ABSTRACT
BACKGROUND: Congenital factor XIII (FXIII) deficiency is an extremely rare bleeding disorder with defects in the F13A1 or F13B genes. Here, we report a case of congenital FXIII deficiency patient who presented with trauma-induced intramuscular hemorrhage accompanied with transient platelet dysfunction with increased endogenous thrombin potential (ETP). METHODS: FXIII antigen and activity, F13A1 gene sequencing, and thrombin generation assay were measured. RESULTS: The diagnosis of FXIII deficiency was confirmed by a double heterozygous mutation of the F13A1 gene and decreased levels of FXIII antigen and activity. Platelet dysfunction caused by an antiplatelet drug was revealed in both platelet aggregation test and PFA-100. After a bleeding event, the PFA-100 results returned to normal and the thrombin generation assay in patient's plasma showed a higher ETP than normal. CONCLUSIONS: This increase in ETP may protect against bleeding and may explain why some patients show only a mild bleeding tendency despite undetectable FXIII activity.
Subject(s)
Factor XIII Deficiency , Factor XIII/genetics , Factor XIII Deficiency/complications , Factor XIII Deficiency/diagnosis , Factor XIII Deficiency/genetics , Hemorrhage/genetics , Humans , Mutation , ThrombinABSTRACT
Measurable residual disease (MRD) testing, a standard procedure in B-lymphoblastic leukemia (B-ALL) diagnostics, is assessed using multiparametric flow cytometry (MFC) and next-generation sequencing (NGS) analysis of immunoglobulin gene rearrangements. We evaluated the concordance between eight-color, two-tube MFC-MRD the LymphoTrack NGS-MRD assays using 139 follow-up samples from 54 pediatric patients with B-ALL. We also assessed the effect of hemodilution in MFC-MRD assays. The MRD-concordance rate was 79.9% (N=111), with 25 (18.0%) and 3 (2.2%) samples testing positive only by NGS-MRD (MFC-NGS+MRD) and MFC-MRD (MFC+NGS-MRD), respectively. We found a significant correlation in MRD values from total nucleated cells between the two methods (r=0.736 [0.647-0.806], P<0.001). The median MRD value of MFC-NGS+MRD samples was estimated to be 0.0012% (0.0001%-0.0263%) using the NGS-MRD assays. Notably, 14.3% of MFC-NGS+MRD samples showed NGS-MRD values below the limit of detection in the MFC-MRD assays. The percentages of hematogones detected in MFC-MRD assays significantly differed between the discordant and concordant cases (P<0.001). MFC and NGS-MRD assays showed relatively high concordance and correlation in MRD assessment, whereas the NGS-MRD assay detected MRD more frequently than the MFC-MRD assay in pediatric B-ALL. Evaluating the hematogone percentages can aid in assessing the impact of sample hemodilution.
Subject(s)
Precursor B-Cell Lymphoblastic Leukemia-Lymphoma , Precursor Cell Lymphoblastic Leukemia-Lymphoma , Humans , Child , Flow Cytometry/methods , Precursor Cell Lymphoblastic Leukemia-Lymphoma/genetics , Neoplasm, Residual/diagnosis , Neoplasm, Residual/genetics , High-Throughput Nucleotide Sequencing/methodsABSTRACT
Background: Pronase pretreatment can reduce rituximab (RTX) interference by degrading CD20 in B-cell flow cytometry crossmatch (FCXM) testing. However, it may also reduce the assay sensitivity by degrading HLA molecules. We investigated the effects of various pronase concentrations on RTX interference and the analytical sensitivity of B-cell FCXM testing. Methods: Using 59 patient serum samples and 38 donor lymphocyte samples, we designed 97 recipient-donor pairs and divided them into three groups according to RTX use and the presence of weak-to-moderate donor HLA-specific antibody (DSA) reactions: RTX+/DSA-, RTX+/DSA+, and RTX-/DSA+. FCXM was performed after pretreating lymphocytes with six different pronase concentrations (0, 0.5, 1, 2, 3, and 4 mg/mL). Results: With B-FCXM testing, false-positive results due to RTX in the RTX+/DSA- group markedly decreased with increasing pronase concentrations. The median channel shift values in the RTX+/DSA+ and RTX-/DSA+ groups did not significantly decrease when the pronase concentration was increased from 1 mg/mL to 2 or 3 mg/mL. All eight RTX+/DSA+ cases that were positive at 1 mg/mL pronase but negative at 2 or 3 mg/mL had mean fluorescence intensity (MFI) DSA values of less than 3,000 except for DQ5 (MFI: 5,226). With T-cell FCXM, false-positive results were observed in 2.9% of 315 FCXM tests with pronase pretreatment. Conclusions: Higher concentrations (2 or 3 mg/mL) of pronase effectively eliminated RTX interference but still carried a risk for false negativity for weak DSA reactions in B-cell FCXM. Higher pronase concentrations can be used as an auxiliary method to detect moderate-to-strong DSA reactions in RTX-treated patients.
ABSTRACT
Background: Inequalities in child mortality occur via interactions between socio-environmental factors and their constituents. Through childhood developmental stages, we can observe changing patterns of mortality. By investigating these patterns and social inequalities by cause and developmental stage, we aim to gain insights into health policies to reduce and equalize childhood mortality. Methods: Using vital statistics, we examined the Korean birth cohort of 2012, including all children born in 2012 up to five years of age (N = 466,636). The dependent variables were all-cause and cause-specific mortality by developmental stage (i.e., neonatal, post-neonatal, and childhood). A Cox proportional hazard regression model was built to compare child mortality according to maternal education. The distribution of inequalities in cause-specific mortality by child age was calculated using the slope index of inequality (SII). Results: Inequalities in child mortality due to maternal education occur during the neonatal period and increase over time. After adjusting for covariates, the Cox proportional hazard models showed that "injury and external causes" (HR = 2.178; 95% CI = [1.283-3.697]) and "unknown causes" (HR = 2.299; 95% CI = [1.572-3.363]) in the post-neonatal period, and "injury and external causes" (HR = 2.153; 95% CI = [1.347-3.440]) in the childhood period significantly contributed to socioeconomic inequalities in child mortality. For each period, the leading causes of inequality were identified as follows: "congenital" (96.7%) for the neonatal period, "unknown causes" (58.2%) and "injury and external causes" (28.4%) for the post-neonatal period, and "injury and external causes" (56.5%) for the childhood period. Conclusion: We confirmed that the main causes of death in mortality inequality vary according to child age, in accordance with the distinctive context of child development. Strengthening the health system and multisectoral efforts that consider families' and children's needs according to spatial contexts (e.g., home, community) may be necessary to address the social inequalities in child health.
ABSTRACT
Background: Several T-cell response assays for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are available; however, their comparability and correlations with antibody responses remain unclear. We compared four SARS-CoV-2 T-cell response assays and two anti-SARS-CoV-2 spike antibody assays. Methods: We enrolled 89 participants who had received a booster dose of the BNT162b2 vaccine after two doses of the ChAdOx1 or BNT162b2 vaccine. Fifty-six participants without breakthrough infection (BI) (ChAdOx1/BNT162b2 group: N=27; BNT162b2 group: N=29) and 33 with BI were included. We evaluated two whole-blood interferon-gamma release assays (IGRAs) (QuantiFERON and Euroimmun), T-SPOT.COVID, an in-house enzyme-linked immunospot (ELISPOT) assay (targeting the spike and nucleocapsid peptides of wild-type and Omicron SARS-CoV-2), Abbott IgG II Quant, and Elecsys Anti-S, using Mann-Whitney U, Wilcoxon signed-rank, and Spearman's correlation tests. Results: The correlations between the IGRAs and between the ELISPOT assays (ρ=0.60-0.70) were stronger than those between the IGRAs and ELISPOT assays (ρ=0.33-0.57). T-SPOT.COVID showed a strong correlation with Omicron ELISPOT (ρ=0.70). The anti-spike antibody assays showed moderate correlations with T-SPOT.COVID, Euroimmun IGRA, and ELISPOT (ρ=0.43-0.62). Correlations tended to be higher in the BI than in the noninfected group, indicating that infection induces a stronger immune response. Conclusions: T-cell response assays show moderate to strong correlations, particularly when using the same platform. T-SPOT.COVID exhibits potential for estimating immune responses to the Omicron variant. To accurately define SARS-CoV-2 immune status, both T-cell and B-cell response measurements are necessary.