Compact In Situ Electrochemical NMR with Wireless and Anti-interference Strategy in Multiscenario Applications.

The integration of electrochemistry with nuclear magnetic resonance (NMR) spectroscopy recently offers a powerful approach to understanding oxidative metabolism, detecting reactive intermediates, and predicting biological activities. This combination is particularly effective as electrochemical methods provide excellent mimics of metabolic processes, while NMR spectroscopy offers precise chemical analysis. NMR is already widely utilized in the quality control of pharmaceuticals, foods, and additives and in metabolomic studies. However, the introduction of additional and external connections into the magnet has posed challenges, leading to signal deterioration and limitations in routine measurements. Herein, we report an anti-interference compact in situ electrochemical NMR system (AICISENS). Through a wireless strategy, the compact design allows for the independent and stable operation of electrochemical NMR components with effective interference isolation. Thus, it opens an avenue toward easy integration into in situ platforms, applicable not only to laboratory settings but also to fieldwork. The operability, reliability, and versatility were validated with a series of biomimetic assessments, including measurements of microbial electrochemical systems, functional foods, and simulated drug metabolisms. The robust performance of AICISENS demonstrates its high potential as a powerful analytical tool across diverse applications.

MRI reconstruction with enhanced self-similarity using graph convolutional network.

BACKGROUND: Recent Convolutional Neural Networks (CNNs) perform low-error reconstruction in fast Magnetic Resonance Imaging (MRI). Most of them convolve the image with kernels and successfully explore the local information. Nonetheless, the non-local image information, which is embedded among image patches relatively far from each other, may be lost due to the limitation of the receptive field of the convolution kernel. We aim to incorporate a graph to represent non-local information and improve the reconstructed images by using the Graph Convolutional Enhanced Self-Similarity (GCESS) network. METHODS: First, the image is reconstructed into the graph to extract the non-local self-similarity in the image. Second, GCESS uses spatial convolution and graph convolution to process the information in the image, so that local and non-local information can be effectively utilized. The network strengthens the non-local similarity between similar image patches while reconstructing images, making the reconstruction of structure more reliable. RESULTS: Experimental results on in vivo knee and brain data demonstrate that the proposed method achieves better artifact suppression and detail preservation than state-of-the-art methods, both visually and quantitatively. Under 1D Cartesian sampling with 4 × acceleration (AF = 4), the PSNR of knee data reached 34.19 dB, 1.05 dB higher than that of the compared methods; the SSIM achieved 0.8994, 2% higher than the compared methods. Similar results were obtained for the reconstructed images under other sampling templates as demonstrated in our experiment. CONCLUSIONS: The proposed method successfully constructs a hybrid graph convolution and spatial convolution network to reconstruct images. This method, through its training process, amplifies the non-local self-similarities, significantly benefiting the structural integrity of the reconstructed images. Experiments demonstrate that the proposed method outperforms the state-of-the-art reconstruction method in suppressing artifacts, as well as in preserving image details.

Antioxidant Activity and the Therapeutic Effect of Sinomenine Hydrochloride-Loaded Liposomes-in-Hydrogel on Atopic Dermatitis.

Sinomenine hydrochloride is an excellent drug with anti-inflammatory, antioxidant, immune-regulatory, and other functions. Atopic dermatitis is an inherited allergic inflammation that causes itchiness, redness, and swelling in the affected area, which can have a significant impact on the life of the patient. There are many therapeutic methods for atopic dermatitis, and sinomenine with immunomodulatory activity might be effective in the treatment of atopic dermatitis. In this study, the atopic dermatitis model was established in experimental mice, and physical experiments were carried out on the mice. In the experiment, sinomenine hydrochloride liposomes-in-hydrogel as a new preparation was selected for delivery. In this case, liposomes were dispersed in the colloidal hydrogel on a mesoscopic scale and could provide specific transfer properties. The results showed that the sinomenine hydrochloride-loaded liposomes-in-hydrogel system could effectively inhibit atopic dermatitis.

Evaluation of Antioxidant Activity and Treatment of Eczema by Berberine Hydrochloride-Loaded Liposomes-in-Gel.

In this paper, berberine hydrochloride-loaded liposomes-in-gel were designed and developed to investigate their antioxidant properties and therapeutic effects on the eczema model of the mouse. Berberine hydrochloride-liposomes (BBH-L) as the nanoparticles were prepared by the thin-film hydration method and then dispersed BBH-L evenly in the gel matrix to prepare the berberine hydrochloride liposomes-gel (BBH-L-Gel) by the natural swelling method. Their antioxidant capacity was investigated by the free radical scavenging ability on 2,2-diphenyl-1-picrylhydrazyl (DPPH) and H2O2 and the inhibition of lipid peroxides malondialdehyde (MDA). An eczema model was established, and the efficacy of the eczema treatment was preliminarily evaluated using ear swelling, the spleen index, and pathological sections as indicators. The results indicate that the entrapment efficiency of BBH-L prepared by the thin-film hydration method was 78.56% ± 0.7%, with a particle size of 155.4 ± 9.3 nm. For BBH-L-Gel, the viscosity and pH were 18.16 ± 6.34 m Pas and 7.32 ± 0.08, respectively. The cumulative release in the unit area of the in vitro transdermal study was 85.01 ± 4.53 µg/cm2. BBH-L-Gel had a good scavenging capacity on DPPH and H2O2, and it could effectively inhibit the production of hepatic lipid peroxides MDA in the concentration range of 0.4-2.0 mg/mL. The topical application of BBH-L-Gel could effectively alleviate eczema symptoms and reduce oxidative stress injury in mice. This study demonstrates that BBH-L-Gel has good skin permeability, excellent sustained release, and antioxidant capabilities. They can effectively alleviate the itching, inflammation, and allergic symptoms caused by eczema, providing a new strategy for clinical applications in eczema treatment.

[Medicinal plant biodiversity in Jilin province based on big data of the fourth national survey of traditional Chinese medicine resources].

Using Origin2022Pro, PAST4.09, GraphPad, and ArcGIS, this study analyzed the big data of the fourth national survey of traditional Chinese medicine resources in Jilin province from five dimensions: differences in resource quantity, taxonomic group, family, and genus, regional distribution, and spatiotemporal distribution, aiming to fully elucidate the biodiversity of medicinal plants in Jilin province. The results indicated that 2 241 species of medicinal plants existed in Jilin province, belonging to 881 genera of 243 families, with 20 dominant families and 3 dominant genera. There were 1 901 species of medicinal plants(belonging to 778 genera of 227 families) in the eastern mountainous region, 1 503 species(belonging to 690 genera of 225 families) in the mid-mountainous areas of the central mountainous region, and 811 species(belonging to 436 genera of 136 families) in the western plain region. The biodiversity of medicinal plants in Jilin province was high and presented a trend of high in the east and low in the west. The medicinal plant resources were mainly concentrated in the eastern mountainous region, and the number of medicinal plant groups had significant diffe-rences between regions, following the trend of western region > central region > eastern region. The species richness was in the order of eastern region > western region > central region. The species diversity structure in the central region was similar to that in the eastern and western regions, while it was significantly different between the western and eastern regions. Compared with the third national survey of traditional Chinese medicine resources, the fourth survey showed an increase of 1 417 species, a decrease of 580 species, and 824 common species, indicating significant changes in the biodiversity of medicinal plants in Jilin province. The reasons for these changes need to be further explored. This article elucidates the background and biodiversity changes of medicinal plant resources in Jilin province, laying a foundation for the protection, utilization, and industrial development of traditional Chinese medicine resources in Jilin province.

Formation control of fixed-wing UAVs with communication delay.

Fixed-wing unmanned aerial vehicles (UAVs) possess high speed and non-hovering capabilities, rendering them uniquely advantages for reconnaissance and detection. The focus of this paper is to addressing the problem of formation control for fixed-wing UAVs in the presence of communication delay. To tackle this problem, for the non-holonomic kinematic model, we propose an intuitive and practical control law based on the leader-follower method to ensure that UAVs maintain a predetermined geometric formation. The stability analysis of the system with communication delay is conducted by constructing a strict Lyapunov-Krasovskii function. Furthermore, we consider the impact of communication delay on formation accuracy and present a prediction algorithm capable of forecasting the actual position of each UAV. To validate our theoretical findings, both digital simulation and hardware-in-loop experiment are conducted.

Integrated Serum Pharmacochemistry and Network Pharmacological Analysis Used To Explore Possible Anti-Aging Mechanisms of the Ginsenosides

BACKGROUND: Although constitutive ginsenosides are credited with ginseng's remarkable anti-aging efficacy, the mechanism of action and bioactive components of ginsenosides are unclear. OBJECTIVE: The goal of the study was to examine the effect of ginsenosides on D-galactose (D-gal)-induced aging in rats and to figure out the underlying molecular mechanism using serum pharmacochemistry and network pharmacology. METHODS: Using behavioral, biochemical indexes, and histological analysis, ginsenosides were evaluated for their anti-aging effects in rats induced by D-gal, and effective ingredients absorbed in the blood were examined by ultra-performance liquid chromatography quadrupole time of flight coupled with mass spectrometry (UPLC-Q/TOF-MS) before being subjected to network pharmacology analysis. RESULTS: As well as improving spatial learning and memory skills, Ginsenosides are known to regulate malondialdehyde (MDA), glutathione peroxidase (GSH-Px), total antioxidant capacity (T-AOC) and superoxide dismutase (SOD) activity. In addition, it improved the ultrastructure of neurons in D-gal-induced rats' hippocampus. Seventy-four absorption components and metabolites of ginsenosides were identified in aging rat serum. According to a network pharmacology study, ginsenosides have anti-aging properties by modulating the phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) and mitogen-activated protein kinases (MAPK) signaling pathways. CONCLUSION: The potential mechanisms of the anti-aging effect of ginsenosides involve multiple components, targets, and pathways. These findings serve as a foundation for further research into the processes behind ginsenoside's anti-aging impact.

Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks With Soft-Thresholding.

Soft-thresholding has been widely used in neural networks. Its basic network structure is a two-layer convolution neural network with soft-thresholding. Due to the network's nature of nonlinear and nonconvex, the training process heavily depends on an appropriate initialization of network parameters, resulting in the difficulty of obtaining a globally optimal solution. To address this issue, a convex dual network is designed here. We theoretically analyze the network convexity and prove that the strong duality holds. Extensive results on both simulation and real-world datasets show that strong duality holds, the dual network does not depend on initialization and optimizer, and enables faster convergence than the state-of-the-art two-layer network. This work provides a new way to convexify soft-thresholding neural networks. Furthermore, the convex dual network model of a deep soft-thresholding network with a parallel structure is deduced.

Quantitative analysis of molecular transport in the extracellular space using physics-informed neural network.

The brain extracellular space (ECS), an irregular, extremely tortuous nanoscale space located between cells or between cells and blood vessels, is crucial for nerve cell survival. It plays a pivotal role in high-level brain functions such as memory, emotion, and sensation. However, the specific form of molecular transport within the ECS remain elusive. To address this challenge, this paper proposes a novel approach to quantitatively analyze the molecular transport within the ECS by solving an inverse problem derived from the advection-diffusion equation (ADE) using a physics-informed neural network (PINN). PINN provides a streamlined solution to the ADE without the need for intricate mathematical formulations or grid settings. Additionally, the optimization of PINN facilitates the automatic computation of the diffusion coefficient governing long-term molecule transport and the velocity of molecules driven by advection. Consequently, the proposed method allows for the quantitative analysis and identification of the specific pattern of molecular transport within the ECS through the calculation of the Péclet number. Experimental validation on two datasets of magnetic resonance images (MRIs) captured at different time points showcases the effectiveness of the proposed method. Notably, our simulations reveal identical molecular transport patterns between datasets representing rats with tracer injected into the same brain region. These findings highlight the potential of PINN as a promising tool for comprehensively exploring molecular transport within the ECS.

A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging.

Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers from prolonged scan time. To alleviate this limitation, advanced fast MRI technology attracts extensive research interests. Recent deep learning has shown its great potential in improving image quality and reconstruction speed. Faithful coil sensitivity estimation is vital for MRI reconstruction. However, most deep learning methods still rely on pre-estimated sensitivity maps and ignore their inaccuracy, resulting in the significant quality degradation of reconstructed images. In this work, we propose a Joint Deep Sensitivity estimation and Image reconstruction network, called JDSI. During the image artifacts removal, it gradually provides more faithful sensitivity maps with high-frequency information, leading to improved image reconstructions. To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results. Results on in vivo datasets and radiologist reader study demonstrate that, for both calibration-based and calibrationless reconstruction, the proposed JDSI achieves the state-of-the-art performance visually and quantitatively, especially when the acceleration factor is high. Additionally, JDSI owns nice robustness to patients and autocalibration signals.

Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal.

OBJECTIVE: Magnetic Resonance Spectroscopy (MRS) is an important technique for biomedical detection. However, it is challenging to accurately quantify metabolites with proton MRS due to serious overlaps of metabolite signals, imperfections because of non-ideal acquisition conditions, and interference with strong background signals mainly from macromolecules. The most popular method, LCModel, adopts complicated non-linear least square to quantify metabolites and addresses these problems by designing empirical priors such as basis-sets, imperfection factors. However, when the signal-to-noise ratio of MRS signal is low, the solution may have large deviation. METHODS: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification. First, a neural network is designed to explicitly predict the imperfection factors and the overall signal from macromolecules. Then, metabolite quantification is solved analytically with the introduced LLS. In our Quantification Network (QNet), LLS takes part in the backpropagation of network training, which allows the feedback of the quantification error into metabolite spectrum estimation. This scheme greatly improves the generalization to metabolite concentrations unseen in training compared to the end-to-end deep learning method. RESULTS: Experiments show that compared with LCModel, the proposed QNet, has smaller quantification errors for simulated data, and presents more stable quantification for 20 healthy in vivo data at a wide range of signal-to-noise ratio. QNet also outperforms other end-to-end deep learning methods. CONCLUSION: This study provides an intelligent, reliable and robust MRS quantification. SIGNIFICANCE: QNet is the first LLS quantification aided by deep learning.

CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI.

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The 'CMRxRecon' dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community.