NMR Spectra Denoising with Vandermonde Constraints

Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering. However, NMR signals are easily contaminated by noise during the data acquisition, which can affect subsequent quantitative analysis. Therefore, denoising NMR signals has been a long-time concern. In this work, we propose an optimization model-based iterative denoising method, CHORD-V, by treating the time-domain NMR signal as damped exponentials and maintaining the exponential signal form with a Vandermonde factorization. Results on both synthetic and realistic NMR data show that CHORD-V has a superior denoising performance over typical Cadzow and rQRd methods, and the state-of-the-art CHORD method. CHORD-V restores low-intensity spectral peaks more accurately, especially when the noise is relatively high.Comment: 10 pages, 9 figure

CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis

Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful analytical tool for studying molecular structure and dynamics in chemistry and biology. However, the processing of raw data acquired from NMR spectrometers and subsequent quantitative analysis involves various specialized tools, which necessitates comprehensive knowledge in programming and NMR. Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation. Thus, NMR processing is not an easy task for chemist and biologists. In this work, we present CloudBrain-NMR, an intelligent online cloud computing platform designed for NMR data reading, processing, reconstruction, and quantitative analysis. The platform is conveniently accessed through a web browser, eliminating the need for any program installation on the user side. CloudBrain-NMR uses parallel computing with graphics processing units and central processing units, resulting in significantly shortened computation time. Furthermore, it incorporates state-of-the-art deep learning-based algorithms offering comprehensive functionalities that allow users to complete the entire processing procedure without relying on additional software. This platform has empowered NMR applications with advanced artificial intelligence processing. CloudBrain-NMR is openly accessible for free usage at https://csrc.xmu.edu.cn/CloudBrain.htmlComment: 11 pages, 13 figure

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 spectra plots and metabolite quantification, the widespread clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: 1) Automatically statistical analysis to find biomarkers for diseases; 2) Consistency verification between the classic and artificial intelligence quantification algorithms; 3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, both healthy and mild cognitive impairment patient data are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing free access and service for two years. Please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.Comment: 11 pages, 12 figure

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 for 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

Interactions Between Emodin and Efflux Transporters on Rat Enterocyte by a Validated Ussing Chamber Technique

Emodin, a major active anthraquinone, frequently interacts with other drugs. As changes of efflux transporters on intestine are one of the essential reasons why the drugs interact with each other, a validated Ussing chamber technique was established to detect the interactions between emodin and efflux transporters, including P-glycoprotein (P-gp), multidrug-resistant associated protein 2 (MRP2), and multidrug-resistant associated protein 3 (MRP3). Digoxin, pravastatin, and teniposide were selected as the test substrates of P-gp, MRP2, and MRP3. Verapamil, MK571, and benzbromarone were their special inhibitors. The results showed that verapamil, MK571, and benzbromarone could increase digoxin, pravastatin, and teniposide absorption, and decrease their Er values, respectively. Verapamil (220 μM) could significantly increase emodin absorption at 9.25 μM. In the presence of MK571 (186 μM), the Papp values of emodin from M-S were significantly increased and the efflux ratio decreased. With the treatment of emodin (185, 370, and 740 μM), digoxin absorption was significantly decreased while teniposide increased. These results indicated that emodin might be the substrate of P-gp and MRP2. Besides, it might be a P-gp inducer and MRP3 inhibitor on enterocyte, which are reported for the first time. These results will be helpful to explain the drug–drug interaction mechanisms between emodin and other drugs and provide basic data for clinical combination therapy

Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence

Diffusion magnetic resonance imaging (MRI) is the only imaging modality for non-invasive movement detection of in vivo water molecules, with significant clinical and research applications. Diffusion MRI (DWI) acquired by multi-shot techniques can achieve higher resolution, better signal-to-noise ratio, and lower geometric distortion than single-shot, but suffers from inter-shot motion-induced artifacts. These artifacts cannot be removed prospectively, leading to the absence of artifact-free training labels. Thus, the potential of deep learning in multi-shot DWI reconstruction remains largely untapped. To break the training data bottleneck, here, we propose a Physics-Informed Deep DWI reconstruction method (PIDD) to synthesize high-quality paired training data by leveraging the physical diffusion model (magnitude synthesis) and inter-shot motion-induced phase model (motion phase synthesis). The network is trained only once with 100,000 synthetic samples, achieving encouraging results on multiple realistic in vivo data reconstructions. Advantages over conventional methods include: (a) Better motion artifact suppression and reconstruction stability; (b) Outstanding generalization to multi-scenario reconstructions, including multi-resolution, multi-b-value, multi-undersampling, multi-vendor, and multi-center; (c) Excellent clinical adaptability to patients with verifications by seven experienced doctors (p<0.001). In conclusion, PIDD presents a novel deep learning framework by exploiting the power of MRI physics, providing a cost-effective and explainable way to break the data bottleneck in deep learning medical imaging.Comment: 23 pages, 16 figure

Large-Scale Expansion of Human Umbilical Cord-Derived Mesenchymal Stem Cells in a Stirred Suspension Bioreactor Enabled by Computational Fluid Dynamics Modeling

Human umbilical cord-derived mesenchymal stem cells (hUCMSCs) hold great potential to generate novel and curative cell therapy products. However, the current large-scale cultivation of hUCMSCs is based on empirical geometry-dependent methods, limiting the generation of high-quantity and high-quality hUCMSCs for clinical therapy. Herein, we develop a novel scale-up strategy based on computational fluid dynamics (CFD) to effectively expand the hUCMSCs in a 3D tank bioreactor. Using a standardized hUCMSCs line on microcarriers, we successfully translated and expanded the hUCMSCs from a 200 mL spinner flask to a 1.5 L computer-controlled bioreactor by matching the shear environment and suspending the microcarrier. Experimental results revealed that the batch-cultured hUCMSCs in bioreactors with an agitation speed of 40 rpm shared a more favorable growth and physiological state, similar to that run at 45 rpm in a 200 mL spinner flask, showing comparability in both culture systems. Notably, the maximum cell density reached up to 27.3 × 105 cells/mL in fed-batch culture, 2.9 folds of that of batch culture and 20.2 times of seeding cells. As such, efficient process optimization and scale-up expansion of hUCMSCs were achieved in the microcarrier-based bioreactor system by the developed CFD simulation strategy, which provided an alternative toolbox to generate massive and standardized curative cell therapy products

Continuous Feeding Reduces the Generation of Metabolic Byproducts and Increases Antibodies Expression in Chinese Hamster Ovary-K1 Cells

Chinese hamster ovary (CHO) cells are the most important host system used for monoclonal antibody (mAb) expression. Moreover, the fed-batch culture mode is the most widely used method to increase mAb expression in CHO cells by increasing the amount of feed. However, a high amount of culture feed results in the production of metabolic byproducts. In this work, we used a continuous feeding strategy to reduce metabolic byproducts and improve mouse–human chimeric anti-epidermal growth factor receptor vIII (EGFRvIII) antibody C12 expression in Chinese hamster ovary-K1 cells. Moreover, the effects of the feeding strategy on the cell culture and monoclonal antibody production were evaluated in chemically defined suspension cultures of recombinant CHO-K1 cells. Compared with bolus feeding methods, the continuous feeding method did not have any advantages when the feeding amount was low, but with a high feeding amount, the continuous feeding method significantly reduced the concentrations of lactate and NH4+ in the later culture stage. At the end of the culture stage, compared with bolus feeding methods, the lactate and NH4+ concentrations under the continuous feeding mode were reduced by approximately 45% and 80%, respectively. In addition, the antibody C12 expression level was also increased by almost 10%. Compared to the bolus feeding method, the antibody C12 produced by the continuous feeding method had a lower content of high-mannose glycoforms. Further analysis found that the osmolality of the continuous feeding method was lower than that of the typical fed-batch bolus feeding method. Conclusively, these results indicate that the continuous feeding method is very useful for reducing metabolic byproducts and achieving higher levels of mAb production