Identification of biomarkers associated with ferroptosis in diabetic retinopathy based on WGCNA and machine learning.

Objective: Diabetic retinopathy (DR) is a chronic progressive eye disease that affects millions of diabetic patients worldwide, and ferroptosis may contribute to the underlying mechanisms of DR. The main objective of this work is to explore key genes associated with ferroptosis in DR and to determine their feasibility as diagnostic markers. Methods: WGCNA identify the most relevant signature modules in DR. Machine learning methods were used to de-screen the feature genes. ssGSEA calculated the scoring of immune cells in the DR versus control samples and compared the associations with the core genes by Spearman correlation. Results: We identified 2,897 differential genes in DR versus normal samples. WGCNA found tan module to have the highest correlation with DR patients. Finally, 20 intersecting genes were obtained from differential genes, tan module and iron death genes, which were screened by LASSO and SVM-RFE method, and together identified 6 genes as potential diagnostic markers. qPCR verified the expression and ROC curves confirmed the diagnostic accuracy of the 6 genes. In addition, our ssGSEA scoring identified these 6 core genes as closely associated with immune infiltrating cells. Conclusion: In conclusion, we analyzed for the first time the potential link of iron death in the pathogenesis of DR. This has important implications for future studies of iron death-mediated pro-inflammatory immune mechanisms.

[Metabolic fingerprint analysis of RAW264.7 inflammatory cell model by using UPLC-Q-TOF/MS].

In order to reveal the properties of polar metabolome in inflammatory cells, we selected LPS-induced RAW264.7 inflammatory cell models as the carrier for the research of metabolic fingerprint analysis. In this study, an ultra performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS)-based metabolomics protocol was optimized for the extraction of polar metabolites from RAW264.7 cell line. Then orthogonal partial least squares discriminant analysis (OPLS-DA) was used to process the metabolic data, and finally, a total of 17 metabolites were selected and identified. The results showed that MeOH-CHCl3-H2O (8∶1∶1) was chosen as the optimal extraction solvent to achieve higher number of chromatographic peaks, with the best relative extraction efficiency and stability. Comparing with the normal cells, the inflammatory cells presented an abnormal metabolism in protein, carbohydrate, nucleotide and phospholipids. In this study, a UPLC-Q-TOF/MS-based metabolomics protocol for the polar metabolites from RAW264.7 cell line was developed, which may provide important information for the study of mechanism of inflammation and the anti-inflammatory drugs.

[The creation of laosun yutie plaster and safety experiment].

OBJECTIVE: To create an externally-applied TCD plaster (Laosun Yutie plaster) for the lumbar muscle strain (LMS). METHOD: (1) The raw material such as angelica, safflower, frankincense, myrrh, clove and so on were turned into solid TCD plaster by a serial of modern treatment technology-distillation, extraction, condense, dehydration. Meanwhile, the naphtha from the distillation process and borneol were dissolved into ethanol solution and the dilution was thus made; (2) The solid TCD plaster and the dilution from the aboved-mentioned process were put together to form the TCD plaster and the corresponding safety experiments were applied to rabits' skin. RESULT: The experimental results showed that it is neither of urgent toxicity, stimulary function, nor of allergy-inducing response. CONCLUSION: The laosun Yutie plaster was a newly externally-applied TCD plaster which could be used safely and handily.