A Novel Mechanism of Action of Histone Deacetylase Inhibitor Chidamide: Enhancing the Chemotaxis Function of Circulating PD-1(+) Cells From Patients With PTCL

BackgroundPeripheral T‐cell lymphomas (PTCLs) are a heterogeneous group of neoplasms characterized by a poor prognosis. Histone deacetylase (HDAC) inhibitors have emerged as novel therapeutic agents for PTCLs. In this study, we aimed to explore the immunomodulatory effect of the HDAC inhibitor chidamide on circulating PD-1(+) cells from patients with PTCL, as well as its correlation with treatment response.MethodsWe enrolled newly diagnosed patients with PTCLs treated with a combination of chidamide and chemotherapy. Gene expression profile analysis was performed on peripheral blood PD-1(+) cells, both at baseline and at the end of treatment. A list of differentially expressed genes (DEGs) was identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate the biological implications of the DEGs. A gene concept network was constructed to identify the key DEGs for further PCR verification.ResultsA total of 302 DEGs were identified in the complete remission (CR) group, including 162 upregulated and 140 downregulated genes. In contrast, only 12 DEGs were identified in the non-CR group. GO analysis revealed that these upregulated DEGs were mainly involved in chemokine activity, cell chemotaxis, and cellular response to interleukin-1 and interferon-γ. Furthermore, KEGG pathway analysis showed that these DEGs were enriched in cytokine-cytokine receptor interaction and chemokine signaling pathways. The innate immune signaling pathways, including the Toll-like and NOD-like receptor signaling pathways, were also influenced. The gene concept network revealed that the key upregulated genes belonged to the C-C chemokine family.ConclusionOur results showed that chidamide treatment notably enhanced the expression of genes associated with chemokine activity and chemotaxis function of circulating PD-1(+) cells. By recruiting immune cells and improving the innate immune function of PD-1(+) cells, chidamide may reshape the tumor microenvironment to an anti-tumor phenotype and synergize with checkpoint inhibitors.

Data-Driven Concept Network for Inspiring Designers’ Idea Generation

Big-data mining brings new challenges and opportunities for engineering design, such as customer-needs mining, sentiment analysis, knowledge discovery, etc. At the early phase of conceptual design, designers urgently need to synthesize their own internal knowledge and wide external knowledge to solve design problems. However, on the one hand, it is time-consuming and laborious for designers to manually browse massive volumes of web documents and scientific literature to acquire external knowledge. On the other hand, how to extract concepts and discover meaningful concept associations automatically and accurately from these textual data to inspire designers’ idea generation? To address the above problems, we propose a novel data-driven concept network based on machine learning to capture design concepts and meaningful concept combinations as useful knowledge by mining the web documents and literature, which is further exploited to inspire designers to generate creative ideas. Moreover, the proposed approach contains three key steps: concept vector representation based on machine learning, semantic distance quantification based on concept clustering, and possible concept combinations based on natural language processing technologies, which is expected to provide designers with inspirational stimuli to solve design problems. A demonstration of conceptual design for detecting the fault location in transmission lines has been taken to validate the practicability and effectiveness of this approach.

Mapping Cultural Schemas: From Theory to Method

A growing body of research in sociology uses the concept of cultural schemas to explain how culture influences beliefs and actions. However, this work often relies on belief or attitude measures gleaned from survey data as indicators of schemas, failing to measure the cognitive associations that constitute schemas. In this article, we propose a concept-association-based approach for collecting data about individuals’ schematic associations, and a corresponding method for modeling concept network representations of shared cultural schemas. We use this method to examine differences between liberal and conservative schemas of poverty in the United States, uncovering patterns of associations expected based on previous research. Examining the structure of schematic associations provides novel insights to long-standing empirical questions regarding partisan attitudes toward poverty. Our method yields a clearer picture of what poverty means for liberals and conservatives, revealing how different concepts related to poverty indeed mean fundamentally different things for these two groups. Finally, we show that differences in schema structure are predictive of individuals’ policy preferences.