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.

Forecasting the spread of COVID-19 based on policy, vaccination, and Omicron data.

The COVID-19 pandemic caused by the novel SARS-COV-2 virus poses a great risk to the world. During the COVID-19 pandemic, observing and forecasting several important indicators of the epidemic (like new confirmed cases, new cases in intensive care unit, and new deaths for each day) helped prepare the appropriate response (e.g.

Genome- and epigenome-wide association studies identify susceptibility of CpG sites and regions for metabolic syndrome in a Korean population.

Background: While multiple studies have investigated the relationship between metabolic syndrome (MetS) and its related traits (fasting glucose, triglyceride, HDL cholesterol, blood pressure, waist circumference) and DNA methylation, our understanding of the epigenetic mechanisms in MetS remains limited. Therefore, we performed an epigenome-wide meta-analysis of blood DNA methylation to identify differentially methylated probes (DMPs) and differentially methylated regions (DMRs) associated with MetS and its components using two independent cohorts comprising a total of 2,334 participants. We also investigated the specific genetic effects on DNA methylation, identified methylation quantitative trait loci (meQTLs) through genome-wide association studies and further utilized Mendelian randomization (MR) to assess how these meQTLs subsequently influence MetS status.

Urinary microbiome-based metagenomic signature for the noninvasive diagnosis of hepatocellular carcinoma.

Background: Gut microbial dysbiosis is implicated in chronic liver disease and hepatocellular carcinoma (HCC), but the role of microbiomes from various body sites remains unexplored. We assessed disease-specific alterations in the urinary microbiome in HCC patients, investigating their potential as diagnostic biomarkers.

Methods: We performed cross-sectional analyses of urine samples from 471 HCC patients and 397 healthy controls and validated the results in an independent cohort of 164 HCC patients and 164 healthy controls.

Type 2 diabetes and its genetic susceptibility are associated with increased severity and mortality of COVID-19 in UK Biobank.

Type 2 diabetes (T2D) is known as one of the important risk factors for the severity and mortality of COVID-19. Here, we evaluate the impact of T2D and its genetic susceptibility on the severity and mortality of COVID-19, using 459,119 individuals in UK Biobank. Utilizing the polygenic risk scores (PRS) for T2D, we identified a significant association between T2D or T2D PRS, and COVID-19 severity.

An ultra-sensitive colloidal quantum dot infrared photodiode exceeding 100 000% external quantum efficiency photomultiplication.

In this study, we present ultrasensitive infrared photodiodes based on PbS colloidal quantum dots (CQDs) using a double photomultiplication strategy that utilizes the accumulation of both electron and hole carriers. While electron accumulation was induced by ZnO trap states that were created by treatment in a humid atmosphere, hole accumulation was achieved using a long-chain ligand that increased the barrier to hole collection. Interestingly, we obtained the highest responsivity in photo-multiplicative devices with the long ligands, which contradicts the conventional belief that shorter ligands are more effective for optoelectronic devices.

The clinical course of hospitalized COVID-19 patients and aggravation risk prediction models: a retrospective, multi-center Korean cohort study.

Background: Understanding the clinical course and pivotal time points of COVID-19 aggravation is critical for enhancing patient monitoring. This retrospective, multi-center cohort study aims to identify these significant time points and associate them with potential risk factors, leveraging data from a sizable cohort with mild-to-moderate symptoms upon admission.

Methods: This study included data from 1,696 COVID-19 patients with mild-to-moderate clinical severity upon admission across multiple hospitals in Daegu-Kyungpook Province (Daegu dataset) between February 18 and early March 2020 and 321 COVID-19 patients at Seoul Boramae Hospital (Boramae dataset) collected from February to July 2020.

An analysis of the waning effect of COVID-19 vaccinations.

Vaccine development is one of the key efforts to control the spread of coronavirus disease 2019 (COVID-19). However, it has become apparent that the immunity acquired through vaccination is not permanent, known as the waning effect. Therefore, monitoring the proportion of the population with immunity is essential to improve the forecasting of future waves of the pandemic.

Development of New Stringency Indices for Nonpharmacological Social Distancing Policies Implemented in Korea During the COVID-19 Pandemic: Random Forest Approach.

Background: In the absence of an effective treatment method or vaccine, the outbreak of the COVID-19 pandemic elicited a wide range of unprecedented restriction policies aimed at mitigating and suppressing the spread of the SARS-CoV-2 virus. These policies and their Stringency Index (SI) of more than 160 countries were systematically recorded in the Oxford COVID-19 Government Response Tracker (OxCGRT) data set. The SI is a summary measure of the overall strictness of these policies.

A multi-threaded approach to genotype pattern mining for detecting digenic disease genes.

To locate disease-causing DNA variants on the human gene map, the customary approach has been to carry out a genome-wide association study for one variant after another by testing for genotype frequency differences between individuals affected and unaffected with disease. So-called digenic traits are due to the combined effects of two variants, often on different chromosomes, while individual variants may have little or no effect on disease. Machine learning approaches have been developed to find variant pairs underlying digenic traits.

Gene-diet interaction analysis using novel weighted food scores discovers the adipocytokine signaling pathway associated with the development of type 2 diabetes.

Introduction: The influence of dietary patterns measured using Recommended Food Score (RFS) with foods with high amounts of antioxidant nutrients for Type 2 diabetes (T2D) was analyzed. Our analysis aims to find associations between dietary patterns and T2D and conduct a gene-diet interaction analysis related to T2D.

Methods: Data analyzed in the current study were obtained from the Korean Genome and Epidemiology Study Cohort.

Clinical features of COVID-19 among patients with end-stage renal disease on hemodialysis in the context of high vaccination coverage during the omicron surge period: a retrospective cohort study.

Background: We determined the clinical presentation and outcomes of the Omicron variant of severe acute respiratory syndrome coronavirus 2 infection in hemodialysis patients and identified the risk factors for severe coronavirus disease (COVID-19) and mortality in the context of high vaccination coverage.

Methods: This was a retrospective cohort study involving hemodialysis patients who were vaccinated against COVID-19 during March-September 2022, when the Omicron variant was predominant, and the COVID-19 vaccination rate was high. The proportion of people with severe COVID-19 or mortality was evaluated using univariate logistic regression.

Longitudinal study investigating serum metabolites and their association with type 2 diabetes risk in a Korean population.

Aim: The lack of longitudinal metabolomics data and the statistical techniques to analyse them has limited the understanding of the metabolite levels related to type 2 diabetes (T2D) onset. Thus, we carried out logistic regression analysis and simultaneously proposed new approaches based on residuals of multiple logistic regression and geometric angle-based clustering for the analysis in T2D onset-specific metabolic changes.

Materials And Methods: We used the sixth, seventh and eighth follow-up data from 2013, 2015 and 2017 among the Korea Association REsource (KARE) cohort data.