c. y. wang

surgery (CRS) and neoadjuvant chemotherapy (NACT) are recommended for advanced ovarian cancer (aOC) treatment. This study aimed to investigate the therapy-induced genomic changes and immune microenvironment alteration in patients with aOC.

The microarray data of ovarian cancer samples from naïve or treated patients were collected from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) between samples were screened. Consensus clustering was conducted to explore the molecular subtypes of ovarian cancer samples. The correlation between tertiary lymphoid structure (TLS) signatures with the molecular subtypes was subject to Gene set variation analysis (GSVA). The prognostic signature of aOC was constructed using machine learning based on lasso-cox regression. Finally, immune infiltration analysis was performed for immune landscape evaluation in aOC.

Totally 28 DEGs were found between the control and treatment groups. Enrichment analysis indicated the association of these genes with the immune changes. Moreover, the cluster 1/2 (C1/C2) of ovarian cancer were identified, and the C1 subtype had higher enrichment of TLS-related biomarkers. Moreover, 15 genes were revealed as independent factors for the prediction of ovarian cancer prognosis. Immune infiltration levels were significantly higher in the C1 subtype, which indicated the distinct immune landscape between the two molecular subtypes of ovarian cancer.

The NACT and CRS induced genomic changes were related to immune response in aOC. The findings of our study might deepen our understanding of the TLS-related signature and immune pattern in aOC patients.

In this study, a dynamic mixer was designed to mix polymer melts online during extrusion, and the flow of a polymer melt in a mixer was simulated using Polyflow software. The Orthogonal experiment was conducted to analyze the effects of three geometrical parameters (i.e. the length of entrance zone (Li), the gap between the rotor and wall (g), and the diameter of cone-shaped rotor (d2)) on mixing properties of a dynamic mixer. The Li, g, and d2 were optimized for the minimum product of segregation scale (S) and power consumption (P). Finally, the mixing properties of the dynamic mixer were compared with those of SK and SX static mixers. The results indicated that among the above-mentioned three parameters, the g was the most important parameter influencing S, and S∙P. The minimum S∙P of 1059 µm·W was obtained when the Li was 16 mm, the g was 1 mm, and the d2 was 24 mm. The S decreased with the increase of the rotation speed from 120 to 360 r/min, and increased with the increase of the flow rate from 15 to 45 mL/min. However, the P increased with the increase of both the rotation speed and flow rate. The maximum shear rate of the melt in the dynamic mixer was observed in the mixing zone, which was mainly affected by the rotation speed rather than the flow rate. To achieve the S of the same size, the length of the dynamic mixer was the shortest, and that of the SK static mixer was the longest. Moreover, to acquire the S of the same size, the dynamic mixer required the largest P, the SX static mixer needed a smaller P, and the SK static mixer required the minimum P.

This paper further studies topological structures induced by L-fuzzifying approximation operators, where L denotes a completely distributive De Morgan algebra. Firstly, the Alexandrov topologies induced by L-fuzzy relations are investigated with respect to L-fuzzifying approximation operators. Especially, the relationships among those Alexandrov topologies are discussed. Secondly, pseudo-similarity sets of L-fuzzy relations are proposed based on the Alexandrov topologies induced by upper and lower L-fuzzifying approximation operators. Meanwhile, the properties of pseudo-similarity sets are discussed, where some examples are presented to show the differences between similarity set of fuzzy relations and pseudo-similarity set of L-fuzzy relations.