Expression of miR-20b in plasma of patients with psoriasis vulgaris and its effect on keratinocytes / 中国医师杂志

Objective:To investigate the expression of microRNA-20b (miR-20b) in peripheral blood plasma of patients with psoriasis vulgis, and to further analyze the effect and potential mechanism of miR-20b on proliferation and apoptosis of human immortals keratinocytes (HaCaT cell line).Methods:The peripheral blood of 36 patients with psoriasis vulgaris and 36 healthy volunteers (as control group) were collected. The relative expression level of miR-20b in plasma was detected by real-time quantitative polymerase chain reaction (qRT-PCR). HaCaT cells were cultured in vitro and randomly divided into control group (untreated), interleukin-22 (IL-22)treatment group (stimulated with 100 ng/ml IL-22 for 24 h), IL-22+ inhibitor control group (after transfection of inhibitor negative control, stimulated with 100 ng/ml IL-22 for 24 h) and IL-22+ miR-20b inhibitor group (treated with 100 ng/ml IL-22 for 24 h after transfection with miR-20b inhibitor). The relative expression of miR-20b in HaCaT cells was detected by qRT-PCR. The proliferation and apoptosis of HaCaT cells were detected by cell counting kit-8 (CCK-8) and flow cytometry, respectively. Bioinformatics software was used to predict the downstream targets of miR-20b. The targeted binding relationship between miR-20b and killin, p53-regulated DNA replication inhibitor (KLLN) 3′UTR was verified by dual luciferase reporter gene assay. The expression level of KLLN protein in HaCaT cells was detected by Western blot. Results:The plasma level of miR-20b in psoriasis patients was significantly higher than that in healthy controls [(1.62±0.53) vs (1.00±0.42), P<0.001]. The results of qRT-PCR showed that the expression of miR-20b in IL-22 treatment group was higher than that in control group ( P<0.05). The expression of miR-20b in IL-22+ miR-20b inhibitor group was lower than that in IL-22+ inhibitor control group ( P<0.05). CCK-8 results showed that the absorbance values of HaCaT cells in IL-22 treatment group were significantly higher than those in control group at 24, 48, 72 and 96 h (all P<0.05). The absorbance values of HaCaT cells in IL-22+ miR-20b inhibitor group at 48, 72 and 96 h were significantly lower than those in IL-22+ inhibitor control group (all P<0.05). The apoptosis rate of IL-22 treatment group was significantly lower than that of control group [(4.12±0.37)% vs (7.06±0.58)%], with statistically significant difference ( P<0.05). The apoptosis rate of HaCaT cells in IL-22+ miR-20b inhibitor group was significantly higher than that in IL-22+ inhibitor control group [(6.59±0.53)% vs (3.94±0.46)%], with statistically significant difference ( P<0.05). Dual luciferase reporter assay was used to verify the interaction between miR-20b and KLLN, and the results showed that the luciferase activity of KLLN wild-type was significantly inhibited by miR-20b mimics. Western blot results showed that the protein expression of KLLN in the IL-22 treatment group was lower than that in the control group ( P<0.05); the protein expression of KLLN in IL-22+ miR-20b inhibitor group was higher than that in IL-22+ inhibitor control group, with statistically significant difference ( P<0.05). Conclusions:MiR-20b is highly expressed in the plasma of patients with psoriasis vulgaris, and miR-20b may promote the proliferation and anti-apoptotic ability of keratinocytes by targeting KLLN.

DEMO: Deep MR Parametric Mapping with Unsupervised Multi-Tasking Framework

Compressed sensing (CS) has been investigated in magnetic resonance (MR) parametric mapping to reduce scan time. However, the relatively long reconstruction time restricts its widespread applications in the clinic. Recently, deep learningbased methods have shown great potential in accelerating reconstruction time and improving imaging quality in fast MR imaging, although their adaptation to parametric mapping is still in an early stage. In this paper, we proposed a novel deep learningbased framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way, which is more practical given that it is difficult to acquire large fully sampled training data of parametric-weighted images. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, thus making it an unsupervised learning approach. DEMO reconstructs parametric weighted images and generates a parametric map simultaneously by unrolling an interaction approach in conventional fast MR parametric mapping, which enables multi-tasking learning. Experimental results showed promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.

A Comparative Study of Unsupervised Deep Learning Methods for MRI Reconstruction

Recently, unsupervised deep learning methods have shown great potential in image processing. Compared with a large-amount demand for paired training data of supervised methods with a specific task, unsupervised methods can learn a universal and explicit prior information on data distribution and integrate it into the reconstruction process. Therefore, it can be used in various image reconstruction environments without showing degraded performance. The importance of unsupervised learning in MRI reconstruction appears to be growing. Nevertheless, the establishment of prior formulation in unsupervised deep learning varies a lot depending on mathematical approximation and network architectures. In this work, we summarized basic concepts of unsupervised deep learning comprehensively and compared performances of several state-of-the-art unsupervised learning methods for MRI reconstruction.

Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-Enhanced Loss Constraint

Dynamic magnetic resonance (MR) imaging has generated great research interest, because it can provide both spatial and temporal information for clinical diagnosis.However, slow imaging speed or long scanning time is still a challenge for dynamic MR imaging. Most existing methods reconstruct dynamic MR images from incomplete k-space data under the guidance of compressed sensing (CS) or lowrank theory, which suffer from long iterative reconstruction time. Recently, deep learning has shown great potential in accelerating dynamic MR. Our previous work proposed a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training. Nevertheless, there was still some smoothing needed in the reconstructed images at high acceleration. In this work, we propose cascaded residual dense networks for dynamic MR imaging with edge-enhanced loss constraint, dubbed cascaded residual dense networks (CRDN).Specifically, the cascaded residual dense networks fully exploit the hierarchical features from all the convolutional layers with both local and global feature fusion.We further use the higher-degree total variation loss function, which has the edge enhancement properties, for training the networks.

A Comparative Study of Unsupervised Deep Learning Methods for MRI Reconstruction

Recently, unsupervised deep learning methods have shown great potential in image processing. Compared with a large-amount demand for paired training data of supervised methods with a specific task, unsupervised methods can learn a universal and explicit prior information on data distribution and integrate it into the reconstruction process. Therefore, it can be used in various image reconstruction environments without showing degraded performance. The importance of unsupervised learning in MRI reconstruction appears to be growing. Nevertheless, the establishment of prior formulation in unsupervised deep learning varies a lot depending on mathematical approximation and network architectures. In this work, we summarized basic concepts of unsupervised deep learning comprehensively and compared performances of several state-of-the-art unsupervised learning methods for MRI reconstruction.

Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-Enhanced Loss Constraint

Dynamic magnetic resonance (MR) imaging has generated great research interest, because it can provide both spatial and temporal information for clinical diagnosis.However, slow imaging speed or long scanning time is still a challenge for dynamic MR imaging. Most existing methods reconstruct dynamic MR images from incomplete k-space data under the guidance of compressed sensing (CS) or lowrank theory, which suffer from long iterative reconstruction time. Recently, deep learning has shown great potential in accelerating dynamic MR. Our previous work proposed a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training. Nevertheless, there was still some smoothing needed in the reconstructed images at high acceleration. In this work, we propose cascaded residual dense networks for dynamic MR imaging with edge-enhanced loss constraint, dubbed cascaded residual dense networks (CRDN).Specifically, the cascaded residual dense networks fully exploit the hierarchical features from all the convolutional layers with both local and global feature fusion.We further use the higher-degree total variation loss function, which has the edge enhancement properties, for training the networks.

Biomarkers for the diagnosis of Kawasaki disease / 国际儿科学杂志

Kawasaki disease (KD) is an acute,self-limiting systemic vascular syndrome that mainly occurs in infants and children under the age of five years,and is the leading cause of acquired heart disease in children.Kawasaki disease can lead to coronary dilatation,coronary aneurysm,which can cause myocardial infarction or death,seriously harm the health of infants and children,and thus early diagnosis is critically important.Till now the diagnosis is based mainly on the clinical symptoms for the lack of diagnostic laboratory tests.In recent years,many studies have been carried out to explore the potential biomarkers with high sensitivity and specificity in the early diagnosis of KD.The article reviews the progress of the related studies.

Level Changes and Significance of IL-2, IFN-γ and TNF-α in Bladder Cancer Patients / 现代生物医学进展

Objective:To discuss the level changes and significance of IL-2,IFN-γ,TNF-α in patients with bladder cancer.Methods:66 patients with bladder cancer who were treated in our hospital from February 2015 to December 2016 were enrolled in this study,which was denoted by bladder cancer group,65 patients with cystitis glandularis who were treated in our hospital during the same period were selected as cystitis group,another 65 healthy persons who were examined in our hospital during the same period were selected as control group,and compared the levels of IL-2,IFN-γ and TNF-α in each group,and the levels of IL-2,IFN-γand TNF-α in patients with bladder cancer of different types and clinical stages.The correlation of IL-2,IFN-γ and TNF-α levels with pathological types and clinical stages were analyzed.Results:The levels ofIL-2 and INF-γ in bladder cancer group were significantly lower than those in cystitis group and control group,the level of TNF-α was significantly higher than that of cystitis group and control group,the difference was statistically significant (P＜0.05).There was no significant difference in IL-2,IFN-γ and TNF-α levels in different types of bladder cancer patients (both P＞0.05).IL-2,IFN-γ levels in T2 to T4 bladder cancer patients were significantly lower than Tis to T1,TNF-α level was significantly higher than Tis to T1,the difference was statistically significant (both P＜0.05).According to Spearman method evaluation correlation founded that IL-2,IFN-γlevels in patients with bladder cancer were negatively correlated with clinical stage,TNF-α level was positively correlated with clinical stage.However,there was no correlation between IL-2,IFN-γ and TNF-α levels in patients with pathological type.Conclusion:IL-2,IFN-γ expression in bladder cancer patients are decreased significantly,while TNF-α expression is increased significantly,and the above three indexes of patients are related to clinical stage,but not related to pathological type.