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The aim of this study was to analyze the research hotspots and mechanisms of luteolin in tumor-related fields using bibliometric and bioinformatic approaches to guide future research. We conducted a comprehensive screening of all articles on luteolin and tumors in Web of Science from 2008 to 2023. The extracted words from these publications were visualized using VOSviewer, Scimago Graphica, and CiteSpace. Public databases were used to collect luteolin and tumor-related targets. GO and KEGG analyses of luteolin antitumor-related genes were performed using Metascape. Protein interaction networks were built with Cytoscape and STRING, identifying hub targets of luteolin in antitumor activity. Subsequently, the binding capacity of luteolin to these hub targets was assessed using molecular docking technology. We found that China dominated this field, the Egyptian Knowledge Bank published the most articles, and Molecules had the highest number of related publications. Recently, network pharmacology, target, traditional Chinese medicine, and nanoparticles have become research hotspots in luteolin’s antitumor research. A total of 483 overlapping genes between luteolin and tumors were identified, and they were closely associated with the cancer-associated pathways, PI3K-Akt, and MAPK signaling pathways. Luteolin forms a complex network of antitumor-related genes, with TP53, TNF, STAT3, AKT1, JUN, IL6, and SRC playing key roles and showing strong binding affinity to luteolin. Computer technology is becoming increasingly integral to the discipline, and future research will focus on more precise antitumor mechanisms and effective clinical applications.

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The two-phase cooling of microelectronics is studied by adapting a mechanistic phase change model that can be used with commercial computational fluid dynamics and heat transfer (CFD–HT) codes. Silicon microdevices with variable density of pin fins and hotspots are simulated, and results are validated with in-house experimental data generated for the purpose of studying the two-phase flow regimes and their thermal/hydraulic implications. The CFD–HT modeling approach is found to constitute a valuable tool in the design and analysis of heterogeneous microfluidic cooling devices under two-phase operation.

Protein–nucleic acid interactions play a crucial role in maintaining cellular homeostasis. Quantitatively, these interactions are described in terms of the dissociation constant or free energy change observed during protein–nucleic acid complex formation. These interactions are impaired in the presence of mutations affecting the binding affinity of the complexes, in turn leading to numerous diseases. Therefore, understanding how binding affinity changes due to mutations in protein-nucleic acid complexes is vital. While experimental techniques are highly accurate, they are also time-consuming and labor-intensive. On the other hand, computational techniques are emerging as valuable alternatives, with numerous databases and computational tools to study protein–RNA complexes. In this chapter, we discuss various databases that provide information on binding affinities and their changes upon mutation in protein–RNA complexes. Additionally, tools available to extract different structural and interaction features from the complexes are given in detail. Furthermore, we offer insights into prediction methods reported to predict the change in binding affinity upon mutation of protein–RNA complexes. We also cover the existing methods for hotspot residue identification in these complexes.