Isoliquiritigenin suppresses the progression of malignant melanoma via targeting H2A.Z.1-E2F1 pathway.

Cutaneous melanoma is one of the most prevalent tumors, and it is still a huge challenge in the current clinical treatment. Isoliquiritigenin (ISL), which is isolated from Glycyrrhiza uralensis Fisch., has been reported for its anti-tumor effect. However, the underlying mechanism and targets of ISL are still not be revealed clearly. In this study, differentiallyexpressedproteins were identified bylabel-free quantitative mass spectrometry. Two isoforms of the histone variant H2A.Z, including H2A.Z.1 and H2A.Z.2, were significantly down regulated after administration of ISL in melanoma. H2A.Z.1 was highly expressed in melanoma and correlated with poor prognosis of melanoma. The expression of H2A.Z was inhibited by ISL in a concentration-dependent manner. Overexpression of H2A.Z.1 in melanoma cell lines partly restored the repressed cell proliferation and cell cycle by ISL. Moreover, E2F1 was identified as one downstream target of H2A.Z.1, which was also highly expressed in melanoma and correlated with poor prognosis of melanoma. Furthermore, in vivo assays validated the inhibitory role of ISL in melanoma proliferation and the expression of H2A.Z.1 and E2F1.Aboveall,it is indicated that ISL inhibit melanoma proliferation via targeting H2A.Z.1-E2F1 pathway. These findings explain the anti-tumor mechanism of ISL and provide potential therapeutic targets for melanoma.

High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning.

Objective. Bloch simulation constitutes an essential part of magnetic resonance imaging (MRI) development. However, even with the graphics processing unit (GPU) acceleration, the heavy computational load remains a major challenge, especially in large-scale, high-accuracy simulation scenarios. This work aims to develop a deep learning-based simulator to accelerate Bloch simulation.Approach. The simulator model, called Simu-Net, is based on an end-to-end convolutional neural network and is trained with synthetic data generated by traditional Bloch simulation. It uses dynamic convolution to fuse spatial and physical information with different dimensions and introduces position encoding templates to achieve position-specific labeling and overcome the receptive field limitation of the convolutional network.Main results. Compared with mainstream GPU-based MRI simulation software, Simu-Net successfully accelerates simulations by hundreds of times in both traditional and advanced MRI pulse sequences. The accuracy and robustness of the proposed framework were verified qualitatively and quantitatively. Besides, the trained Simu-Net was applied to generate sufficient customized training samples for deep learning-basedT2mapping and comparable results to conventional methods were obtained in the human brain.Significance. As a proof-of-concept work, Simu-Net shows the potential to apply deep learning for rapidly approximating the forward physical process of MRI and may increase the efficiency of Bloch simulation for optimization of MRI pulse sequences and deep learning-based methods.

Learning from synthetic data for reference-free Nyquist ghost correction and parallel imaging reconstruction of echo planar imaging.

BACKGROUND: Echo planar imaging (EPI) suffers from Nyquist ghost caused by eddy currents and other non-ideal factors. Deep learning has received interest for EPI ghost correction. However, large datasets with qualified labels are usually unavailable, especially for the under-sampled EPI data due to the imperfection of traditional ghost correction algorithms. PURPOSE: To develop a multi-coil synthetic-data-based deep learning method for the Nyquist ghost correction and reconstruction of under-sampled EPI. METHODS: Our network is trained purely with synthetic data. The labels of the training samples are generated by combining a public magnetic resonance imaging dataset and a few pre-collected coil sensitivity maps. The input is synthesized by under-sampling (for the accelerated case) and adding phase errors between the even and odd echoes of the label. To bridge the gap between synthetic data and data from real acquisition, linear and non-linear 2D phase errors are considered during the training data generation. RESULTS: The proposed method outperformed the existing mainstream approaches in several experiments. The average ghost-to-signal ratios of our/3-line navigator-based methods were 0.51%/5.36% and 0.42%/8.64% in fully-sampled and under-sampled in vivo experiments, respectively. In the sagittal experiments, our method successfully corrected higher-order and 2D phase errors. Our method also outperformed other reference-based methods on motion-corrupted data. In the simulation experiments, the peak signal-to-noise ratios were 37.6/38.3 dB for 2D linear/non-linear simulated phase errors, indicating that our method was consistently reliable for different kinds of phase errors. CONCLUSION: Our method achieves superb ghost correction and parallel imaging reconstruction without any calibration information, and can be readily adapted to other EPI-based applications.

Single-shot multi-parametric mapping based on multiple overlapping-echo detachment (MOLED) imaging.

Multi-parametric quantitative magnetic resonance imaging (mqMRI) allows the characterization of multiple tissue properties non-invasively and has shown great potential to enhance the sensitivity of MRI measurements. However, real-time mqMRI during dynamic physiological processes or general motions remains challenging. To overcome this bottleneck, we propose a novel mqMRI technique based on multiple overlapping-echo detachment (MOLED) imaging, termed MQMOLED, to enable mqMRI in a single shot. In the data acquisition of MQMOLED, multiple MR echo signals with different multi-parametric weightings and phase modulations are generated and acquired in the same k-space. The k-space data is Fourier transformed and fed into a well-trained neural network for the reconstruction of multi-parametric maps. We demonstrated the accuracy and repeatability of MQMOLED in simultaneous mapping apparent proton density (APD) and any two parameters among T2, T2*, and apparent diffusion coefficient (ADC) in 130-170 ms. The abundant information delivered by the multiple overlapping-echo signals in MQMOLED makes the technique potentially robust to system imperfections, such as inhomogeneity of static magnetic field or radiofrequency field. Benefitting from the single-shot feature, MQMOLED exhibits a strong motion tolerance to the continuous movements of subjects. For the first time, it captured the synchronous changes of ADC, T2, and T1-weighted APD in contrast-enhanced perfusion imaging on patients with brain tumors, providing additional information about vascular density to the hemodynamic parametric maps. We expect that MQMOLED would promote the development of mqMRI technology and greatly benefit the applications of mqMRI, including therapeutics and analysis of metabolic/functional processes.