Objective To explore the method and feasibility of preparing MTX+VCR-loaded erythrocytes using healthy adult erythrocytes. Methods We used the modified hypotonic pre-swelling and isotonic resealing technique to prepare MTX+VCR-loaded erythrocytes. The loading parameters and releasing rate were evaluated with high penformance liquid chromatograph (HPLC) and auto-cytoanalysis. We observed the osmotic fragility of MTX+VCR-loaded erythrocytes conventionally. The change in innate immune activity of co-encapsulated erythrocytes was measured by counting the rosette rate of MTX+VCR-loaded erythrocytes adhering to S180. Results The concentration of MTX showed significant influence on the co-encapsulation efficiency (P0.05). Conclusions The preparation and biological characteristics of MTX+VCR-loaded erythrocytes were not different from those of mono-loaded erythrocytes, and erythrocytes were practically capable to be used as carriers for co-encapsulation of MTX and VCR.

Purpose: We used bioinformatics methods and Mendelian randomization (MR) analysis to investigate the hub genes involved in acute myeloid leukemia (AML) and their causal relationship with hemolysis, to explore a new direction for molecular biology research of AML. Methods: We first differentially analyzed peripheral blood samples from 62 healthy volunteers and 65 patients with AML from the Gene Expression Omnibus database to obtain differentially expressed genes (DEGs), and intersected them with genes sourced from weighted gene co-expression network analysis (WGCNA) and the GeneCards database to obtain target genes. Target genes were screened using protein–protein interaction (PPI) network analysis and ROC curves to identify genes associated with AML. Finally, we analyzed the correlation between genes and immune cells and the relationship between toll-like receptor 4 (TLR4) and AML using MR. Results: We compared peripheral blood expression profiles using an array of 62 healthy volunteers (GSE164191) and 65 patients with AML (GSE89565) (M0:25; M1:11; M2:10; M3:1; M4:7; M4 eo t [16;16] ou inv [16]:4; M5:6; M6:1) and obtained 7,339 DEGs (3,733 upregulated and 3,606 downregulated). We intersected these DEGs with 4,724 genes from WGCNA and 1,330 genes related to hemolysis that were identified in the GeneCards database to obtain 190 target genes. After further screening these genes using the PPI network, we identified TLR4, PTPRC, FCGR3B, STAT1, and APOE, which are closely associated with hemolysis in patients with AML. Finally, we found a causal relationship between TLR4 and AML occurrence using MR analysis (p < 0.05). Conclusion We constructed a WGCNA-based co-expression network and identified hemolysis-associated AML genes.

Purpose: We used bioinformatics methods and Mendelian randomization (MR) analysis to investigate the hub genes involved in acute myeloid leukemia (AML) and their causal relationship with hemolysis, to explore a new direction for molecular biology research of AML. Methods: We first differentially analyzed peripheral blood samples from 62 healthy volunteers and 65 patients with AML from the Gene Expression Omnibus database to obtain differentially expressed genes (DEGs), and intersected them with genes sourced from weighted gene co-expression network analysis (WGCNA) and the GeneCards database to obtain target genes. Target genes were screened using protein–protein interaction (PPI) network analysis and ROC curves to identify genes associated with AML. Finally, we analyzed the correlation between genes and immune cells and the relationship between toll-like receptor 4 (TLR4) and AML using MR. Results: We compared peripheral blood expression profiles using an array of 62 healthy volunteers (GSE164191) and 65 patients with AML (GSE89565) (M0:25; M1:11; M2:10; M3:1; M4:7; M4 eo t [16;16] ou inv [16]:4; M5:6; M6:1) and obtained 7,339 DEGs (3,733 upregulated and 3,606 downregulated). We intersected these DEGs with 4,724 genes from WGCNA and 1,330 genes related to hemolysis that were identified in the GeneCards database to obtain 190 target genes. After further screening these genes using the PPI network, we identified TLR4, PTPRC, FCGR3B, STAT1, and APOE, which are closely associated with hemolysis in patients with AML. Finally, we found a causal relationship between TLR4 and AML occurrence using MR analysis (p < 0.05). Conclusion We constructed a WGCNA-based co-expression network and identified hemolysis-associated AML genes.

Purpose: We used bioinformatics methods and Mendelian randomization (MR) analysis to investigate the hub genes involved in acute myeloid leukemia (AML) and their causal relationship with hemolysis, to explore a new direction for molecular biology research of AML. Methods: We first differentially analyzed peripheral blood samples from 62 healthy volunteers and 65 patients with AML from the Gene Expression Omnibus database to obtain differentially expressed genes (DEGs), and intersected them with genes sourced from weighted gene co-expression network analysis (WGCNA) and the GeneCards database to obtain target genes. Target genes were screened using protein–protein interaction (PPI) network analysis and ROC curves to identify genes associated with AML. Finally, we analyzed the correlation between genes and immune cells and the relationship between toll-like receptor 4 (TLR4) and AML using MR. Results: We compared peripheral blood expression profiles using an array of 62 healthy volunteers (GSE164191) and 65 patients with AML (GSE89565) (M0:25; M1:11; M2:10; M3:1; M4:7; M4 eo t [16;16] ou inv [16]:4; M5:6; M6:1) and obtained 7,339 DEGs (3,733 upregulated and 3,606 downregulated). We intersected these DEGs with 4,724 genes from WGCNA and 1,330 genes related to hemolysis that were identified in the GeneCards database to obtain 190 target genes. After further screening these genes using the PPI network, we identified TLR4, PTPRC, FCGR3B, STAT1, and APOE, which are closely associated with hemolysis in patients with AML. Finally, we found a causal relationship between TLR4 and AML occurrence using MR analysis (p < 0.05). Conclusion We constructed a WGCNA-based co-expression network and identified hemolysis-associated AML genes.

This study aimed at developing restraint standards for quality and safety of blood transfusion services for blood transfusion administration in clinical blood use, as well as such issues before, during and after transfusion. Such means as literature analysis, blood transfusion adverse event analysis, and system standard interpretation were used to study these issues found in clinical blood use at home and abroad, with key points identified according to the universality, high-prevalence rate, criticality and impact degree. A standard framework is established centering on transfusion service and key points as nodes. Hence a standard text comes into being, comprising four sections and 20 key points.

【Objective】 To optimize the existing spin-EB method and promote human induced pluripotent stem cells (hiPSCs) differentiate into megakaryocytes (MKs). 【Methods】 In this study, the initial inoculation amount of hiPSCs was increased from 3 500 cells/well to 8 000 cells/well, and the size of EB was increased. By observing the generation time of EB- hematopoietic cells during differentiation, and detecting the proliferation of CD34⁺ hematopoietic progenitor cells and CD41⁺ MKs in different stages, it was studied whether the optimized scheme could promote the differentiation of hiPSCs into hematopoietic progenitor cells(HPCs) and MKs. 【Results】 By increasing the initial inoculation amount of hiPSCs and the size of EB, the differentiation of hiPSCs into HPCs and MKs and the cell production efficiency can be promoted. 【Conclusion】 Our research describes an optimized and repeatable differentiation method, which can produce hematopoietic progenitor cells and mature MKs from hiPSCs in a relatively short time with higher yield. It is of great clinical significance and broad scientific research prospect to continuously optimize the culture scheme of hiPSCs differentiation to produce MKs and platelets in vitro, and to promote large-scale platelet generation in vitro in transfusion medicine.