Network pharmacology and molecular docking analysis on the mechanism of Tripterygium wilfordii Hook in the treatment of Sjögren syndrome.

Tripterygium wilfordii Hook. F (TWH) has significant anti-inflammatory and immunosuppressive effects, and is widely used in the inflammatory response mediated by autoimmune diseases. However, the multi-target mechanism of TWH action in Sjögren syndrome (SS) remains unclear. Therefore, the aim of this study was to explore the molecular mechanism of TWH in the treatment of SS using network pharmacology and molecular docking methods. TWH active components and target proteins were screened from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. SS-related targets were obtained from the GeneCards database. After overlap, the therapeutic targets of TWH in the treatment of SS were screened. Protein-protein interaction and core target analysis were performed by STRING network platform and Cytoscape software. In addition, the affinity between TWH and the disease target was confirmed by molecular docking. Finally, the DAVID (visualization and integrated) database was used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of overlapping targets. The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database shows that TWH contains 30 active components for the treatment of SS. Protein-protein interaction and core target analysis suggested that TNF, MMP9, TGFB1, AKT1, and BCL2 were the key targets of TWH in the treatment of SS. In addition, the molecular docking method confirmed that the bioactive molecules of TWH had a high affinity with the target of SS. Enrichment analysis showed that TWH active components were involved in multiple signaling pathways. Pathways in cancer, Lipid and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications is the main pathway. It is associated with a variety of biological processes such as inflammation, apoptosis, immune injury, and cancer. Based on data mining network pharmacology, and molecular docking method validation, TWH is likely to be a promising candidate for the treatment of SS drug, but still need to be further verified experiment.

Dual adversarial models with cross-coordination consistency constraint for domain adaption in brain tumor segmentation.

The brain tumor segmentation task with different domains remains a major challenge because tumors of different grades and severities may show different distributions, limiting the ability of a single segmentation model to label such tumors. Semi-supervised models (e.g., mean teacher) are strong unsupervised domain-adaptation learners. However, one of the main drawbacks of using a mean teacher is that given a large number of iterations, the teacher model weights converge to those of the student model, and any biased and unstable predictions are carried over to the student. In this article, we proposed a novel unsupervised domain-adaptation framework for the brain tumor segmentation task, which uses dual student and adversarial training techniques to effectively tackle domain shift with MR images. In this study, the adversarial strategy and consistency constraint for each student can align the feature representation on the source and target domains. Furthermore, we introduced the cross-coordination constraint for the target domain data to constrain the models to produce more confident predictions. We validated our framework on the cross-subtype and cross-modality tasks in brain tumor segmentation and achieved better performance than the current unsupervised domain-adaptation and semi-supervised frameworks.

MAD-Net: Multi-attention dense network for functional bone marrow segmentation.

Radiotherapy is the main treatment modality for various pelvic malignancies. However, high intensity radiation can damage the functional bone marrow (FBM), resulting in hematological toxicity (HT). Accurate identification and protection of the FBM during radiotherapy planning can reduce pelvic HT. The traditional manual method for contouring the FBM is time-consuming and laborious. Therefore, development of an efficient and accurate automatic segmentation mode can provide a distinct leverage in clinical settings. In this paper, we propose the first network for performing the FBM segmentation task, which is referred to as the multi-attention dense network (named MAD-Net). Primarily, we introduce the dense convolution block to promote the gradient flow in the network as well as incite feature reuse. Next, a novel slide-window attention module is proposed to emphasize long-range dependencies and exploit interdependencies between features. Finally, we design a residual-dual attention module as the bottleneck layer, which further aggregates useful spatial details and explores intra-class responsiveness of high-level features. In this work, we conduct extensive experiments on our dataset of 3838 two-dimensional pelvic slices. Experimental results demonstrate that the proposed MAD-Net transcends previous state-of-the-art models in various metrics. In addition, the contributions of the proposed components are verified by ablation analysis, and we conduct experiments on three other datasets to manifest the generalizability of MAD-Net.

Identification of diagnostic markers related to fecal and plasma metabolism in primary Sjögren's syndrome.

BACKGROUND: Accurate diagnostic techniques for patients with primary Sjögren's syndrome (pSS) are needed. This study aimed to investigate new biomarkers related to fecal and plasma metabolism from pSS patients. METHODS: The feces and plasma of 21 pSS patients and 18 controls admitted to the Second Hospital of Shanxi Medical University were collected for analysis. Metabolites in feces and plasma were quantified using liquid chromatography-mass spectrometry. The metabolic pathway alterations caused by pSS were studied and the expression of metabolites in the intersecting pathway was analyzed in the feces and plasma of pSS patients. Metabolites that showed the same alterations in feces and plasma in pSS patients were considered as diagnostic markers and receiver operating characteristic curves were generated to analyze the sensitivity of these markers in diagnosing pSS. RESULTS: There were 114 and 92 upregulated metabolites and 54 and 125 downregulated metabolites in the feces and plasma of pSS patients, respectively. These metabolites were enriched in 8 pathways for feces and 12 pathways for plasma. Arginine biosynthesis, Linoleic acid metabolism, Tyrosine metabolism, Taurine and hypotaurine metabolism were pathways enriched by metabolites in both samples. Twelves metabolites were enriched in the above four pathways, while only 9,10-12,13-Diepoxyoctadecanoate, Tyramine, 9-OxoODE and 2-Hydroxyethanesulfonate showed the same trend. The candidate diagnostic markers were all predictive, with better diagnostic sensitivity in plasma samples. CONCLUSIONS: 9,10-12,13-Diepoxyoctadecanoate, Tyramine, 9-OxoODE, 2-Hydroxyethanesulfonate were metabolism-related diagnostic markers for pSS feces and plasma.

MEA-Net: multilayer edge attention network for medical image segmentation.

Medical image segmentation is a fundamental step in medical analysis and diagnosis. In recent years, deep learning networks have been used for precise segmentation. Numerous improved encoder-decoder structures have been proposed for various segmentation tasks. However, high-level features have gained more research attention than the abundant low-level features in the early stages of segmentation. Consequently, the learning of edge feature maps has been limited, which can lead to ambiguous boundaries of the predicted results. Inspired by the encoder-decoder network and attention mechanism, this study investigates a novel multilayer edge attention network (MEA-Net) to fully utilize the edge information in the encoding stages. MEA-Net comprises three major components: a feature encoder module, a feature decoder module, and an edge module. An edge feature extraction module in the edge module is designed to produce edge feature maps by a sequence of convolution operations so as to integrate the inconsistent edge information from different encoding stages. A multilayer attention guidance module is designed to use each attention feature map to filter edge information and select important and useful features. Through experiments, MEA-Net is evaluated on four medical image datasets, including tongue images, retinal vessel images, lung images, and clinical images. The evaluation values of the Accuracy of four medical image datasets are 0.9957, 0.9736, 0.9942, and 0.9993, respectively. The values of the Dice coefficient are 0.9902, 0.8377, 0.9885, and 0.9704, respectively. Experimental results demonstrate that the network being studied outperforms current state-of-the-art methods in terms of the five commonly used evaluation metrics. The proposed MEA-Net can be used for the early diagnosis of relevant diseases. In addition, clinicians can obtain more accurate clinical information from segmented medical images.

[The Value of Serum HMGB1 and sRAGE in the Diagnosis, Efficacy Monitoring and Prognosis of Newly Diagnosed Multiple Myeloma].

OBJECTIVE: To evaluate the value of high mobility group protein B1 (HMGB1) and soluble receptor for advanced glycation end products (sRAGE) in the diagnosis, efficacy monitoring and prognosis of newly diagnosed multiple myeloma (MM) patients. METHODS: Fifty newly diagnosed MM patients before and after chemotherapy and 50 hematological outpatients from October 2018 to May 2020 were selected. Enzyme linked immunosorbent assay (ELISA) was used to detect the serum HMGB1 and sRAGE levels of the patients. ROC was used to further analyze the efficacy of serum HMGB1 and sRAGE levels on the diagnosis of MM. At the same time, the serum levels of HMGB1 and sRAGE before and after chemotherapy were compared, and their values in the evaluation of curative effect of MM patients were analyzed. According to the mean values of serum HMGB1 and sRAGE, all the patients were divided into different groups, the clinical characteristics and survival status of the patients were compared. RESULTS: Before treatment the serum HMGB1 level of the patients in MM group was higher than that in control group, while sRAGE level was lower (t=11.363,6.127, P<0.001). The AUC of serum HMGB1 and sRAGE in the MM patients was 0.955 and 0.811, respectively. After 3 courses of chemotherapy, HMGB1 level of the patients in CR group was lower than before chemotherapy, while in PD group was higher, as well as sRAGE level of the patients in PR group (P<0.05). There were significant differences in R-ISS stage, HGB, CRP, ESR, CD56, CD117, D13S319 deletion between HMGB1 high expression group and HMGB1 low expression group (χ2=3.920, 6.522, 6.65, 4.16, 3.945, 6.65, 4.16, P<0.05), while there were significant differences in ISS stage, CRP and CD56 between sRAGE low expression group (28 cases) and sRAGE high expression group (22 cases) (χ2=4.565, 4.711, 5.547, P<0.05). Kaplan-Meier survival analysis showed that the patients in HMGB1 low expression group had better survival condition, for PFS Tlow>Thigh (χ2=9.470, P<0.05), and for OS Tlow>Thigh (χ2=7.808, P<0.05); there was no difference in the survival of sRAGE high expression group and low expression group, for PFS Tlow0.05), and for OS Tlow0.05). Cox analysis showed that LDH and HMGB1 were the factors affecting the prognosis of the patients, and both of them affected PFS (HR=2.771, 95% CI: 1.002-7.662, P=0.049; HR=6.022, 95% CI: 1.689-21.470, P=0.006), while HMGB1 also affected OS (HR=4.275, 95% CI: 1.183-15.451, P=0.027). CONCLUSION: The serum HMGB1 and sRAGE have certain auxiliary value for the diagnosis and curative effect monitoring of newly diagnosed MM patients, and serum HMGB1 is expected to be an auxiliary detection index for the prognosis of MM.

Improved U-Net3+ with stage residual for brain tumor segmentation.

BACKGROUND: For the encoding part of U-Net3+,the ability of brain tumor feature extraction is insufficient, as a result, the features can not be fused well during up-sampling, and the accuracy of segmentation will reduce. METHODS: In this study, we put forward an improved U-Net3+ segmentation network based on stage residual. In the encoder part, the encoder based on the stage residual structure is used to solve the vanishing gradient problem caused by the increasing in network depth, and enhances the feature extraction ability of the encoder which is instrumental in full feature fusion when up-sampling in the network. What's more, we replaced batch normalization (BN) layer with filter response normalization (FRN) layer to eliminate batch size impact on the network. Based on the improved U-Net3+ two-dimensional (2D) model with stage residual, IResUnet3+ three-dimensional (3D) model is constructed. We propose appropriate methods to deal with 3D data, which achieve accurate segmentation of the 3D network. RESULTS: The experimental results showed that: the sensitivity of WT, TC, and ET increased by 1.34%, 4.6%, and 8.44%, respectively. And the Dice coefficients of ET and WT were further increased by 3.43% and 1.03%, respectively. To facilitate further research, source code can be found at: https://github.com/YuOnlyLookOne/IResUnet3Plus . CONCLUSION: The improved network has a significant improvement in the segmentation task of the brain tumor BraTS2018 dataset, compared with the classical networks u-net, v-net, resunet and u-net3+, the proposed network has smaller parameters and significantly improved accuracy.

Research on multi-path dense networks for MRI spinal segmentation.

Accurate and robust segmentation of anatomical structures from magnetic resonance images is valuable in many computer-aided clinical tasks. Traditional codec networks are not satisfactory because of their low accuracy of edge segmentation, the low recognition rate of the target, and loss of detailed information. To address these problems, this study proposes a series of improved models for semantic segmentation and progressively optimizes them from the three aspects of convolution module, codec unit, and feature fusion. Instead of the standard convolution structure, we apply a new type of convolution module for the feature extraction. The networks integrate a multi-path method to obtain richer-detail edge information. Finally, a dense network is utilized to strengthen the ability of the feature fusion and integrate more different-level information. The evaluation of the Accuracy, Dice coefficient, and Jaccard index led to values of 0.9855, 0.9185, and 0.8507, respectively. These metrics of the best network increased by 1.0%, 4.0%, and 6.1%, respectively. Boundary F1-Score reached 0.9124 indicating that the proposed networks can segment smaller targets to obtain smoother edges. Our methods obtain more key information than traditional methods and achieve superiority in segmentation performance.

Catheter ablation for atrial fibrillation in a low-volume center using contemporary technology.

BACKGROUND: Catheter ablation is increasingly being performed worldwide for atrial fibrillation (AF). However, there are concerns of lower success rates and higher complications of AF ablations performed in low-volume centers. Thus, we sought to evaluate the safety and efficacy of AF catheter ablation in a low-volume center using contemporary technologies. METHODS AND RESULTS: 71 consecutive patients (50 paroxysmal AF [pAF] vs 21 persistent AF) who underwent first catheter ablation were studied. Primary outcome was AF recurrence rate. Secondary outcomes included periprocedural complications, hospitalization for symptomatic tachy-arrhythmias post-ablation and number of repeat ablations. Mean age of our cohort was 59.1 ± 9.7 years, of which 56 (78.9%) were males. 1-year AF recurrence was 19.5% in pAF and 23.8% in persistent AF (p = 0.694). Ablation in persistent AF group required longer procedural (197.76 ± 48.60 min [pAF] vs 238.67 ± 70.50 min [persistent AF], p = 0.006) and ablation duration (35.08 ± 15.84 min [pAF] vs 52.65 ± 28.46 min [persistent AF], p = 0.001). There were no significant differences in secondary outcomes. Major periprocedural complication rate was 2.8%.Subset analysis on (i) cryoablation vs radiofrequency, (ii) Ensite vs CARTO navigational system and (iii) circular vs high density mapping catheter did not yield significant differences in primary or secondary outcomes. CONCLUSIONS: The AF ablation complication and recurrence free rates in both paroxysmal and persistent AF at one year were comparable to high-volume centers. Long-term follow up is needed. In addition, first AF catheter ablation in a low-volume center is realistic with comparable efficacy and safety outcomes to high-volume centers using contemporary ablation technologies.

WITHDRAWN: Comparison of the accuracy between imageless navigation and manual freehand approaches for total hip arthroplasty: a systematic review and meta-analysis.

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MicroRNA­1 regulates the development of osteoarthritis in a Col2a1­Cre­ERT2/GFPfl/fl­RFP­miR­1 mouse model of osteoarthritis through the downregulation of Indian hedgehog expression.

The present study assessed the effects of microRNA­1 (miR­1) on the development of osteoarthritis using human tissues and a Col2a1­Cre­ERT2/GFPfl/fl­RFP­miR­1 mouse model of osteoarthritis. Human cartilage tissues (n=20) were collected for reverse transcription­quantitative polymerase chain reaction (RT­qPCR), histological analysis and immunohistochemistry experiments. A transgenic mouse model of osteoarthritis was established by subjecting Col2a1­Cre­ERT2/GFPfl/fl­RFP­miR­1 transgenic mice to anterior cruciate ligament transection (ACLT). Mice were subjected to radiography and in vivo fluorescence molecular tomography (FMT), while mouse tissues were collected for histological analysis, RT­qPCR and Safranin O staining. It was found that the miR­1 level was downregulated, whereas the levels of Indian hedgehog (Ihh), as well as those of its downstream genes were upregulated in human osteoarthritic cartilage. In the transgenic mice, treatment with tamoxifen induced miR­1, as well as collagen, type II (Col2a1) and Aggrecan (Acan) expression; however, it decreased Ihh, glioma­associated oncogene homolog (Gli)1, Gli2, Gli3, smoothened homolog (Smo), matrix metalloproteinase (MMP)­13 and collagen type X (Col10) expression. Safranin O staining revealed cartilage surface damage in the non­tamoxifen + ACLT group, compared with that in the tamoxifen + ACLT group. Histologically, an intact cartilage surface and less fibrosis were observed in the tamoxifen + ACLT group. Immunohistochemistry revealed that the protein expression of Ihh, Col10, and MMP­13 was significantly higher in the joint tissues of the non­tamoxifen + ACLT group than in those of the tamoxifen + ACLT group. However, Col2a1 expression was lower in the joint tissues of the non­tamoxifen + ACLT group than in those of the tamoxifen + ACLT group. The results of RT­qPCR and FMT further confirmed these findings. On the whole, the findings of the present study demonstrate that miR­1 expression protects against osteoarthritis­induced cartilage damage and gene expression by inhibiting Ihh signaling.

The fecal metabolome is associated with gestational diabetes mellitus.

Dysbiosis of gut microbiota has been linked to gestational diabetes mellitus (GDM), and grows as a resource for GDM biomarkers. However, the contributions of gut microbiota to GDM remain incompletely understood. Metabolites are key messengers in the interactions between gut microbiota and the host. Metabolomics is emerging as an essential tool in exploring the contributions of gut microbiota to diseases. In this study, we performed 1H-NMR based metabolomics on the feces of 62 pregnant women, including 31 women with GDM, and 31 women as the non-diabetes (NDM) control. Using Principle Component Analysis (PCA) and Orthogonal Projection to Latent Structures Discrimination Analysis (OPLS-DA), we observed clear cluster separation of the fecal metabolome between women with GDM and the NDM control. We further applied several feature selection methods to find five fecal metabolites contributing to the cluster separation of the fecal metabolome. These five metabolites, namely dibutyl decanedioate, N-acetylgalactosamine-4-sulphate, homocysteine, l-malic acid, and butanone, were significantly correlated with the clinical indices of GDM. Metabolite enrichment and pathway analysis on the five metabolites suggested that the fecal citrate cycle and sulfur metabolism were correlated with GDM. The results of this study demonstrated that disorders in the fecal metabolome are associated with GDM.

Abnormal glucose tolerance and insulin resistance in polycystic ovary syndrome amongst the Taiwanese population- not correlated with insulin receptor substrate-1 Gly972Arg/Ala513Pro polymorphism.

BACKGROUND: Insulin resistance and glucose dysmetabolism in polycystic ovary syndrome (PCOS) are related with the polymorphisms in the genes encoding the insulin receptor substrate (IRS) proteins, especially Gly972Arg/Ala513Pro polymorphism being reported to be associated with type-2 diabetes and PCOS. We intended to assess the prevalence of abnormal glucose tolerance (AGT) and insulin resistance in Taiwanese PCOS women. We also tried to assess whether the particular identity of Gly972Arg/Ala513Pro polymorphic alleles of the IRS-1 gene mutation can be used as an appropriate diagnostic indicator for PCOS. METHODS: We designed a prospective clinical study. Forty-seven Taiwanese Hoklo and Hakka women, diagnosed with PCOS were enrolled in this study as were forty-five healthy Hoklo and Hakka women as the control group. Insulin resistance was evaluated with fasting insulin, fasting glucose/insulin ratio, and homeostasis model assessment index for insulin resistance (HOMAIR). The genomic DNA of the subjects was amplified by PCR and digested by restriction fragmented length polymorphism (RFLP) with Bst N1 used for codon 972 and Dra III for codon 513. RESULTS: AGT was found in 46.8% of these PCOS patients and was significantly related to high insulin resistance rather than the low insulin resistance. Those patients with either insulin resistance or AGT comprised the majority of PCOS affected patients (AGT + fasting insulin > or =17: 83%, AGT + glucose/insulin ratio > or =6.5: 85.1%, AGT + HOMAIR > or = 2: 87.2%, and AGT + HOMAIR > or = 3.8: 72.3%). None of the tested samples revealed any polymorphism due to the absence of any Dra III recognition site or any Bst N1 recognition site in the amplified PCR fragment digested by restriction fragmented length polymorphism. CONCLUSION: There is significantly high prevalence of AGT and insulin resistance in PCOS women, but Gly972Arg and Ala513Pro polymorphic alleles of IRS-1 are rare and are not associated with the elevated risk of PCOS amongst Taiwanese subjects. This is quite different from the similar study in phylogenetically diverged Caucasian subjects.