ABSTRACT
BACKGROUND: Ischemic stroke (IS) is a fearful disease that can cause a variety of immune events. Nevertheless, precise immune-related mechanisms have yet to be systematically elucidated. This study aimed to identify immune-related signatures using machine learning and to validate them with animal experiments and single cell analysis. METHODS: In this study, we screened 24 differentially expressed genes (DEGs) while identifying immune-related signatures that may play a key role in IS development through a comprehensive strategy between least absolute shrinkage and selection operation (LASSO) regression, support vector machine (SVM) and immune-related genes. In addition, we explored immune infiltration using the CIBERSORT algorithm. Finally, we performed validation in mouse brain tissue and single cell analysis. RESULTS: We identified 24 DEGs for follow-up analysis. ID3 and SLC22A4 were finally identified as the better immune-related signatures through a comprehensive strategy among DEGs, LASSO, SVM and immune-related genes. RT-qPCR, western blot, and immunofluorescence revealed a significant decrease in ID3 and a significant increase in SLC22A4 in the middle cerebral artery occlusion group. Single cell analysis revealed that ID3 was mainly concentrated in endothelial_2 cells and SLC22A4 in astrocytes in the MCAO group. A CIBERSORT finds significantly altered levels of immune infiltration in IS patients. CONCLUSIONS: This study focused on immune-related signatures after stroke and ID3 and SLC22A4 may be new therapeutic targets to promote functional recovery after stroke. Furthermore, the association of ID3 and SLC22A4 with immune cells may be a new direction for post-stroke immunotherapy.
Subject(s)
Inhibitor of Differentiation Proteins , Ischemic Stroke , Organic Cation Transport Proteins , Stroke , Symporters , Animals , Humans , Mice , Algorithms , Astrocytes , Blotting, Western , Inhibitor of Differentiation Proteins/immunology , Inhibitor of Differentiation Proteins/metabolism , Ischemic Stroke/genetics , Neoplasm Proteins , Organic Cation Transport Proteins/immunology , Organic Cation Transport Proteins/metabolism , Stroke/immunology , Stroke/metabolism , Symporters/immunology , Symporters/metabolismABSTRACT
In recent years, traceability systems have been developed as effective tools for improving the transparency of supply chains, thereby guaranteeing the quality and safety of food products. In this study, we proposed a cattle/beef supply chain traceability model and a traceability system based on radio frequency identification (RFID) technology and the EPCglobal network. First of all, the transformations of traceability units were defined and analyzed throughout the cattle/beef chain. Secondly, we described the internal and external traceability information acquisition, transformation, and transmission processes throughout the beef supply chain in detail, and explained a methodology for modeling traceability information using the electronic product code information service (EPCIS) framework. Then, the traceability system was implemented based on Fosstrak and FreePastry software packages, and animal ear tag code and electronic product code (EPC) were employed to identify traceability units. Finally, a cattle/beef supply chain included breeding business, slaughter and processing business, distribution business and sales outlet was used as a case study to evaluate the beef supply chain traceability system. The results demonstrated that the major advantages of the traceability system are the effective sharing of information among business and the gapless traceability of the cattle/beef supply chain.
Subject(s)
Food Supply , Meat Products , Models, Theoretical , Radio Frequency Identification Device , Animal Husbandry , Animals , Cattle , ChinaABSTRACT
A method, which we named surface plasmon interferometric microscopy, for real-time displaying of the dynamic evolution of the refractive index (RI) of a sample in three-dimensions is demonstrated experimentally. The Fourier fringe analysis technique is employed to get the phase variations of the samples by demodulating the interference patterns captured by a CCD camera, and the 3D RI distribution can be obtained through numerical interpolation from the relation between the phase and the RI of the samples. Our method may provide an interesting way to monitor fast dynamics of physical, biological, and chemical processes in real time.
