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IntroductionTwo approaches were compared for the calculation of coefficient of variation (CV) and bias, and their effect on sigma calculation, when different allowable total error (TEa) values were used to determine the optimal method for Six Sigma quality management in the clinical laboratory.Materials and methodsSigma metrics for routine clinical chemistry tests using three systems (Beckman AU5800, Roche C8000, Siemens Dimension) were determined in June 2017 in the laboratory of Peking Union Medical College Hospital. Imprecision (CV%) and bias (bias%) were calculated for ten routine clinical chemistry tests using a proficiency testing (PT)- or an internal quality control (IQC)-based approach. Allowable total error from the Clinical Laboratory Improvement Amendments of 1988 and the Chinese Ministry of Health Clinical Laboratory Center Industry Standard (WS/T403-2012) were used with the formula: Sigma = (TEa − bias) / CV to calculate the Sigma metrics (σCLIA, σWS/T) for each assay for comparative analysis.ResultsFor the PT-based approach, eight assays on the Beckman AU5800 system, seven assays on the Roche C8000 system and six assays on the Siemens Dimension system showed σCLIA > 3. For the IQC-based approach, ten, nine and seven assays, respectively, showed σCLIA > 3. Some differences in σ were therefore observed between the two calculation methods and the different TEa values.ConclusionsBoth methods of calculating σ can be used for Six Sigma quality management. In practice, laboratories should evaluate Sigma multiple times when optimizing a quality control schedule.

Background DNA polymerase delta 1 catalytic subunit (POLD1) plays a key role in DNA replication and damage repair. A defective DNA proofreading function caused by POLD1 mutation contributes to carcinogenesis, while POLD1 overexpression predicts poor prognosis in cancers. However, the effect of POLD1 in hepatocellular carcinoma (HCC) is not well-understood. Methods Expression patterns of POLD1 were evaluated in TCGA and the HPA databases. Kaplan-Meier curves and Cox regression were used to examine the prognostic value of POLD1. The prognostic and predictive value of POLD1 was further validated by another independent cohort from ICGC database. The influences of DNA copy number variation, methylation and miRNA on POLD1 mRNA expression were examined. The correlation between infiltrating immune cells and POLD1 expression was analyzed. GO and KEGG enrichment analyses were performed to detect biological pathways associated with POLD1 expression in HCC. Results POLD1 was overexpressed in HCC (n = 369) compared with adjacent normal liver (n = 50). POLD1 upregulation was significantly correlated with positive serum AFP and advanced TNM stage. Kaplan–Meier and multivariate analyses suggested that POLD1 overexpression predicts poor prognosis in HCC. DNA copy gain, low POLD1 methylation, and miR‑139-3p downregulation were associated with POLD1 overexpression. Besides, POLD1 expression was associated with the infiltration levels of dendritic cell, macrophage, B cell, and CD4 + T cell in HCC. Functional enrichment analysis suggested “DNA replication”, “mismatch repair” and “cell cycle” pathways might be involved in the effect of POLD1 on HCC pathogenesis. Additionally, POLD1 mRNA expression was significantly associated with tumor mutation burden, microsatellite instability, and prognosis in various tumors. Conclusions POLD1 may be a potential prognostic marker and promising therapeutic target in HCC.

Analysis of circulating tumor cells (CTCs) is regarded as a useful diagnostic index to monitor tumor development and guide precision medicine. Although the immunoassay is a common strategy for CTC identification and heterogeneity characterization, it is challenged by poor reaction efficiency and laborious manipulations in microdevices, which hinder the sensitivity, throughput, simplification, and applicability. To meet the need for rapid, sensitive, and simple CTC analysis, we developed an efficient CTC detection system by integrating a 3D printed off-chip multisource reagent platform, a bubble retainer, and a single CTC capture microchip, which can achieve CTC capture and identification within 90 min. Compared with traditional CTC identification methods, this system decreases immunostaining time and antibody consumption by 90% and performs the on-chip immunoassay in a fully automated manner. Using this system, CTCs from the peripheral blood of 19 patients with various cancers were captured, detected, and compared with clinical data. The system shows great potential for early screening, real-time monitoring, and precision medicine for hepatocellular carcinoma (HCC). With the advantages of automation, stability, economy, and user-friendly operation, the proposed system is promising for clinical scenarios.