Annals of Laboratory Medicine最新文献

Total laboratory automation (TLA) is a transformative solution in clinical laboratories that addresses growing demands for operational efficiency, accuracy, and rapid turnaround times in patient care. TLA integrates advanced technologies across pre-analytical, analytical, and post-analytical phases, thereby streamlining workflows, reducing manual intervention, and enhancing QC. TLA adoption is driven by factors such as increasing test volumes, the need for cost reduction and regulatory compliance, and labor shortages. Key benefits of TLA include improved accuracy through error minimization, optimized resource utilization, enhanced staff well-being, and consistent delivery of high-quality results. Leading companies, including Abbott, Roche, Siemens, and Beckman Coulter, dominate the global TLA market with innovative solutions. Recent developments incorporate artificial intelligence (AI), machine learning, robotics, and Internet-of-things technologies, which enable predictive analytics and automated data management. However, challenges remain, including high implementation costs, the need for workforce training, cybersecurity concerns, and system integration complexities. Future trends indicate that TLA will advance through enhanced AI integration, sustainable practices, and big data analytics, fostering continuous improvements in precision diagnostics and clinical outcomes. Moreover, TLA has the potential to revolutionize laboratory operations globally, driving efficiency, accuracy, and sustainability while ultimately improving patient care. Successful adoption of TLA will require strategic planning, interdisciplinary collaboration, and alignment with emerging healthcare needs. In this review, we emphasize that overcoming these challenges through innovation and robust management is essential for ensuring that TLA continues to play a vital role in modern healthcare systems.

Background: Understanding the virulence and pathogenicity of invasive nontyphoidal Salmonella (iNTS) in children may support timely treatment and enable closer monitoring of chronic infections. iNTS epidemiology in Asia remains inadequately described. We analyzed the genetic diversity and virulence genes associated with extra-intestinal invasion in Korean children.

Methods: Salmonella isolates from children <18 yrs of age diagnosed with moderate-to-severe salmonellosis between January 2019 and December 2021 were subjected to antibiotic susceptibility testing and whole-genome sequencing.

Results: In total, 58 cases were included. We identified 20 serotypes, the most prevalent being Salmonella Enteritidis (N=21), followed by Infantis (N=6), I 4,[5],12:i:- (N=5), and Bareilly (N=5). Extra-intestinal invasion occurred in 12 (20.7%) cases involving Salmonella Oranienburg (2/2), Give (1/1), Javiana (1/1), Paratyphi B var. L(+) tartrate+ (1/1), Schwarzengrund (1/1), Singapore (1/1), Montevideo (1/2), Saintpaul (1/2), I 4:b:- (1/2), Infantis (1/6), and Enteritidis (1/21). While the numbers of total virulence genes and genes belonging to major virulence categories did not significantly differ between iNTS and non-iNTS, several genetic factors, including Salmonella pathogenicity island (SPI)-1 (P=0.039), SPI-2 (P=0.020), SPI-5 (P=0.014), SPI-13 (P=0.010), cytolethal distending toxin-related genes (P=1.4×10^-4), fepC (P=0.021), and tcpC (P=0.040) were more frequent in invasive isolates.

Conclusions: Salmonella Enteritidis-ST11 predominated in infections among Korean children, but invasive isolates were rare. Early detection of genetic factors associated with extra- intestinal invasion will be helpful for prompt and appropriate treatment.

Current ABO titration methods lack standardization and harmonization. We analyzed the consistency of ABO antibody titer testing among Korean laboratories and discussed future directions for standardization by analyzing external quality control data collected by the Korean Association of External Quality Assessment Service over 5 yrs (2019-2023). The analysis included the number of participating institutions and methods, as well as the proportion of acceptable results. To compare column agglutination technology (CAT) and tube methods, we created a normalized variable: ([log2 titer of laboratory test result]-[mean of log2 titer for the peer group]). The number of participating institutions and methods increased over time. The use of CAT methods expanded, whereas that of tube methods declined. The proportion of acceptable results ranged from 84.0% to 100%, with no significant differences between CAT and tube methods. An F-test revealed no significant variance differences among institutions using these methods. Tube methods demonstrated lower variance in anti-human globulin testing, and room temperature tube methods exhibited lower variance than that of CAT methods. Domestic laboratories demonstrated high-quality performance in ABO antibody titer testing, with no significant differences in acceptable result rates or variance across methods. Continuous efforts toward standardization remain essential.

Background: Korea National Health and Nutrition Examination Survey (KNHANES) triglyceride testing changed from the glycerol blanking method (2005-2021) to the glycerol non-blanking method (2022). We converted triglyceride data from 2005-2021 to that obtained since 2022 with different analytical methods.

Methods: To develop a conversion equation, 98 fresh serum specimen pairs were compared using Passing-Bablok regression analysis. Implications of the conversion equation on epidemiological data were evaluated using KNHANES data from 2019-2021. Bias estimations determined using the Lipid Standardization Program (LSP) of the United States Centers for Disease Control and Prevention (CDC) enhanced the accuracy and comparability of the triglyceride results.

Results: Triglyceride concentrations measured via the glycerol non-blanking method were 10.7 mg/dL (0.12 mmol/L, 10.0%) higher than those from the glycerol blanking method, with a 9.9 mg/dL (0.11 mmol/L, 5.0%) difference at a concentration of 200 mg/dL (2.26 mmol/L, N=98). The conversion equation y (glycerol non-blanking, 2022)=11.94+0.99x(glycerol blanking, 2005-2021) changed the mean triglyceride concentrations of the KNHANES 2019-2021 data (N=16,015) from 123.7 mg/dL (1.40 mmol/L, 95% confidence interval [CI]: 122.2-125.1 mg/dL [1.38-1.41 mmol/L]) to 134.3 mg/dL (1.52 mmol/L, 95% CI: 132.9-135.8 mg/dL [1.50-1.53 mmol/L]). Since 2022, bias monitoring using the CDC's LSP has remained within a 5.0% limit.

Conclusions: KNHANES triglyceride values in 2022 (non-blanking) were substantially higher than those from 2005-2021 (blanking). Conversion equations helped effectively adjust 2005-2021 data. Researchers should consider adjusting the KNHANES triglyceride data based on their study characteristics.

Background: Pancreatic cancer (PC)-screening methods have limited accuracy despite their high clinical demand. Differential diagnosis of chronic pancreatitis (CP) poses another challenge for PC diagnosis. Therefore, we aimed to identify blood protein biomarkers for PC diagnosis and differential diagnosis of CP using high-throughput multiplex proteomic analysis.

Methods: Two independent cohorts (N=88 and 80) were included, and residual serum samples were collected from all individuals (N=168). Each cohort consisted of four groups: healthy (H) individuals and those with CP, stage I/II PC (PC1), or stage III/IV PC (PC2). Protein expression in the first cohort was quantified using the Olink Immuno-Oncology and Oncology 3 proximity extension assay (PEA) panels and was analyzed using machine-learning (ML)-based analyses. Samples in the second cohort were utilized to verify candidate biomarkers in immunoassays.

Results: Both the PEA and immunoassay results confirmed that previously recognized biomarkers, such as the mucin-16 and interleukin-6 proteins, were more highly expressed in the PC (PC1 and PC2) groups than in the non-PC (CP and H) groups. Several novel biomarkers for PC diagnosis were identified via ML-based feature extraction, including C1QA and CDHR2, whereas pro-neuropeptide Y (NPY) appeared to be a promising biomarker for the differential diagnosis of CP. Applying XGBoost classification incorporating the selected features resulted in an area under the curve of 0.92 (0.85-0.98) for differentiating the PC group from the CP and H groups.

Conclusions: Promising blood biomarkers for PC diagnosis and differential diagnosis of CP were identified using a PEA platform and ML techniques.

Pharmacogenomics is a rapidly evolving field with a strong foundation in basic science dating back to 1960. Pharmacogenomic findings have been translated into clinical care through collaborative efforts of clinical practitioners, pharmacists, clinical laboratories, and research groups. The methods used have transitioned from targeted genotyping of relatively few variants in individual genes to multiplexed multi-gene panels, and sequencing-based methods are likely on the horizon; however, no system exists for classifying and reporting rare variants identified via sequencing-based approaches. Laboratory testing in pharmacogenomics is complex for several genes, including cytochrome P450 2D6 (CYP2D6), HLA-A, and HLA-B, owing to a high degree of polymorphisms, homology with other genes, and copy-number variation. These loci require specialized methods and familiarity with each gene, which may persist during the transition to next-generation sequencing. Increasing implementation across laboratories and clinical facilities has required cooperative efforts to develop standard testing targets, nomenclature, and reporting practices and guidelines for applying the results clinically. Beyond standardization, harmonization between pharmacogenomics and the broader field of genomic medicine may be essential for facilitating further adoption and realizing the full potential of personalized medicine. In this review, we describe the evolution of clinical laboratory testing for pharmacogenomics, including standardization efforts and the anticipated transition from targeted genotyping to sequencing-based pharmacogenomics. We speculate on potential upcoming developments, including pharmacoepigenetics, improved understanding of the impact of non-coding variants, use of large-scale functional genomics to characterize rare variants, and a renewed interest in polygenic risk or combinatorial approaches, which will drive the progression of the field.

Background: FISH is the standard method for detecting cytogenetic abnormalities (CAs) in patients with multiple myeloma, and pre-enrichment of plasma cells is recommended to increase detection rates. However, optimal strategies to ensure sufficient plasma cell retrieval when standard enrichment techniques fail remain underexplored. We investigated factors influencing the success of fluorescence-activated cell sorting (FACS) and assessed the use of direct FISH in cases in which FACS failed.

Methods: A retrospective analysis was conducted on 457 bone marrow samples submitted for FISH between November 2016 and May 2022. FACS was considered successful when plasma cells (CD38+ and CD138+ cells) constituted >1% of the total number of cells. Direct FISH was performed for samples with FACS failure.

Results: FACS was successful in 70.9% of cases and had a high positivity rate (94.8%). Shorter sample transfer times significantly improved FACS success, with a 77.1% success rate for transfer times <2 hrs, compared with 67.8% for longer times (P =0.0388). Plasma cell percentage was a strong determinant of FACS success, with a median of 31.2% in successful cases versus 8.5% in failures (P <0.0001). Even when FACS failed, direct FISH detected CAs in 43.6% of cases.

Conclusions: Plasma cell percentage and sample transfer time are critical factors influencing FACS success. While FACS-FISH demonstrates superior sensitivity in detecting CAs, direct FISH serves as a valuable alternative when FACS fails. These findings highlight the importance of optimizing sample handling and FISH protocols for accurate cytogenetic analysis of multiple myeloma.

Background: Exemestane, an aromatase inhibitor commonly used for breast cancer treatment, shares structural similarities with sex steroids analyzed in clinical laboratories. We aimed to investigate the influence of exemestane cross-reactivity in the measurement of sex steroids across various immunoassays.

Methods: We conducted a multicenter study involving measurements of androstenedione, testosterone, estradiol, progesterone, and 17-hydroxyprogesterone in serum samples from women undergoing exemestane therapy (N=15; 25 mg/day). Measurements were performed using liquid chromatography-mass spectrometry (LC-MS) and various commercially available chemiluminescence immunoassays, ELISA, and radioimmunoassay. In-vitro cross-reactivity was assessed by adding exemestane and 17-hydroexemestane to serum samples.

Results: Patients undergoing exemestane therapy had markedly falsely elevated androstenedione results in all immunoassays evaluated (N=4), which correlated with serum exemestane levels. In-vitro experiments confirmed this interference to be caused by cross-reactivity with exemestane. Additionally, one immunoassay yielded falsely elevated estradiol results in 20% of patients. However, in-vitro experiments did not confirm this to be caused by cross-reactivity with exemestane or 17-hydroexemestane.

Conclusions: Exemestane cross-reacts with androstenedione immunoassays, causing falsely elevated results in treated patients. This analytical interference may raise unnecessary concerns, leading to expensive diagnostic workups.

Despite primary glomerulonephritis (PGN) being a leading cause of chronic kidney disease and end-stage renal disease, specific and sensitive biomarkers for the early detection and monitoring of this condition are lacking. We evaluated the value of the combined detection of serum cystatin C (CYSC), β2-microglobulin (β2-MG), and urine transferrin (TRF) for diagnosing early-stage PGN. From May 2021 to May 2023, we enrolled 105 patients in our hospital as the observation group and 50 healthy volunteers as the control group. Their serum expression levels of CYSC, β2-MG, and TRF were evaluated. We plotted separate ROC curves and calculated the area under the curve (AUC) values of CYSC, β2-MG, and TRF to assess their diagnostic performance in PGN. The levels of CYSC, β2-MG, and TRF were significantly higher (P <0.05) in the observation group than in the healthy control group. CYSC, β2-MG, and TRF were expressed at significantly higher levels in G2, G3a, and G3b of PGN than in G1. The combined use of CYSC, β2-MG, and TRF as biomarkers could significantly improve the early diagnosis and monitoring of PGN and may lead to better patient outcomes by facilitating earlier intervention and treatment strategies.