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Computational Drug Discovery in Diaphragm Dysfunction via Text Mining and Biomedical Database.
Hailiang, Bai; Xiafen, Bai; Xingxia, Hao; Jiake, Chai; Yunfei, Chi; Shaofang, Han; Chen, Chen; Yang, Chang; Hongjie, Duan.
Afiliación
  • Hailiang B; Chinese PLA Medical School (Chinese PLA General Hospital), Beijing 100853, China.
  • Xiafen B; Department of Burns and Plastic Surgery, The Fourth Medical Center of PLA General Hospital, Beijing 100048, China.
  • Xingxia H; Chinese PLA Medical School (Chinese PLA General Hospital), Beijing 100853, China.
  • Jiake C; Department of Special Medical Service, PLA strategic support force Medical Center, Beijing 100101, China.
  • Yunfei C; Chinese PLA Medical School (Chinese PLA General Hospital), Beijing 100853, China.
  • Shaofang H; Department of Burns and Plastic Surgery, The Fourth Medical Center of PLA General Hospital, Beijing 100048, China.
  • Chen C; The Inner Mongolia Medical University, Hohhot 010107, China.
  • Yang C; Department of Burns and Plastic Surgery, The Fourth Medical Center of PLA General Hospital, Beijing 100048, China.
  • Hongjie D; Department of Burns and Plastic Surgery, The Fourth Medical Center of PLA General Hospital, Beijing 100048, China.
J Burn Care Res ; 45(5): 1192-1206, 2024 Sep 06.
Article en En | MEDLINE | ID: mdl-38512012
ABSTRACT
The diaphragm, which is crucial for ventilation, is the primary muscle responsible for inspiration. Patients with severe burns who experience diaphragmatic dysfunction have an increased risk of mortality. Unfortunately, there are currently no effective medications available to prevent or treat this condition. The objective of our study is to utilize bioinformatics to identify potential genes and drugs associated with diaphragmatic dysfunction. In this study, text-mining techniques were utilized to identify genes associated with diaphragmatic dysfunction and recovery. Common genes were then analyzed using GO and KEGG pathway analysis, as well as protein-protein interaction network analysis. The obtained hub genes were processed using Cytoscape software, and their expression levels in diaphragmatic dysfunction were validated using quantitative real-time polymerase chain reaction (qRT-PCR) in severe burn rats. Genes that were confirmed were then examined in drug-gene interaction databases to identify potential drugs associated with these genes. Our analysis revealed 96 genes that were common to both the "diaphragm dysfunction" and "functional recovery" text mining concepts. Gene enrichment analysis identified 19 genes representing 10 pathways. qRT-PCR showed a significant increase in expression levels of 13 genes, including CCL2, CCL3, CD4, EGF, HGF, IFNG, IGF1, IL17A, IL6, LEP, PTGS2, TGFB1, and TNF, in samples with diaphragmatic dysfunction. Additionally, we found that a total of 56 drugs targeted 5 potential genes. These findings provide new insights into the development of more effective drugs for treating diaphragmatic dysfunction and also present substantial opportunities for researching new target pharmacology and promoting drugs in the pharmaceutical industry.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Diafragma / Descubrimiento de Drogas / Minería de Datos Límite: Animals Idioma: En Revista: J Burn Care Res Asunto de la revista: TRAUMATOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Diafragma / Descubrimiento de Drogas / Minería de Datos Límite: Animals Idioma: En Revista: J Burn Care Res Asunto de la revista: TRAUMATOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China