Development of a duplex chamber digital PCR to quantify twelve genetically modified maize events.

Accurate quantification of genetically modified organisms (GMOs) is essential for regulatory compliance, especially under threshold-based labeling policies. In this study, we developed and validated twelve event-specific duplex chamber- or chip-based digital PCR (cdPCR) methods using microfluidic array plates to quantify GM maize events approved in South Korea. In contrast to conventional real-time PCR, the cdPCR approach allows for absolute quantification without standard curve calibration and incorporates event-specific zygosity ratio correction to improve accuracy of the measurement.

Surface passivation engineering for stable optoelectronic devices via hydroxyl-free ZnMgO nanoparticles.

ZnMgO nanoparticles (ZMO NPs) are widely used as electron transport layers in optoelectronic devices such as light-emitting diodes (LEDs) and photodiodes (PDs) primarily because of their facile synthesis and excellent electron transport properties. However, the surface hydroxyl groups (‒OH) on the ZMO NPs introduce charge traps, inhibit electron transport, and reduce device stability, particularly under ambient humidity and oxygen. Therefore, in this study, an alcohol treatment (AT) method was developed to remove surface ‒OH via proton transfer to effectively reduce trap states and dipole moments and enhance surface passivation.

Comprehensive analysis of high-throughput transcriptomics to distinguish drug-induced liver injury (DILI) phenotypes.

Drug-Induced Liver Injury (DILI) is a major challenge in drug development, occurring due to liver damage caused by the adverse effects of drugs or xenobiotics. High-throughput transcriptomics (HTTr) provides mechanistic insights into drug-induced hepatotoxicity, complementing traditional chemical structure-based methods. To address the challenges posed by DILI, this study aimed to evaluate the suitability of HTTr data for DILI classification and prediction.

Deep learning health space model for ordered responses.

Background: As personalized medicine becomes more prevalent, the objective measurement and visualization of an individual's health status are becoming increasingly crucial. However, as the dimensions of data collected from each individual increase, this task becomes more challenging. The Health Space (HS) model provides a statistical framework for visualizing an individual's health status on biologically meaningful axes.

Nanoseed-based physically unclonable function for on-demand encryption.

A physically unclonable function (PUF) is a promising hardware-based cryptographic primitive to prevent confidential information leakage. However, conventional techniques, such as weak and strong PUFs, have limitations in overcoming the trade-off between security and storage volume. This study introduces nanoseed-based PUFs that overcome the drawbacks of conventional PUFs using optical and electrical randomness originated from nanoseeds and a unique on-demand cryptographic algorithm.

Assessing the causal effects of type 2 diabetes and obesity-related traits on COVID-19 severity.

Background: Type 2 diabetes (T2D) and obesity-related traits are highly comorbid with coronavirus disease 2019 (COVID-19), but their causal relationships with disease severity remain unclear. While recent Mendelian randomization (MR) studies suggest a causal link between obesity-related traits and COVID-19 severity, findings regarding T2D are inconsistent, particularly when adjusting for body mass index (BMI). This study aims to clarify these relationships.

Specific detection of genetically modified potatoes containing and genes derived from potato.

Several genetically modified (GM) potatoes have been developed by introducing endogenous genes derived from potatoes, such as () and (), to improve quality. Therefore, it is difficult to distinguish between GM and non-GM potatoes. In this study, we developed a sequence-specific polymerase chain reaction (PCR) detection method to identify innate and inserted genes.

Erratum: Correction of Fig. 2 in the Article "Temporal Radiographic Trajectory and Clinical Outcomes in COVID-19 Pneumonia: A Longitudinal Study".

This corrects the article on p. e25 in vol. 40, PMID: 40065711.

Machine learning models for pancreatic cancer diagnosis based on microbiome markers from serum extracellular vesicles.

Pancreatic cancer (PC) is a fatal disease with an extremely low 5-year survival rate, mainly because of its poor detection rate in early stages. Given emerging evidence of the relationship between microbiota composition and diseases, this study aims to identify microbiome markers linked to the diagnosis of pancreatic cancer. We utilized extracellular vesicles (EVs) data obtained from blood samples of 38 pancreatic cancer patients and 51 health controls.

An ensemble approach improves the prediction of the COVID-19 pandemic in South Korea.

Background: Modelling can contribute to disease prevention and control strategies. Accurate predictions of future cases and mortality rates were essential for establishing appropriate policies during the COVID-19 pandemic. However, no single model yielded definite conclusions, with each having specific strengths and weaknesses.

Composite quantile regression approach to batch effect correction in microbiome data.

Background: Batch effects refer to data variations that arise from non-biological factors such as experimental conditions, equipment, and external factors. These effects are considered significant issues in the analysis of biological data since they can compromise data consistency and distort actual biological differences, which can severely skew the results of downstream analyses.

Method: In this study, we introduce a new approach that comprehensively addresses two types of batch effects: "systematic batch effects" which are consistent across all samples in a batch, and "nonsystematic batch effects" which vary depending on the variability of operational taxonomic units (OTUs) within each sample in the same batch.

Sex Differences in Apolipoprotein E and Alzheimer Disease Pathology Across Ancestries.

Importance: Age, sex, and apolipoprotein E (APOE) are the strongest risk factors for late-onset Alzheimer disease (AD). The role of APOE in AD varies with sex and ancestry. While the association of APOE with AD biomarkers also varies across sex and ancestry, no study has systematically investigated both sex-specific and ancestry differences of APOE on cerebrospinal fluid (CSF) biomarkers together, resulting in limited insights and generalizability.

Temporal Radiographic Trajectory and Clinical Outcomes in COVID-19 Pneumonia: A Longitudinal Study.

Background: Currently, little is known about the relationship between the temporal radiographic latent trajectories, which are based on the extent of coronavirus disease 2019 (COVID-19) pneumonia and clinical outcomes. This study aimed to elucidate the differences in the temporal trends of critical laboratory biomarkers, utilization of critical care support, and clinical outcomes according to temporal radiographic latent trajectories.

Methods: We enrolled 2,385 patients who were hospitalized with COVID-19 and underwent serial chest radiographs from December 2019 to March 2022.

Analyzing COVID-19 progression with Markov multistage models: insights from a Korean cohort.

Background: Understanding the progression and recovery process of COVID-19 is crucial for guiding public health strategies and developing targeted interventions. This longitudinal cohort study aims to elucidate the dynamics of COVID-19 severity progression and evaluate the impact of underlying health conditions on these transitions, providing critical insights for more effective disease management.

Methods: Data from 4549 COVID-19 patients admitted to Seoul National University Boramae Medical Center between February 5th, 2020, and October 30th, 2021, were analyzed using a 5-state continuous-time Markov multistate model.

DMOIT: denoised multi-omics integration approach based on transformer multi-head self-attention mechanism.

Multi-omics data integration has become increasingly crucial for a deeper understanding of the complexity of biological systems. However, effectively integrating and analyzing multi-omics data remains challenging due to their heterogeneity and high dimensionality. Existing methods often struggle with noise, redundant features, and the complex interactions between different omics layers, leading to suboptimal performance.

Enhanced adaptive permutation test with negative binomial distribution in genome-wide omics datasets.

Background: The permutation test has been widely used to provide the p-values of statistical tests when the standard test statistics do not follow parametric null distributions. However, the permutation test may require huge numbers of iterations, especially when the detection of very small p-values is required for multiple testing adjustments in the analysis of datasets with a large number of features.

Objective: To overcome this computational burden, we suggest a novel enhanced adaptive permutation test that estimates p-values using the negative binomial (NB) distribution.

Kernel-based hierarchical structural component models for pathway analysis on survival phenotype.

Background: High-throughput sequencing, particularly RNA-sequencing (RNA-seq), has advanced differential gene expression analysis, revealing pathways involved in various biological conditions. Traditional pathway-based methods generally consider pathways independently, overlooking the correlations among them and ignoring quite a few overlapping biomarkers between pathways. In addition, most pathway-based approaches assume that biomarkers have linear effects on the phenotype of interest.

Multipartite network analysis to identify environmental and genetic associations of metabolic syndrome in the Korean population.

Network analysis has become a crucial tool in genetic research, enabling the exploration of associations between genes and diseases. Its utility extends beyond genetics to include the assessment of environmental factors. Unipartite network analysis is commonly used in genomics to visualize initial insights and relationships among variables.

A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data.

Metabolic syndrome (MetS) is a complex disorder characterized by a cluster of metabolic abnormalities, including abdominal obesity, hypertension, elevated triglycerides, reduced high-density lipoprotein cholesterol, and impaired glucose tolerance. It poses a significant public health concern, as individuals with MetS are at an increased risk of developing cardiovascular diseases and type 2 diabetes. Early and accurate identification of individuals at risk for MetS is essential.

Single stranded 1D-helical Cu coordination polymer for ultra-sensitive ammonia sensing at room temperature.

With the increasing demand for ammonia applications, there is a significant focus on improving NH detection performance at room temperature. In this study, we introduce a groundbreaking NH gas sensor based on Cu(I)-based coordination polymers, featuring semiconducting, single stranded 1D-helical nanowires constructed from Cu-Cl and -methylthiourea (MTCP). The MTCP demonstrates an exceptional response to NH gas (>900% at 100 ppm) and superior selectivity at room temperature compared to current materials.

The association between sleep quality and accelerated epigenetic aging with metabolic syndrome in Korean adults.

Background: Healthy sleep is vital for maintaining optimal mental and physical health. Accumulating evidence suggests that sleep loss and disturbances play a significant role in the biological aging process, early onset of disease, and reduced lifespan. While numerous studies have explored the association between biological aging and its drivers, only a few studies have examined its relationship with sleep quality.

Sex and aging signatures of proteomics in human cerebrospinal fluid identify distinct clusters linked to neurodegeneration.

Sex and age are major risk factors for chronic diseases. Recent studies examining age-related molecular changes in plasma provided insights into age-related disease biology. Cerebrospinal fluid (CSF) proteomics can provide additional insights into brain aging and neurodegeneration.

K-Track-Covid: interactive web-based dashboard for analyzing geographical and temporal spread of COVID-19 in South Korea.

The COVID-19 pandemic has necessitated the development of robust tools for tracking and modeling the spread of the virus. We present 'K-Track-Covid,' an interactive web-based dashboard developed using the R Shiny framework, to offer users an intuitive dashboard for analyzing the geographical and temporal spread of COVID-19 in South Korea. Our dashboard employs dynamic user interface elements, employs validated epidemiological models, and integrates regional data to offer tailored visual displays.

Oxidative stress in patients with coronavirus disease and end-stage renal disease: a pilot study.

Background: Oxidative stress, an imbalance between reactive oxygen species production and antioxidant capacity, increases in patients with coronavirus disease (COVID-19) or renal impairment. We investigated whether combined COVID-19 and end-stage renal disease (ESRD) would increase oxidative stress levels compared to each disease alone.

Methods: Oxidative stress was compared among three groups.