Transcriptomics explores potential mechanisms for the development of Primary Sjogren's syndrome to diffuse large B-cell lymphoma in B cells.

PURPOSE: Primary Sjogren's syndrome (pSS) is a prevalent autoimmune disease. The immune dysregulation it causes often leads to the development of diffuse large B-cell lymphoma (DLBCL) in clinical practice. However, how it contributes to these two disorders at the molecular level is not yet known. This study explored the potential molecular mechanisms associated with the differences between DLBCL and pSS. PATIENTS AND METHODS: Gene expression matrices from discovery cohort 1, discovery cohort 2, and the validation cohort were downloaded from the GEO and TCGA databases. Weighted gene coexpression network analysis (WGCNA) was performed to identify the coexpression modules of DLBCL and pSS in discovery cohort 1 and obtain shared genes. GO and KEGG enrichment analyses and PPI network analysis were performed on the shared genes. Immune-related genes (IRGs) were intersected with shared genes to obtain common genes. Afterward, common genes were identified via machine learning methods. The immune infiltration analysis, miRNA-TF-hub gene regulatory chart, gene interactions of the hub genes, and gene‒drug target analysis were performed. Finally, STAT1 was identified as a possible essential gene by the above analysis, and immune infiltration and GSEA pathway analyses were performed in the high- and low-expression groups in discovery cohort 2. The diagnostic efficacy of the hub genes was assessed in the validation cohort, and clinical samples were collected for validation. RESULTS: By WGCNA, one modular gene in each group was considered highly associated with the disease, and we obtained 28 shared genes. Enrichment analysis revealed shared genes involved in the viral response and regulation. We obtained four hub genes (ISG20, STAT1, TLR7, and RSAD2) via the algorithm. Hub genes and similar genes are primarily involved in regulating type I IFNs. The construction of a miRNA-TF-hub gene regulatory chart revealed that hsa-mir-155-5p, hsa-mir-146b-5p, hsa-mir-21-3p, and hsa-mir-126-3p play essential roles in both diseases. Hub genes were differentially expressed in B-cell memory according to immune infiltration analysis. Hub genes had a strong diagnostic effect on both diseases. STAT1 plays an essential role in immune cells in both diseases. CONCLUSION: We identified hub susceptibility genes for DLBCL and pSS and identified hub genes and potential therapeutic targets that may act as biomarkers.

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.

[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.

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.

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.