Annals of Laboratory Medicine最新文献

Background: Droplet digital (dd)PCR is a new-generation PCR technique with high precision and sensitivity; however, the positive and negative droplets are not always effectively separated because of the "rain" phenomenon. We aimed to develop a practical optimization and evaluation process for the ddPCR assay and to apply it to the detection of BRAF V600E in fine-needle aspiration (FNA) specimens of thyroid nodules, as an example.

Methods: We optimized seven ddPCR parameters that can affect "rain." Analytical and clinical performance were analyzed based on histological diagnosis after thyroidectomy using a consecutive prospective series of 242 FNA specimens.

Results: The annealing time and temperature, number of PCR cycles, and primer and probe concentrations were found to be more important considerations for assay optimization than the denaturation time and ramp rate. The limit of blank and 95% limit of detection were 0% and 0.027%, respectively. The sensitivity of ddPCR for histological papillary thyroid carcinoma (PTC) was 82.4% (95% confidence interval [CI], 73.6%-89.2%). The pooled sensitivity of BRAF V600E in FNA specimens for histological PTC was 78.6% (95% CI, 75.9%-81.2%, I²=60.6%).

Conclusions: We present a practical approach for optimizing ddPCR parameters that affect the separation of positive and negative droplets to reduce rain. Our approach to optimizing ddPCR parameters can be expanded to general ddPCR assays for specific mutations in clinical laboratories. The highly sensitive ddPCR can compensate for uncertainty in cytological diagnosis by detecting low levels of BRAF V600E.

Background: Healthcare 4.0. refers to the integration of advanced technologies, such as artificial intelligence (AI) and big data analysis, into the healthcare sector. Recognizing the impact of Healthcare 4.0 technologies in laboratory medicine (LM), we seek to assess the overall awareness and implementation of Healthcare 4.0 among members of the Korean Society for Laboratory Medicine (KSLM).

Methods: A web-based survey was conducted using an anonymous questionnaire. The survey comprised 36 questions covering demographic information (seven questions), big data (10 questions), and AI (19 questions).

Results: In total, 182 (17.9%) of 1,017 KSLM members participated in the survey. Thirty-two percent of respondents considered AI to be the most important technology in LM in the era of Healthcare 4.0, closely followed by 31% who favored big data. Approximately 80% of respondents were familiar with big data but had not conducted research using it, and 71% were willing to participate in future big data research conducted by the KSLM. Respondents viewed AI as the most valuable tool in molecular genetics within various divisions. More than half of the respondents were open to the notion of using AI as assistance rather than a complete replacement for their roles.

Conclusions: This survey highlighted KSLM members' awareness of the potential applications and implications of big data and AI. We emphasize the complexity of AI integration in healthcare, citing technical and ethical challenges leading to diverse opinions on its impact on employment and training. This highlights the need for a holistic approach to adopting new technologies.

Cardiac biomarkers, especially high-sensitivity cardiac troponin C or I (hs-cTnC or hs-cTnI, respectively), are vital for diagnosing acute myocardial infarction (AMI). Despite the specificity of hs-cTn as a biomarker, the creatine kinase-myocardial band (CK-MB) is commonly used alongside hs-cTn in emergency departments (EDs). We analyzed 23,771 simultaneous hs-cTn (hs-cTnT or hs-cTnI) and CK-MB requests for 17,185 patients in tertiary hospital ED in 2022. The objective of this study was to assess their practical value in diagnosing AMI in real-world settings. Among all 17,185 patients tested, 98.0% underwent hs-cTnT and CK-MB tests, and substantially fewer underwent hs-cTnI testing. We observed concordance between the initial hs-cTn and CK-MB results in 71.3% of patients. Of 131 AMI cases, 57 were positive for both biomarkers, 63 for hs-cTn only, and none for CK-MB alone. CK-MB positivity was often found in the absence of AMI. Discrepancies between the hs-cTnT and hs-cTnI results occurred in 30.0% of patients. Indiscriminate CK-MB testing for diagnosing AMI in EDs should be reconsidered. Efficient use of CK-MB is important for reducing costs and ensuring optimal patient care.

Background: In recent decades, the analytical quality of clinical laboratory results has substantially increased because of collaborative efforts. To effectively utilize laboratory results in applications, such as machine learning through big data, understanding the level of harmonization for each test would be beneficial. We aimed to develop a quantitative harmonization index that reflects the harmonization status of real-world laboratory tests.

Methods: We collected 2021-2022 external quality assessment (EQA) results for eight tests (HbA1c, creatinine, total cholesterol, HDL-cholesterol, triglyceride, alpha-fetoprotein [AFP], carcinoembryonic antigen [CEA], and prostate-specific antigen [PSA]). This EQA was conducted by the Korean Association of External Quality Assessment Service, using commutable materials. The total analytical error of each test was determined according to the bias% and CV% within peer groups. The values were divided by the total allowable error from biological variation (minimum, desirable, and optimal) to establish a real-world harmonization index (RWHI) at each level (minimum, desirable, and optimal). Good harmonization was arbitrarily defined as an RWHI value ≤ 1 for the three levels.

Results: Total cholesterol, triglyceride, and CEA had an optimal RWHI of ≤ 1, indicating an optimal harmonization level. Tests with a desirable harmonization level included HDL-cholesterol, AFP, and PSA. Creatinine had a minimum harmonization level, and HbA1c did not reach the minimum harmonization level.

Conclusions: We developed a quantitative RWHI using regional EQA data. This index may help reflect the actual harmonization level of laboratory tests in the field.

Bacteroides fragilis is the most common opportunistic anaerobic pathogen. In the absence of appropriate antimicrobial therapy, mortality rates associated with B. fragilis group infections can reach as high as 50%. Therefore, we aimed to elucidate the clinical characteristics and outcomes of B. fragilis infections and the molecular genetic characteristics of B. fragilis isolates. Forty B. fragilis clinical isolates were collected at Hanyang University Hospital between January 2022 and December 2023. Antimicrobial susceptibility was tested using the agar dilution method. Whole-genome sequencing was conducted using the Illumina platform (Illumina, San Diego, CA, USA). Various multilocus sequence types of B. fragilis were identified, including ST149 (N=4), ST11 (N=4), ST1 (N=3), ST21 (N=2), and ST157 (N=1). The insertion sequence (IS) IS1187, located upstream of cfiA, was associated with high-level carbapenem resistance in the ST157 isolate. B. fragilis toxin genes (bft ) were identified in 30% of isolates. The most common comorbidities were diabetes mellitus (26.5%) and non-metastatic cancer (23.5%). Five patients (14.7%) died within 30 days, and two (5.9%) deaths were directly attributable to B. fragilis infection. The emergence of high-level MIC carbapenem-resistant B. fragilis ST157 has led to caution in the presence of B. fragilis infections.

Hypertrophic cardiomyopathy (HCM) is a genetic cardiac muscle disease characterized by clinical and genetic heterogeneity. Genetic testing can reveal the presence of disease-causing variants in genes encoding sarcomere proteins. However, it yields inconclusive or negative results in 40-60% of HCM cases, owing to, among other causes, technical limitations such as the inability to detect pathogenic intronic variants. Therefore, we aimed to increase the diagnostic yield of molecular analysis for HCM by improving the in-silico detection of intronic variants in MYBPC3 that may escape detection by algorithms normally used with tagged diagnostic panels. We included 142 HCM probands with negative results in Illumina TruSight Cardio panel analysis, including exonic regions of 174 cardiomyopathy genes. Raw data were re-analyzed using existing bioinformatics tools. The spliceogenic variant c.1224-80G>A was detected in three patients (2.1%), leading us to reconsider their molecular diagnosis. These patients showed late onset and mild symptoms, although no peculiar phenotypic characteristics were shared. Collectively, rare spliceogenic MYBPC3 variants may play a role in causing HCM, and their systematic detection should be performed to provide more comprehensive solutions in genetic testing using multigenic panels.