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Background Data mining of The Cancer Genome Atlas (TCGA) data has significantly facilitated cancer genome research and provided unprecedented opportunities for cancer researchers. However, existing web applications for DNA methylation analysis does not adequately address the need of experimental biologists, and many additional functions are often required. Results To facilitate DNA methylation analysis, we present the SMART (Shiny Methylation Analysis Resource Tool) App, a user-friendly and easy-to-use web application for comprehensively analyzing the DNA methylation data of TCGA project. The SMART App integrates multi-omics and clinical data with DNA methylation and provides key interactive and customized functions including CpG visualization, pan-cancer methylation profile, differential methylation analysis, correlation analysis and survival analysis for users to analyze the DNA methylation in diverse cancer types in a multi-dimensional manner. Conclusion The SMART App serves as a new approach for users, especially wet-bench scientists with no programming background, to analyze the scientific big data and facilitate data mining. The SMART App is available at http://www.bioinfo-zs.com/smartapp .

The development of students’ core disciplinary competencies is crucial to the quality of talent cultivation against the background of China’s New Engineering Education initiative. We condensed a literacy–ability–knowledge model of the core competence structure of university students based on the results of a survey and then applied this model with a special focus on competence structures. We coded the core competency indicators of the course area using a software engineering course as an example to construct a map of the core competencies of the course (area). New teaching and instruction methods to promote the development of students’ core competencies were then explored. Finally, to shape core subject competence, we suggest building a course recommendation system that aligns with an employment guidance service based on the certification of core subject competence to realize a new mode of cultivating competence.

Lung squamous cell carcinoma (LUSC) is associated with poor clinical prognosis and lacks available targeted therapy. Novel molecules are urgently required for the diagnosis and prognosis of LUSC. Here, we conducted our data mining analysis for LUSC by integrating the differentially expressed genes acquired from Gene Expression Omnibus (GEO) database by comparing tumor tissues versus normal tissues (GSE8569, GSE21933, GSE33479, GSE33532, GSE40275, GSE62113, GSE74706) into The Cancer Genome Atlas (TCGA) database which includes 502 tumors and 49 adjacent non-tumor lung tissues. We identified intersections of 129 genes (91 up-regulated and 38 down-regulated) between GEO data and TCGA data. Based on these genes, we conducted our downstream analysis including functional enrichment analysis, protein-protein interaction, competing endogenous RNA (ceRNA) network and survival analysis. This study may provide more insight into the transcriptomic and functional features of LUSC through integrative analysis of GEO and TCGA data and suggests therapeutic targets and biomarkers for LUSC.

The complexity of the tumor microenvironment (TME) is a crucial factor in lung adenocarcinoma (LUAD) progression. To gain deeper insights into molecular mechanisms of LUAD, we perform an integrative single-cell RNA sequencing (scRNA-seq) data analysis of 377,574 cells from 117 LUAD patient samples. By linking scRNA-seq data with bulk gene expression data, we identify a cluster of prognostic-related UPP1 high tumor cells. These cells, primarily situated at the invasive front of tumors, display a stronger association with the immunosuppressive components in the TME. Our cytokine array analysis reveals that the upregulation of UPP1 in tumor cells leads to the increased release of various immunosuppressive cytokines, with TGF-β1 being particularly prominent. Furthermore, this UPP1 upregulation also elevates the expression of PD-L1 through the PI3K/AKT/mTOR pathway, which contributes to the suppression of CD8 + T cells. Cytometry by time-of-flight (CyTOF) analysis provides additional evidence of the role of UPP1 in shaping the immunosuppressive nature of the TME. Using patient-derived organoids (PDOs), we discover that UPP1 high tumors exhibit relatively increased sensitivity to Bosutinib and Dasatinib. Collectively, our study highlights the immunosuppressive role of UPP1 in LUAD, and these findings may provide insights into the molecular features of LUAD and facilitate the development of personalized treatment strategies. Characterising the tumour microenvironment features of lung adenocarcinoma (LUAD) remains crucial. Here, the authors perform single cell RNA sequencing data analysis of 117 LUAD samples and functional assays and highlight the immunosuppressive role of UPP1high tumour cells.

Background Predicting lung adenocarcinoma (LUAD) risk is crucial in determining further treatment strategies. Molecular biomarkers may improve risk stratification for LUAD. Methods We analyzed the gene expression profiles of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). We initially used three distinct algorithms (sigFeature, random forest, and univariate Cox regression) to evaluate each gene’s prognostic relevance. Survival related genes were then fitted into the least absolute shrinkage and selection operator (LASSO) model to build a risk prediction model for LUAD. After 100,000 times of calculation and model construction, a 16-gene-based prediction model capable of classifying LUAD patients into high-risk and low-risk groups was successfully built. Results Using a combined strategy, we initially identified 2472 significant survival-related genes. Functional enrichment analysis demonstrated these genes’ relevance to tumor initiation and progression. Using the LASSO method, we successfully built a reliable risk prediction model. The risk model was validated in two external sets and an independent set. The expression of these 16 genes was highly correlated with patients’ risk. High-risk group patients witnessed poorer recurrence-free survival (RFS) and overall survival (OS) compared to low-risk group patients. Moreover, stratification analysis and decision curve analysis (DCA) confirmed the independence and potential translational value of this predictive tool. We also built a nomogram comprising risk model and stage to predict OS for LUAD patients. Conclusions Our risk model may serve as a practical and reliable prognosis predictive tool for LUAD and could provide novel insights into the understanding of the molecular mechanism of this disease.

This study aimed to summarize the distribution pattern of traditional Chinese medicine (TCM) syndromes in patients with type 2 diabetes mellitus (T2DM). The frequency, characteristics and distribution of all TCM syndromes of 549 patients with T2DM were analyzed. The average age of T2DM onset was higher in women than in men (ie, men experienced earlier onset). The distribution of TCM syndromes, in order of frequency, was as follows: damp-heat trapping spleen (including spleen deficiency and dampness, damp heat due to spleen deficiency, and qi weakness due to spleen deficiency) (58.29%), qi-yin deficiency (16.03%), deficiency of yin and excessive heat (12.93%), blood stasis in collaterals (9.41%), and yin-yang deficiency (3.21%). The physical intensity of patients' occupational activity was mainly light (49.6%), followed by heavy (31.4%) and moderate (19.0%). Damp-heat trapping spleen is the most common TCM syndrome in patients with T2DM, with damp heat due to spleen deficiency the most significant subtype. This syndrome tends to occur in people over the age of 60 and those undertaking too much or too little physical activity in their occupational activities. The traditional \"three more and one less\" symptoms do not adequately describe the clinical symptoms of T2DM.

In our country, the Yellow River Basin ecological protection and development are put forward under the background of high quality. Among the 14 large-scale coal bases in our country, 9 coal bases are located in the Yellow River Basin, and the Shenfu-Dongsheng Coalfield, which is currently the largest under development, is located here. The region is in the process of coal mining, and the movement of overlying strata will cause the stress redistribution and coal seam in overlying aquifers also due to the effect of pore water pressure along the seepage of rock fracture and damage of overlying aquifer, so in the same formation, stress and the coupled action of seepage flow will produce mutual influence. This article through the early stage of the theoretical results discussed the application of numerical simulation method for simulating 2301 face, and the effect of stress on seepage is concluded. It is proved that the numerical simulation analysis has an important reference value for the coupling problem of stress and seepage. At the same time, the protective mining of aquifers is the basic condition for the surface ecological protection of the area, and it also provides a theoretical basis for the restoration of the ecological environment in the coal mining areas of the Yellow River Basin.

Robust, real-time event detection from physiological signals acquired during long-term ambulatory monitoring still represents a major challenge for highly-artifacted signals. In this paper, we propose an original and generic multi-feature probabilistic detector (MFPD) and apply it to real-time QRS complex detection under noisy conditions. The MFPD method calculates a binary Bayesian probability for each derived feature and makes a centralized fusion, using the Kullback-Leibler divergence. The method is evaluated on two ECG databases: 1) the MIT-BIH arrhythmia database from Physionet containing clean ECG signals, 2) a benchmark noisy database created by adding noise recordings of the MIT-BIH noise stress test database, also from Physionet, to the MIT-BIH arrhythmia database. Results are compared with a well-known wavelet-based detector, and two recently published detectors: one based on spatiotemporal characteristic of the QRS complex and the second, as the MFDP, based on feature calculations from the University of New South Wales detector (UNSW). For both benchmark Physionet databases, the proposed MFPD method achieves the lowest standard deviation in sensitivity and positive predictivity (+P) despite its online algorithm architecture. While the statistics are comparable for low-to mildly artifactual ECG signals, the MFPD outperforms reference methods for artifacted ECG with low SNR levels reaching 87.48 ± 14.21% in sensitivity and 89.39 ± 14.67% in +P as compared to 88.30 ± 17.66% and 86.06 ± 19.67% respectively from UNSW, the best performing reference method. With demonstrations on the extensively studied QRS detection problem, we consider that the proposed generic structure of the multi-feature probabilistic detector should offer promising perspectives for long-term monitoring applications for highly-artifacted signals.

Histone deacetylases (HDACs) have been proved to be promising targets for the treatment of cancer, and five histone deacetylase inhibitors (HDACis) have been approved on the market for the treatment of different lymphomas. In our previous work, we designed a series of novel coumarin-containing hydroxamate HDACis, among which compounds 6 and 7 displayed promising activities against tumor growth. Based on a molecular docking study, we further developed 26 additional analogues with the aim to improve activity of designed compounds. Several of these new derivatives not only showed excellent HDAC1 inhibitory effects, but also displayed significant growth inhibitory activities against four human cancer cell lines. Representative compounds, 13a and 13c, showed potent anti-proliferative activities against solid tumor cell lines with IC50 values of 0.36–2.91 µM and low cytotoxicity against Beas-2B and L-02 normal cells. Immunoblot analysis revealed that 13a and 13c dose-dependently increased the acetylation of histone H3 and H4. Importantly, the two compounds displayed much better anti-metastatic effects than SAHA against the MDA-MB-231 cell line. Moreover, 13a and 13c arrested MDA-MB-231 cells at G2/M phase and induced MDA-MB-231 cell apoptosis. Finally, the molecular docking study rationalized the high potency of compound 13c.

Rock burst is a common tunnel and mine dynamic disaster, especially for deep buried tunnels, which often leads to tunnel construction delay and even induces tunnel collapse and subsidence of strata. Rock drilling is one of the effective pressure relief methods to prevent these disasters. In order to study the influence of borehole diameter on rock mass pressure relief effect, indoor acoustic emission characteristics and numerical simulation of rock samples with different borehole diameter were studied. The research result shows that with the increase in borehole diameter, the effect of borehole pressure relief is better. Different borehole diameters do not change the overall trend of acoustic emission evolution, but it will lead to different acoustic emission count characteristics of rock damage and failure, especially the maximum acoustic emission count characteristics and corresponding strain values. The existence of drilling will lead to the failure stress of rock in advance. Moreover, the existence of drilling causes a great change in the failure mode of the specimen.