The rapid increase in genetic dataset volume has demanded extensive adoption of biological knowledge to reduce the computational complexity, and the biological pathway is one well-known source of such knowledge. In this regard, we have introduced a novel statistical method that enables the pathway-based association study of large-scale genetic dataset—namely, PHARAOH. However, researcher-level application of the PHARAOH method has been limited by a lack of generally used file formats and the absence of various quality control options that are essential to practical analysis. In order to overcome these limitations, we introduce our integration of the PHARAOH method into our recently developed all-in-one workbench. The proposed new PHARAOH program not only supports various de facto standard genetic data formats but also provides many quality control measures and filters based on those measures. We expect that our updated PHARAOH provides advanced accessibility of the pathway-level analysis of large-scale genetic datasets to researchers.
Dataset ; Genetic Association Studies ; Methods ; Quality Control

Gene-gene interaction (GGI) analysis is known to play an important role in explaining missing heritability. Many previous studies have already proposed software to analyze GGI, but most methods focus on a binary phenotype in a case-control design. In this study, we developed “Hierarchical structural CoMponent analysis of Gene-Gene Interactions” (HisCoM-GGI) software for GGI analysis with a continuous phenotype. The HisCoM-GGI method considers hierarchical structural relationships between genes and single nucleotide polymorphisms (SNPs), enabling both gene-level and SNP-level interaction analysis in a single model. Furthermore, this software accepts various types of genomic data and supports data management and multithreading to improve the efficiency of genome-wide association study data analysis. We expect that HisCoM-GGI software will provide advanced accessibility to researchers in genetic interaction studies and a more effective way to understand biological mechanisms of complex diseases.
Case-Control Studies ; Genome-Wide Association Study ; Methods ; Phenotype ; Polymorphism, Single Nucleotide ; Statistics as Topic

PURPOSE: This study investigated the effects of shoulder protraction exercise according to weight by examining the surface electromyography (EMG) amplitude in the serratus anterior (SA), upper trapezius (UT), and pectoralis major (PM) as well as the activity ratio of each muscle. METHODS: Twenty three winging scapula subjects participated in the study. The subjects performed scapula protraction at shoulder 90° flexion and 60° horizontal abduction with up to four (none, 1kg, 1.5kg, and 2kg) dumbbells in the supine position. The EMG data were collected from the dominant side muscles during a shoulder protraction exercise according to weight in the supine position. One way repeated measures analysis of variance (ANOVA) was used to compare the normalized activities of the SA, UT, and PM and the ratios of PM/SA and UT/SA. RESULTS: The results showed that the activities of both the SA and UT were highest for the shoulder protraction exercise at 2kg in the supine position. The UT/SA ratio also was the lowest for exercise at 2kg. On the other hand, the activities of both the UT and PM/SA ratio were similar under all conditions. CONCLUSION: These results show that there is a need to selectively strengthen the SA muscle in the case of patients with the shoulder dysfunction. In particular, it is necessary to weigh 2kg when performing shoulder protraction exercises in the supine position to activate the SA muscle in patients with a winging scapula.
Electromyography ; Exercise ; Hand ; Humans ; Muscles ; Scapula ; Shoulder ; Superficial Back Muscles ; Supine Position

In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM for pathway analysis of common variants [HisCoM-PCA]) which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis.

In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM for pathway analysis of common variants [HisCoM-PCA]) which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis.

Background: Blepharitis, a chronic inflammatory condition of the eyelid margin, can cause significant discomfort and impair the quality of life. While traditional treatments like eyelid hygiene and medications are often insufficient, non-invasive therapies such as micro-vibration devices are gaining popularity despite minimal empirical validation. This study evaluated the efficacy and safety of a commercially available microvibration device in a rat model of blepharitis. Methods: Blepharitis was induced in rats using complete Freund’s adjuvant injections. The treatment groups included micro-vibration (180 Hz), micro-vibration with thermal enhancement, and pharmacological interventions with cyclosporine or hyaluronic acid. Ocular signs, including tear break-up time (TBUT), fluorescein staining, eyelid swelling, and telangiectasia, were assessed weekly. Immunohistochemical analysis quantified inflammatory cytokines (interleukin [IL]-1β, IL-6, and tumor necrosis factor-α) in ocular tissues. Results: The micro-vibration therapy significantly improved TBUT, reduced eyelid swelling, and lowered inflammatory cytokine levels. Combining thermal therapy with micro-vibration yielded enhanced antiinflammatory effects, though uncontrolled heating caused tissue damage. Pharmacological treatments were effective but less comprehensive compared to micro-vibration therapies. Conclusion Micro-vibration therapy at 180 Hz demonstrated substantial efficacy in alleviating symptoms of blepharitis and reducing inflammation. However, the uncontrolled thermal function of the device posed safety concerns, emphasizing the need for stringent regulation and parameter control in commercially available devices. This study highlights the potential of micro-vibration therapy as a non-invasive treatment for blepharitis while underscoring the importance of device safety.

Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (GxG) interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.
Body Mass Index ; Genome-Wide Association Study ; Hypertension ; Multifactor Dimensionality Reduction ; Obesity ; Resin Cements

Trimethyltin chloride (TMT) has been used as a neurotoxin for inducing brain dysfunction and neuronal death. Neuronal death in the hippocampus by TMT may generate excessive nitric oxide, but there are few studies about nitric oxide synthase enzyme involved in the synthesis of nitric oxide. The purpose of present study is to analyze the TMT toxicity in each region of rat hippocampus. To evaluate the involvement of nitric oxide, we analyzed the effects of aminoguanidine known as a selective inhibitor for inducible nitric oxide synthase on behavioral changes and the hippocampus of rat by TMT toxicity. 6-week-old male Sprague-Dawley rats were administered with a single dose of TMT (8 mg/kg b.w., i.p.) and the control group was similarly administered with distilled water. TMT + aminoguanidine-treated groups were administered with aminoguanidine (10 mg/kg or 100 mg/kg b.w., i.p.) for 3 days prior to TMT injection. The rats were sacrificed 2 days after TMT administration. In the TMT-treated group, a number of cell losses were seen in CA1, CA3 and the dentate gyrus. In the TMT + aminoguanidine-treated group, neuronal death was seen in CA1 and CA3, but reduced in the dentate gyrus compared to the TMT-treated group. Western blot analysis showed that cleaved caspase-3 expression was increased in the TMT-treated group compared to the control group. However, the expression significantly declined in the TMT + aminoguanidine-treated group. The present findings suggest that inducible nitric oxide synthase is involved in neuronal death induced by TMT.
Animals ; Blotting, Western ; Brain ; Caspase 3 ; Dentate Gyrus ; Guanidines ; Hippocampus ; Humans ; Male ; Neurons ; Nitric Oxide ; Nitric Oxide Synthase ; Nitric Oxide Synthase Type II ; Rats ; Rats, Sprague-Dawley ; Trimethyltin Compounds ; Water

Background and Objectives: Self-monitoring of blood pressure (SMBP) is a reliable method used to assess BP accurately. However, patients do not often know how to respond to the measured BP value. We developed a mobile application-based feed-back algorithm (SMBPApp) for tailored recommendations. In this study, we aim to evaluate whether SMBP-App is superior to SMBP alone in terms of BP reduction and drug adherence improvement in patients with hypertension. Methods: Self-Monitoring of blood pressure and Feed-back using APP in TReatment of UnconTrolled Hypertension (SMART-BP) is a prospective, randomized, open-label, multicenter trial to evaluate the efficacy of SMBP-App compared with SMBP alone. Patients with uncomplicated essential hypertension will be randomly assigned to the SMBP-App (90 patients) and SMBP alone (90 patients) groups. In the SMBP group, the patients will perform home BP measurement and receive the standard care, whereas in the SMBP-App group, the patients will receive additional recommendations from the application in response to the obtained BP value. Follow-up visits will be scheduled at 12 and 24 weeks after randomization. The primary endpoint of the study is the mean home systolic BP. The secondary endpoints include the drug adherence, the home diastolic BP, home and office BP. Conclusions SMART-BP is a prospective, randomized, open-label, multicenter trial to evaluate the efficacy of SMBP-App. If we can confirm its efficacy, SMBP-App may be scaled-up to improve the treatment of hypertension.Trial Registration: ClinicalTrials.gov Identifier: NCT04470284

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.