The bioinformatics analysis of quercetin in octagonal lotus for the screening of breast cancer MYC, CXCL10, CXCL11, and E2F1.

BACKGROUND: Comprehensive bioinformatics analysis of the effective molecular screening of Podophyllum octagonal in breast cancer treatment by using network pharmacology. METHODS: We collected the active ingredients and target genes of Chinese medicine octagonal lotus through the Traditional Chinese Medicine System Pharmacology Analysis Platform (TCMSP); downloaded human protein annotation information on the protein database Uniport; and collected data from five databases: GeneCards, OMIM, PharmGkb, TDD, and DrugBank. Construct the practical ingredient-target gene data intersection to obtain the target gene-disease gene and draw the Venn diagram. We use Cytoscape 3.8.0 software to construct the effective component-target gene-disease gene network. The STRING database protein interaction (PPI) networks were erected, and we used Cytoscape 3.8.0 software to screen out its core sub-networks and hub gene networks. Through survival analysis, core genes and hub genes were screened to identify several key genes. We performed key target gene ontology (GO) analysis and gene interaction (KEGG) analysis, which were followed by molecular docking of the key active ingredients in the star anise corresponding to several key genes. RESULTS: 19 active ingredients, 444 drug targets, and 10,941 disease-related genes were obtained. The key active ingredient was quercetin. GO analysis revealed 2471 affected biological processes, and 167 pathways were obtained in KEGG enrichment analysis. CONCLUSION: This study initially screened the key active ingredients of star aniseed lotus and analyzed key genes and several essential pathways. Traditional Chinese medicine is expected to provide new evidence and research ideas to prevent and treat breast cancer.

Reflections on the Analysis of Interfaces and Grain Boundaries by Atom Probe Tomography.

Interfaces play critical roles in materials and are usually both structurally and compositionally complex microstructural features. The precise characterization of their nature in three-dimensions at the atomic scale is one of the grand challenges for microscopy and microanalysis, as this information is crucial to establish structure-property relationships. Atom probe tomography is well suited to analyzing the chemistry of interfaces at the nanoscale. However, optimizing such microanalysis of interfaces requires great care in the implementation across all aspects of the technique from specimen preparation to data analysis and ultimately the interpretation of this information. This article provides critical perspectives on key aspects pertaining to spatial resolution limits and the issues with the compositional analysis that can limit the quantification of interface measurements. Here, we use the example of grain boundaries in steels; however, the results are applicable for the characterization of grain boundaries and transformation interfaces in a very wide range of industrially relevant engineering materials.

3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning methods.

Boosting is a family of supervised learning algorithm that convert a set of weak learners into a single strong one. It is popular in the field of object tracking, where its main purpose is to extract the position, motion, and trajectory from various features of interest within a sequence of video frames. A scientific application explored in this study is to combine the boosting tracker and the Hough transformation, followed by principal component analysis, to extract the location and trace of grain boundaries within atom probe data. Before the implementation of this method, these information could only be extracted manually, which is time-consuming and error-prone. The effectiveness of this method is demonstrated on an experimental dataset obtained from a pure aluminum bi-crystal and validated on simulated data. The information gained from this method can be combined with crystallographic information directly contained within the data, to fully define the grain boundary character to its 5 degrees of freedom at near-atomic resolution in three dimensions. It also enables local atomic compositional and geometric information, i.e. curvature, to be extracted directly at the interface.

An Automated Computational Approach for Complete In-Plane Compositional Interface Analysis by Atom Probe Tomography.

We introduce an efficient, automated computational approach for analyzing interfaces within atom probe tomography datasets, enabling quantitative mapping of their thickness, composition, as well as the Gibbsian interfacial excess of each solute. Detailed evaluation of an experimental dataset indicates that compared with the composition map, the interfacial excess map is more robust and exhibits a relatively higher resolution to reveal compositional variations. By field evaporation simulations with a predefined emitter mimicking the experimental dataset, the impact of trajectory aberrations on the measurement of the thickness, composition, and interfacial excess of the decorated interface are systematically analyzed and discussed.

Unraveling the Metastability of C n2+ ( n = 2-4) Clusters.

Pure carbon clusters have received considerable attention for a long time. However, fundamental questions, such as what the smallest stable carbon cluster dication is, remain unclear. We investigated the stability and fragmentation behavior of C n2+ ( n = 2-4) dications using state-of-the-art atom probe tomography. These small doubly charged carbon cluster ions were produced by laser-pulsed field evaporation from a tungsten carbide field emitter. Correlation analysis of the fragments detected in coincidence reveals that they only decay to C n-1+ + C+. During C22+ → C+ + C+, significant kinetic energy release (∼5.75-7.8 eV) is evidenced. Through advanced experimental data processing combined with ab initio calculations and simulations, we show that the field-evaporated diatomic 12C22+ dications are either in weakly bound 3Πu and 3Σg- states, quickly dissociating under the intense electric field, or in a deeply bound electronic 5Σu- state with lifetimes >180 ps.

On the detection of multiple events in atom probe tomography.

In atom probe tomography (APT), multiple events can arise as a consequence of e.g. correlated field evaporation and molecular ion dissociation. They represent challenging cases for single-particle detectors and can cause compositional as well as spatial inaccuracies. Here, two state-of-the-art atom probe microscopes (Cameca LEAP 5000 XS and 5000 XR) were used to investigate cemented tungsten carbide, which exhibits high amounts of multiple events. By advanced data analysis methods, the natural character of the multiple events, as well as the performance of the APT detectors, are assessed. Accordingly, possible signal loss mechanisms are discussed.

Evaluation of Analysis Conditions for Laser-Pulsed Atom Probe Tomography: Example of Cemented Tungsten Carbide.

Cemented tungsten carbide has been analyzed using laser-pulsed atom probe tomography (APT). The influence of experimental parameters, including laser pulse energy, pulse repetition rate, and specimen base temperature, on the acquired data were evaluated from different aspects, such as mass spectrum, chemical composition, noise-to-signal ratio, and multiple events. Within all the applied analysis conditions, only 1 MHz pulse repetition rate led to a strong detector saturation effect, resulting in a largely biased chemical composition. A comparative study of the laser energy settings showed that an ~12 times higher energy was required for the less focused green laser of the LEAPTM 3000X HR system to achieve a similar evaporation field as the finer spot ultraviolet laser of the LEAPTM 5000 XS system.