Data pipeline for real-time energy consumption data management and prediction.

With the increasing utilization of data in various industries and applications, constructing an efficient data pipeline has become crucial. In this study, we propose a machine learning operations-centric data pipeline specifically designed for an energy consumption management system. This pipeline seamlessly integrates the machine learning model with real-time data management and prediction capabilities. The overall architecture of our proposed pipeline comprises several key components, including Kafka, InfluxDB, Telegraf, Zookeeper, and Grafana. To enable accurate energy consumption predictions, we adopt two time-series prediction models, long short-term memory (LSTM), and seasonal autoregressive integrated moving average (SARIMA). Our analysis reveals a clear trade-off between speed and accuracy, where SARIMA exhibits faster model learning time while LSTM outperforms SARIMA in prediction accuracy. To validate the effectiveness of our pipeline, we measure the overall processing time by optimizing the configuration of Telegraf, which directly impacts the load in the pipeline. The results are promising, as our pipeline achieves an average end-to-end processing time of only 0.39 s for handling 10,000 data records and an impressive 1.26 s when scaling up to 100,000 records. This indicates 30.69-90.88 times faster processing compared to the existing Python-based approach. Additionally, when the number of records increases by ten times, the increased overhead is reduced by 3.07 times. This verifies that the proposed pipeline exhibits an efficient and scalable structure suitable for real-time environments.

Attention-based speech feature transfer between speakers.

In this study, we propose a simple yet effective method for incorporating the source speaker's characteristics in the target speaker's speech. This allows our model to generate the speech of the target speaker with the style of the source speaker. To achieve this, we focus on the attention model within the speech synthesis model, which learns various speaker features such as spectrogram, pitch, intensity, formant, pulse, and voice breaks. The model is trained separately using datasets specific to the source and target speakers. Subsequently, we replace the attention weights learned from the source speaker's dataset with the attention weights from the target speaker's model. Finally, by providing new input texts to the target model, we generate the speech of the target speaker with the styles of the source speaker. We validate the effectiveness of our model through similarity analysis utilizing five evaluation metrics and showcase real-world examples.

An experimental study of animating-based facial image manipulation in online class environments.

Recent advances in artificial intelligence technology have significantly improved facial image manipulation, which is known as Deepfake. Facial image manipulation synthesizes or replaces a region of the face in an image with that of another face. The techniques for facial image manipulation are classified into four categories: (1) entire face synthesis, (2) identity swap, (3) attribute manipulation, and (4) expression swap. Out of them, we focus on expression swap because it effectively manipulates only the expression of the face in the images or videos without creating or replacing the entire face, having advantages for the real-time application. In this study, we propose an evaluation framework of the expression swap models targeting the real-time online class environments. For this, we define three kinds of scenarios according to the portion of the face in the entire image considering actual online class situations: (1) attendance check (Scenario 1), (2) presentation (Scenario 2), and (3) examination (Scenario 3). Considering the manipulation on the online class environments, the framework receives a single source image and a target video and generates the video that manipulates a face of the target video to that in the source image. To this end, we select two models that satisfy the conditions required by the framework: (1) first order model and (2) GANimation. We implement these models in the framework and evaluate their performance for the defined scenarios. Through the quantitative and qualitative evaluation, we observe distinguishing properties of the used two models. Specifically, both models show acceptable results in Scenario 1, where the face occupies a large portion of the image. However, their performances are significantly degraded in Scenarios 2 and 3, where the face occupies less portion of the image; the first order model causes relatively less loss of image quality than GANimation in the result of the quantitative evaluation. In contrast, GANimation has the advantages of representing facial expression changes compared to the first order model. Finally, we devise an architecture for applying the expression swap model to the online video conferencing application in real-time. In particular, by applying the expression swap model to widely used online meeting platforms such as Zoom, Google Meet, and Microsoft Teams, we demonstrate its feasibility for real-time online classes.

Association between Helicobacter pylori Infection and Eosinophilic Esophagitis / 대한소화기학회지

Background/Aims@#This study examined the association between eosinophilic esophagitis (EoE) and Helicobacter pylori (H. pylori) infection. @*Methods@#A single tertiary referral center case-control study was performed. EoE patients diagnosed at Seoul National University Bundang Hospital from July 2003 to March 2022 were reviewed retrospectively. Forty-five EoE patients were included in the analysis.For each EoE patient, two age and sex-matched normal controls were selected randomly from an outpatient population who received upper gastrointestinal endoscopy. @*Results@#Although 17 out of 90 (18.9%) controls had a H. pylori infection, only two out of 45 (4.4%) EoE patients showed evidence of a H. pylori infection. EoE was inversely associated with a H. pylori infection (odds ratio 0.20, 95% confidence interval 0.04–0.91, p=0.044). @*Conclusions@#An inverse association was observed between H. pylori infection and EoE. Further prospective studies will be needed to validate the protective effects of H. pylori infection for EoE.

The Clinicopathological Features of Mixed Carcinoma in 7,215Patients with Gastric Cancer in a Tertiary Hospital in South Korea

Background/Aims@#There are few reports regarding mixed carcinoma, defined as a mixture of glandular and poorly cohesive components, in patients with gastric cancer (GC). The aim of this study was to evaluate the proportion and characteristics of mixed carcinoma in GC patients. @*Methods@#A total of 7,215 patients diagnosed with GC at Seoul National University Bundang Hospital were enrolled from March 2011 to February 2020. GC was divided into four groups (wellmoderately differentiated GC, poorly differentiated GC, poorly cohesive carcinoma, and mixed carcinoma). The proportion of each GC type and the clinicopathological features were analyzed and divided into early GC and advanced GC. @*Results@#The proportion of mixed carcinoma was 10.9% (n=787). In early GC, submucosal invasion was the most common in poorly differentiated (53.7%), and mixed carcinoma ranked second (41.1%). Mixed carcinoma showed the highest proportion of lymph node metastasis in early GC (23.0%) and advanced GC (78.3%). In advanced GC, the rate of distant metastasis was 3.6% and 3.9% in well-moderately differentiated GC and mixed carcinoma, respectively, lower than that in poorly differentiated GC (6.4%) and poorly cohesive carcinoma (5.7%), without statistical significance. @*Conclusions@#Mixed carcinoma was associated with lymph node metastasis compared to other histological GC subtypes. And it showed relatively common submucosal invasion in early GC, but the rates of venous invasion and distant metastasis were lower in advanced GC. Further research is needed to uncover the mechanism underlying these characteristics of mixed carcinoma (Trial registration number: NCT04973631).

Concomitant ankylosing spondylitis can increase the risk of biologics or small molecule therapies to control inflammatory bowel disease

Background/Aims@#Patients with inflammatory bowel disease (IBD) are diagnosed with ankylosing spondylitis (AS) often. However, the disease course of patients with both IBD and AS is not well understood. This study aims to evaluate the effect of concomitant AS on IBD outcomes. @*Methods@#Among the 4,722 patients with IBD who were treated in 3 academic hospitals from 2004 to 2021, 55 were also diagnosed with AS (IBD-AS group). Based on patients’ electronic medical records, the outcomes of IBD in IBD-AS group and IBD group without AS (IBD-only group) were appraised. @*Results@#The proportion of patients treated with biologics or small molecule therapies was significantly higher in IBD-AS group than the proportion in IBD-only group (27.3% vs. 12.7%, P= 0.036). Patients with both ulcerative colitis and AS had a significantly higher risk of biologics or small molecule therapies than patients with only ulcerative colitis (P< 0.001). For univariable logistic regression, biologics or small molecule therapies were associated with concomitant AS (odds ratio, 4.099; 95% confidence interval, 1.863–9.021; P< 0.001) and Crohn’s disease (odds ratio, 3.552; 95% confidence interval, 1.590–7.934; P= 0.002). @*Conclusions@#Concomitant AS is associated with the high possibility of biologics or small molecule therapies for IBD. IBD patients who also had AS may need more careful examination and active treatment to alleviate the severity of IBD.

Practical strategies for the prevention and management of chronic postsurgical pain

Chronic postsurgical pain (CPSP) is a multifactorial condition that affects a significant proportion of patients undergoing surgery. The prevention and management of CPSP require the identification of preoperative risk factors to screen high-risk patients and establish appropriate perioperative pain management plans to prevent its development. Active postoperative pain management should be provided to prevent CPSP in patients with severe pain following surgery. These tasks have become important for perioperative team members in the management of CPSP. This review article provides a comprehensive overview of the latest research on the role of perioperative team members in preventing and managing CPSP. Additionally, it highlights practical strategies that can be employed in clinical practice, covering the definition and risk factors for CPSP, including preoperative, intraoperative, and postoperative factors, as well as a risk prediction model. The article also explores various treatments for CPSP, as well as preventive measures, including preemptive analgesia, regional anesthesia, pharmacological interventions, psychoeducational support, and surgical technique modification. This article emphasizes the importance of a comprehensive perioperative pain management plan that includes multidisciplinary interventions, using the transitional pain service as an example. By adopting a multidisciplinary and collaborative approach, perioperative team members can improve patient outcomes, enhance patient satisfaction, and reduce healthcare costs. However, further research is necessary to establish targeted interventions to effectively prevent and manage CPSP.

Analysis of the gut microbiome using extracellular vesicles in the urine of patients with colorectal cancer

Background/Aims@#We evaluated the gut microbiome using extracellular vesicles (EVs) in the urine of patients with colorectal cancer (CRC) to determine whether gut-microbe-derived EVs could be a potential biomarker for the diagnosis of CRC. @*Methods@#EVs were isolated from the urine of patients with CRC and healthy controls. DNA was extracted from the EVs, and the bacterial composition was analyzed using next-generation sequencing of the 16S rRNA. @*Results@#A total of 91 patients with CRC and 116 healthy controls were enrolled. We found some specific microbiomes that were more or less abundant in the CRC group than in the control group. The alpha-diversity of the gut microbiome was significantly lower in the CRC group than in the control group. A significant difference was observed in the beta-diversity between the groups. The alpha-diversity indices between patients with early- and late-stage CRC showed conflicting results; however, there was no significant difference in the beta-diversity according to the stage of CRC. There was no difference in the alpha- and beta-diversity of the gut microbiome corresponding to the location of CRC (proximal vs. distal). @*Conclusions@#A distinct gut microbiome is reflected in the urine EVs of patients with CRC compared with that in the healthy controls. Microbial signatures from EVs in urine could serve as potential biomarkers for the diagnosis of CRC.

Real-world effectiveness and safety of ustekinumab induction therapy for Korean patients with Crohn’s disease: a KASID prospective multicenter study

Background/Aims@#We investigated the real-world effectiveness and safety of ustekinumab (UST) as induction treatment for Koreans with Crohn’s disease (CD). @*Methods@#CD patients who started UST were prospectively enrolled from 4 hospitals in Korea. All enrolled patients received intravenous UST infusion at week 0 and subcutaneous UST injection at week 8. Clinical outcomes were assessed using Crohn’s Disease Activity Index (CDAI) scores at weeks 8 and 20 among patients with active disease (CDAI ≥150) at baseline. Clinical remission was defined as a CDAI <150, and clinical response was defined as a reduction in CDAI ≥70 points from baseline. Safety and factors associated with clinical remission at week 20 were also analyzed. @*Results@#Sixty-five patients were enrolled between January 2019 and December 2020. Among 49 patients with active disease at baseline (CDAI ≥150), clinical remission and clinical response at week 8 were achieved in 26 (53.1%) and 30 (61.2%) patients, respectively. At week 20, 27 (55.1%) and 35 (71.4%) patients achieved clinical remission and clinical response, respectively. Twenty-seven patients (41.5%) experienced adverse events, with serious adverse events in 3 patients (4.6%). One patient (1.5%) stopped UST therapy due to poor response. Underweight (body mass index <18.5 kg/m2) (odds ratio [OR], 0.085; 95% confidence interval [CI], 0.014–0.498; P=0.006) and elevated C-reactive protein at baseline (OR, 0.133; 95% CI, 0.022–0.823; P=0.030) were inversely associated with clinical remission at week 20. @*Conclusions@#UST was effective and well-tolerated as induction therapy for Korean patients with CD.

Impact of Body Mass Index on Survival Depending on Sex in 14,688 Patients with Gastric Cancer in a Tertiary Hospital in South Korea

Background/Aims@#The incidence and prognosis of gastric cancer (GC) shows sex difference.This study aimed to evaluate the effect of body mass index (BMI) on GC survival depending on sex. @*Methods@#The sex, age, location, histology, TNM stages, BMI, and survival were analyzed in GC patients from May 2003 to February 2020 at the Seoul National University Bundang Hospital. @*Results@#Among 14,688 patients, there were twice as many males (66.6%) as females (33.4%).However, under age 40 years, females (8.6%) were more prevalent than males (3.1%). Cardia GC in males showed a U-shaped distribution for underweight (9.6%), normal (6.4%), overweight (6.1%), obesity (5.6%), and severe obesity (9.3%) but not in females (p=0.003). Females showed decreased proportion of diffuse-type GC regarding BMI (underweight [59.9%], normal [56.8%], overweight [49.5%], obesity [44.8%], and severe obesity [41.7%]), but males did not (p<0.001). Both sexes had the worst prognosis in the underweight group (p<0.001), and the higher BMI, the better prognosis in males, but not females. Sex differences in prognosis according to BMI tended to be more prominent in males than in females in subgroup analysis of TNM stages I, II, and III and the operative treatment group. @*Conclusions@#GC-specific survival was affected by BMI in a sex-dependent manner. These differences may be related to genetic, and environmental, hormonal factors; body composition; and muscle mass (Trial registration number: NCT04973631).

Long-term Effects of the Eradication of Helicobacter pylori on Metabolic Parameters, Depending on Sex, in South Korea

Background/Aims@#Helicobacter pylori (HP) infection is positively associated with metabolic syndrome (MS). However, the long-term effects of eradication therapy on MS and sex differences have not been thoroughly studied. We aimed to investigate the long-term effects of HP eradication on MS and sex differences. @*Methods@#This study included 2,267 subjects who visited a tertiary referral center between May 2003 and May 2019. HP was diagnosed by histology, a Campylobacter-like organism test, and culture, and the subjects were prospectively followed up. The participants were categorized into three groups: HP uninfected, HP infected but non-eradicated, and HP eradicated. The baseline characteristics and changes in metabolic parameters after HP eradication were compared over a 5-year follow-up period. @*Results@#Among 1,521 subjects, there was no difference in baseline metabolic parameters between the HP-uninfected (n=509) and HP-infected (n=1,012) groups, regardless of sex. Analysis of the metabolic parameters during follow-up among HP-uninfected (n=509), HP-non-eradicated (n=346), and HP-eradicated (n=666) groups showed that high-density lipoprotein (HDL) and the body mass index (BMI) increased after eradication, with a significant difference at 1-year of follow-up. In females, HDL increased after eradication (p=0.023), and the BMI increased after eradication in male subjects (p=0.010). After propensity score matching, the HDL change in female remained significant, but the statistical significance of the change in BMI in the male group became marginally significant (p=0.089). @*Conclusions@#HP eradication affected metabolic parameters differently depending on sex. HDL significantly increased only in females over time, especially at 1-year of follow-up. In contrast, BMI showed an increasing tendency over time in males, especially at the 1-year follow-up.

Potential of Gut Microbe-Derived Extracellular Vesicles to Differentiate Inflammatory Bowel Disease Patients from Healthy Controls

Background/Aims@#This study aimed to evaluate the potential of the stool microbiome and gut microbe-derived extracellular vesicles (EVs) to differentiate between patients with inflammatory bowel disease (IBD) and healthy controls, and to predict relapse in patients with IBD. @*Methods@#Metagenomic profiling of the microbiome and bacterial EVs in stool samples of controls (n=110) and patients with IBD (n=110) was performed using 16S rRNA sequencing and then compared. Patients with IBD were divided into two enterotypes based on their microbiome, and the cumulative risk of relapse was evaluated. @*Results@#There was a significant difference in the composition of the stool microbiome and gut microbe-derived EVs between patients with IBD and controls. The alpha diversity of the microbiome in patients with IBD was significantly lower than that in controls, while the beta diversity also differed significantly between the two groups. These findings were more prominent in gut microbe-derived EVs than in the stool microbiome. The survival curve tended to be different for enterotypes based on the gut microbe-derived EVs; however, this difference was not statistically significant (log-rank test, p=0.166). In the multivariable analysis, elevated fecal calprotectin (>250 mg/kg) was the only significant risk factor associated with relapse (adjusted hazard ratio, 3.147; 95% confidence interval, 1.545 to 6.408; p=0.002). @*Conclusions@#Analysis of gut microbe-derived EVs is better at differentiating patients with IBD from healthy controls than stool microbiome analysis.

Development and Validation of Models to Predict Lymph Node Metastasis in Early Gastric Cancer Using Logistic Regression and Gradient Boosting Machine Methods / Journal of the Korean Cancer Association, 대한암학회지

Purpose@#To identify important features of lymph node metastasis (LNM) and develop a prediction model for early gastric cancer (EGC) using a gradient boosting machine (GBM) method. @*Materials and Methods@#The clinicopathologic data of 2556 patients with EGC who underwent gastrectomy were used as training set and the internal validation set (set 1) at a ratio of 8:2. Additionally, 548 patients with EGC who underwent endoscopic submucosal dissection (ESD) as the initial treatment were included in the external validation set (set 2). The GBM model was constructed, and its performance was compared with that of the Japanese guidelines. @*Results@#LNM was identified in 12.6% (321/2556) of the gastrectomy group (training set & set 1) and 4.3% (24/548) of the ESD group (set 2). In the GBM analysis, the top five features that most affected LNM were lymphovascular invasion, depth, differentiation, size, and location. The accuracy, sensitivity, specificity, and the area under the receiver operating characteristics of set 1 were 0.566, 0.922, 0.516, and 0.867, while those of set 2 were 0.810, 0.958, 0.803, and 0.944, respectively. When the sensitivity of GBM was adjusted to that of Japanese guidelines (beyond the expanded criteria in set 1 [0.922] and eCuraC-2 in set 2 [0.958]), the specificities of GBM in sets 1 and 2 were 0.516 (95% confidence interval, 0.502-0.523) and 0.803 (0.795-0.805), while those of the Japanese guidelines were 0.502 (0.488-0.509) and 0.788 (0.780-0.790), respectively. @*Conclusion@#The GBM model showed good performance comparable with the eCura system in predicting LNM risk in EGCs.

Large-scale digital forensic investigation for Windows registry on Apache Spark.

In this study, we investigate large-scale digital forensic investigation on Apache Spark using a Windows registry. Because the Windows registry depends on the system on which it operates, the existing forensic methods on the Windows registry have been targeted on the Windows registry in a single system. However, it is a critical issue to analyze large-scale registry data collected from several Windows systems because it allows us to detect suspiciously changed data by comparing the Windows registry in multiple systems. To this end, we devise distributed algorithms to analyze large-scale registry data collected from multiple Windows systems on the Apache Spark framework. First, we define three main scenarios in which we classify the existing registry forensic studies into them. Second, we propose an algorithm to load the Windows registry into the Hadoop distributed file system (HDFS) for subsequent forensics. Third, we propose a distributed algorithm for each defined forensic scenario using Apache Spark operations. Through extensive experiments using eight nodes in an actual distributed environment, we demonstrate that the proposed method can perform forensics efficiently on large-scale registry data. Specifically, we perform forensics on 1.52 GB of Windows registry data collected from four computers and show that the proposed algorithms can reduce the processing time by up to approximately 3.31 times, as we increase the number of CPUs from 1 to 8 and the number of worker nodes from 2 to 8. Because the distributed algorithms on Apache Spark require the inherent network and MapReduce overheads, this improvement of the processing performance verifies the efficiency and scalability of the proposed algorithms.

Effective Model Update for Adaptive Classification of Text Streams in a Distributed Learning Environment.

In this study, we propose dynamic model update methods for the adaptive classification model of text streams in a distributed learning environment. In particular, we present two model update strategies: (1) the entire model update and (2) the partial model update. The former aims to maximize the model accuracy by periodically rebuilding the model based on the accumulated datasets including recent datasets. Its learning time incrementally increases as the datasets increase, but we alleviate the learning overhead by the distributed learning of the model. The latter fine-tunes the model only with a limited number of recent datasets, noting that the data streams are dependent on a recent event. Therefore, it accelerates the learning speed while maintaining a certain level of accuracy. To verify the proposed update strategies, we extensively apply them to not only fully trainable language models based on CNN, RNN, and Bi-LSTM, but also a pre-trained embedding model based on BERT. Through extensive experiments using two real tweet streaming datasets, we show that the entire model update improves the classification accuracy of the pre-trained offline model; the partial model update also improves it, which shows comparable accuracy with the entire model update, while significantly increasing the learning speed. We also validate the scalability of the proposed distributed learning architecture by showing that the model learning and inference time decrease as the number of worker nodes increases.

A comparative experimental study of distributed storage engines for big spatial data processing using GeoSpark.

With increasing numbers of GPS-equipped mobile devices, we are witnessing a deluge of spatial information that needs to be effectively and efficiently managed. Even though there are several distributed spatial data processing systems such as GeoSpark (Apache Sedona), the effects of underlying storage engines have not been well studied for spatial data processing. In this paper, we evaluate the performance of various distributed storage engines for processing large-scale spatial data using GeoSpark, a state-of-the-art distributed spatial data processing system running on top of Apache Spark. For our performance evaluation, we choose three distributed storage engines having different characteristics: (1) HDFS, (2) MongoDB, and (3) Amazon S3. To conduct our experimental study on a real cloud computing environment, we utilize Amazon EMR instances (up to 6 instances) for distributed spatial data processing. For the evaluation of big spatial data processing, we generate data sets considering four kinds of various data distributions and various data sizes up to one billion point records (38.5GB raw size). Through the extensive experiments, we measure the processing time of storage engines with the following variations: (1) sharding strategies in MongoDB, (2) caching effects, (3) data distributions, (4) data set sizes, (5) the number of running executors and storage nodes, and (6) the selectivity of queries. The major points observed from the experiments are summarized as follows. (1) The overall performance of MongoDB-based GeoSpark is degraded compared to HDFS- and S3-based GeoSpark in our experimental settings. (2) The performance of MongoDB-based GeoSpark is relatively improved in large-scale data sets compared to the others. (3) HDFS- and S3-based GeoSpark are more scalable to running executors and storage nodes compared to MongoDB-based GeoSpark. (4) The sharding strategy based on the spatial proximity significantly improves the performance of MongoDB-based GeoSpark. (5) S3- and HDFS-based GeoSpark show similar performances in all the environmental settings. (6) Caching in distributed environments improves the overall performance of spatial data processing. These results can be usefully utilized in decision-making of choosing the most adequate storage engine for big spatial data processing in a target distributed environment.

Effects of Aging and Gender on the Anorectal Function of Healthy Subjects Assessed with Conventional Anorectal Manometry / 대한소화기학회지

Background/Aims@#Anorectal functions are influenced by gender and age. This study sought to define the normal anorectal pressure values measured with conventional anorectal manometry (ARM) and to evaluate the effects of age and gender on anorectal function in asymptomatic subjects. @*Methods@#Conventional ARM was used to measure the anorectal pressures of 164 asymptomatic healthy subjects, including 86 males and 76 females. @*Results@#The resting anal pressures of males and females aged >60 years were significantly lower than those ≤60 years (males, 44.09±14.22 vs. 57.45±17.69, p<0.001; females, 44.09±14.22 vs. 57.45±17.69, p<0.001). The anal high-pressure zone was significantly lower in older males than in younger males (2.42±0.93 vs. 2.82±0.739, p=0.048). In both age groups (<60 and ≥60 years), the anal squeezing pressures of males were significantly higher than those of females (<60 years old, 168.40±75.94 vs. 119.15±57.53, p=0.001; ≥60 years, 149.61±64.68 vs. 101.3±54.92, p=0.006). @*Conclusions@#The normal anorectal pressure values measured with ARM in males and females were different. Older males and females had lower anal resting pressures than those of the younger subjects, but squeezing pressure was not affected by age.

Increased interleukin-6 and TP53 levels in rotator cuff tendon repair patients with hypercholesterolemia

Background@#A previous study reported that hyperlipidemia increases the incidence of tears in the rotator cuff tendon and affects healing after repair. The aim of our study was to compare the gene and protein expression of torn rotator cuff tendons in patients both with and without hypercholesterolemia. @*Methods@#Thirty patients who provided rotator cuff tendon samples were classified into either a non-hypercholesterolemia group (n=19, serum total cholesterol [TC] <200 mg/dL) and hypercholesterolemia group (n=11, serum TC ≥240 mg/dL) based on their concentrations of serum TC. The expression of various genes of interest, including COL1A1, IGF1, IL-6, MMP2, MMP3, MMP9, MMP13, TNMD, and TP53, was analyzed by real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR). In addition, Western blot analysis was performed on the proteins encoded by interleukin (IL)-6 and TP53 that showed significantly different expression levels in real-time qRT-PCR. @*Results@#Except for IGF1, the gene expression levels of IL-6, MMP2, MMP9, and TP53 were significantly higher in the hypercholesterolemic group than in the non-hypercholesterolemia group. Western blot analysis confirmed significantly higher protein levels of IL-6 and TP53 in the hypercholesterolemic group (p<0.05). @*Conclusions@#We observed an increase in inflammatory cytokine and matrix metalloproteinase (MMP) levels in hypercholesterolemic patients with rotator cuff tears. Increased levels of IL-6 and TP53 were observed at both the mRNA and protein levels. We suggest that the overexpression of IL-6 and TP53 may be a specific feature in rotator cuff disease patients with hypercholesterolemia.

A Randomized, Double-blind, Active-controlled Exploratory Clinical Trial for the Evaluation of the Efficacy and Safety of Goodmorning S Granule® on Constipation / 대한소화기학회지

Background/Aims@#Constipation is a common gastrointestinal disease that reduces the quality of life and incurs considerable medical expenses. Bisacodyl and sodium docusate are generally used to treat constipation. This study assessed the effectiveness and safety of Goodmorning S Granule® (Hanpoong Pharm. Co., Ltd., Wanju, Korea) in functional constipation by a comparison with bisacodyl. @*Methods@#A 2-week randomized, double-blind, active-controlled exploratory clinical trial was conducted to compare the treatment (Goodmorning S Granule® ) with the control (bisacodyl). The efficacy was measured by the changes in transition, Bristol stool type, stomachache, clinical manifestation, defecation time after drug consumption, 36-item short-form survey (SF-36), and the results of improvement evaluation. The safety was evaluated by the incidence of adverse drug events and vital signs. Additional analyses were conducted by dividing the severity according to the proportion of Bristol Stool Scale types 1 and 2. @*Results@#Subjects were randomized to the treatment (n=24) or control (n=26) groups. No significant differences were observed in demographics. After 2 weeks from the baseline, the changes in the complete spontaneous bowel movement (CSBM) were higher in the treatment (4.00±2.62) group than in the control group (1.40±2.34) (p<0.05). The treatment group exhibited significant improvement in the score on the SF-36 questionnaire. The clinical side effects, such as stomachache and borborygmus, were reduced in the moderate constipation patients in the treatment group, according to additional analyses. @*Conclusions@#Goodmorning S Granule® , a herbal medicine, was more effective in improving quality of life and CSBM per week and safer in the moderate constipation groups because of the reduced clinical side effects.

Long-term effect of the eradication of Helicobacter pylori on the hemoglobin A1c in type 2 diabetes or prediabetes patients

Background/Aims@#The long-term effect of Helicobacter pylori eradication on the metabolic syndrome or diabetes are unclear. The aim of this study was to evaluate the effect of H. pylori eradication on glycemic control in type 2 diabetes mellitus (T2DM) or prediabetes mellitus (preDM). @*Methods@#A total of 124 asymptomatic subjects with T2DM or preDM were divided into H. pylori-negative (n = 40), H. pylori-positive with non-eradicated (n = 34), and eradicated (n = 50) groups. We measured H. pylori status (culture, histology, and rapid urease test) and glycated hemoglobin A1c (A1C) levels and followed-up at the 1st year and the 5th year of follow-up. @*Results@#The A1C levels significantly decreased in the eradicated group compared to the negative group and the non-eradicated groups (at the 1st year, p = 0.024; at the 5th year, p = 0.009). The A1C levels decreased in male, and/or subjects < 65 years of age in subgroup analyses (in male subjects, p = 0.047 and p = 0.020 at the 1st and the 5th year; in subjects < 65 years of age, p = 0.028 and p = 0.006 at the 1st and the 5th year; in male subjects < 65 years of age, p = 0.039 and p = 0.032 at the 1st and the 5th year). The eradication of H. pylori was related to the decrease in A1C values throughout the follow-up period, compared to the non-eradicated group (p = 0.017). @*Conclusions@#H. pylori eradication was related to the decreasing of A1C levels in patients with T2DM or preDM over a long-term follow-up period, especially in male and subjects < 65 years of age.