EHR coding with hybrid attention and features propagation on disease knowledge graph.

And sentences associated with these attributes and relationships have been neglected. in this paper ►We propose an end-to-end model called Knowledge Graph Enhanced neural network (KGENet) to address the above shortcomings. specifically ►We first construct a disease knowledge graph that focuses on the multi-view disease attributes of ICD codes and the disease relationships between these codes. we also use a long sequence encoder to get EHR document representation. most importantly ►KGENet leverages multi-view disease attributes and structured disease relationships for knowledge enhancement through hybrid attention and graph propagation ►Respectively. furthermore ►The above processes can provide attribute-aware and relationship-augmented explainability for the model prediction results based on our disease knowledge graph. experiments conducted on the MIMIC-III benchmark dataset show that KGENet outperforms state-of-the-art models in both model effectiveness and explainability Electronic health record (EHR) coding assigns International Classification of Diseases (ICD) codes to each EHR document. These standard medical codes represent diagnoses or procedures and play a critical role in medical applications. However, EHR is a long medical text that is difficult to represent, the ICD code label space is large, and the labels have an extremely unbalanced distribution. These factors pose challenges to automatic EHR coding. Previous studies have not explored the disease attributes (e.g., symptoms, tests, medications) of ICD codes and the disease relationships (e.g., causes, risk factors, comorbidities) between them. In addition, the important roles of medical.

Stability Analysis of Anionic-CO2-Soluble Surfactant Di-(2-ethylhexyl) Sodium Sulfosuccinate-Assisted Oily Foam Based on Statistical Analysis of Bubble Dynamic Characteristics.

Currently, oily foam stability in CO2 injection for heavy oil recovery exhibits inadequacies that considerably constrain its extensive application. Some scholars have conducted research demonstrating that CO2-soluble surfactants can assist in inducing heavy oil to form oil-based foams (oily foam). In this study, stability tests for the oily foam were conducted at different surfactant concentrations using a visualized PVT cell. Oily foam stability was assessed by calculating the comprehensive foam index (S) and analyzing the bubble images. The research indicates that AOT can effectively reduce the interfacial tension between oil and gas. At a concentration of 0.1 wt % AOT, the interfacial tension can be effectively reduced from 1.75 to 1.14 mN/m. The concentration of 0.3 wt % AOT represents a turning point, with an S of 16 101.7 mL·min. Beyond this concentration, the increase in S becomes less pronounced. As the concentration of CO2-soluble surfactant is increased from 0.1 to 0.5 wt %, the average bubble radius decreases from 2.74 to 0.43 mm, while the number of bubbles per unit area increases from 5.56 to 81.1 per cm2. With an increasing concentration of the CO2-soluble surfactant, the system generates more and smaller gas bubbles within the oily foam, resulting in a slower bubble coalescence. The findings of this study are poised to play a pivotal role in enhancing heavy oil recovery efficiency.

Cancer-associated fibroblasts-derived FMO2 as a biomarker of macrophage infiltration and prognosis in epithelial ovarian cancer.

OBJECTIVE: Recent molecular profiling revealed that cancer-associated fibroblasts (CAFs) are essential for matrix remodeling and tumor progression. Our study aimed to investigate the role of flavin-containing monooxygenase 2 (FMO2) in epithelial ovarian cancer (EOC) as a novel CAF-derived prognostic biomarker. METHODS: Primary fibroblasts were isolated from EOC samples. Microdissection and single-cell RNA sequencing (scRNA-seq) datasets (including TCGA, GSE9891, GSE63885, GSE118828 and GSE178913) were retrieved to determine the expression profiles. Gene set enrichment analysis (GSEA) was used to explore the correlation between FMO2 and stromal activation as well as immune infiltration. The predictive value of FMO2 and combined macrophage infiltration level was verified in an independent EOC cohort (n = 113). RESULTS: We demonstrated that FMO2 was upregulated in tumor stroma and correlated with fibroblast activation. Besides, FMO2 had the predictive power for worse clinical outcome of EOC patients. In the mesenchymal subtype of EOC, the FMO2-defined signature revealed that FMO2 contributed to infiltration of tumor-infiltrating lymphocytes. Moreover, we confirmed the positive correlation between FMO2 and CD163+ cell infiltration level in EOC tissues, and showed that combination of FMO2 expression with CD163+ cell infiltration level in the tumor stroma could predict poor overall survival (HR = 3.63, 95% CI = 1.93-6.84, p = 0.0008). Additionally, FMO2 also predicted the prognosis of patients with ovarian cancer based on the expression of immune checkpoints (such as PD-L1 and PD1). CONCLUSION: Our results address the tumor-supporting role of FMO2 in EOC and its association with immune components, and it might be a prospective target for stroma-oriented therapies against EOC.

Emerging role of RNA sensors in tumor microenvironment and immunotherapy.

RNA sensors detect foreign and endogenous RNAs to protect the host by initiating innate and adaptive immune response. In tumor microenvironment (TME), activation of RNA sensors induces tumor-inhibitory cytotoxic T lymphocyte responses and inhibits the activity of immunosuppressive cells though stimulating type I IFN signaling pathway. These characteristics allow RNA sensors to be prospective targets in tumor immunotherapy. Therefore, a comprehensive understanding of the roles of RNA sensors in TME could provide new insight into the antitumor immunotherapy. Moreover, RNA sensors could be prominent triggering targets to synergize with immunotherapies. In this review, we highlight the diverse mechanisms of RNA sensors in cancer immunity and their emerging contributions in cancer immunotherapy, including monotherapy with RNA sensor agonists, as well as combination with chemotherapy, radiotherapy, immune checkpoint blockade or cancer vaccine.

Dysregulated tumor-associated macrophages in carcinogenesis, progression and targeted therapy of gynecological and breast cancers.

Gynecological and breast cancers are a group of heterogeneous malignant tumors. Although existing treatment strategies have ameliorated the clinical outcomes of patients, the overall survival rate of advanced diseases remains unsatisfactory. Increasing evidence has indicated that the development and prognosis of tumors are closely related to the tumor microenvironment (TME), which restricts the immune response and provokes malignant progression. Tumor-associated macrophages (TAMs) are the main component of TME and act as a key regulator in tumor metastasis, immunosuppression and therapeutic resistance. Several preclinical trials have studied potential drugs that target TAMs to achieve potent anticancer therapy. This review focuses on the various functions of TAMs and how they influence the carcinogenesis of gynecological and breast cancers through regulating cancer cell proliferation, tumor angiogenesis and tumor-related immunosuppression. Besides, we also discuss the potential application of disabling TAMs signaling as a part of cancer therapeutic strategies, as well as CAR macrophages, TAMs-based vaccines and TAMs nanobiotechnology. These research advances support that targeting TAMs combined with conventional therapy might be used as effective therapeutics for gynecological and breast cancers in the future.

Analogy model test by experimental simulation of the subsidence zone associated with the 3 September 2017 North Korean nuclear test.

The surveillance of nuclear tests relies on the seismic network and the interferometric synthetic aperture radar (InSAR) data. Using the surface displacements such as subsidence craters caused by a nuclear test is an effective tool to monitor and estimate source characteristics of nuclear tests. In this study, based on the scaling laws of the process of subsidence crater formation, we developed an explosive model test in a vacuum chamber using the yield and buried depth estimated by Wang et al. to simulate the surface subsidence zone and collapse the crater associated with the 3 September 2017 North Korean nuclear test, and compared with their research results. The explosive simulation apparatus independently developed by the Army Engineering University of PLA is also introduced. The simulated results indicate that the radii of the subsidence zone and collapse crater are ∼257 m and ∼154 m, respectively, which are close to the empirical formula and InSAR observations. The explosive model test in a vacuum chamber could help to characterize the surface displacements induced by nuclear tests and provide an effective method for nuclear monitoring and characterization around the world.