Annals of Laboratory Medicine最新文献

Background: Coronavirus disease (COVID-19) induces inflammation, coagulopathy following platelet and monocyte activation, and fibrinolysis, resulting in elevated D-dimer levels. Activated platelets and monocytes produce microvesicles (MVs). We analyzed the differences in platelet and monocyte MV counts in mild, moderate, and severe COVID-19, as well as their correlation with D-dimer levels.

Methods: In this cross-sectional study, blood specimens were collected from 90 COVID-19 patients and analyzed for D-dimers using SYSMEX CS-2500. Platelet MVs (PMVs; PMVCD42b⁺ and PMVCD41a⁺), monocyte MVs (MMVs; MMVCD14⁺), and phosphatidylserine-binding annexin V (PS, AnnV⁺) were analyzed using a BD FACSCalibur instrument.

Results: PMV and MMV counts were significantly increased in COVID-19 patients. AnnV⁺ PMVCD42b⁺ and AnnV⁺ PMVCD41a⁺ cell counts were higher in patients with severe COVID-19 than in those with moderate clinical symptoms. The median (range) of AnnV⁺ PMVCD42b⁺ (MV/μL) in mild, moderate, and severe COVID-19 was 1,118.3 (328.1-1,910.5), 937.4 (311.4-2,909.5), and 1,298.8 (458.2-9,703.5), respectively (P =0.009). The median (range) for AnnV⁺ PMVCD41a⁺ (MV/μL) in mild, moderate, and severe disease was 885.5 (346.3-1,682.7), 663.5 (233.8-2,081.5), and 1,146.3 (333.3-10,296.6), respectively (P =0.007). D-dimer levels (ng/mL) weak correlated with AnnV⁺ PMVCD41a⁺ (P =0.047, r=0.258).

Conclusions: PMV PMVCD42b⁺ and PMVCD41a⁺ counts were significantly increased in patients with severe clinical symptoms, and PMVCD41a⁺ counts correlated with D-dimer levels. Therefore, MV counts can be used as a potential biomarker of COVID-19 severity.

Background: The Jr^a antigen is a high-prevalence red blood cell (RBC) antigen. Reports on cases of fatal hemolytic disease of the fetus and newborn and acute hemolytic transfusion reactions suggest that antibodies against Jr^a (anti-Jr^a) have potential clinical significance. Identifying anti-Jr^a is challenging owing to a lack of commercially available antisera. We developed an alternative approach to rapidly predict the presence of anti-Jr^a using the TaqMan single-nucleotide polymorphism (SNP)-genotyping method.

Methods: Residual peripheral blood samples from 10 patients suspected of having the anti-Jr^a were collected. Two samples with confirmed Jr(a-) RBCs and anti-Jr^a were used to validate the TaqMan genotyping assay by comparing the genotyping results with direct sequencing. The accuracy of the assay in predicting the presence of anti-Jr^a was verified through crossmatching with in-house Jr(a-) O+ RBCs.

Results: The TaqMan-genotyping method was validated with two Jr(a-) RBC- and anti-Jr^a-confirmed samples that showed concordant Jr^a genotyping and direct sequencing results. Jr^a genotyping for the remaining samples and crossmatching the serum samples with inhouse Jr(a-) O+ RBCs showed consistent results.

Conclusions: We validated a rapid, simple, accurate, and cost-effective method for predicting the presence of anti-Jr^a using a TaqMan-based SNP-genotyping assay. Implementing this method in routine practice in clinical laboratories will assist in solving difficult problems regarding alloantibodies to high-prevalence RBC antigens and ultimately aid in providing safe and timely transfusions and proper patient care.

Background: Metformin, a drug prescribed for patients with type 2 diabetes, has potential efficacy in enhancing antitumor immunity; however, the detailed underlying mechanisms remain to be elucidated. Therefore, we aimed to identify the inhibitory molecular mechanisms of metformin on programmed death ligand 1 (PD-L1) expression in cancer cells and programmed death 1 (PD-1) expression in immune cells.

Methods: We employed a luciferase reporter assay, quantitative real-time PCR, immunoblotting analysis, immunoprecipitation and ubiquitylation assays, and a natural killer (NK) cell-mediated tumor cell cytotoxicity assay. A mouse xenograft tumor model was used to evaluate the effect of metformin on tumor growth, followed by flow-cytometric analysis using tumor-derived single-cell suspensions.

Results: Metformin decreased AKT-mediated β-catenin S552 phosphorylation and subsequent β-catenin transactivation in an adenosine monophosphate-activated protein kinase (AMPK) activation-dependent manner, resulting in reduced CD274 (encoding PD-L1) transcription in cancer cells. Tumor-derived soluble factors enhanced PD-1 protein stability in NK and T cells via dissociation of PD-1 from ubiquitin E3 ligases and reducing PD-1 polyubiquitylation. Metformin inhibited the tumor-derived soluble factor-reduced binding of PD-1 to E3 ligases and PD-1 polyubiquitylation, resulting in PD-1 protein downregulation in an AMPK activation-dependent manner. These inhibitory effects of metformin on both PD-L1 and PD-1 expression ameliorated cancer-reduced cytotoxic activity of immune cells in vitro and decreased tumor immune evasion and growth in vivo.

Conclusions: Metformin blocks both PD-L1 and PD-1 within the tumor microenvironment. This study provided a mechanistic insight into the efficacy of metformin in improving immunotherapy in human cancer.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Vancomycin variable Enterococcus (VVE) bacteria are phenotypically susceptible to vancomycin, but they harbor the vanA gene. We aimed to ascertain the prevalence of VVE among clinically isolated vancomycin-susceptible Enterococcus faecium (VSE) isolates, as well as elucidate the molecular characteristics of the vanA gene cluster within these isolates. Notably, we investigated the prevalence and structure of the vanA gene cluster of VVE. Between June 2021 and May 2022, we collected consecutive, non-duplicated vancomycin-susceptible Enterococcus faecium (VSE) samples. Real-time PCR was performed to detect the presence of vanA, vanB, and vanC. Overlapping PCR with sequencing and whole-genome sequencing were performed for structural analysis. Sequence types (STs) were determined by multilocus sequence typing. Exposure testing was performed to assess the ability of the isolates to acquire vancomycin resistance. Among 282 VSE isolates tested, 20 isolates (7.1%) were VVE. Among them, 17 isolates had partial deletions in the IS1216 or IS1542 sequences in vanS (N=10), vanR (N=5), or vanH (N=2). All VVE isolates belonged to the CC17 complex and comprised five STs, namely ST17 (40.0%), ST1421 (25.0%), ST80 (25.0%), ST787 (5.0%), and ST981 (5.0%). Most isolates were related to three hospital-associated clones (ST17, ST1421, and ST80). After vancomycin exposure, 18 of the 20 VVEs acquired vancomycin resistance. Considering the high reversion rate, detecting VVE by screening VSE for vanA is critical for appropriate treatment and infection control.

Background: Mass spectrometry (MS) methods exhibit higher accuracy and comparability in measuring serum C-peptide concentrations than immunoassays. We developed and validated a novel isotope dilution-ultraperformance liquid chromatography-tandem MS (ID-UPLC-MS/MS) assay to measure serum C-peptide concentrations.

Methods: Sample pretreatment involved solid-phase extraction, ion-exchange solid-phase extraction, and derivatization with 6-aminoquinolyl-N-hydroxysuccinimidylcarbamate (Cayman Chemical, Ann Arbor, Michigan, USA). We used an ExionLC UPLC system (Sciex, Framingham, MA, USA) and a Sciex Triple Quad 6500⁺ MS/MS system (Sciex) for electrospray ionization in positive-ion mode with multiple charge states of [M+3H]3+ and multiple reaction monitoring transitions. The total run time was 50 mins, and the flow rate was 0.20 mL/min. We evaluated the precision, trueness, linearity, lower limit of quantitation (LLOQ), carryover, and matrix effects. Method comparison with electrochemiluminescence immunoassay (ECLIA) was performed in 138 clinical specimens.

Results: The intra- and inter-run precision coefficients of variation were <5% and the bias values for trueness were <4%, which were all acceptable. The verified linear interval was 0.050-15 ng/mL, and the LLOQ was 0.050 ng/mL. No significant carryover or matrix effects were observed. The correlation between this ID-UPLC-MS/MS method and ECLIA was good (R=0.995, slope=1.564); however, the ECLIA showed a positive bias (51.8%).

Conclusions: The developed ID-UPLC-MS/MS assay shows acceptable performance in measuring serum C-peptide concentrations. This will be useful in situations requiring accurate measurement of serum C-peptide in clinical laboratories.

Background: The Molecular International Prognostic Scoring System (IPSS-M) has improved the prediction of clinical outcomes for myelodysplastic syndromes (MDS). The Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS), based on classical clinical parameters, has outperformed the IPSS, revised version (IPSS-R). For the first time, we validated the IPSS-M and other molecular prognostic models and compared them with the established IPSS-R and AIPSS-MDS models using data from South American patients.

Methods: Molecular and clinical data from 145 patients with MDS and 37 patients with MDS/myeloproliferative neoplasms were retrospectively analyzed.

Results: Prognostic power evaluation revealed that the IPSS-M (Harrell's concordance [C]-index: 0.75, area under the receiver operating characteristic curve [AUC]: 0.68) predicted overall survival better than the European MDS (EuroMDS; C-index: 0.72, AUC: 0.68) and Munich Leukemia Laboratory (MLL) (C-index: 0.70, AUC: 0.64) models. The IPSS-M prognostic discrimination was similar to that of the AIPSS-MDS model (C-index: 0.74, AUC: 0.66) and outperformed the IPSS-R model (C-index: 0.70, AUC: 0.61). Considering simplified low- and high-risk groups for clinical management, after restratifying from IPSS-R (57% and 32%, respectively, hazard ratio [HR]: 2.8; P=0.002) to IPSS-M, 12.6% of patients were upstaged, and 5% were downstaged (HR: 2.9; P=0.001). The AIPSS-MDS recategorized 51% of the low-risk cohort as high-risk, with no patients being downstaged (HR: 5.6; P<0.001), consistent with most patients requiring disease-modifying therapy.

Conclusions: The IPSS-M and AIPSS-MDS models provide more accurate survival prognoses than the IPSS-R, EuroMDS, and MLL models. The AIPSS-MDS model is a valid option for assessing risks for all patients with MDS, especially in resource-limited centers where molecular testing is not currently a standard clinical practice.