A schematic diagram of the synthesis of PTh/MoS2 and PThM/MoS2 and their use in the inactivation of E. coli.

No abstract

Objective: According to a growing body of research, long non-coding RNAs (lncRNAs) participate in the progress of hepatocellular carcinoma (HCC). Cuproptosis is a distinct kind of programmed cell death, separating it from several other forms of programmed cell death that may be caused by genetic programming. Consequently, our aim was to investigate the relationship between Differentially Expressed Cuproptosis-Related lncRNAs (DECRLs) and clinical outcome and immune characteristics of HCC. Method: The Cancer Genome Atlas (TCGA) database was used to retrieve related data. The GSE101728 dataset was downloaded from the Gene Expression Omnibus (GEO) database. A list of cuproptosis-related genes (CRGs) was obtained from a recently published article in Science. Combined analysis of TCGA dataset and the GSE101728 dataset identified differentially expressed CRGs(DECRGs).We can obtain DECRLs via co-expression. Then, using DECRLs, we developed a risk prediction model using Cox regression analysis and the least absolute shrinkage selection operator (LASSO) regression analysis. To evaluate the diagnostic accuracy of this model, a Kaplan-Meier (K-M) survival analysis and a receiver operating characteristic (ROC) curve analysis were used. Next, principal component analysis (PCA) was carried out.Moreover, the relationships between the risk model and immune characteristics, somatic mutation, and drug sensitivity were also investigated. Finally Real-Time quantitative PCR(RT-qPCR) and Western Blot confirmed the expression of DECRGs or DECRLs in HCC. Results: Three high-risk DECRLs(AL031985.3,AC107959.3,MKLN1-AS) that can guide HCC prognosis and immune microenvironment were obtained through cox regression analysis.Immune functions such as APC co-inhibition,Type-II-IFN-Reponse,Parainflammation,MHC-class-I, and Tumor Immune Dysfunction and Exclusion(TIDE) score, and Tumor Mutation Burden(TMB) were significantly different in high-risk and low-risk groups.Moreover, this research also found that the IC50 values for 87 chemotherapeutic drugs varied widely across patients within high and low-risk groups.The expression of GLS at both mRNA and protein levels was significantly raised in HCC,and that of CDKN2A was dropped in HCC. The mRNA expression level of AL031985.3,AC107959.3 and MKLN1-AS was upregulated in HCC. Conclusion: The proposed 3-DECRLs that can predict clinical prognosis or guide the immune characteristics and drugs that may have a potential curative effect on HCC received in our research may play a major role in patient management and immunotherapy.

Virtual surgery is a typical application of virtual reality technology in the medical field, which can help improve the success rate of surgery and reduce medical costs in various aspects such as medical training, surgery planning, and intraoperative navigation, and is becoming a hot research topic and a frontier subject in the medical field. The establishment of virtual surgery simulation system involves the intersection and penetration of various disciplines, and the research is difficult, and many functional modules are still not perfect. This study focuses on the key technologies in the virtual surgery simulation system, focusing on two core modules, the soft tissue modeling method and the collision detection algorithm, to improve the accuracy of the soft tissue model deformation under the condition of meeting the real-time system. The biomechanical properties of soft tissues are studied, the viscoelastic properties are analyzed, and the viscoelastic theory is used as the basis for soft tissue modeling; the geometric model is established by using a complementary method of surface model and tetrahedral mesh cells, with the surface model covering the outer surface for visual rendering and the tetrahedral mesh cells for skeleton support of the physical model, so that the model has a better visual effect and deformation effect, which enhances the model fidelity that is enhanced by the better visual effect and deformation effect; the soft tissue physical modeling method is summarized and summarized to lay the foundation for soft tissue model optimization. The experimental results show that the bilateral teleoperation system under analog control can give the operator a better haptic sensation (smaller value of input impedance felt by the operator when doing free motion from the robot) while ensuring good positional tracking and force tracking effects. This method solves the problems of collapse distortion and lack of viscoelastic properties of the traditional mass-spring model, and improves the accuracy of model deformation. Based on the improved algorithm, the viscoelastic hybrid filled sphere model of the liver organ is successfully established, which proves that the modeling method is feasible and effective.