Tailoring thresholds for interpreting plasma p-tau217 levels

BACKGROUND: Plasma phosphorylated tau (p-tau) 217 test has emerged as a minimally invasive and accessible alternative to positron emission tomography imaging and cerebrospinal fluid analysis for Alzheimer's disease (AD) diagnostics. However, the diagnostic performance of p-tau217 across diverse cognitive and demographic subgroups remains underexplored. This multicentre cross-sectional study aimed to assess the diagnostic utility of plasma p-tau217 using a double cut-off approach in a large, diverse cohort, focusing on subgroup analyses based on cognitive status, age, sex, body mass index and APOE ε4 carrier status. METHODS: Plasma p-tau217 levels were analysed in cognitively unimpaired (CU) and cognitively impaired (CI) individuals. Double cut-offs for p-tau217 levels were selected to classify participants into amyloid-negative, intermediate and amyloid-positive groups. Diagnostic performance metrics including sensitivity, specificity, positive predictive value and negative predictive value were evaluated across subgroups, and tailored cut-off strategies were explored for specific populations. RESULTS: The optimal cut-offs differed between CU and CI groups. In the CI group, diagnostic accuracy was consistently high across all subgroups, meeting confirmatory test standards with sensitivity and specificity ≥90%. In the CU group, the appropriate standards varied by subgroup. Participants aged <65 years required alternative cut-offs to improve sensitivity to 85.0% and maintain specificity at 95.7%. CONCLUSION: Plasma p-tau217 demonstrated robust diagnostic accuracy across CI subgroups and highlighted the importance of tailored cut-off thresholds for CU populations. These findings support the integration of plasma p-tau217 into clinical workflows for AD diagnostics, emphasising its potential for early detection and risk stratification

Information Security Modeling for the Operation of a Novel Highly Trusted Network in a Virtualization Environment

A novel network architecture to be deployed in Korea is so called HTN (highly trusted network). The aim of HTN is the seamless communication of information in a secure manner, anytime and anywhere, in the national administration network infrastructure. In this paper, we present the results of information security modeling for the HTN. Through the use of security modeling procedure, we derive the requirements and corresponding technology for security control of the system by analyzing threat elements and attack possibility. First we analyze threat of each component for the HTN by STRIDE modeling and later construct an attack tree by analyzing attack examples for every threat. Finally we propose the security requirements and technology to respond against them, based on the analyzed threats and attack examples

Hydrogel-based strong and fast actuators by electroosmotic turgor pressure

Hydrogels are promising as materials for soft actuators because of qualities such as softness, transparency, and responsiveness to stimuli. However, weak and slow actuations remain challenging as a result of low modulus and osmosis-driven slow water diffusion, respectively. We used turgor pressure and electroosmosis to realize a strong and fast hydrogel-based actuator. A turgor actuator fabricated with a gel confined by a selectively permeable membrane can retain a high osmotic pressure that drives gel swelling; thus, our actuator exerts large stress [0.73 megapascals (MPa) in 96 minutes (min)] with a 1.16 cubic centimeters of hydrogel. With the accelerated water transport caused by electroosmosis, the gel swells rapidly, enhancing the actuation speed (0.79 MPa in 9 min). Our strategies enable a soft hydrogel to break a brick and construct underwater structures within a few minutes.</jats:p

Prediction of homologous recombination deficiency from Oncomine Comprehensive Assay Plus correlating with SOPHiA DDM HRD Solution.

ObjectivePoly(ADP-ribose) polymerase (PARP) inhibitors are used for targeted therapy for ovarian cancer with homologous recombination deficiency (HRD). In this study, we aimed to develop a homologous recombination deficiency prediction model to predict the genomic integrity (GI) index of the SOPHiA DDM HRD Solution from the Oncomine Comprehensive Assay (OCA) Plus. We also tried to a find cut-off value of the genomic instability metric (GIM) of the OCA Plus that correlates with the GI index of the SOPHiA DDM HRD Solution.MethodsWe included 87 cases with high-grade ovarian serous carcinoma from five tertiary referral hospitals in Republic of Korea. We developed an HRD prediction model to predict the GI index of the SOPHiA DDM HRD Solution. As predictor variables in the model, we used the HRD score, which included percent loss of heterozygosity (%LOH), percent telomeric allelic imbalance (%TAI), percent large-scale state transitions (%LST), and the genomic instability metric (GIM). To build the model, we employed a penalized logistic regression technique.ResultsThe final model equation is -21.77 + 0.200 × GIM + 0.102 × %LOH + 0.037 × %TAI + 0.261 × %LST. To improve the performance of the prediction model, we added a borderline result category to the GI results. The accuracy of our HRD status prediction model was 0.958 for the test set. The accuracy of HRD status using GIM with a cut-off value of 16 was 0.911.ConclusionThe Oncomine Comprehensive Assay Plus provides a reliable biomarker for homologous recombination deficiency

Identification of High-Priority Tributaries for Water Quality Management in Nakdong River Using Neural Networks and Grade Classification

To determine the high-priority tributaries that require water quality improvement in the Nakdong River, which is an important drinking water resource for southeastern Korea, data collected at 28 tributaries between 2013 and 2017 were analyzed. To analyze the water quality characteristics of the tributary streams, principal component analysis and factor analysis were performed. COD (chemical oxygen demand), TOC (total organic carbon), TP (total phosphorus), SS (suspended solids), and BOD (biochemical oxygen demand) were classified as the primary factors. In the self-organizing maps analysis using the unsupervised learning neural network model, the first factor showed a highly relevant pattern. To perform the grade classification, 11 parameters were selected. Six parameters are concentrations of the main parameters for the water quality standard assessment in South Korea. We added the pollution load densities for the selected five primary factors. Joochungang showed the highest pollution load density despite its small watershed area. According to the results of the grade classification method, Joochungang, Topyeongcheon, Hwapocheon, Chacheon, Gwangyeocheon, and Geumhogang were selected as tributaries requiring high-priority water quality management measures. From this study, it was concluded that neural network models and grade classification methods could be utilized to identify the high-priority tributaries for more directed and effective water quality management.</jats:p