Journal of Clinical Laboratory Analysis最新文献

Background: In the oral environment, the production of bacteriocins or antimicrobial peptides (AMPs) plays a crucial role in maintaining ecological balance by impeding the proliferation of closely related microorganisms. This study aims to conduct in silico genome screening of Streptococcus salivarius to identify potential antimicrobial compounds existing as hypothetical peptides, with the goal of developing novel synthetic antimicrobial peptides.

Methods: Draft genomes of various oral Streptococcus salivarius strains were obtained from the NCBI database and subjected to analysis using bioinformatic tools, viz. Expert Protein-Analysis System (Expasy), UniProt Knowledgebase (UniProtKB), European Molecular Biology Open Software Suite (EMBOSS), Pepwheel, and PEP-FOLD Peptide Structure Prediction Server. The antimicrobial potential of peptides was assessed through the Antimicrobial Peptide Database (AMP) and Bactibase. Two short peptides, viz. synthetic antimicrobial peptides (SAMPs), were designed based on current knowledge of hydrophobic and cationic residues, synthesized, and their efficacy against biofilm formation was evaluated with standard microbiological methods.

Results: The synthesized short peptides reduced the growth and effectively inhibited biofilm formation by specific oral microbial strains, demonstrating their potential as antimicrobial peptides. Furthermore, the alignment of bacteriocin biosynthetic clusters among streptococcus strains revealed variations in putative bacteriocin amino acid sequences across different strains of the same organism.

Conclusion: Streptococcus salivarius emerges as a promising bioresource for the development of novel antimicrobial agents, particularly for combating biofilm-associated oral infections.

Objective: To explore the impact of in vitro cell subculture on prenatal diagnostic sample results and compare the efficacy of conventional karyotyping and chromosomal microarray analysis (CMA) in detecting chromosome mosaicism.

Methods: We conducted a retrospective analysis of G-banding karyotyping and CMA data from 2007 amniocentesis cases to investigate chromosome mosaicism.

Results: Chromosome mosaicism was detected in 1.49% of cases (30/2007). Sex chromosome mosaicism was the most common form of mosaicism. Among the 30 mosaicisms, 18 results were consistent between the two methods. In four cases, CMA indicated mosaicism but the karyotypes were normal. In eight cases, CMA was normal while the karyotypes suggested mosaicism.

Conclusions: CMA and karyotyping complement each other in prenatal genetic diagnosis. Combining both methods enhances detection accuracy, particularly in cases of chromosomal mosaicism, which may be missed after the subculture of adherent cells in karyotype analysis.

Background: Pseudomonas aeruginosa is a significant opportunistic pathogen, especially in hospital-acquired infections, with plasmid-mediated fluoroquinolone resistance posing a major healthcare threat. This research aimed to isolate fluoroquinolone-resistant P. aeruginosa from patients at Aleppo University Hospital, assess the prevalence of fluoroquinolone resistance, confirm molecular identity, identify plasmid-associated resistance genes, and investigate virulence factors.

Methods: A total of 430 samples were collected from patients and cultured on selective media for identification. Molecular confirmation was achieved through PCR techniques. Various media were used to assess virulence factors and antibiotic resistance while also investigating the prevalence of resistance-related genes.

Results: The study identified 29 fluoroquinolone-resistant P. aeruginosa isolates. These strains exhibited complete resistance to penicillins and all four generations of cephalosporins while remaining 100% sensitive to colistin. Notably, both hemolysin and gelatinase production rates were found to be 100%, and 48.2% of the isolates formed strong biofilms. The aac(6')-Ib gene was present in 72.4% of the isolates, the qnrS gene in 44.8%, and the qnrB gene in 13.7%. Additionally, 37.8% of the isolates contained two types of resistance genes, while 62% had one type. Importantly, all resistant isolates (100%) possessed at least four virulence factors.

Conclusion: The findings indicate a prevalence of plasmid-associated fluoroquinolone resistance genes in the studied isolates. It is recommended to rationalize fluoroquinolone use to preserve their effectiveness against multidrug-resistant strains.

Background and objective: The objective of this study was to investigate the antibody to RBC in autoimmune disease patients with ANA using sensitive Coombs test based on flow cytometry method.

Materials and methods: Antinuclear antibodies (ANA) of autoimmune disease patients were added to red blood cells (RBCs) of blood group O. At the same time, healthy individuals' serums were also checked. The sample tubes were incubated for 30 min at 37°C. After incubation, each sample was analyzed on flow cytometry.

Results: The agglutination of antinuclear antibodies in autoimmune patients was observed, while in healthy people, there was no agglutination between RBCs and serum. A significant difference was found between the disease group and healthy group (p < 0.0001) showing that a statistical analysis was conducted to compare the presence of ANA agglutination between the two groups. The reported p-value of less than 0.0001 shows that the observed difference is highly significant. The serum stability test conducted over ten consecutive days demonstrated a CV of 6.90% in the test results, indicating favorable stability.

Conclusion: In conclusion, this study emphasizes the effectiveness of flow cytometry as a valuable tool for detecting RBC-bound antibodies and new antibodies in autoimmune disease patients. Its high sensitivity and accuracy have the potential to greatly improve diagnostic capabilities in clinical laboratories.

Background: Hepatocellular carcinoma (HCC) is a ubiquitous malignancy linked to significant mortality. The abnormal expression of β-1,4-N-acetyl-galactosaminyltransferase 1 (B4GALNT1) seemed to be implicated in tumorigenesis. Nonetheless, this enzyme's roles in HCC are unclear.

Methods: By analyzing the TCGA_LIHC, GSE77509, and GSE135631 datasets, the levels of B4GALNT1 expression in HCC and surrounding non-cancerous tissues were compared. The prognostic implications of B4GALNT1 were assessed using the Cox regression analysis (CRA). The relationship of B4GALNT1 mutations with CpG island methylation levels and prognosis was examined by analyzing the cBioPortal and MethSurv databases. We sifted the evidence of B4GALNT1 expression correlating with 28 immune cell types' infiltration by harnessing the "GSVA" R package. To delve into the influences of genes associated with B4GALNT1 on HCC, we implemented gene set enrichment analysis (GSEA). We constructed a lentiviral vector expressing B4GALNT1 and knocked down B4GALNT1 in HepG2 cells. The resulting effects on HCC cell proliferation and apoptosis were analyzed via cell proliferation assays and flow cytometry.

Results: HCC tissues presented significant B4GALNT1 overexpression relative to surrounding non-cancerous tissues, marking it as a standalone risk factor for HCC progression. Methylation levels of two CpG islands were high, suggesting poor prognosis. It was detectable that B4GALNT1 expression interrelated with the infiltration extent of natural killer T cells in HCC tissues. B4GALNT1-fueled cell proliferation and enhanced resistance to apoptosis in HCC cells.

Conclusion: B4GALNT1 is a strong regulator of HCC progression and holds promise as a marker for prognosis and a hallmark for therapy in HCC.

Background: In this study, we attempted to select the optimum cases for a prostate biopsy based on routine laboratory test results in addition to prostate-specific antigen (PSA) blood test using H2O automated machine learning (AutoML) software, which includes many common machine learning algorithms.

Methods: The study included 737 patients (46-88 years old). Routine laboratory measurements were used to train machine learning models using H2O AutoML. We created a model that classifies prostate biopsy results as malignant or benign. The performance of the best model was evaluated using the area under the receiver operating characteristic curve (AUC), log-loss metric, F1 score, positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity. The model's performance was evaluated through the SHapley Additive exPlanations (SHAP) analysis feature-based interpretation method applied to comprehend the machine learning model.

Results: The gradient boosting machine model was the most successful. The best result was obtained in the model with 11 parameters, including PSA, free PSA, free PSA to PSA, hemoglobin, neutrophils, platelets, neutrophil-to-lymphocyte ratio (NLR), glucose, platelet-to-lymphocyte ratio (PLR), lymphocytes, and age. The AUC of this model was 0.72, the specificity was 0.84, the PPV was 0.65, the NPV was 0.69, and the accuracy was 0.68.

Conclusion: Our results suggest that adding only routine laboratory parameters to the PSA test and developing machine learning algorithms can help reduce the number of unnecessary prostate biopsies without overlooking the diagnosis of PCa.

Background: As a complex disease, hypertension (HTN) is influenced by both genetic and environmental factors and their interaction. The calcium signaling pathway is known to be involved in the regulation of blood pressure, and dysfunction in this pathway may contribute to the development of hypertension. Genome-wide association studies (GWAS) have identified several genes in the calcium signaling pathway associated with susceptibility to HTN, including PLCB1, ATP2B1, and ADRB1. The aim of this study was to investigate the possible association between single nucleotide variants (SNVs) in the calcium signaling pathway and HTN.

Methods: We genotyped three SNVs: rs1801253 (ADRB1), rs6108168 (PLCB1), and rs17249754 (ATP2B1) in a population of 131 patients with hypertension and 115 healthy controls from Kermanshah province, Iran.

Results: Our results showed a strong and significant association between the G allele and the GG and CG genotypes of the rs1801253 variant in the ADRB1 gene and susceptibility to hypertension. However, no significant association was found for the other two SNVs. In addition, the presence of the GCG haplotype (alleles from left to right: rs1801253, rs6108168, and rs17249754) appeared to confer a protective role against HTN.

Conclusion: This study has made a significant contribution towards enhancing the comprehension of hypertension development, as well as the early identification of individuals who are at risk.

Background: The management of multiple myeloma is challenging because the disease is incurable and unexpected relapses can threaten a patient's survival. Several assessment systems are currently available, but they often require invasive or costly procedures (e.g., instrumental bone marrow and whole-body examinations) or rely on non-specific markers in blood and urine that may not be sufficient to assess and monitor the disease.

Aims: To address some of these limitations, the aim of this study was to evaluate the potential use of soluble B-Cell Maturation Antigen (BCMA), a promising new serum biomarker, as a toll for moniting multiple myeloma patients.

Materials & methods: An unselected cohort of 57 newly diagnosed or relapsed myeloma patients was followed up for 6 months after starting a new therapy. Soluble BCMA levels were measured in peripheral blood using a simple and inexpensive ELISA assay.

Results: Soluble BCMA was detectable in peripheral blood by a simple and inexpensive assay in all patients, even in non-secretory disease or during BCMA-targeted therapies, and significant changes in its levels were observed over time. The analysis showed that the decrease in sBCMA at 1 and 6 months reflects the quality of the clinical response to anti-myeloma regimens.

Discussion & conclusion: The data provide interesting insights into the usefulness of sBCMA as a non-invasive tool for early assessment of treatment efficacy. Its simple and cost-effective detection in peripheral blood could provide clinicians with an addiotional resource for monitoring disease progression and tailoring treatment strategies.

Introduction: Neurofilament light chain (NfL) is one of the most important biomarkers in the field of clinical neurochemistry. Several analytical methods have been developed in the last decade. Recently, Fujirebio introduced a ready-to-use assay kit for measuring NfL levels in the cerebrospinal fluid (CSF) on the fully automated LUMIPULSE G System. In this study, we established the decisional cutoffs for CSF NfL.

Materials and methods: We performed a retrospective observational study including patients with cognitive decline. CSF NfL levels were measured by two analytical methods: the NF-light ELISA kit (UmanDiagnostics) and the Lumipulse G1200 fully automated system (Fujirebio). We calculated the cutoffs for the Lumipulse, starting from the consolidated cutoffs of the ELISA method for each age and using the equation obtained by the regression analysis.

Results: The study population consisted of 100 patients with cognitive decline. The median levels of CSF NfL measured by Lumipulse and ELISA were 776.5 ± 772.6 pg/mL and 473.5 ± 443.5 pg/mL, respectively, significantly different (p < 0.001). The Spearman's rank correlation coefficient was 0.962, indicating a robust positive correlation between the two measurement methods. The equation derived from the Passing-Bablok regression analysis was CSF CLEIA = -61.16 + 1.83 × CSF ELISA. Based on this equation, we defined the decisional cutoff values.

Conclusions: Decisional cutoffs are fundamental tools for guiding clinicians to use biomarkers' results and interpretation appropriately. This is the first study establishing the decisional cutoff value of NfL measured by Lumipulse, a fully automated platform widely used in clinical laboratories.

Background: Mycoplasma pneumoniae (MP) is a major cause of community-acquired pneumonia (CAP), posing diagnostic challenges. This study evaluates novel inflammatory biomarkers, including neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII) and system inflammation response index (SIRI) for MP diagnosis in children.

Methods: Complete blood count (CBC) results of 424 children with MP infection and 150 health children were collected. NLR, MLR, PLR, SII and SIRI, were respectively calculated. Shapiro-Wilk test, Student's t-test, Mann-Whitney U-test and Pearson chi-squared test were used to analyze the clinical data of the patients and participants. Multiple logistic regression analysis was conducted based on the results of single factor analysis. Receiver operating characteristic (ROC) curve was drawn to evaluate the potential of the above biomarkers for MP infection.

Results: Compared with the control group, white blood cell (WBC) count, neutrophil (NEU) count, monocyte (MON) count, NLR, MLR, PLR, SII and SIRI were significantly higher and lymphocyte count (LYM) and platelet (PLT) were significantly lower than those in MP group. The results of multivariate logistic regression analysis indicate that MLR and SIRI can serve as major risk factors for MP infection in children. The predictive accuracy of logistic regression model based on MLR and SIRI is 83.28%. The area under the curve (AUC) results showed that SIRI has better predicting value of MP infection (AUC = 0.892, Sensitivity = 75.7%, Specificity = 92.0%).

Conclusion: This study described the significance of novel inflammatory biomarkers in children with MP infection and may provide new auxiliary diagnostic indicators for MP infection.