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Haemorrhage and coagulation disorders are common complications in cirrhotic patients, which cause blood products transfusion, and mounting evidence suggested that red blood cells (RBCs) were associated with pathologic thrombosis and RBC transfusion increased the risk of venous thromboembolism (VTE).The aim of the study was to investigate the association of RBC transfusion with splanchnic vein thrombosis (SVT) in cirrhotic patients.We retrospectively reviewed patients with cirrhosis admitted in the Hunan Provincial People's Hospital between January 2010 and September 2020. Demographic data, the development of SVT, blood transfusion product type and RBC transfusion dose were collected. Multivariate logistic regression analyses and propensity matching analysis (PSM) were performed to identify the association between RBC transfusion and development of SVT.A total of 4479 patients with cirrhosis were enrolled in the study. SVT occurred in 48 (12.4%) cirrhotic patients in RBC transfusion group, and 233 (5.7%) cirrhotic patients in non-RBC transfusion group. RBC transfusion was significantly associated with an increased risk of SVT (unadjusted odds ratio [OR] 2.345, 95% confidence interval [CI] 1.686-3.262, p 0.001). Notably, this association remained robust after PSM, and the volume of RBC transfusion was associated with SVT in a dose-dependent manner.This study suggested that RBC transfusion was associated with an increased risk of SVT in cirrhotic patients. High quality clinical study will be needed to further validate the association between RBC transfusion and SVT.

Numb is known as a cell fate determinant as it identifies the direction of cell differentiation via asymmetrical partitioning during mitosis. It is considered as a tumour suppressor, and a frequent loss of Numb expression in breast cancer is noted. Numb forms a tri‐complex with p53 and E3 ubiquitin ligase HDM2 (also known as MDM2), thereby preventing the ubiquitination and degradation of p53. In this study, we examined Numb expression in 125 patients with triple‐negative breast cancer (TNBC). The results showed that 61 (48.8%) patients presented with a deficient or decreased Numb expression. The percentage of Ki67 > 14% in the retained Numb group was significantly lower than that in the decreased and deficient Numb groups (86.00% vs. 98.40%, P = .0171). This study aimed to detect the expression and migration of Numb, HDM2 and p53 in the membrane, cytoplasmic and nuclear fractions of normal mammary epithelial cell line MCF‐10A and basal‐like TNBC cell line MDA‐MB‐231. We obtained the cell fractions to identify changes in these three protein levels after the re‐expression of NUMB in the MDA‐MB‐231 cells and the knocking down of NUMB in the MCF‐10A cells. Results showed that Numb regulates p53 levels in the nucleus where the protein levels of Numb are positively correlated with p53 levels, regardless if it is re‐expressed in the MDA‐MB‐231 cells or knocked down in the MCF‐10A cells. Moreover, HDM2 was remarkably decreased only in the membrane fraction of NUMB knock‐down cells; however, its mRNA levels were increased significantly. Our results reveal a previously unknown molecular mechanism that Numb can migrate into the nucleus and interact with HDM2 and p53.

Background Six Sigma methodology with a zero‐defect goal has long been applied in commercial settings and was utilized in this study to assure/improve the quality of various analytes. Methods Daily internal quality control (QC) and external quality assessment data were collected and analyzed by calculating the sigma (σ) values for 19 analytes based on the coefficient of variation, bias, and total error allowable. Standardized QC sigma charts were established with these parameters. Quality goal index (QGI) analysis and root cause analysis (RCA) were used to discover potential problems for the analytes. Results Five analytes with σ ≥ 6 achieved world‐class performance, and only the Westgard rule (13s) with one control measurement at two QC material levels (N2) per QC event and a run size of 1000 patient samples between QC events (R1000) was needed for QC. In contrast, more control rules (22s/R4s/41s) along with high N values and low R values were needed for quality assurance for five analytes with 4 ≤ σ