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In this paper, based on first-principles theoretical calculation and Vesta modeling, nitrogen atoms were doped in the Ir-C of Pnma phase and carbon atoms were doped in the Ir-N of Pnnm phase to obtain a new IR-C-N system.The structure optimization and static self-consistent cycle optimization of the doped structure were carried out. The elastic calculation of the optimized structure was carried out under the pressure of 0Gpa. Through the analysis of the calculation results, the compound has shown good brittleness under certain doping ratios.

Osteosarcoma (OS) is a malignant bone tumor of mesenchymal origin. Tripterygii Wilfordii (TW) is a traditional Chinese medicine widely used for its anti-inflammatory and immunomodulatory effects. Various components of TW have been shown to have antitumor effects, however, no systematic study has been conducted to prove the anti-OS effects of TW. This study aimed to investigate the effects of TW on OS and its mechanism based on network pharmacology and molecular docking. The web pharmacology section includes the gathering of the active components of TW, the collection of predicted targets of TW and OS-related targets, the analysis of therapeutic targets of TW, the enrichment of gene ontology (GO), and the enrichment of Kyoto Encyclopedia of Genes and Genomes (KEGG). The Veen diagram showed 451 targets for OS treatment in TW. The therapeutic target enrichment analysis results showed that TW treated OS via multiple targets and pathways. TW can affect OS proliferation, apoptosis, migration, infiltration, and angiogenesis through a signaling network formed by hub genes that cascade through numerous signaling pathways. In addition, molecular docking results showed that triptolide, kaempferol, and 5,8-Dihydroxy-7-(4-hydroxy-5-methyl-coumarin-3)-coumarin have relatively high potential to become drugs for patients with OS and improve the 5-year survival rate of patients with OS. Network pharmacology and molecular docking suggest that TW affects the biological behavior of OS through multiple pathways involving multiple targets, such as proliferation, apoptosis, migration, and infiltration. Upregulation of the cellular tumor antigen p53 (TP53) gene and downregulation of peroxisome proliferator-activated receptor gamma (PPARG) and signal transducer and activator of transcription 1-alpha/beta (STAT1) genes can prolong the survival time of patients with OS. Triptolide, kaempferol, and 5,8-Dihydroxy-7-(4-hydroxy-5 methyl-coumarin-3)-coumarin have a relatively high potential to become a treatment for patients with OS and improve 5-year survival of OS patients.

Osteosarcoma (OS) is a malignant bone tumor of mesenchymal origin. Angelica dahurica is a typical traditional Chinese herb. Angelica dahurica is used in the treatment of a variety of tumors. However, the studies of Angelica dahurica for OS have not been reported. To investigate Angelica dahurica's potential mechanism of action in the treatment of OS, we used network pharmacology and molecular docking methods in this study. Of which the network pharmacology includes the collection of active ingredients of Angelica dahurica, the collection of predicted targets of Angelica dahurica and predicted targets of OS, the analysis of therapeutic targets of Angelica dahurica, gene ontology (GO) enrichment, and Kyoto encyclopedia of genes and genomes (KEGG) enrichment. The Venn plot performance showed that there were 225 predicted targets of Angelica dahurica for the treatment of OS. The therapeutic targets enrichment analysis results showed that Angelica dahurica treated OS through multiple targets and pathways. Angelica dahurica could affect OS's proliferation, apoptosis, migration, infiltration, and angiogenesis through a signaling network formed by pivotal genes crosstalking numerous signaling pathways. In addition, molecular docking results showed that sen-byakangelicol, beta-sitosterol, and Prangenin, have a relatively high potential to become a treatment for patients with OS and improve 5-year survival in OS patients. We used network pharmacology and molecular docking methods to predict the active ingredients and significant targets of Angelica dahurica for the treatment of OS and, to a certain extent, elucidated the potential molecular mechanism of Angelica dahurica in the treatment of OS. This study provided a theoretical basis for Angelica dahurica in the treatment of OS.

BACKGROUND Leiomyosarcoma (LMS) is one of the most common soft tissue sarcomas. LMS is prone to distant metastasis (DM), and patients with DM have a poor prognosis. AIM In this study, we investigated the risk factors of DM in LMS patients and the prognostic factors of LMS patients with DM. METHODS AND RESULTS LMS patients diagnosed between 2010 and 2016 were extracted from the Surveillance, Epidemiology, and End Result (SEER) database. Patients were randomly divided into the training set and validation set. Univariate and multivariate logistic regression analyses were performed, and a nomogram was established. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram. Based on the nomogram, a web-based nomogram is established. The univariate and multivariate Cox regression analyses were used to assess the prognostic risk factors of LMS patients with DM. Eventually, 2184 patients diagnosed with LMS were enrolled, randomly divided into the training set (n = 1532, 70.14%) and validation set (n = 652, 29.86%). Race, primary site, grade, T stage, and tumor size were correlated with DM incidence in LMS patients. The AUC of the nomogram is 0.715 in training and 0.713 in the validation set. The calibration curve and DCA results showed that the nomogram performed well in predicting the DM risk. A web-based nomogram was established to predict DM's risk in LMS patients (https://wenn23.shinyapps.io/riskoflmsdm/). Epithelioid LMS, in uterus, older age, giant tumor, multiple organ metastasis, without surgery, and chemotherapy had a poor prognosis. CONCLUSIONS The established web-based nomogram (https://wenn23.shinyapps.io/riskoflmsdm/) is an accurate and personalized tool to predict the risks of LMS developing DM. Advanced age, larger tumor, multiple organ metastasis, epithelioid type, uterine LMS, no surgery, and no chemotherapy were associated with poor prognosis in LMS patients with DM.

Objective To identify risk factors and develop predictive web-based nomograms for the early death of patients with bone metastasis of lung adenocarcinoma (LUAD). Methods Patients in the Surveillance, Epidemiology, and End Results database diagnosed with bone metastasis of LUAD between 2010 and 2016 were included and randomly divided into training and validation sets. Early death-related risk factors (survival time ≤7 months) were evaluated by logistic regression. Two predictive nomograms were established and validated by calibration curves, receiver operating characteristic curves, and decision curve analysis. Results A total of 9189 patients (56.59%) died from all causes within 7 months of being diagnosed, including 8585 patients (56.67%) who died from cancer-specific causes. Age >65 years, sex (men), T stage (T3 and T4), N stage (N2 and N3), brain metastasis, and liver metastasis were risk factors for all-cause and cancer-specific early death. The area under the curves of the nomograms for all-cause and cancer-specific early death prediction were 0.754 and 0.753 (training set) and 0.747 and 0.754 (validation set), respectively. Further analysis showed that the two nomograms performed well. Conclusions Our two web-based nomograms for all-cause and cancer-specific early death provide valuable tools for predicting early death in these patients.

Background: Studies have shown that the RNA N6-methyladenosine (m6A) modification patterns are extensively involved in the development of multiple tumors. However, the association between the m6A regulator expression patterns and the sarcoma tumor immune microenvironment (TIME) remains unclear. Methods: We systematically evaluated the m6A regulator expression patterns in patients with sarcoma based on known 23 m6A regulators. Different m6A regulator expression patterns were analyzed using gene set variation analysis and a single-sample gene set enrichment analysis algorithm. According to the results of consensus clustering, we classified the patients into four different clusters. Next, we subjected the four clusters to differential genetic analysis and established m6A-related differentially expressed genes (DEGs). We then calculated the m6A-related DEGs score and constructed the m6A-related gene signature, named m6A score. Finally, the 259 sarcoma samples were divided into high- and low-m6A score groups. We further evaluated the TIME landscape between the high- and low-m6A score groups. Results: We identified four different m6A modification clusters and found that each cluster had unique metabolic and immunological characteristics. Based on the 19 prognosis-related DEGs, we calculated the principal component analysis scores for each patient with sarcoma and classified them into high- and low-m6A score groups. Conclusions: The m6A regulator expression patterns and complexity of the sarcoma TIME landscape are closely related to each other. Systematic evaluation of m6A regulator expression patterns and m6A scores in patients with sarcoma will enhance our understanding of TIME characteristics.

In the context of today’s informatization and big data, the traditional financial management model of power grid projects has fallen behind. The financial department is unable to calculate and control key indicators in the process of engineering projects, but can only calculate the results and solidify assets. In response to such problems, in order to advance the role of financial to important nodes in the process of projects, this paper proposes a method for analyzing and predicting business financial data based on LSTM neural network, which uses multi-source heterogeneous data at the current stage of the project to affect future costs Forecast with income, and the financial department calculates indicators based on the forecast results. When the indicators are poor, the project decision of the current stage can be adjusted to realize the closed-loop monitoring of the business process. The results show that the method is accurate and effective.

Traffic classification is of great significance to network management. With the wide application of encryption technology, traditional traffic classification methods are no longer applicable. In recent years, the machine learning-based traffic classification method has become more and more popular. However, this method not only needs complex feature engineering but also is difficult to adapt to different network environments. A traffic classification method based on text convolution neural network is proposed in this paper, which represents traffic data as vectors, then use text convolution neural networks to extract key features for traffic classification. This method is validated on ISCX VPN-non VPN dataset and achieves better classification performance than the previous traffic classification method. For class imbalance problem, a new loss function and an appropriate method of class weight allocation are used in multi-class classification task, which can effectively deal with class imbalance problem.