Elucidation of the anti-colon cancer mechanism of Phellinus baumii polyphenol by an integrative approach of network pharmacology and experimental verification.

Colon cancer, a prevalent malignant tumor affecting the digestive system, presents a substantial risk to human health due to its high occurrence and mortality rates. Phellinus baumii polyphenol (PBP), a natural product derived from traditional Chinese medicine, has gained widespread popularity due to its low toxicity and minimal side effects, compared to radiation and chemotherapy. This study used an integrated approach of network pharmacology and experimental verification to elucidate the anti-colon cancer effects of PBP and its potential mechanisms. In network pharmacology, the identification of relevant targets involved a comprehensive search across multiple databases using keywords such as "active components of PBP" and "colon cancer". Venn diagram analysis was subsequently performed to ascertain the shared targets. To identify the key active components and core targets, we constructed a network of "Disease-Drug-Pathways-Targets" and a protein-protein interaction (PPI) network among the targets using Cytoscape 3.9.1. Furthermore, molecular docking was carried out to predict the binding affinity and conformation between the main active compounds (davallialactone and citrinin) of PBP and the core targets (TP53, STAT3, CASP3, CTNNB1, PARP1, MYC). To validate our findings, in vitro experiments were conducted. We verified that PBP exerted an anti-colon cancer effect on human colon cancer HCT116 cells by significantly inhibiting cell proliferation, promoting apoptosis and arresting the cell cycle in S phase by using Cell Counting Kit-8 (CCK-8) and flow cytometry. Finally, we determined the key regulatory proteins related to apoptosis and the cell cycle by western blot analysis, and proposed the potential mechanism by which PBP exerts an anti-colon cancer effect by inducing the caspase-dependent mitochondrial-mediated intrinsic apoptotic pathway and arresting the cell cycle in S phase in HCT116 cells. These results suggest that PBP possesses substantial potential for the treatment of colon cancer and may serve as a viable alternative therapeutic strategy in colon cancer treatment.

Phellinus baumii Polyphenol: A Potential Therapeutic Candidate against Lung Cancer Cells.

Phellinus baumii, a fungus that grows on mulberry trees and is used in traditional Chinese medicine, exerts therapeutic effects against various diseases, including cancer. Polyphenols, generally considered to be antioxidants, have antitumor and proapoptotic effects. In this study, we identified the composition of Phellinus baumii polyphenol (PBP) and characterized its 17 chemical components by UPLC-ESI-QTOF-MS. Furthermore, to clarify the potential mechanism of PBP against Lung Cancer Cells, network pharmacology and experimental verification were combined. Molecular docking elucidated the binding conformation and mechanism of the primary active components (Osmundacetone and hispidin) to the core targets CASP3, PARP1 and TP53. In addition, potential molecular mechanisms of PBP predicted by network pharmacology analysis were validated in vitro. PBP significantly inhibited the human lung cancer A549 cells and showed typical apoptotic characteristics, without significant cytotoxicity to normal human embryonic kidney (HEK293) cells. Analysis using flow cytometry and western blot indicated that PBP caused apoptosis, cell cycle arrest, reactive oxygen species (ROS) accumulation, and mitochondrial membrane potential (MMP) depression in A549 cells to exercise its antitumor effects. These results reveal that PBP has great potential for use as an active ingredient for antitumor therapy.

A Survey on Deep Learning Event Extraction: Approaches and Applications.

Event extraction (EE) is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, EE based on deep learning technology has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This article fills the research gap by reviewing the state-of-the-art approaches, especially focusing on the general domain EE based on deep learning models. We introduce a new literature classification of current general domain EE research according to the task definition. Afterward, we summarize the paradigm and models of EE approaches, and then discuss each of them in detail. As an important aspect, we summarize the benchmarks that support tests of predictions and evaluation metrics. A comprehensive comparison among different approaches is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area.

Determination of ethyl carbamate in wine by matrix modification-assisted headspace single-drop microextraction and gas chromatography-mass spectrometry technique.

A novel method for the analysis of ethyl carbamate in wine has been developed by coupling matrix modification-assisted headspace single-drop microextraction and gas chromatography-mass spectrometry (GC-MS) techniques. The method was developed by optimizing the matrix modifier and extraction parameters. The calibration method was followed by quantifying the internal isotope standard. The results suggested that the method was linear in the concentration range of 2-1000 ng/mL (R2 = 0.9996). The method presents a detection limit of 1.5 ng/mL, and the quantification limit is 5 ng/mL. The accuracy ranged between 94.9 and 99.9%, and the precision of the method was less than 5%. The method was applied for the detection of wine samples, and the results exhibited no significant difference when compared to the solid phase extraction method.