Purpose: We aimed to investigate how genetic predisposition to drinking and gastric cancer (GC) modifies the association between alcohol consumption and GC risk in the Korean population. Materials and Methods: Polygenic risk scores for GC (PRS-GC) and alcohol consumption (PRS-Alcohol) were formulated using genome-wide association results from BioBank Japan. Validation was performed using Korean cohorts (SNUBH-GENIE cohort), incorporating 8,846 controls and 531 patients with GC. Subsequently, these PRSs were applied to an independent Korean cohort of 67,771 participants, including 313 patients with GC during the follow-up for 14 years (KoGES cohort). Results: In KoGES cohort, the influence of alcohol consumption on GC risk was significantly altered by the PRS-GC and exhibited a synergistic interaction effect. PRS-Alcohol itself shows a negative correlation with GC risk. However, when actual alcohol consumption significantly exceeded genetically predicted levels, the risk of alcohol-related GC was notably increased (adjusted hazard ratio, 1.32; 95% confidence interval, 1.01 to 1.72). Heavy drinkers in the high–PRS-GC/low–PRS-Alcohol group had a 2.16 times higher risk of GC than non-to-light drinkers, which was prominent in males. Conclusion Korean drinkers with higher PRS-GC who consume alcohol more than genetically predicted levels are susceptible to GC. PRS-GC and PRS-Alcohol may be beneficial for assessing the impact of alcohol consumption on GC risk in Koreans.

BACKGROUND: Mesenchymal stem cells (MSCs) derived from bone marrow (BM), adipose tissue (AD), and umbilical cord (UC) exhibit therapeutic potential in regenerative medicine. However, their properties, including transcriptomic profiles, vary based on tissue origin, passage stage, and isolation method, complicating their clinical standardization.Addressing these unresolved differences requires comprehensive approaches, such as RNA sequencing, to analyze transcriptomic profiles in detail. METHODS: In this study, RNA-seq was employed to analyze MSC transcriptomes from BM, AD, and UC tissues. UC MSCs were isolated using enzymatic digestion or the Minimal Cube Explant (MCE) method (smumf cells), and transcriptomes of early (P3–4) and late (P10) passages of smumf cells were compared. Differentially expressed genes (DEGs) were identified, followed by transcription factor (TF) and pathway analyses. RESULTS: Fetal MSCs (UC and smumf cells) exhibited distinct transcriptomic profiles compared to adult MSCs (BM and AD), with 2,208 upregulated and 2,594 downregulated DEGs. Key transcription factors, such as E2F1 and NF-jB1, and pathways, including glycolysis, cholesterol biosynthesis, and TNF-a signaling, were enriched in fetal MSCs. smumf cells demonstrated transcriptomic stability between early and late passages, with only 12 DEGs identified. Additionally, smumf cells showed enhanced innate immune responses and cholesterol metabolism compared to enzymatically isolated UC MSCs. CONCLUSION This study provides a comprehensive transcriptomic comparison of MSCs, highlighting the superior transcriptional stability, immunomodulatory capacity, and metabolic flexibility of fetal MSCs, particularly smumf cells. These findings underscore their potential as a reliable cell source for therapeutic applications and encourage further exploration of their clinical application.

This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.

This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.

This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.

This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.

Recently, there has been considerable progress in developing new technologies and equipment for the medical field, including minimally invasive surgeries. Evaluating the effectiveness of these treatments requires study designs like randomized controlled trials.However, due to the nature of certain treatments, randomization is not always feasible, leading to the use of observational studies. The effect size estimated from observational studies is subject to selection bias caused by confounders. One method to reduce this bias is propensity scoring. This study aimed to introduce a propensity score matching process between two groups using a practical example with R. Additionally, Rex, an Excel add-in graphical user interface statistical program, is provided for researchers unfamiliar with R programming.Further techniques, such as matching with three or more groups, propensity score weighting and stratification, and imputation of missing values, are summarized to offer approaches for more complex studies not covered in this tutorial.

Background: Achilles tendon is composed of dense connective tissue and is one of the largest tendons in the body. In veterinary medicine, acute ruptures are associated with impact injury or sharp trauma. Healing of the ruptured tendon is challenging because of poor blood and nerve supply as well as the residual cell population. Platelet-rich plasma (PRP) contains numerous bioactive agents and growth factors and has been utilized to promote healing in bone, soft tissue, and tendons. Objective: The purpose of this study was to evaluate the healing effect of PRP injected into the surrounding fascia of the Achilles tendon after allograft in rabbits. Methods: Donor rabbits (n = 8) were anesthetized and 16 lateral gastrocnemius tendons were fully transected bilaterally. Transected tendons were decellularized and stored at −80°C prior to allograft. The allograft was placed on the partially transected medial gastrocnemius tendon in the left hindlimb of 16 rabbits. The allograft PRP group (n = 8) had 0.3 mL of PRP administered in the tendon and the allograft control group (n = 8) did not receive any treatment. After 8 weeks, rabbits were euthanatized and allograft tendons were transected for macroscopic, biomechanical, and histological assessment. Results: The allograft PRP group exhibited superior macroscopic assessment scores, greater tensile strength, and a histologically enhanced healing process compared to those in the allograft control group. Conclusions Our results suggest administration of PRP on an allograft tendon has a positive effect on the healing process in a ruptured Achilles tendon.

Background: Genome-wide association studies (GWAS) on type 2 diabetes mellitus (T2DM) have identified more than 400 distinct genetic loci associated with diabetes and nearly 120 loci for fasting plasma glucose (FPG) and fasting insulin level to date. However, genetic risk factors for the longitudinal deterioration of FPG have not been thoroughly evaluated. We aimed to identify genetic variants associated with longitudinal change of FPG over time. Methods: We used two prospective cohorts in Korean population, which included a total of 10,528 individuals without T2DM. GWAS of repeated measure of FPG using linear mixed model was performed to investigate the interaction of genetic variants and time, and meta-analysis was conducted. Genome-wide complex trait analysis was used for heritability calculation. In addition, expression quantitative trait loci (eQTL) analysis was performed using the Genotype-Tissue Expression project. Results: A small portion (4%) of the genome-wide single nucleotide polymorphism (SNP) interaction with time explained the total phenotypic variance of longitudinal change in FPG. A total of four known genetic variants of FPG were associated with repeated measure of FPG levels. One SNP (rs11187850) showed a genome-wide significant association for genetic interaction with time. The variant is an eQTL for NOC3 like DNA replication regulator (NOC3L) gene in pancreas and adipose tissue. Furthermore, NOC3L is also differentially expressed in pancreatic β-cells between subjects with or without T2DM. However, this variant was not associated with increased risk of T2DM nor elevated FPG level. Conclusion We identified rs11187850, which is an eQTL of NOC3L, to be associated with longitudinal change of FPG in Korean population.

Background: Nonsteroidal anti-inflammatory drugs (NSAID) are currently among the most prescribed medications worldwide to relieve pain and reduce inflammation, especially in patients suffering osteoarthritis (OA). However, NSAIDs are known to have adverse effects on the gastrointestinal system. If a gastric ulcer occurs, planned OA treatment needs to be changed, incurring additional treatment costs and causing discomfort for both patients and clinicians. Therefore, it is necessary to create a gastric ulcer prediction model that can reflect the detailed health status of each individual and to use it when making treatment plans. Methods: Using sample cohort data from 2008 to 2013 from the National Health Insurance Service in South Korea, we developed a prediction model for NSAID-induced gastric ulcers using machine-learning algorithms and investigated new risk factors associated with medication and comorbidities. Results: The population of the study consisted of 30,808 patients with OA who were treated with NSAIDs between 2008 and 2013. After a 2-year follow-up, these patients were divided into two groups: without gastric ulcer (n=29,579) and with gastric ulcer (n=1,229). Five machine-learning algorithms were used to develop the prediction model, and a gradient boosting machine (GBM) was selected as the model with the best performance (area under the curve, 0.896; 95% confidence interval, 0.883–0.909). The GBM identified 5 medications (loxoprofen, aceclofenac, talniflumate, meloxicam, and dexibuprofen) and 2 comorbidities (acute upper respiratory tract infection [AURI] and gastroesophageal reflux disease) as important features. AURI did not have a dose-response relationship, so it could not be interpreted as a significant risk factor even though it was initially detected as an important feature and improved the prediction performance. Conclusions We obtained a prediction model for NSAID-induced gastric ulcers using the GBM method. Since personal prescription period and the severity of comorbidities were considered numerically, individual patients’ risk could be well reflected. The prediction model showed high performance and interpretability, so it is meaningful to both clinicians and NSAID users.