Deep learning network for NMR spectra reconstruction in time-frequency domain and quality assessment.

High-quality nuclear magnetic resonance (NMR) spectra can be rapidly acquired by combining non-uniform sampling techniques (NUS) with reconstruction algorithms. However, current deep learning (DL) based reconstruction methods focus only on single-domain reconstruction (time or frequency domain), leading to drawbacks like peak loss and artifact peaks and ultimately failing to achieve optimal performance. Moreover, the lack of fully sampled spectra makes it difficult, even impossible, to determine the quality of reconstructed spectra, presenting challenges in the practical applications of NUS.

The state-of-the-art in cardiac MRI reconstruction: Results of the CMRxRecon challenge in MICCAI 2023.

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart's structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data.

Current status of FAP-directed cancer theranostics: a bibliometric analysis.

Fibroblast activation protein (FAP) is a key molecule in the field of oncology, with significant impacts on tumor diagnosis and treatment. Importantly, it has paved the way for the development of radiotracers for quinoline-based FAP inhibitors (FAPIs), which are currently among the most promising radiotracers for PET imaging in cancer. We performed a bibliometric analysis of scientific publications related to FAP and FAPI-based radiotracers, which included the quantification and visualization of current research trends and prospects based on various bibliometric indicators.

A risk-based unmanned aerial vehicle path planning scheme for complex air-ground environments.

Multifarious applications of unmanned aerial vehicles (UAVs) are thriving in extensive fields and facilitating our lives. However, the potential third-party risks (TPRs) on the ground are neglected by developers and companies, which limits large-scale commercialization. Risk assessment is an efficacious method for mitigating TPRs before undertaking flight tasks.

Ga-FAPI-46 PET/CT in the evaluation of gliomas: comparison with F-FDG PET/CT and contrast-enhanced MRI.

This study compared Ga-FAPI-46 PET/CT, F-fluorodeoxyglucose (FDG) PET/CT, and contrast-enhanced MRI (CE-MRI) for glioma imaging, classification, and recurrence detection and explored PET parameters and molecular pathological profiles. Between June 2020 and June 2024, we prospectively enrolled patients with space-occupying lesions in the brain or previously treated gliomas. All patients underwent sequential CE-MRI, Ga-FAPI-46, and F-FDG PET/CT.

[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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

CloudBrain-MRS: An intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis.

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectrum plots and metabolite quantification, the spread of clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform.

Evaluation of the diagnostic utility on 1.5T and 3.0T H magnetic resonance spectroscopy for temporal lobe epilepsy.

Background: H magnetic resonance spectroscopy (H-MRS) can be used to study neurological disorders because it can be utilized to examine the concentrations of related metabolites. However, the diagnostic utility of different field strengths for temporal lobe epilepsy (TLE) remains unclear. The purpose of this study is to make quantitative comparisons of metabolites of TLE at 1.

Can language models be used for real-world urban-delivery route optimization?

Language models have contributed to breakthroughs in interdisciplinary research, such as protein design and molecular dynamics understanding. In this study, we reveal that beyond language, representations of other entities, such as human behaviors, that are mappable to learnable sequences can be learned by language models. One compelling example is the real-world delivery route optimization problem.

Comparative study of bilateral putamen for patients with severe Parkinson's disease detected by H magnetic resonance spectroscopy.

Background: The diagnosis of Parkinson's disease (PD) is challenging because the clinical symptoms overlap with other neurodegenerative diseases. The discovery of reliable biomarkers is highly expected to facilitate clinical diagnosis. Through the analysis of the H magnetic resonance spectroscopy (H-MRS) in the putamen, the purpose of the study was to discuss the possibility of the difference in metabolite concentrations between the left and right putamen as biomarkers for patients with severe PD.