Childhood adversity and suicidal ideation in older Korean adults: unraveling the mediating mechanisms of mental health, physical health, and social relationships.

BACKGROUND: Suicide rates in older adults are much higher than those in younger age groups. Given the rapid increase in the proportion of older adults in Korea and the high suicide rate of this age group, it is worth investigating the mechanism of suicidal ideation for older adults. Generally, adverse childhood experiences are positively associated with suicidal ideation; however, it is not fully understood what mediating relationships are linked to the association between these experiences and current suicidal ideation. METHODS: The data from 685 older Korean adults were analyzed utilizing logistic regression, path analyses, and structural equation modeling. Based on our theoretical background and the empirical findings of previous research, we examined three separate models with mental health, physical health, and social relationship mediators. After that, we tested a combined model including all mediators. We also tested another combined model with mediation via mental health moderated by physical health and social relationships. RESULTS: The univariate logistic regression results indicated that childhood adversity was positively associated with suicidal ideation in older adults. However, multivariate logistic regression results demonstrated that the direct effect of childhood adversity became nonsignificant after accounting all variables. Three path models presented significant mediation by depression and social support in the association between childhood adversity and suicidal ideation. However, combined structural equation models demonstrated that only mediation by a latent variable of mental health problems was statistically significant. Social relationships moderated the path from mental health problems to suicidal ideation. CONCLUSIONS: Despite several limitations, this study has clinical implications for the development of effective strategies to mitigate suicidal ideation. In particular, effectively screening the exposure to adverse childhood experiences, early identification and treatment of depressive symptoms can play a crucial role in weakening the association between childhood adversity and suicidal ideation in older adults.

Developing prediction algorithms for late-life depression using wearable devices: a cohort study protocol.

INTRODUCTION: Despite the high prevalence of major depressive disorder (MDD) among the elderly population, the rate of treatment is low due to stigmas and barriers to medical access. Wearable devices such as smartphones and smartwatches can help to screen MDD symptoms earlier in a natural setting while forgoing these concerns. However, previous research using wearable devices has mostly targeted the younger population. By collecting longitudinal data using wearable devices from the elderly population, this research aims to produce prediction algorithms for late-life depression and to develop strategies that strengthen medical access in community care systems. METHODS AND ANALYSIS: The current cohort study recruited a subsample of 685 elderly people from the Korean Genome and Epidemiology Study-Cardiovascular Disease Association Study, a national large cohort established in 2004. The current study has been conducted over a 3-year period to explore the development patterns of late-life depression. Participants have completed three annual face-to-face interviews (baseline, the first follow-up and the second follow-up) and 2 years of app-based surveys and passive sensing data collection. All the data collection will end at the second follow-up interview. The collected self-report, observational and passive sensing data will be primarily analysed by machine learning. ETHICS AND DISSEMINATION: This study protocol has been reviewed and approved by the Yonsei University Mirae Campus Institutional Review Board (1041849-2 02 111 SB-180-06) in South Korea. All participants provided written informed consent. The findings of this research will be disseminated by academic publications and conference presentations.

Development of a formula for estimated glomerular filtration rate in pregnant women from physiological hyperfiltration of serum creatinine.

Increased body fluids during pregnancy complicates the application of estimated glomerular filtration rate (eGFR) formulas that are based on body surface area. Furthermore, gestational renal dysfunction cannot be identified if the serum creatinine (SCr) concentration is within the non-pregnant reference interval (RI) despite inadequate pregnancy-related renal hyperfiltration. 1484 SCr measurements from 957 healthy pregnant women were collected. The average SCr value of gestational week (GW) 0-3 was the representative SCr value of non-pregnant status. While the distribution of SCr measurements varied across GWs, it was transformed into a normal distribution using the bootstrap resampling method. A polynomial linear regression method was applied to achieve a continuous and smooth transformation of values. The normally distributed SCr values of each GW were compared to the non-pregnant status, leading to the calculation of SCr hyperfiltration. The final equation, (2 - SCr (µmol/L) / 55.25) × 103.1 × 55.25/(56.7 - 0.223 × GW - 0.113 × GW2 + 0.00545 × GW3 - 0.0000653 × GW4), and reference intervals for both SCr and eGFR for each GW were obtained. These RIs and novel equations can be effectively used to monitor renal dysfunction in pregnant women.

Comparative Analysis of Biological Signatures between Freshly Preserved and Cryo-Preserved Bone Marrow Mesenchymal Stem Cells.

Mesenchymal stem cells (MSCs) can differentiate into multiple connective tissue lineages, including osteoblasts, chondrocytes, and adipocytes. MSCs secrete paracrine molecules that are associated with immunomodulation, anti-fibrotic effects, and angiogenesis. Due to their orchestrative potential, MSCs have been therapeutically applied for several diseases. An important aspect of this process is the delivery of high-quality MSCs to patients at the right time, and cryo-biology and cryo-preservation facilitate the advancement of the logistics thereof. This study aimed to compare the biological signatures between freshly preserved and cryo-preserved MSCs by using big data sourced from the Pharmicell database. From 2011 to 2022, data on approximately 2300 stem cell manufacturing cases were collected. The dataset included approximately 60 variables, including viability, population doubling time (PDT), immunophenotype, and soluble paracrine molecules. In the dataset, 671 cases with no missing data were able to receive approval from an Institutional Review Board and were analyzed. Among the 60 features included in the final dataset, 20 were selected by experts and abstracted into two features by using a principal component analysis. Circular clustering did not introduce any differences between the two MSC preservation methods. This pattern was also observed when using viability, cluster of differentiation (CD) markers, and paracrine molecular indices as inputs for unsupervised analysis. The individual average PDT and cell viability at most passages did not differ according to the preservation method. Most immunophenotypes (except for the CD14 marker) and paracrine molecules did not exhibit different mean levels or concentrations between the frozen and unfrozen MSC groups. Collectively, the biochemical signatures of the cryo-preserved and unfrozen bone marrow MSCs were comparable.

Enhancing X-ray-Based Wrist Fracture Diagnosis Using HyperColumn-Convolutional Block Attention Module.

Fractures affect nearly 9.45% of the South Korean population, with radiography being the primary diagnostic tool. This research employs a machine-learning methodology that integrates HyperColumn techniques with the convolutional block attention module (CBAM) to enhance fracture detection in X-ray radiographs. Utilizing the EfficientNet-B0 and DenseNet169 models bolstered by the HyperColumn and the CBAM, distinct improvements in fracture site prediction emerge. Significantly, when HyperColumn and CBAM integration is applied, both DenseNet169 and EfficientNet-B0 showed noteworthy accuracy improvements, with increases of approximately 0.69% and 0.70%, respectively. The HyperColumn-CBAM-DenseNet169 model particularly stood out, registering an uplift in the AUC score from 0.8778 to 0.9145. The incorporation of Grad-CAM technology refined the heatmap's focus, achieving alignment with expert-recognized fracture sites and alleviating the deep-learning challenge of heavy reliance on bounding box annotations. This innovative approach signifies potential strides in streamlining training processes and augmenting diagnostic precision in fracture detection.

Childhood adversity and late-life depression: moderated mediation model of stress and social support.

Background: As life expectancy increases, understanding the mechanism for late-life depression and finding a crucial moderator becomes more important for mental health in older adults. Childhood adversity increases the risk of clinical depression even in old age. Based on the stress sensitivity theory and stress-buffering effects, stress would be a significant mediator, while social support can be a key moderator in the mediation pathways. However, few studies have tested this moderated mediation model with a sample of older adults. This study aims to reveal the association between childhood adversity and late-life depression in older adults, taking into consideration the effects of stress and social support. Methods: This study used several path models to analyze the data from 622 elderly participants who were never diagnosed with clinical depression. Results: We found that childhood adversity increases the odds ratio of depression by approximately 20% in older adults. Path model with mediation demonstrates that stress fully mediates the pathway from childhood adversity to late-life depression. Path model with moderated mediation also illustrates that social support significantly weakens the association between childhood adversity and perceived stress. Conclusion: This study provides empirical evidence to reveal a more detailed mechanism for late-life depression. Specifically, this study identifies one crucial risk factor and one protective factor, stress and social support, respectively. This brings insight into prevention of late-life depression among those who have experienced childhood adversity.

Changes in the mean incidence and variance of orthopedic diseases before and during the COVID-19 pandemic in Korea: a retrospective study.

BACKGROUND: During the coronavirus disease (COVID-19) pandemic, the amount of moderate- to high-intensity physical activity significantly decreased. Therefore, the epidemiology of musculoskeletal diseases could possibly have changed. We assessed changes in the incidence of and variance in non-traumatic orthopedic diseases before and after the COVID-19 pandemic in Korea. METHODS: This study included data from the Korea National Health Insurance Service, which covers the entire Korean population (approximately 50 million), from January 2018 to June 2021. Using International Classification of Diseases, Tenth Revision codes, 12 common orthopedic diseases were evaluated, including cervical disc disorders, lumbar disc disorders, forward head posture, myofascial pain syndrome, carpal tunnel syndrome, tennis elbow, frozen shoulder, rheumatoid arthritis, gout, hip fracture, distal radius fracture, and spine fracture diseases. "Pre-COVID-19" was the period until February 2020, and "COVID-19 pandemic period" was the period starting March 2020. Differences in the mean incidence and variance of diseases before and during the COVID-19 pandemic were compared. RESULTS: In most cases, the incidence of orthopedic diseases decreased at the beginning of the pandemic and then increased thereafter. Among the 12 diseases, the incidence of three diseases showed a statistically significant change. The incidence of myofascial pain syndrome (P < 0.001) was lower during the COVID-19 pandemic than during the pre-COVID-19 period. The incidences of frozen shoulder (P < 0.001) and gout (P = 0.043) were higher during the COVID-19 pandemic than during the pre-COVID-19 period. However, no statistical difference in disease variations was observed between the two periods. CONCLUSIONS: The incidence of orthopedic diseases varied during the COVID-19 pandemic among the Korean population. Although the incidence of myofascial pain syndrome was lower, that of frozen shoulder and gout was higher during the COVID-19 pandemic than during the pre-COVID-19 period. No disease variations during the COVID-19 pandemic were found.

Prediction Model for Pre-Eclampsia Using Gestational-Age-Specific Serum Creatinine Distribution.

Pre-eclampsia (PE) is a pregnancy-related disease, causing significant threats to both mothers and babies. Numerous studies have identified the association between PE and renal dysfunction. However, in clinical practice, kidney problems in pregnant women are often overlooked due to physiologic adaptations during pregnancy, including renal hyperfiltration. Recent studies have reported serum creatinine (SCr) level distribution based on gestational age (GA) and demonstrated that deviations from the expected patterns can predict adverse pregnancy outcomes, including PE. This study aimed to establish a PE prediction model using expert knowledge and by considering renal physiologic adaptation during pregnancy. This retrospective study included pregnant women who delivered at the Wonju Severance Christian Hospital. Input variables, such as age, gestational weeks, chronic diseases, and SCr levels, were used to establish the PE prediction model. By integrating SCr, GA, GA-specific SCr distribution, and quartile groups of GA-specific SCr (GAQ) were made. To provide generalized performance, a random sampling method was used. As a result, GAQ improved the predictive performance for any cases of PE and triple cases, including PE, preterm birth, and fetal growth restriction. We propose a prediction model for PE consolidating readily available clinical blood test information and pregnancy-related renal physiologic adaptations.

Internet of medical things-based real-time digital health service for precision medicine: Empirical studies using MEDBIZ platform.

The aim of this study was to introduce the implemented MEDBIZ platform based on the internet of medical things (IoMT) supporting real-time digital health services for precision medicine. In addition, we demonstrated four empirical studies of the digital health ecosystem that could provide real-time healthcare services based on IoMT using real-world data from in-hospital and out-hospital patients. Implemented MEDBIZ platform based on the IoMT devices and big data to provide digital healthcare services to the enterprise and users. The big data platform is consisting of four main components: IoMT, core, analytics, and services. Among the implemented MEDBIZ platform, we performed four clinical trials that designed monitoring services related to chronic obstructive pulmonary disease, metabolic syndrome, arrhythmia, and diabetes mellitus. Of the four empirical studies on monitoring services, two had been completed and the rest were still in progress. In the metabolic syndrome monitoring service, two studies were reported. One was reported that intervention components, especially wearable devices and mobile apps, made systolic blood pressure, diastolic blood pressure, waist circumference, and glycosylated hemoglobin decrease after 6 months. Another one was presented that increasing high-density lipoprotein cholesterol and triglyceride levels were prevented in participants with the pre-metabolic syndrome. Also, self-care using healthcare devices might help prevent and manage metabolic syndrome. In the arrhythmia monitoring service, during the real-time monitoring of vital signs remotely at the monitoring center, 318 (15.9%) general hikers found abnormal signals, and 296 (93.1%) people were recommended for treatment. We demonstrated the implemented MEDBIZ platform based on IoMT supporting digital healthcare services by acquiring real-world data for getting real-world evidence. And then through this platform, we were developing software as a medical device, digital therapeutics, and digital healthcare services, and contributing to the development of the digital health ecosystem.

Identification of Cardiovascular Disease-Related Genes Based on the Co-Expression Network Analysis of Genome-Wide Blood Transcriptome.

Inference of co-expression network and identification of disease-related modules and gene sets can help us understand disease-related molecular pathophysiology. We aimed to identify a cardiovascular disease (CVD)-related transcriptomic signature, specifically, in peripheral blood tissue, based on differential expression (DE) and differential co-expression (DcoE) analyses. Publicly available blood sample datasets for coronary artery disease (CAD) and acute coronary syndrome (ACS) statuses were integrated to establish a co-expression network. A weighted gene co-expression network analysis was used to construct modules that include genes with highly correlated expression values. The DE criterion is a linear regression with module eigengenes for module-specific genes calculated from principal component analysis and disease status as the dependent and independent variables, respectively. The DcoE criterion is a paired t-test for intramodular connectivity between disease and matched control statuses. A total of 21 and 23 modules were established from CAD status- and ACS-related datasets, respectively, of which six modules per disease status (i.e., obstructive CAD and ACS) were selected based on the DE and DcoE criteria. For each module, gene-gene interactions with extremely high correlation coefficients were individually selected under the two conditions. Genes displaying a significant change in the number of edges (gene-gene interaction) were selected. A total of 6, 10, and 7 genes in each of the three modules were identified as potential CAD status-related genes, and 14 and 8 genes in each of the two modules were selected as ACS-related genes. Our study identified gene sets and genes that were dysregulated in CVD blood samples. These findings may contribute to the understanding of CVD pathophysiology.

Gestational age-specific serum creatinine can predict adverse pregnancy outcomes.

Serum creatinine level (SCr) typically decreases during pregnancy due to physiologic glomerular hyperfiltration. Therefore, the clinical practice of estimated glomerular filtration rate (eGFR) based on SCr concentrations might be inapplicable to pregnant women with kidney disease since it does not take into account of the pregnancy-related biological changes. We integrated the Wonju Severance Christian Hospital (WSCH)-based findings and prior knowledge from big data to reveal the relationship between the abnormal but hidden SCr level and adverse pregnancy outcomes. We analyzed 4004 pregnant women who visited in WSCH. Adverse pregnancy outcomes included preterm birth, preeclampsia, fetal growth retardation, and intrauterine fetal demise. We categorized the pregnant women into four groups based on the gestational age (GA)-unadjusted raw distribution (Q1-4raw), and then GA-specific (Q1-4adj) SCr distribution. Linear regression analysis revealed that Q1-4adj groups had better predictive outcomes than the Q1-4raw groups. In logistic regression model, the Q1-4adj groups exhibited a robust non-linear U-shaped relationship with the risk of adverse pregnancy outcomes, compared to the Q1-4raw groups. The integrative analysis on SCr with respect to GA-specific distribution could be used to screen out pregnant women with a normal SCr coupled with a decreased renal function.

Diffusion of a Lifelog-Based Digital Healthcare Platform for Future Precision Medicine: Data Provision and Verification Study.

We propose a method for data provision, validation, and service expansion for the spread of a lifelog-based digital healthcare platform. The platform is an operational cloud-based platform, implemented in 2020, that has launched a tool that can validate and de-identify personal information in a data acquisition system dedicated to a center. The data acquired by the platform can be processed into products of statistical analysis and artificial intelligence (AI)-based deep learning modules. Application programming interfaces (APIs) have been developed to open data and can be linked in a programmatic manner. As a standardized policy, a series of procedures were performed from data collection to external sharing. The proposed platform collected 321.42 GB of data for 146 types of data. The reliability and consistency of the data were evaluated by an information system audit institution, with a defects ratio of approximately 0.03%. We presented definitions and examples of APIs developed in 17 functional units for data opening. In addition, the suitability of the de-identification tool was confirmed by evaluating the reduced risk of re-identification using quasi-identifiers. We presented specific methods for data verification, personal information de-identification, and service provision to ensure the sustainability of future digital healthcare platforms for precision medicine. The platform can contribute to the diffusion of the platform by linking data with external organizations and research environments in safe zones based on data reliability.

Concept and Proof of the Lifelog Bigdata Platform for Digital Healthcare and Precision Medicine on the Cloud.

PURPOSE: We propose the Lifelog Bigdata Platform as a sustainable digital healthcare system based on individual-centric lifelog datasets and describe the standardization of lifelog and clinical data in its full-cycle management system. MATERIALS AND METHODS: The Lifelog Bigdata Platform was developed by Yonsei Wonju Health System on the cloud to support digital healthcare and precision medicine. It consists of five core components: data acquisition system, de-identification of individual information, lifelog integration, analyzer, and service. We designed a gathering system into a dedicated virtual machine to save lifelog or clinical outcomes and established standard guidelines for maintaining the quality of gathering procedures. We used standard integration keys to integrate the lifelog and clinical data. Metadata were generated from the data warehouse after loading combined or fragmented data on it. We analyzed the de-identified lifelog and clinical data using the lifelog analyzer to prevent and manage acute and chronic diseases through providing results of statistics on analysis. RESULTS: The big data centers were built in four hospitals and seven companies for integrating lifelog and clinical data to develop the Lifelog Bigdata Platform. We integrated and loaded lifelog big data and clinical data for 3 years. In the first year, we uploaded 94 types of data on the platform with a total capacity of 221 GB. CONCLUSION: The Lifelog Bigdata Platform is the first to combine lifelog and clinical data. The proposed standardization guidelines can be used for future platforms to achieve a virtuous cycle structure of lifelogging big data and an industrial ecosystem.

Multidisciplinary aerodigestive program at a children's hospital: A protocol for a prospective observational study.

BACKGROUND: Children with complex chronic multisystemic diseases frequently require care from multiple pediatric subspecialists. The aerodigestive program is a multidisciplinary program that diagnoses and treats pediatric patients with complex multi-systematic problems affecting airway, breathing, feeding, swallowing, or growth. The aim of this study is to present the protocol of the aerodigestive program of a children's hospital. METHODS AND DESIGN: This study is a prospective study to evaluate and compare the overall improvement of patients' objective and subjective conditions before and after the AeroDigestive Team (ADT) program. Among children from 1 month to 18 years of age, patients with complex problems of the airway, breathing, feeding, swallowing, or growth meeting at least two parameters of the inclusion criteria were enrolled. The overall process included referral based on the inclusion criteria, enrollment of ADT program with informed consents, interview and questionnaire for assessing patients' medical condition, prescheduling appointment, multi-specialists' evaluation, monthly team meetings, wrap-up discussion with the patients and family, therapeutic intervention, and follow-up at 6 months with the assessment of outcome measures. The outcome was evaluated objectively and subjectively. The objective outcome measure was divided into surgical or medical intervention, assessment of changes in medical condition, and follow-up study. Both caregiver interviews and questionnaires using a scoring system were used as subjective outcome measures before and after the ADT program. Children were scheduled to be followed-up at 6 months after the interventions or ADT meeting. DISCUSSION: The aerodigestive program is expected to provide comprehensive and multidisciplinary management of children with complex airway and digestive tract disorders.

A Deep Neural Network for Estimating Low-Density Lipoprotein Cholesterol From Electronic Health Records: Real-Time Routine Clinical Application.

BACKGROUND: Previously, we constructed a deep neural network (DNN) model to estimate low-density lipoprotein cholesterol (LDL-C). OBJECTIVE: To routinely provide estimated LDL-C levels, we applied the aforementioned DNN model to an electronic health record (EHR) system in real time (deep LDL-EHR). METHODS: The Korea National Health and Nutrition Examination Survey and the Wonju Severance Christian Hospital (WSCH) datasets were used as training and testing datasets, respectively. We measured our proposed model's performance by using 5 indices, including bias, root mean-square error, P10-P30, concordance, and correlation coefficient. For transfer learning (TL), we pretrained the DNN model using a training dataset and fine-tuned it using 30% of the testing dataset. RESULTS: Based on 5 accuracy criteria, deep LDL-EHR generated inaccurate results compared with other methods for LDL-C estimation. By comparing the training and testing datasets, we found an overfitting problem. We then revised the DNN model using the TL algorithms and randomly selected subdata from the WSCH dataset. Therefore, the revised model (DNN+TL) exhibited the best performance among all methods. CONCLUSIONS: Our DNN+TL is expected to be suitable for routine real-time clinical application for LDL-C estimation in a clinical laboratory.

Development of a Multidisciplinary Aerodigestive Program: An Institutional Experience.

We share our experience on the implementation of a multidisciplinary aerodigestive program comprising an aerodigestive team (ADT) so as to evaluate its feasibility. We performed a retrospective chart review of the patients discussed at the monthly ADT meetings and analyzed the data. A total of 98 children were referred to the ADT during the study period. The number of cases increased steadily from 3.5 cases per month in 2019 to 8.5 cases per month in 2020. The median age of patients was 34.5 months, and 55% were male. Among the chronic comorbidities, neurologic disease was the most common (85%), followed by respiratory (36%) and cardiac (13%) disorders. The common reasons for consultation were suspected aspiration (56%), respiratory difficulty (44%), drooling/stertor (30%), regurgitation/vomiting (18%), and feeding/swallowing difficulty (17%). Following discussions, 58 patients received active interventions, including fundoplication, gastrostomy, laryngomicrosurgery, tracheostomy, and primary dilatation of the airway. According to the questionnaire of the caregiver, the majority agreed that the main symptoms and quality of life of patients had improved (88%), reducing the burden on caregivers (77%). Aerodigestive programs may provide comprehensive and multidisciplinary management for children with complex airway and digestive tract disorders.

Video-assisted thoracoscopic surgical wedge resection using multiplanar computed tomography reconstruction-fluoroscopy after CT guided microcoil localization.

BACKGROUND: When early-stage lung cancer is diagnosed, the recommended treatment is anatomical resection using video-assisted thoracoscopic surgery (VATS) or robotic lobectomy. However, nonanatomical resection, known as wedge resection (WR), which is performed to diagnose pulmonary nodules, can be problematic for clinicians performing VATS or robotic-assisted thoracic surgery (RATS). The purpose of this study was to evaluate the safety and effectiveness of VATS WR using multiplanar computed tomography reconstruction (CT MPR)-fluoroscopy after CT guided microcoil localization to achieve complete pulmonary nodule resection. METHODS: Between January 2016 to December 2020, the medical records of patients who underwent CT-guided microcoil localization for suspicious malignant pulmonary nodules and VATS WR with CT MPR and intraoperative fluoroscopy were retrospectively reviewed. RESULTS: All 130 patients successfully underwent CT-guided localization. The success rate of VATS WR with CT MPR-intraoperative fluoroscopy was 98.5%. Mean operation time was 58 min (range 50-84 min). The postoperative complication rate was 3.1%, and no major postoperative complications were reported. The mean postoperative length of hospital stay was 4.7 days (range 4-8 days). CONCLUSIONS: VATS WR using CT MPR-fluoroscopy after CT guided microcoil localization is a safe and highly effective approach for complete pulmonary nodule resection. However, even in uniport VATS or recently performed robotic surgery, localization and resection of nonvisible, nonpalpable pulmonary nodules is a challenging problem. Consequently, satisfactory outcomes can be expected if this technique is used for suspicious malignant pulmonary nodule resection.

Association of Metabolically Healthy Obesity and Future Depression: Using National Health Insurance System Data in Korea from 2009-2017.

(1) Background: The health implications associated with the metabolically healthy obese (MHO) phenotype, in particular related to symptoms of depression, are still not clear. the purpose of this study is to check whether depression and metabolic status are relevant by classifying them into four groups in accordance with the MHO diagnostic standard. Other impressions seen were the differences between sexes and the effects of the MHO on the occurrence of depression. (2) Methods: A sample of 3,586,492 adult individuals from the National Health Insurance Database of Korea was classified into four categories by their metabolic status and body mass index: (1) metabolically healthy non-obese (MHN); (2) metabolically healthy obese (MHO); (3) metabolically unhealthy non-obese (MUN); and (4) metabolically unhealthy obese (MUO). Participants were followed for six to eight years for new incidences of depression. The statistical significance of the general characteristics of the four groups, as well as the mean differences in metabolic syndrome risk factors, was assessed with the use of a one-way analysis of variance (ANOVA). (3) Results: The MHN ratio in women was higher than in men (men 39.3%, women 55.2%). In both men and women, depression incidence was the highest among MUO participants (odds ratio (OR) = 1.01 in men; OR = 1.09 in women). It was concluded as well that, among the risk factors of metabolic syndrome, waist circumference was the most related to depression. Among the four groups, the MUO phenotype was the most related to depression. Furthermore, in women participants, MHO is also related to a higher risk of depressive symptoms. These findings indicate that MHO is not a totally benign condition in relation to depression in women. (4) Conclusion: Therefore, reducing metabolic syndrome and obesity patients in Korea will likely reduce the incidence of depression.

Effectiveness of Intramuscular Electrical Stimulation on Postsurgical Nociceptive Pain for Patients Undergoing Open Pancreaticoduodenectomy: A Randomized Clinical Trial.

BACKGROUND: After pylorus-preserving pancreaticoduodenectomy (PPPD), incision and suture of the abdominal muscles cause inflammatory changes and elicit somatic pain that deteriorates the quality of life. There have been no previous reports on needle electrical twitch obtaining intramuscular stimulation (NETOIMS) in abdominal open operation; this study aimed to apply NETOIMS for postoperative somatic pain in patients undergoing PPPD as a new treatment modality for pain control. METHODS: Between June 2018 and January 2019, 44 patients who underwent PPPD were randomly assigned to a control group and the NETOIMS group. The NETOIMS group received NETOIMS in the transverse abdominis muscle under ultrasound guidance right after operation under general anesthesia. The pain score (visual analog scale), peak cough flow (PCF), and gait speed were repetitively measured from 1 day before operation to 2 weeks after discharge as scheduled. Data were analyzed by the linear mixed model and repeated-measures analysis of variance. RESULTS: Of the 44 patients recruited, data from 38 patients were finally analyzed. The pain scores were significantly lower in the NETOIMS group after PPPD (p = 0.01). Although the PCF at each measuring time point did not show inter-group difference (p = 0.20), improvement of PCF from the second day after operation to discharge was greater (p = 0.02) and gait speed improved significantly faster (p < 0.01) in the NETOIMS group than in the control group. CONCLUSIONS: NETOIMS helps in rapid reduction of postoperative somatic pain developed after PPPD and in improvement of PCF and gait speed.

A LSTM-Hawkes hybrid model for posterior click distribution forecast in the advertising network environment.

In the field of advertising technology, it is a key task to forecast posterior click distribution since 66% of advertising transactions depend on cost per click model. However, due to the General Data Protection Regulation, machine learning techniques to forecast posterior click distribution based on the sequences of an identified user's actions are restricted in European countries. To overcome this barrier, we introduce a contextual behavior concept for the advertising network environment and propose a new hybrid model, which we call the Long Short Term Memory-Hawkes model by combining a stochastic-based generative model and a machine learning-based predictive model. Also, to meet the computational efficiency for the heavy demand in mobile advertisement market, we define gradient exponential kernel with just three hyper parameters to minimize residuals. We have carefully tested our proposed model with production data and found that the LSTM-Hawkes model reduces the Mean Squared Error by at least 27.1% and up to 83.8% on average in comparison to the existing Hawkes Process based algorithm, Hawkes Intensity Process, as well as 39.77% on average in comparison to Multivariate Linear Regression. We have also found that our proposed model improves the forecast accuracy by about 21.2% on average.