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Respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infections and hospitalization in infants and young children. Here, we analyzed the genetic diversity of RSV using partial G gene sequences in 84 RSV-A and 78 RSV- B positive samples collected in Seoul, South Korea, for 10 consecutive years, from 2010 to 2019. Our phylogenetic analysis revealed that RSV-A strains were classified into either the ON1 (80.9%) or NA1 (19.0%) genotypes. On the other hand, RSV-B strains demonstrated diversified clusters within the BA genotype. Notably, some sequences designated as BA-SE, BA-SE1, and BA-DIS did not cluster with previously identified BA genotypes in the phylogenetic trees. Despite this, they did not meet the criteria for the assignment of a new genotype based on recent classification methods. Selection pressure analysis identified three positive selection sites (amino acid positions 273, 274, and 298) in RSV-A, and one possible positive selection site (amino acid position 296) in RSV-B, respectively. The mean evolutionary rates of Korean RSV-A from 1999 to 2019 and RSV-B strains from 1991 and 2019 were estimated at 3.51 × 10 −3 nucleotides (nt) substitutions/site/year and 3.32 × 10 −3 nt substitutions/site/year, respectively. The population dynamics in the Bayesian skyline plot revealed fluctuations corresponding to the emergence of dominant strains, including a switch of the dominant genotype from NA1 to ON1. Our study on time-scaled cumulative evolutionary analysis contributes to a better understanding of RSV epidemiology at the local level in South Korea.

Background: Rigorous evaluation of vacuum blood collection tubes is essential to ensure the reliability of laboratory results. Methods: In this study, we compared the serum separator tube V-Tube™ (V-Tube SST), the quick-clotting serum separator tube VQ-Tube™ (VQ-Tube SST), and the K2EDTA V-Tube™ (V-Tube K2EDTA) manufactured by AB Medical (Seoul, Republic of Korea), with their respective counterparts from Becton Dickinson (BD, Franklin Lakes, NJ, USA): BD Vacutainer® SST™ II Advance Tube (BD SST) and BD Vacutainer® K2EDTA 5.4 mg Tube (BD K2EDTA). The evaluation encompassed 61 measurands across the fields of chemistry, immunology, and hematology, and incorporated a stability assessment for the VQ-Tube SST. Results: The V-Tube SST, VQ-Tube SST, and V-Tube K2EDTA demonstrated comparable analytical performance to the BD tubes for the majority of measurands. However, glucose, lactate dehydrogenase, mean corpuscular volume, and mean corpuscular hemoglobin concentration indicated clinically significant differences according to the desirable biological variation database (Ricos). Conclusions: These findings suggest that, while the V-tube and VQ-tube SST generally serve as alternatives to BD tubes, caution should be taken when interpreting results for specific measurands that demonstrated clinically significant discrepancies.

Background/Objectives: Whole exome sequencing (WES) is an effective method for detecting disease-causing variants. However, copy number variation (CNV) detection using WES data often has limited sensitivity and high false-positive rates. Methods: In this study, we constructed a reference CNV set using chromosomal microarray analysis (CMA) data from 44 of 180 individuals who underwent WES and CMA and evaluated four WES-based CNV callers (CNVkit, CoNIFER, ExomeDepth, and cn.MOPS) against this benchmark. For each tool, we first defined software-specific problematic genomic regions across the full WES cohort and filtered out the CNVs that overlapped these regions. Results: The four algorithms showed low mutual concordance and distinct distributions in the problematic regions. On average, 2210 sequencing target baits (1.23%) were classified as problematic; these baits had lower mappability scores and higher coefficients of variation in RPKM than the remaining probes. After the supplementary filtration step, all tools demonstrated improved performance. Notably, ExomeDepth achieved gains of 14.4% in sensitivity and 7.9% in positive predictive value. Conclusions: We delineated software-specific problematic regions and demonstrated that targeted filtration markedly reduced false positives in WES-based CNV detection.

Smartphone-based point-of-care testing (POCT) is rapidly emerging as an alternative to traditional screening and laboratory testing, particularly in resource-limited settings. In this proof-of-concept study, we present a smartphone- and cloud-based artificial intelligence quantitative analysis system (SCAISY) for relative quantification of SARS-CoV-2-specific IgG antibody lateral flow assays that enables rapid evaluation (<60 s) of test strips. By capturing an image with a smartphone camera, SCAISY quantitatively analyzes antibody levels and provides results to the user. We analyzed changes in antibody levels over time in more than 248 individuals, including vaccine type, number of doses, and infection status, with a standard deviation of less than 10%. We also tracked antibody levels in six participants before and after SARS-CoV-2 infection. Finally, we examined the effects of lighting conditions, camera angle, and smartphone type to ensure consistency and reproducibility. We found that images acquired between 45° and 90° provided accurate results with a small standard deviation and that all illumination conditions provided essentially identical results within the standard deviation. A statistically significant correlation was observed (Spearman correlation coefficient: 0.59, p = 0.008; Pearson correlation coefficient: 0.56, p = 0.012) between the OD450 values of the enzyme-linked immunosorbent assay and the antibody levels obtained by SCAISY. This study suggests that SCAISY is a simple and powerful tool for real-time public health surveillance, enabling the acceleration of quantifying SARS-CoV-2-specific antibodies generated by either vaccination or infection and tracking of personal immunity levels.

The complete blood count (CBC) is one of the most important clinical steps in clinical diagnosis. The instruments used for CBC are usually expensive and bulky and require well-trained operators. Therefore, it is difficult for medical institutions below the tertiary level to provide these instruments, especially in underprivileged countries. Several reported on-chip blood cell tests are still in their infancy and do not deviate from conventional microscopic or impedance measurement methods. In this study, we (i) combined magnetically activated cell sorting and the differential density method to develop a method to selectively isolate three types of leukocytes from blood and obtain samples with high purity and concentration for portable leukocyte classification using the lens-free shadow imaging technique (LSIT), and (ii) established several shadow parameters to identify the type of leukocytes in a complete leukocyte shadow image by shadow image analysis. The purity of the separated leukocytes was confirmed by flow cytometry. Several shadow parameters such as the “order ratio” and “minimum ratio” were developed to classify the three types of leukocytes. A shadow image library corresponding to each type of leukocyte was created from the tested samples. Compared with clinical reference data, a correlation index of 0.98 was obtained with an average error of 6% and a confidence level of 95%. This technique offers great potential for biological, pharmaceutical, environmental, and clinical applications, especially where point-of-care detection of rare cells is required.

Background: Accurate and timely diagnosis of mycobacterial infections, including Mycobacterium tuberculosis complex (MTBC) and nontuberculous mycobacteria (NTM), is crucial for effective disease management. Methods: This study evaluated the performance of the NeoPlex TB/NTM-5 Detection Kit (NeoPlex assay, Seongnam, Republic of Korea), a multiplex real-time PCR assay that incorporates melting curve analysis, compared with the line-probe assay (LPA). The NeoPlex assay could simultaneously detect and differentiate MTBC from five other NTM species: Mycobacterium intracellulare, Mycobacterium avium, Mycobacterium kansasii, Mycobacterium abscessus, and Mycobacterium massiliense. A total of 91 acid-fast bacillus culture-positive samples, comprising 36 MTBC and 55 NTM isolates, were collected from the Korea University Anam Hospital. Results: The NeoPlex assay successfully detected nucleic acids in 87 of the 91 isolates (95.6%). Notably, it identified additional mycobacterial nucleic acids not detected by the LPA in eight isolates. These findings were confirmed via DNA sequencing. The assay had 100% sensitivity and specificity for M. intracellulare, M. abscessus, M. massilense, NTM, and MTBC, whereas it had 100% specificity and sensitivity of 90.9% and 75.0% for M. avium and M. kansasii, respectively. Conclusions: These results highlight the potential of the NeoPlex assay to enhance rapid and accurate diagnosis of mycobacterial infections, particularly in settings in which prompt treatment initiation is essential.

Accurate and efficient classification and quantification of CD34+ cells are essential for the diagnosis and monitoring of leukemia. Current methods, such as flow cytometry, are complex, time-consuming, and require specialized expertise and equipment. This study proposes a novel approach for the label-free identification of CD34+ cells using a deep learning model and lens-free shadow imaging technology (LSIT). LSIT is a portable and user-friendly technique that eliminates the need for cell staining, enhances accessibility to nonexperts, and reduces the risk of sample degradation. The study involved three phases: sample preparation, dataset generation, and data analysis. Bone marrow and peripheral blood samples were collected from leukemia patients, and mononuclear cells were isolated using Ficoll density gradient centrifugation. The samples were then injected into a cell chip and analyzed using a proprietary LSIT-based device (Cellytics). A robust dataset was generated, and a custom AlexNet deep learning model was meticulously trained to distinguish CD34+ from non-CD34+ cells using the dataset. The model achieved a high accuracy in identifying CD34+ cells from 1929 bone marrow cell images, with training and validation accuracies of 97.3% and 96.2%, respectively. The customized AlexNet model outperformed the Vgg16 and ResNet50 models. It also demonstrated a strong correlation with the standard fluorescence-activated cell sorting (FACS) technique for quantifying CD34+ cells across 13 patient samples, yielding a coefficient of determination of 0.81. Bland–Altman analysis confirmed the model’s reliability, with a mean bias of −2.29 and 95% limits of agreement between 18.49 and −23.07. This deep-learning-powered LSIT offers a groundbreaking approach to detecting CD34+ cells without the need for cell staining, facilitating rapid CD34+ cell classification, even by individuals without prior expertise.

Morphological changes in leukocytes are valuable markers for diseases and immune responses. In our earlier work, we presented Cellytics, a device that uses lens-free shadow imaging technology (LSIT) to monitor natural killer cell activity. Here, we present an improved Cellytics system that has been upgraded to a four-channel configuration to achieve higher throughput while maintaining robust reproducibility for rapid and cost-effective leukocyte analysis. The performance of this multi-channel Cellytics system was improved through refinements to the micro-pinhole chip. Etched pinholes provided better image resolution and clarity compared to drilled pinholes. To stimulate leukocytes, we used an activation stimulator cocktail (ASC) and quantified the resulting morphological changes using shadow-based metrics, including peak-to-peak distance (PPD) and maxima-to-minima standard deviation (MMD-SD). In addition, we developed a new leukocyte activation parameter (LAP) to specifically assess these activation-induced morphological changes. After ASC stimulation, leukocytes showed significantly increased PPD and LAP values and decreased MMD-SD compared to non-activated leukocytes. These results are consistent with the results of the flow cytometric analysis. These results emphasize the potential of Cellytics for the rapid and accurate assessment of leukocyte activation and provide a valuable tool for both clinical diagnostics and basic immunological research.

The objective of this survey was to gain a real-world perspective on coagulation testing by evaluating the availability of various coagulation laboratory tests, assessing specific analytic and postanalytic steps in clinical laboratories in Korea. Participants were surveyed using a 65-question questionnaire specifically focused on their coagulation testing practices related to prothrombin time (PT), activated partial thromboplastin time (aPTT), plasma-mixing studies, lupus anticoagulant (LA) tests, platelet function tests, coagulation factor assays, and the composition of hemostasis and thrombosis test panels. The survey was performed between July and September 2022. The survey achieved a 77.9% (81 of 104) response rate. PT or aPTT tests were performed directly at all participating institutions, followed by D-dimer and fibrinogen tests, platelet function test, and plasma-mixing studies in order of frequency. Variations existed in the performance of mixing test and LA assessment. Patterns of coagulating testing differed depending on the size of the hospital. The survey revealed that most laboratories conducted coagulation tests following the international guidelines such as Clinical Laboratory Standards Institute guidelines and the Korean Laboratory Certification system. However, some coagulation tests, including mixing test and LA tests, are yet to be standardized in Korea. Continuous education on coagulation test methods and internal and external quality control are required to encourage laboratories to enhance the performance of coagulation testing.

Background Neutrophil gelatinase‐associated lipocalin (NGAL) is a urine biomarker related to acute renal injury. Whereas several studies have evaluated NGAL levels in hematological malignancy, using peripheral blood (PB). Recently, bone marrow (BM) NGAL level was reported to be higher than PB NGAL level in individuals with hematological malignancy, suggesting that BM NGAL would reflect BM microenvironment better than PB NGAL. We measured BM NGAL levels in patients with hematological malignancy, comparing those with NGAL levels in normal BM. We evaluated the association of BM NGAL with hematological parameters including neutrophil counts. Methods BM samples were collected from 107 patients who underwent BM examination. Immunoassays were used to assess NGAL levels. Data on hematological parameters were collected from medical records. Intergroup comparisons were performed using the Kruskal‐Wallis H test and Pearson chi‐square test. Single and multiple regression analyses were performed to analyze the relationships. Results The independent factors that affected the BM NGAL level were neutrophil counts and BM band neutrophil%, while neutrophil count was the main influencing factor. The acute myeloid leukemia (n = 18) and myelodysplastic syndrome (n = 25) groups showed statistically lower BM NGAL levels than patients with normal BM. The myeloproliferative neoplasm group (n = 34) showed higher BM NGAL levels than patients with normal BM, but this difference was not statistically significant. Neutrophil counts and BM band neutrophil% showed intergroup patterns similar to those of BM NGAL levels. Conclusion BM NGAL was related to neutrophil count and BM band neutrophil%, showing different levels according to hematological malignant disease entities.