Clinica Chimica Acta最新文献

Background: Prostate cancer (PC) is the second most common malignant tumor in males and a leading cause of cancer-related morbidity and mortality among men. Early detection is essential for improving outcomes. Although prostate-specific antigen (PSA) is widely used for PC screening, it suffers from high false positive and false negative rates. Our study aimed to identify a panel of serum mature microRNAs (miRNAs) for PC diagnosis.

Methods: We conducted a PubMed search to identify candidate mature miRNAs associated with PC. We then used quantitative reverse transcription-polymerase chain reaction (RT-qPCR) to assess the expression profiles of these mature miRNAs in serum samples from 112 PC patients and 112 healthy controls (HCs). We selected mature miRNAs with favorable diagnostic potential by analyzing receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). Subsequently, we developed a highly diagnostically efficient panel of three mature miRNAs using stepwise logistic regression based on their expression levels.

Results: We identified three mature miRNAs (hsa-miR-143-5p, hsa-miR-23b-3p, and hsa-miR-148b-3p) with significant diagnostic value, constructing a panel with an AUC of 0.891, sensitivity of 84.15%, and specificity of 80.49%. Bioinformatics analysis also revealed LDB3 and RBMS3 as potential therapeutic targets for PC.

Conclusions: Our study introduces a novel diagnostic approach by identifying a panel of three mature miRNAs (hsa-miR-143-5p, hsa-miR-23b-3p, and hsa-miR-148b-3p) as novel and non-intrusive biomarkers for PC.

Early-onset preeclampsia (EOPE) is a serious pregnancy complication. Understanding its underlying mechanisms could lead to improved diagnosis and management. Genome-wide DNA methylation changes in circulating Extracellular Vesicle DNA (EV-DNA) from women with EOPE could serve as a non-invasive approach to identify key regions and genes that could serve as biomarkers to understand placental pathophysiology. In this case-control study, serum extracellular vesicles were isolated from 3rd trimester pregnant women and characterized using Nanoparticle Tracking Analysis and Transmission Electron Microscopy. The circulating EV-DNA samples were subjected to Whole Genome Bisulfite Sequencing analysis (WGBS) to identify differentially methylated CpGs (DMCs) sites in EOPE cases compared to control. A total of 154 DMCs were identified in EV-DNA, of which 131 were hypomethylated and 23 were hypermethylated. Majority of DMCs were of mitochondrial origin. Previously, it has been reported that oxidative stress, decreased trophoblast differentiation, and invasion are linked to preeclampsia pathogenesis and are related to mitochondrial dysfunction. Therefore, DMCs of the mitochondrial genes like MT-ND1, MT-ND4, MT-CO2, MT-CO3, and MT-RNR1 were selected for validation and showed a similar trend by pyrosequencing. The expression of these genes were also altered in circulating extracellular vesicles. Our study shows changes in the DNA methylation patterns of circulating EV-DNA in women with EOPE. These changes, especially in mitochondrial genes, could lead to mitochondrial dysfunction and contribute EOPE pathogenesis. These findings suggest that these alterations could be explored as non-invasive approach to better understand placental health and improve disease management.

Background and objective: Serum protein electrophoresis (SPEP) plays a critical role in diagnosing diseases associated with M-proteins. However, its clinical application is limited by a heavy reliance on experienced experts.

Methods: A dataset comprising 85,026 SPEP outcomes was utilized to develop artificial intelligence diagnostic models for the classification and localization of M-proteins. These models were trained and validated using three data features, and their performance was evaluated using comprehensive metrics, including sensitivity, positive predictive value (PPV), specificity, negative predictive value (NPV), F1 score, accuracy, area under the receiver operating characteristic curve (AUC), Matthews correlation coefficient (MCC), and Intersection over Union (IoU). The best-performing machine learning (ML) and deep learning (DL) models were further tested on a separate dataset of 1,079 samples. The localization ability of the DL model was compared against three clinical experts.

Results: Among the four ML models, the extreme gradient boosting (XGB) model achieved the best performance, with MCC, AUC, F1 score, sensitivity, specificity, accuracy, PPV, and NPV of 0.847, 0.903, 0.875, 0.822, 0.985, 0.951, 0.934, and 0.955, respectively. Different feature extraction methods significantly influenced model performance. The DL models outperformed the ML models in comprehensive performance. The U-Net combined with Transformer model demonstrated localization ability comparable to that of clinical experts, achieving sensitivity, specificity, accuracy, PPV, NPV, F1 score, AUC, MCC, and IoU of 0.947, 0.984, 0.976, 0.938, 0.986, 0.942, 0.966, 0.927, and 0.877, respectively.

Conclusion: The U-Net combined with the Transformer model demonstrated expert-level performance in M-protein classification and localization, achieving an accuracy of 0.976 and an IoU of 0.877. This exceptional performance highlights the potential of this combined model for automating clinical SPEP workflows.

Background: Urothelial carcinoma (UC) is a common malignancy worldwide. Aberrant DNA methylation is implicated in UC carcinogenesis. This study sought to delineate the DNA methylation landscape in UC and identify DNA methylation-based biomarkers for early detection of UC.

Methods: Whole genome bisulfite sequencing (WGBS) was conducted on bladder cancer tissues and paired normal tissues. By integrating WGBS data with The Cancer Genome Atlas (TCGA) UBC data, a DNA methylation-based biomarker was identified. When combined with a known UC biomarker AL021918.2, the performance of the dual-target test was evaluated in voided urine samples from 224 UC patients and 419 controls.

Results: Notable hypomethylation was observed in UC samples compared to normal samples. Through differential methylation analysis, differential methylation CpG sites, regions, and genes were identified. Of these, Transmembrane protein 106A gene (TMEM106A) was screened as a new UC biomarker. In a dual-target test, using triplex quantitative methylation-specific PCR (qMSP) to examine TMEM106A and AL021918.2 methylation levels, the training set showed a sensitivity of 89.0 %, a specificity of 92.9 %, and an area under the curve (AUC) value of 0.941 (95 % confidence interval [CI]: 0.913-0.969). Similarly, the validation set showed a sensitivity of 90.0 %, a specificity of 91.1 %, and an AUC value of 0.922 (95 % CI: 0.881-0.962). In addition, our dual-target test demonstrated outstanding detection rates for low-grade or early-stage tumors.

Conclusions: We provide a comprehensive analysis of DNA methylation profiles in UC, and highlight the promising clinical potential of dual-target urine tests for UC detection.

Noninvasive detection of BK virus, for early detection of BK polyomavirus-associated nephropathy post-renal transplantation, is currently an active subject of investigation. In this study, we developed and validated a novel risk score diagnostic assay (PymiR Score) based on measurements of three urine miRNAs, including BKV-related miRNA (bkv-miR-B1-5p), polyomavirus-related miRNA (bkv-miR-B1-3p) and renal tubular injury-related miRNA (miR-21-5p), by quantitative polymerase chain reaction. The limit of detection of the three miRNAs was 2 × 10³ copies/mL, while the intra- and inter-assay coefficients of variation were in the ranges of 2.13 %-3.59 % and 2.30 %-3.35 %, respectively. In the training set, we identified that at a PymiR Score of 71.78, the maximum sensitivity and specificity for the detection of BKVAN were 76.9 % and 100 % respectively, with the receiver-operator characteristic (ROC) analysis showing an area under the curve of 0.8681. In the validation set, we observed a significant difference in the PymiR Score between BKV infection and biopsy-proven BKVAN (P ＜ 0.05), with the ROC analysis showing a sensitivity of 72.73 %, specificity of 100 %, and an AUC of 0.8571. Compared with the area under the ROC curve of plasma BK virus DNA load (AUC = 0.7266), the PymiR Score exhibited higher discrimination capacity (P＜0.05) between BKV infection and biopsy-proven BKVAN. Overall, this non-invasive approach offers a robust and convenient alternative for the diagnosis of BK polyomavirus-associated nephropathy.

Background: Lactate dehydrogenase (LDH) is a critical enzyme widely used in clinical diagnostics. However, variations in measurement systems can lead to inconsistent results, potentially impacting clinical decision-making. This study aimed to evaluate the comparability of lactate dehydrogenase measurements by comparing routine methods with the IFCC reference method, and to analyze the standardization status of LDH testing through external quality assessment (EQA).

Methods: We analyzed 40 individual serum samples using four routine LD measurement systems: Siemens ADVIA 2400, Hitachi 7600/BioSino, Beckman AU5800, and Roche Cobas C501, alongside the IFCC reference method. The analytical quality specification (±6.4 %) was based on biological variation. Our study also included a comprehensive external quality assessment (EQA) program conducted by the Beijing Center for Clinical Laboratory (BCCL) over three years (2020, 2021, and 2023) to evaluate the consistency and reliability of LD measurements across different reagents. The acceptance limit is based on the requirement of desirable biological variability (±11.4 %).

Results: All four measurement systems showed strong correlations with the IFCC reference method (r ≥ 0.98). Relative average deviations ranged from -5.23 % to 3.71 %, within the acceptable biological variation limit. External quality assessment revealed high pass rates (94.9 %-98.7 %) across reagent groups, with significant improvements observed in some reagent groups from 2020 to 2023..

Conclusions: While the four LD measurement systems demonstrated good correlation with the IFCC reference method, some systems require further standardization. The study provides valuable insights into LD measurement accuracy and supports ongoing efforts to enhance laboratory testing comparability.

Introduction: There is a general impression that patient-based quality control (PBQC) requires a high volume of laboratory results to detect errors effectively. However, internal quality control (IQC) performed infrequently may be associated with increased risk of missed error (i.e. low power of error detection).

Methods: Using simulations and routine sodium and aspartate aminotransferase (AST) as examples, this study examined how the "average number of patient samples affected before error detection' (ANPed) metrics can provide linkage to compare relative performance of QC practices in various IQC and PBQC settings.

Results: Smaller numbers of IQC samples tested per IQC run or larger average number of patient samples measured between IQC runs are associated with higher ANPed. The ANPed for sodium and AST PBQC models were smaller than IQC performed once every 100 patient samples, except when the systematic error was small for AST.

Discussion: Use of ANPed clearly illustrate the relative impact of different IQC frequencies and number of IQC levels tested. Patient-based quality control can outperform IQC even for laboratories with small testing volume. Laboratory practitioners can use this metric to design a QC strategy that suit their desired risk profile.

Objective: This exploratory study investigates if neurofilament light chain (NfL) is excreted in the urine and whether this depends on plasma NfL (pNfL) levels and kidney function in terms of eGFR and U-albumin-creatinine ratio (uACR).

Methods: Using a computer algorithm, we identified excess urine and plasma from routine testing of uACR and eGFR in patients 45-50 years old. Up to 17 paired urine-plasma samples in each of six categories of kidney function defined by uACR and eGFR were analysed for NfL, and the urinary NfL-creatinine ratio (uNCR) was calculated to correct for urine dilution.

Results: In the 35 subjects with normal eGFR (>90 ml/min/1.73 m²) and varying degrees of albuminuria, uNfL was only above the lower limit of quantification in one subject with microalbuminuria (uACR 30-300 mg/g), and in none of the subjects with uACR < 30 mg/g. In the 47 subjects with impaired eGFR (15-60 ml/min/1.73 m²), the percentage of subjects with detectable uNfL and the average level of uNCR increased with increasing albuminuria. However, multiple regression analysis revealed that uNCR only significantly correlated with pNfL, not eGFR and uACR. pNfL correlated inversely with eGFR, but not uACR.

Conclusions: Our results show that the urinary NfL excretion in subjects with normal kidney function and normal pNfL levels is very low. Thus, the increased pNfL often observed in patients with low eGFR seems not to be explained by impaired urinary NfL excretion, and urine is generally not a suitable matrix for NfL measurements.

Background: The liver function tests and noninvasive tests (NITs) play important roles in the follow-up and monitoring of fatty liver disease (FLD). Our aim is to establish annual biological variation (BV) and personalized reference intervals (prRIs) of liver function tests for the first time in order to accurately assess the status and progress of FLD.

Methods: 67 fatty liver patients who participated in regular physical examination once a year for six consecutive years, were enrolled. Based on these patients, we calculated annual BV and derived parameters, including reference change value (RCV), index of individuality (II), and total variation around the true homeostatic set point (TV_set) which could further be used to derive prRI.

Results: We calculated the annual within-subject BV (CV_I), within-person BV (CV_P), RCV, II, TV_set of 8 liver function tests and 3 NITs for fatty liver patients. CV_I estimates of fatty liver patients for half of liver function tests were significantly lower than those of healthy people and these could lead to a lower RCV. IIs of all measurands were < 1.4 except for total bile acids (TBA). The mean of CV_P is similar to the CV_I; however, there is a significant heterogeneity in CV_P among different subjects. Annual TV_set estimates for 7 measurands were lower and prRI was also narrower in fatty liver patients than that of healthy people.

Conclusion: Annual BV and their derived parameters based on fatty liver patients can provide an objective basis for the monitoring and follow-up of FLD, a global epidemic disease.

The integration of genomics into personalized medicine has the potential to transform healthcare by customizing treatments according to individual genetic profiles. This paper examines the diverse applications of genomics, including the identification of disease susceptibility, improvement of diagnostic methods, optimization of drug therapies, and monitoring of treatment responses. It also explores the expanding global market for genetic testing and the increasing implementation of whole-genome sequencing in clinical practice, with a focus on pilot programs that are advancing comprehensive genomic analysis. Despite challenges such as high costs, data interpretation complexities, and ethical concerns, significant efforts are being made to address these issues. Additionally, the creation of biobanks as vital resources for preserving high-quality biosamples and supporting research highlights the critical need for infrastructure development in genomics. By fostering interdisciplinary collaboration and establishing robust ethical and regulatory frameworks, personalized medicine can ensure equitable access to tailored therapies and enhance health outcomes for everyone. This abstract provides an overview of the transformative potential of genomics and personalized medicine in ushering in a new era of precision healthcare.