚, while reducing BRAM resources occupied by four channels by approximately 21.4% compared to the traditional DDS solution. This design offers a feasible solution for achieving miniaturization and high performance in the RF front-end of NMR instruments.
Oxidative modification of cytochrome c (Cyt c) may influence the local conformation of protein, yet the mechanism by which structural alterations of Cyt c affect its degree of oxidative modification remains unclear. In this study, Girard’s reagent T (GRT) was employed as a nuclear magnetic resonance (NMR) probe to investigate the oxidative modification levels of human Cyt c under varying environmental conditions. Experimental results demonstrated that protecting lysine residues through reductive methylation effectively reduced protein oxidation. Partial unfolding of Cyt c was found to enhance its oxidative modification, while binding Cyt c with cardiolipin significantly increased the extent of oxidation. Additionally, other factors such as protein aggregation exhibited inhibitory effects on oxidative modification.
Intracranial tumors represent a serious neurological disorder, and early detection is critical for improving patient survival rates. However, current deep learning models for intracranial tumor image classification often suffer from insufficient feature extraction, high model complexity, and class imbalance. To address these challenges, this study proposes a lightweight deep learning architecture, the adaptive dynamic network (AD-Net). The network innovatively incorporates a dynamic convolution mechanism that adaptively adjusts filter responses, thereby enhancing the representation of complex and imbalanced tumor features. Additionally, the integration of a channel attention mechanism enables the model to focus on critical channel information, further improving classification accuracy and interpretability. This study also introduces a combined binary and ternary classification training strategy, which significantly reduces training time and computational resource requirements, making the model more suitable for resource-constrained medical settings. Experimental results demonstrate that AD-Net outperforms existing mainstream deep learning models in accuracy, precision, recall, F1 score, and Cohen’s Kappa coefficient, confirming its effectiveness and practical value for intracranial tumor classification.
Due to their millimeter-scale size and low pump power threshold, Kerr optical soliton frequency combs have emerged as a key technology for chip-scale optical atomic clock research. However, the abrupt intracavity power drop during Kerr optical soliton formation leads to cavity frequency drift, which significantly shortens the lifetime of Kerr optical soliton frequency combs. Some active control methods have been reported for long-term stabilization of Kerr solitons, such as soliton power control, Pound-Drever-Hall frequency locking, and auxiliary laser mode. However, the electronic control systems used for these methods are rarely reported. This work presents an active control system for stabilizing Kerr optical soliton frequency combs based on Field-Programmable Gate Array (FPGA). It achieves long-term stable operation of Kerr optical soliton combs in both MgF₂ and CaF₂ microresonators by power control and PDH frequency locking. Furthermore, the system can be extended to other microresonator platforms (e.g., Si₃N₄, AlN, SiO₂) for Kerr optical soliton frequency generation and stabilization.
With the widespread application of nuclear magnetic resonance (NMR) technology in fields such as food safety inspection and petroleum exploration, there is a growing demand for miniaturized and portable NMR spectrometers. In response to this demand, this paper proposes a system design framework for a palm-sized NMR spectrometer console and develops a comprehensive and flexible software architecture. The software system consists of two main components: embedded control software and host computer application software. The embedded control software is responsible for real-time control of spectrometer hardware and communication with the host computer. The host software handles user interface interaction, sequence parameter configuration, and post-processing of acquired data. Furthermore, an open and lightweight NMR data communication protocol is designed to support custom host software development based on specific user requirements, thereby significantly enhancing system flexibility and scalability. Experimental results demonstrate that the proposed palm-sized NMR spectrometer console delivers reliable performance and substantial practical value.
High-field solid-state nuclear magnetic resonance (NMR) technology boasts high sensitivity and multi-nucleus detection capability. However, when studying paramagnetic materials like lithium-ion batteries, the strong paramagnetism of transition metal ions (such as Mn3+, Fe3+, etc.) leads to issues like magnetic field inhomogeneity, spectral line broadening, signal attenuation, and inability to perform magic-angle spinning (MAS) at high magnetic fields. Under low-field conditions, the magnetic field distortion induced by paramagnetic effects is significantly reduced, offering a potential solution to these problems. This paper presents a theoretical analysis of the advantages of low-field environments for studying paramagnetic substances. A low-field solid-state MAS probe for a 0.5 T Halbach magnet was developed. Furthermore, a complete low-field solid-state MAS spectrometer was constructed. In experiments, 7Li NMR signals of various paramagnetic samples were acquired at a spinning speed of 12 kHz, verifying the feasibility of the self-developed low-field solid-state MAS technology for paramagnetic samples. This approach addresses the problems of overlapping spin sidebands and MAS failure at high fields, providing a new pathway for NMR research on paramagnetic materials.
In the evaluation of low-porosity and low-permeability light oil reservoirs in the eastern South China Sea using nuclear magnetic resonance(NMR) logging data, the permeability values tend to be overestimated. To improve the accuracy of NMR-derived permeability calculations, it is essential to thoroughly analyze the causes of this overestimation and establish corresponding correction methods. This paper systematically conducted NMR transverse relaxation time (T2) spectroscopy experiments under different saturation states, including water-saturated, bound water, oil-saturated, and residual oil states. By comparing the T2 spectra of the same core sample under water-saturated and oil-containing states, it was observed that light crude oil causes a significant rightward shift in the distribution of macropores within the T2 spectrum, while the distribution of micropores remains largely unchanged. This indicates that light oil is the primary cause of T2 spectrum anomalies and NMR permeability deviations. Based on this finding, T2 cutoff values for different reservoir types were determined through core classification, and a T2 spectrum correction method calibrated for macropore components was established. Results demonstrate that this method effectively corrects T2 spectra from light oil-bearing reservoirs to fully water-saturated state, significantly enhancing the accuracy of NMR-derived permeability measurements.
