Search

Background. Gastric cancer (GC) is an extremely heterogeneous malignancy with a complex tumor microenvironment (TME) that contributes to unsatisfactory prognosis. Methods. The overall activity score for assessing the immune activity of GC patients was developed based on cancer immune cycle activity index in the Tracking Tumor Immunophenotype (TIP). Genes potentially affected by the overall activity score were screened using weighted gene co-expression network analysis (WGCNA). Based on the expression profile data of GC in The Cancer Genome Atlas (TCGA) database, COX analysis was applied to create an immune activity score (IAS). Differences in TME activity in the IAS groups were analyzed. We also evaluated the value of IAS in estimating immunotherapy and chemotherapy response based on immunotherapy cohort. Gene expression in IAS model and cell viability were determined by real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) and Cell Counting Kit-8 (CCK-8) assay, respectively. Results. WGCAN analysis screened 629 overall activity score-related genes, which were mainly associated with T cell response and B cell response. COX analysis identified AKAP5, CTLA4, LRRC8C, AOAH-IT1, NPC2, RGS1 and SLC2A3 as critical genes affecting the prognosis of GC, based on which the IAS was developed. Further RT-qPCR analysis data showed that the expression of AKAP5 and CTLA4 was downregulated, while that of LRRC8C, AOAH-IT1, NPC2, RGS1 and SLC2A3 was significantly elevated in GC cell lines. Inhibition of AKAP5 increased cell viability but siAOAH-IT1 promoted viability of GC cells. IAS demonstrated excellent robustness in predicting immunotherapy outcome and GC prognosis, with low-IAS patients having better prognosis and immunotherapy. In addition, resistance to Erlotinib, Rapamycin, MG- 132, Cyclopamine, AZ628, and Sorafenib was reduced in patients with low IAS. Conclusion. IAS was a reliable prognostic indicator. ForGCpatients, IAS showed excel- lent robustness in predic

In this study, we aimed to identify potential targets modulating the progression of nasopharyngeal carcinoma (NPC) using integrated bioinformatics analysis and functional assays. Differentially expressed genes (DEGs) between NPC and normal tissues samples were obtained from publicly availably microarray datasets (GSE68799, GSE34573, and GSE53819) in the Gene Expression Omnibus (GEO) database. The bioinformatics analysis identified 49 common DEGs from three GEO datasets, which were mainly enriched in cytokine/chemokine pathways and extracellular matrix organization pathway. Further protein–protein interaction network analysis identified 11 hub genes from the 49 DEGs. The 11 hub genes were significantly up-regulated in the NPC tissues when compared to normal tissues by analyzing the Oncomine database. The 8 hub genes including COL5A1, COL7A1, COL22A1, CXCL11, IFI44L, IFIT1, RSAD2, and USP18 were significantly up-regulated in the NPC tissues when compared to normal tissues by using the Oncomine database. Further validation studies showed that IFIT1 was up-regulated in the NPC cells. Knockdown of IFI1T1 suppressed the proliferation, migration, and invasion of NPC cells; while IFIT1 overexpression promoted the proliferation, migration, and invasion of NPC cells. In conclusion, a total of 49 DEGs and 11 hub genes in NPC using the integrated bioinformatics analysis. IFIT1 was up-regulated in the NPC cells lines, and IFIT1 may act as an oncogene by promoting NPC cell proliferation, migration, and invasion. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

In this study, we aimed to identify potential targets modulating the progression of nasopharyngeal carcinoma (NPC) using integrated bioinformatics analysis and functional assays. Differentially expressed genes (DEGs) between NPC and normal tissues samples were obtained from publicly availably microarray datasets (GSE68799, GSE34573, and GSE53819) in the Gene Expression Omnibus (GEO) database. The bioinformatics analysis identified 49 common DEGs from three GEO datasets, which were mainly enriched in cytokine/chemokine pathways and extracellular matrix organization pathway. Further protein–protein interaction network analysis identified 11 hub genes from the 49 DEGs. The 11 hub genes were significantly up-regulated in the NPC tissues when compared to normal tissues by analyzing the Oncomine database. The 8 hub genes including COL5A1, COL7A1, COL22A1, CXCL11, IFI44L, IFIT1, RSAD2, and USP18 were significantly up-regulated in the NPC tissues when compared to normal tissues by using the Oncomine database. Further validation studies showed that IFIT1 was up-regulated in the NPC cells. Knockdown of IFI1T1 suppressed the proliferation, migration, and invasion of NPC cells; while IFIT1 overexpression promoted the proliferation, migration, and invasion of NPC cells. In conclusion, a total of 49 DEGs and 11 hub genes in NPC using the integrated bioinformatics analysis. IFIT1 was up-regulated in the NPC cells lines, and IFIT1 may act as an oncogene by promoting NPC cell proliferation, migration, and invasion. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

In this study, we aimed to identify potential targets modulating the progression of nasopharyngeal carcinoma (NPC) using integrated bioinformatics analysis and functional assays. Differentially expressed genes (DEGs) between NPC and normal tissues samples were obtained from publicly availably microarray datasets (GSE68799, GSE34573, and GSE53819) in the Gene Expression Omnibus (GEO) database. The bioinformatics analysis identified 49 common DEGs from three GEO datasets, which were mainly enriched in cytokine/chemokine pathways and extracellular matrix organization pathway. Further protein–protein interaction network analysis identified 11 hub genes from the 49 DEGs. The 11 hub genes were significantly up-regulated in the NPC tissues when compared to normal tissues by analyzing the Oncomine database. The 8 hub genes including COL5A1, COL7A1, COL22A1, CXCL11, IFI44L, IFIT1, RSAD2, and USP18 were significantly up-regulated in the NPC tissues when compared to normal tissues by using the Oncomine database. Further validation studies showed that IFIT1 was up-regulated in the NPC cells. Knockdown of IFI1T1 suppressed the proliferation, migration, and invasion of NPC cells; while IFIT1 overexpression promoted the proliferation, migration, and invasion of NPC cells. In conclusion, a total of 49 DEGs and 11 hub genes in NPC using the integrated bioinformatics analysis. IFIT1 was up-regulated in the NPC cells lines, and IFIT1 may act as an oncogene by promoting NPC cell proliferation, migration, and invasion. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Deep models have improved state-of-the-art for both supervised and unsupervised learning. For example, deep embedded clustering (DEC) has greatly improved the unsupervised clustering performance, by using stacked autoencoders for representation learning. However, one weakness of deep modeling is that the local neighborhood structure in the original space is not necessarily preserved in the latent space. To preserve local geometry, various methods have been proposed in the supervised and semi-supervised learning literature (e.g., spectral clustering and label propagation) using graph Laplacian regularization. In this paper, we combine the strength of deep representation learning with measure propagation (MP), a KL-divergence based graph regularization method originally used in the semi-supervised scenario. The main assumption of MP is that if two data points are close in the original space, they are likely to belong to the same class, measured by KL-divergence of class membership distribution. By taking the same assumption in the unsupervised learning scenario, we propose our Deep Embedded Clustering Aided by Measure Propagation (DECAMP) model. We evaluate DECAMP on short text clustering tasks. On three public datasets, DECAMP performs competitively with other state-of-the-art baselines, including baselines using additional data to generate word embeddings used in the clustering process. As an example, on the Stackoverflow dataset, DECAMP achieved a clustering accuracy of 79%, which is about 5% higher than all existing baselines. These empirical results suggest that DECAMP is a very effective method for unsupervised learning., Comment: This work was presented as a poster in 14th Annual Machine Learning Symposium in The New York Academy of Sciences

Jihui wang, Xiaoli Wu, Wenjing Liang, Minhua chen, Chongbang Zhao, Xianglan Wang Department of Psychiatry, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, People’s Republic of ChinaCorrespondence: Xianglan WangDepartment of Psychiatry, The Third Affiliated Hospital, Sun Yat-Sen University, No. 60, Tianhe Road, Tianhe District, Guangzhou 510630, Guangdong, People’s Republic of ChinaTel +8602085253129Email wxiangl@mail.sysu.edu.cnObjective: Insomnia with objective short sleep duration (IOSSD) is associated with an increased risk of cardiovascular morbidity, diabetes, neurocognitive impairment, and mortality. Inflammation is believed to be one of the main links between IOSSD and these diseases. The role of nucleotide-binding domain and leucine-rich repeat protein-3 (NLRP3) inflammasome in inducing activation of inflammatory signaling in IOSSD is not clear. In this study, we investigated the expression of NLRP3 inflammasome in patients with IOSSD to clarify this issue.Methods: Thirty-six patients with insomnia and 20 age- and sex-matched healthy controls were sequentially recruited. Subjects were categorized into three groups: IOSSD (sleep duration < 6h, n=20), insomnia with objective normal sleep duration (IONSD, sleep duration ≥ 6h, n=16) and healthy controls (n=20). Objective sleep parameters were measured by overnight polysomnography. Peripheral NLRP3 inflammasome protein levels [NLRP3, apoptosis-associated speck-like protein containing a CARD (ASC), caspase l] and cytokines [interleukin (IL)-1β and IL-18] were assessed by Western blotting and ELISA, respectively.Results: IOSSD group showed significantly increased protein expressions of ASC and caspase-1 compared to IONSD and healthy controls and significantly increased IL-18 levels compared to healthy controls. On correlation analysis, total sleep time showed an inverse correlation with NLRP3, ASC, IL-18, and IL-1β levels. Wake after sleep

Jihui Wang,1 Xiaodong Chen,2 Xuejiao Men,2 Minhua Chen,1 Jiong Tao,1 Zhengqi Lu2 1Department of Psychiatry, the Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, People’s Republic of China; 2Department of Neurology, the Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, People’s Republic of ChinaCorrespondence: Jiong TaoDepartment of Psychiatry, The Third Affiliated Hospital, Sun Yat-sen University, No.600 Tianhe Road, Tianhe District, Guangzhou, Guangdong 510630, People’s Republic of ChinaTel +8613600071635Email tj2023@163.comZhengqi LuDepartment of Neurology, The Third Affiliated Hospital, Sun Yat-sen University, No.600 Tianhe Road, Tianhe District, Guangzhou, Guangdong 510630, People’s Republic of ChinaTel +8618922102767Email lzq1828@outlook.comObjective: Chronic inflammatory responses and leukocyte infiltration are classical pathological features of cerebral small vessel disease (CSVD). To date, limited evidence of a relationship between chronic insomnia and inflammatory responses in patients with CSVD has been uncovered. The purpose of the present study was to investigate the potential relationship between chronic insomnia and pro-inflammatory cytokine levels in patients with atherosclerotic CSVD (A-CSVD).Methods: In total, 76 A-CSVD patients with or without chronic insomnia (CI) confirmed using magnetic resonance (MR) were prospectively recruited. Overnight polysomnography (PSG) was performed and serum levels of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), IL-6, IL-17A, IL-8, and IL-12 assessed. Cytokine levels were compared between CSVD+CI (study group) and CSVD without CI (control group) patients, and the correlations between PSG parameters and cytokine levels were explored in all patients via multiple linear regression analyses.Results: The serum IL-8 level of the study group (12.3± 4.4 pg/mL) was significantly higher tha

Kanglai Li,1,* Jiong Tao,2,* Yuemei Li,3 Minhua Chen,2 Xiuhua Wu,2 Yingtao Liao,2 Xiaolan Lin,4 Zhaoyu Gan2 1Department of Very Important Patient, the 3rd Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China; 2Department of Psychiatry, the 3rd Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China; 3Department of Obstetrics, Wuzhou Gongren Hospital, Wuzhou, People’s Republic of China; 4Department of Infectious Diseases, the 3rd Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China *These authors contributed equally to this work Background: The aim of the study was to describe and compare the patterns of medication persistence among patients with unipolar depression (UD) or bipolar depression in a 5-year follow-up, and explore their impact on long-term outcome. Patients and methods: A total of 333 eligible patients with current major depressive episode were observed and followed up from the first index prescription for 5 years. Lack of persistence or treatment interruption was defined as a gap of at least 2 consecutive months without taking any medication. Time to lack of persistence in the first (TLP1) and the second (TLP2) episode of treatment, number of visits before the first treatment interruption (NV) and number of treatment interruptions (NTI) were measured. Results: During the 5-year follow-up, nearly 50% of patients experienced at least two times of treatment interruption. Pattern of medication persistence did not significantly differ between UD and bipolar disorder (BD) patients. TLP1 was positively associated with TLP2. Shorter TLP1 predicted a higher possibility of subsequent visits because of recurrence or relapse and more NTI meant a lower likelihood of achieving full remission in the fifth year for both UD and BD patients. For UD patients, shorter TLP1 or less NV predicted a lower chance of achieving remission, while for

There is great potential for host-based gene expression analysis to impact the early diagnosis of infectious diseases. In particular, the influenza pandemic of 2009 highlighted the challenges and limitations of traditional pathogen-based testing for suspected upper respiratory viral infection. We inoculated human volunteers with either influenza A (A/Brisbane/59/2007 (H1N1) or A/Wisconsin/67/2005 (H3N2)), and assayed the peripheral blood transcriptome every 8 hours for 7 days. Of 41 inoculated volunteers, 18 (44%) developed symptomatic infection. Using unbiased sparse latent factor regression analysis we generated a gene signature (or factor) for symptomatic influenza capable of detecting 94% of infected cases. This gene signature is detectable as early as 29 hours post-exposure and achieves maximal accuracy on average 43 hours (p = 0.003 H1N1) and 38 hours (p-value = 0.005, H3N2) before peak clinical symptoms. In order to test the relevance of these findings in naturally acquired disease, a composite influenza A signature built from these challenge studies was applied to Emergency Department patients where it discriminates between swine-origin influenza A/H1N1 (2009) infected and non-infected individuals with 92% accuracy. The host genomic response to Influenza infection is robust and may provide the means for detection before typical clinical symptoms are apparent., Pub. in PLoS One, v8 n1, p1-9, Jan 2013.

We consider the recovery of an underlying signal x \in C^m based on projection measurements of the form y=Mx+w, where y \in C^l and w is measurement noise; we are interested in the case l < m. It is assumed that the signal model p(x) is known, and w CN(w;0,S_w), for known S_W. The objective is to design a projection matrix M \in C^(l x m) to maximize key information-theoretic quantities with operational significance, including the mutual information between the signal and the projections I(x;y) or the Renyi entropy of the projections h_a(y) (Shannon entropy is a special case). By capitalizing on explicit characterizations of the gradients of the information measures with respect to the projections matrix, where we also partially extend the well-known results of Palomar and Verdu from the mutual information to the Renyi entropy domain, we unveil the key operations carried out by the optimal projections designs: mode exposure and mode alignment. Experiments are considered for the case of compressive sensing (CS) applied to imagery. In this context, we provide a demonstration of the performance improvement possible through the application of the novel projection designs in relation to conventional ones, as well as justification for a fast online projections design method with which state-of-the-art adaptive CS signal recovery is achieved., Comment: 25 pages, 7 figures, parts of material published in IEEE ICASSP 2012, submitted to SIIMS

We study the problem of supervised linear dimensionality reduction, taking an information-theoretic viewpoint. The linear projection matrix is designed by maximizing the mutual information between the projected signal and the class label (based on a Shannon entropy measure). By harnessing a recent theoretical result on the gradient of mutual information, the above optimization problem can be solved directly using gradient descent, without requiring simplification of the objective function. Theoretical analysis and empirical comparison are made between the proposed method and two closely related methods (Linear Discriminant Analysis and Information Discriminant Analysis), and comparisons are also made with a method in which Renyi entropy is used to define the mutual information (in this case the gradient may be computed simply, under a special parameter setting). Relative to these alternative approaches, the proposed method achieves promising results on real datasets., Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)

This paper addresses the problem of simultaneous signal recovery and dictionary learning based on compressive measurements. Multiple signals are analyzed jointly, with multiple sensing matrices, under the assumption that the unknown signals come from a union of a small number of disjoint subspaces. This problem is important, for instance, in image inpainting applications, in which the multiple signals are constituted by (incomplete) image patches taken from the overall image. This work extends standard dictionary learning and block-sparse dictionary optimization, by considering compressive measurements, e.g., incomplete data). Previous work on blind compressed sensing is also generalized by using multiple sensing matrices and relaxing some of the restrictions on the learned dictionary. Drawing on results developed in the context of matrix completion, it is proven that both the dictionary and signals can be recovered with high probability from compressed measurements. The solution is unique up to block permutations and invertible linear transformations of the dictionary atoms. The recovery is contingent on the number of measurements per signal and the number of signals being sufficiently large; bounds are derived for these quantities. In addition, this paper presents a computationally practical algorithm that performs dictionary learning and signal recovery, and establishes conditions for its convergence to a local optimum. Experimental results for image inpainting demonstrate the capabilities of the method.