EarlyTect BCD, a Streamlined PENK Methylation Test in Urine DNA, Effectively Detects Bladder Cancer in Patients with Hematuria.

The current noninvasive diagnostic approaches for detecting bladder cancer (BC) often exhibit limited clinical performance, especially for the initial diagnosis. This study aims to evaluate the validity of a streamlined urine-based PENK methylation test called EarlyTect BCD in detecting BC in patients with hematuria scheduled for cystoscopy in Korean and American populations. The test seamlessly integrates two steps, linear target enrichment and quantitative methylation-specific PCR within a single closed tube. The detection limitation of the test was approximately two genome copies of methylated PENK per milliliter of urine. In the retrospective training set (n = 105), an optimal cutoff value was determined to distinguish BC from non-BC, resulting in a sensitivity of 87.3% and a specificity of 95.2%. In the prospective validation set (n = 210, 122 Korean and 88 American patients), the overall sensitivity for detecting all stages of BC was 81.0%, with a specificity of 91.5% and an area under the curve value of 0.889. There was no significant difference between the two groups. The test achieved a sensitivity of 100% in detecting high-grade Ta and higher stages of BC. The negative predictive value of the test was 97.7%, and the positive predictive value was 51.5%. The findings of this study demonstrate that EarlyTect BCD is a highly effective noninvasive diagnostic tool for identifying BC among patients with hematuria.

Identification and validation of PCDHGA12 and PRRX1 methylation for detecting lung cancer in bronchial washing sample.

Bronchoscopy is a frequently used initial diagnostic procedure for patients with suspected lung cancer (LC). Cytological examinations of bronchial washing (BW) samples obtained during bronchoscopy often yield inconclusive results regarding LC diagnosis. The present study aimed to identify molecular biomarkers as a non-invasive method for LC diagnosis. Aberrant DNA methylation is used as a useful biomarker for LC. Therefore, microarray-based methylation profiling analyses on 13 patient-matched tumor tissues at stages I-III vs. non-tumor tissues were performed, and a group of highly differentially methylated genes was identified. A subsequent analysis using bisulfite-pyrosequencing with additional tissues and cell lines revealed six methylated genes [ADAM metallopeptidase with thrombospondin type 1 motif 20, forkhead box C2 (mesenchyme forkhead 1), NK2 transcription factor related, locus 5 (Drosophila), oligodendrocyte transcription factor 3, protocadherin γ subfamily A 12 (PCDHGA12) and paired related homeobox 1 (PRRX1)] associated with LC. Next, a highly sensitive and accurate detection method, linear target enrichment-quantitative methylation-specific PCR in a single closed tube, was applied for clinical validation using BW samples from patients with LC (n=68) and individuals with benign diseases (n=33). PCDHGA12 and PRRX1 methylation were identified as the best-performing biomarkers to detect LC. The two-marker combination showed a sensitivity of 82.4% and a specificity of 87.9%, with an area under the curve of 0.891. Notably, the sensitivity for small cell LC was 100%. The two-marker combination had a positive predictive value of 93.3% and a negative predictive value of 70.7%. The sensitivity was higher than that of cytology, which only had a sensitivity of 50%. The methylation status of the two-marker combination showed no association with sex, age or stage, but was associated with tumor location and histology. In conclusion, the present study showed that the regulatory regions of PCDHGA12 and PRRX1 are highly methylated in LC and can be used to detect LC in BW specimens as a diagnostic adjunct to cytology in clinical practice.

Evaluation of Sensitive Urine DNA-Based PENK Methylation Test for Detecting Bladder Cancer in Patients with Hematuria.

Hematuria is a prevalent symptom associated with bladder cancer (BC). However, the invasiveness and cost of cystoscopy, the current gold standard for BC diagnosis in patients with hematuria, necessitate the development of a sensitive and accurate noninvasive test. This study introduces and validates a highly sensitive urine-based DNA methylation test. The test improves sensitivity in detecting PENK methylation in urine DNA using linear target enrichment followed by quantitative methylation-specific PCR. In a case-control study comprising 175 patients with BC and 143 patients without BC with hematuria, the test's optimal cutoff value was determined by distinguishing between two groups, achieved an overall sensitivity of 86.9% and a specificity of 91.6%, with an area under the curve of 0.892. A prospective validation clinical study involving 366 patients with hematuria scheduled for cystoscopy assessed the test's performance. The test demonstrated an overall sensitivity of 84.2% in detecting 38 cases of BC, a specificity of 95.7%, and an area under the curve of 0.900. Notably, the sensitivity for detecting Ta high grade and higher stages of BC reached 92.3%. The test's negative predictive value was 98.2%, and the positive predictive value was 68.7%. These findings highlight the potential of the PENK methylation in urine DNA using linear target enrichment followed by quantitative methylation-specific PCR test in urine as a promising molecular diagnostic tool for detecting primary BC in patients with hematuria, which may reduce the need for cystoscopy.

Acquisition of Dynamic Material Properties in the Electrohydraulic Forming Process Using Artificial Neural Network.

Electrohydraulic forming is a high-velocity forming process that deforms sheet metals with velocities above 100 m/s and strain rates more than 100 s-1. This experiment was conducted in a closed space because of safety concerns related to the high-velocity conditions; therefore, we were not able to examine the deformation process of the sheet metal. To observe the electrohydraulic forming process in detail, we performed virtual numerical simulations using accurate material properties. Therefore, in this paper, we obtained the material property of a sheet metal from a numerical estimation by using a surrogate model based on the reduced order model and the artificial neural network. The Cowper-Symonds constitutive equation was selected for the Al 6061-T6 sheet metal, and two strain rate parameters were adopted as the unknown parameters. From the two sampling techniques, the training and test samples were extracted from the specific ranges of two unknown parameters, and a numerical simulation was performed for these samples by using the LS-DYNA program. The z-axis displacements of the deformed sheet metal were obtained from the results of the numerical simulation, and two basis vectors were extracted by using principal component analysis. In addition, to predict the weighting coefficients of the two basis vectors at the defined range of parameters, we used the artificial neural network technique as a surrogate model. By comparing the surrogate model and the experimental results and calculating the root mean square error value, we estimated the optimal parameter for Al 6061-T6. Finally, the reliability of the obtained material parameters was proved by comparing the experimental results, the surrogate model, and LS-DYNA.