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“The Expert Group on Tumor Ablation Therapy of Chinese Medical Doctor Association, The Tumor Ablation Committee of Chinese College of Interventionalists, The Society of Tumor Ablation Therapy of Chinese Anti-Cancer Association and The Ablation Expert Committee of the Chinese Society of Clinical Oncology” have organized multidisciplinary experts to formulate the consensus for thermal ablation of pulmonary subsolid nodules or ground-glass nodule (GGN). The expert consensus reviews current literatures and provides clinical practices for thermal ablation of GGN. The main contents include: (1) clinical evaluation of GGN, (2) procedures, indications, contraindications, outcomes evaluation and related complications of thermal ablation for GGN and (3) future development directions.

This paper proposes an image reconstruction method for simultaneous multi-slice imaging (SMS) by combining the virtual conjugate coil (VCC) technology and robust artificial-neural-networks for k-space interpolation (RAKI). This method can effectively improve the reconstruction quality, and is named VIRGINIA (VIRtual conjuGate coIls Neural-networks InterpolAtion). VIRGINIA utilizes the complex conjugate symmetry property of the virtual coil concept to generate virtual coil data for training, and obtains better image quality by applying the trained network to the original aliased SMS data. With experimental data, the VIRGINIA method was compared to other reconstruction methods (i.e., RAKI only and slice-GRAPPA) in terms of quantitative indices such as structural similarity index (SSIM), peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). The results demonstrated that, under some certain slice-acceleration factors, VIRGINIA produced better reconstruction quality than those obtainable by Slice-GRAPPA and RAKI.

Fast magnetic resonance imaging (MRI) has been attracting more and more research interests in recent years. With the emergence of big data and development of advanced deep learning algorithms, neural network has become a common and effective tool for image reconstruction in fast MRI. One main challenge to the deep learning-based methods for fast MRI reconstruction is the trade-off between the network performance and the network capacity. Few previous studies have used the deep learning-based methods in parallel imaging. In this work, a deep recursive cascaded convolutional network (DRCCN) architecture was designed for parallel MRI, with reduced number of network parameters while maintaining a satisfactory performance. The experimental results demonstrated that, compared to the classical methods, image reconstruction with the well-trained DRCCN networks were more accurate and less time consuming.

Ground object information of remote sensing images covered with thin clouds is obscure. An information recovery algorithm for ground objects in thin cloud images is proposed by fusing guide filter and transfer learning. Firstly, multi-resolution decomposition of thin cloud target images and cloud-free guidance images is performed by using multi-directional nonsubsampled dual-tree complex wavelet transform. Then the decomposed low frequency subbands are processed by using support vector guided filter and transfer learning respectively. The decomposed high frequency subbands are enhanced by using modified Laine enhancement function. The low frequency subbands output by guided filter and those predicted by transfer learning model are fused by the method of selection and weighting based on regional energy. Finally, the enhanced high frequency subbands and the fused low frequency subbands are reconstructed by using inverse multi-directional nonsubsampled dual-tree complex wavelet transform to obtain the ground object information recovery images. Experimental results of Landsat-8 OLI multispectral images show that, support vector guided filter can effectively preserve the detail information of the target images, domain adaptive transfer learning can effectively extend the range of available multi-source and multi-temporal remote sensing images, and good effects for ground object information recover are obtained by fusing guide filter and transfer learning to remove thin cloud on the remote sensing images.

Objective To investigate the effect of human umbilical cord mesenchymal stem cells (hUC-MSCs) on macrophage polarization under hypoxia. Methods hUC-MSCs were obtained by explants adherent culture and cultured under normoxia (21% O2) and hypoxia (5% O2). The multi-directional differentiation of hUC-MSCs was observed by osteogenic and adipogenic differentiation induction. Live/death staining was performed to detect the cell viability, and ELISA was executed to detect the protein content in supernatant of hUC-MSCs. Transwell chamber was employed to co-culture the hUC-MSCs cultured under normoxia and hypoxia and macrophage (THP-1) stimulated by lipopolysaccharide (IPS), then the polarization of THP-1 was detected by immunofluorescence, and the secretions of inflammatory factor and anti-inflammatory factor of THP-1 were detected by ELISA. Results hUC-MSCs cultured under hypoxia showed the ability of osteogenic and adipogenic multi-directional differentiation. Live/death staining showed the high cell viability of hUC-MSCs cultured under normoxia and hypoxia. The expression levels of prostaglandin E2 (PGE2) and indoleamine 2,3-dioxygenase (IDO) were significantly higher in the hUC-MSCs cultured under hypoxia than in those cultured under normoxia. hUCMSCs cultured under hypoxia promoted the polarization of THP-1 to M2, obviously reduced the expression of TNF-α and IL-1β, and increased the expression of IL-10 significantly. Conclusion hUC-MSCs cultured under hypoxia may promote the polarization of THP-1 to M2 and improve the viability of anti-inflammatory. DOI: 10.11855/j.issn.0577-7402.2016.07.01

Objective To study the therapeutic effect of exosome derived from lipopolysaccharides (LPS) priming mesenchymal stem cells (MSCs) for diabetic wound healing. Methods Human umbilical cord MSCs were treated with LPS (100ng/ml) for 2 days, the supernatant were then collected, and exosomes were harvested by density gradient centrifugation and identified. Diabetic cutaneous wounds were prepared and the animals were divided into the following three groups: control group, untreated MSCs derived exosome (un-exosome) treatment group and LPS primed MSCs derived exosome (LPS-exosome) treatment group. Exosomes (60μg) were injected dispersively into the wound edge daily for 10 days. After treatment, the therapeutic results were evaluated by gross observation of the wounds, the expression levels of inflammation related factors and macrophage subtype markers in the injured sites were detected by qRT-PCR at day 3, 7 and 14 after treatment. Results Compared with control group, the diabetic wound healing was obviously improved in LPS-exosome treatment group after treatment for 7 and 14 days, with faster wound close, depressed expression of pro-inflammatory factors IL -1, IL -12 and M1 macrophage surface marker iNOS, and up-regulation of anti-inflammatory factors IL-10, TGF-βand M2 macrophage surface marker CD163, the differences were significant (P