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1.
Front Immunol ; 15: 1444923, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-39165358

RESUMO

Histone methylation can affect chromosome structure and binding to other proteins, depending on the type of amino acid being modified and the number of methyl groups added, this modification may promote transcription of genes (H3K4me2, H3K4me3, and H3K79me3) or reduce transcription of genes (H3K9me2, H3K9me3, H3K27me2, H3K27me3, and H4K20me3). In addition, advances in tumor immunotherapy have shown that histone methylation as a type of protein post-translational modification is also involved in the proliferation, activation and metabolic reprogramming of immune cells in the tumor microenvironment. These post-translational modifications of proteins play a crucial role in regulating immune escape from tumors and immunotherapy. Lysine methyltransferases are important components of the post-translational histone methylation modification pathway. Lysine methyltransferase 2C (KMT2C), also known as MLL3, is a member of the lysine methyltransferase family, which mediates the methylation modification of histone 3 lysine 4 (H3K4), participates in the methylation of many histone proteins, and regulates a number of signaling pathways such as EMT, p53, Myc, DNA damage repair and other pathways. Studies of KMT2C have found that it is aberrantly expressed in many diseases, mainly tumors and hematological disorders. It can also inhibit the onset and progression of these diseases. Therefore, KMT2C may serve as a promising target for tumor immunotherapy for certain diseases. Here, we provide an overview of the structure of KMT2C, disease mechanisms, and diseases associated with KMT2C, and discuss related challenges.


Assuntos
Neoplasias , Humanos , Neoplasias/imunologia , Neoplasias/terapia , Metilação , Processamento de Proteína Pós-Traducional , Animais , Histona-Lisina N-Metiltransferase/metabolismo , Histona-Lisina N-Metiltransferase/genética , Histonas/metabolismo , Proteínas de Ligação a DNA/metabolismo , Proteínas de Ligação a DNA/genética , Microambiente Tumoral/imunologia , Proteínas de Neoplasias/genética , Proteínas de Neoplasias/imunologia , Proteínas de Neoplasias/metabolismo , Regulação Neoplásica da Expressão Gênica
2.
Sci Rep ; 14(1): 485, 2024 Jan 04.
Artigo em Inglês | MEDLINE | ID: mdl-38177292

RESUMO

In order to improve the precision of goaf fire early warning outcomes, this paper obtains the temperature characteristic index of goaf fire early warning by using a coal spontaneous combustion thermogravimetric test and a coal spontaneous combustion programmed heating test. The major gas index and auxiliary gas index for early warning are derived using the integration of the Graham coefficient and grey correlation approach. The D-S evidence theory, which involves optimizing weight allocation, is utilized to integrate the early warning temperature index and various gas indexes. Based on the fusion results, a coal mine goaf fire early warning index system is developed through multi-parameter fusion. The early warning index system is then validated through a programmed heating experiment. The results show that the process of coal spontaneous combustion can be categorized into six distinct stages: latent stage, oxidation stage, critical stage, pyrolysis stage, fission stage, and combustion stage. These stages are determined by the characteristic temperatures of coal spontaneous combustion, which are 31.7 °C, 54.8 °C, 153.7 °C, 204.5 °C, and 241.6 °C. The major gas index for early warning of goaf fires can be determined by 100∆(CO)/∆O2(%). Additionally, auxiliary gas indexes such as C3H8/CH4, C3H8/C2H6, C2H4/C2H6, and C2H2 can be used for goaf fire early warning. The programmed heating experiment shows that the early warning system software is designed by the multi-parameter fusion goaf fire early warning index system is accurate and effective. The selection of the goaf fire early warning index is more rational and precise when using the multi-parameter fusion goaf fire early warning index system based on the D-S evidence theory of weight allocation. It offers robust support for enhancing the goaf fire early warning index system and predicting coal mine goaf fires.

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