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Introduction: Previous studies have investigated predictive factors for parenting stress in caregivers of autism spectrum disorder (ASD) patients using traditional statistical approaches, but their study settings and results were inconsistent. Herein, this study aimed to identify major predictors for parenting stress in this population by developing explainable machine learning models. Methods: Study participants were collected from the Department of Child and Adolescent Psychiatry, Severance Hospital, Yonsei University College of Medicine, Seoul, the Republic of Korea between March 2016 and October 2020. A total of 36 model features were used, which include subscales of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) for caregivers' psychopathology, Social Responsiveness Scale-2 for core symptoms, and Child Behavior Checklist (CBCL) for behavioral problems. Machine learning classifiers [eXtreme Gradient Boosting (XGBoost), random forest (RF), logistic regression, and support vector machine (SVM) classifier] were generated to predict severe total parenting stress and its subscales (parental distress, parent-child dysfunctional interaction, and difficult child). Model performance was assessed by area under the receiver operating curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. We utilized the SHapley Additive exPlanations tree explainer to investigate major predictors. Results: A total of 496 participants were included [mean age of ASD patients 6.39 (SD 2.24); 413 men (83.3%)]. The best-performing models achieved an AUC of 0.831 (RF model; 95% CI 0.740-0.910) for parental distress, 0.814 (SVM model; 95% CI 0.720-0.896) for parent-child dysfunctional interaction, 0.813 (RF model; 95% CI 0.724-0.891) for difficult child, and 0.862 (RF model; 95% CI 0.783-0.930) for total parenting stress on the test set. For the total parenting stress, ASD patients' aggressive behavior and anxious/depressed, and caregivers' depression, social introversion, and psychasthenia were the top 5 leading predictors. Conclusion: By using explainable machine learning models (XGBoost and RF), we investigated major predictors for each subscale of the parenting stress index in caregivers of ASD patients. Identified predictors for parenting stress in this population might help alert clinicians whether a caregiver is at a high risk of experiencing severe parenting stress and if so, providing timely interventions, which could eventually improve the treatment outcome for ASD patients. [ABSTRACT FROM AUTHOR]

We assess the sensitivity of the Weather Research and Forecasting (WRF) Model to the use of different planetary boundary layer (PBL) parameterizations focusing on air temperature and extreme heat conditions. This work aims to evaluate the performance of the WRF Model in simulating temperatures across the Middle East–North Africa (MENA) domain, explain the model biases resulting from the choice of different PBL schemes, and identify the best-performing configuration for the MENA region. Three different PBL schemes are used to downscale the ECMWF ERA-Interim climate over the MENA region at a horizontal resolution of 24 km, for the period 2000–10. These are the Mellor–Yamada–Janjić (MYJ), Yonsei University (YSU), and the asymmetric convective model, version 2 (ACM2). For the evaluation of the WRF runs, we used related meteorological variables from the ERA5 reanalysis, including summer maximum and minimum 2-m air temperature and heat extreme indices. Our results indicate that simulations tend to overestimate maximum temperatures and underestimate minimum temperatures, and we find that model errors are very dependent on the geographic location. The possible physical causes of model biases are investigated through the analysis of additional variables (such as boundary layer height, moisture, and heat fluxes). It is shown that differences among the PBL schemes are associated with differences in vertical mixing strength, which alters the magnitude of the entrainment of free-tropospheric air into the PBL. The YSU is found to be the best-performing scheme, and it is recommended in WRF climate simulations for the MENA region. [ABSTRACT FROM AUTHOR]

This study investigated the performance of the Weather Research Forecasting (WRF) model at 4‐km horizontal grid spacing in simulating precipitation, 2 m air temperature (T2), snowfall, and lake‐effect snow (October 4–8, 2018) over the Tibetan Plateau (TP). Multiple simulations with different physical parameterization schemes (PPSs), including two planetary boundary layer schemes (Yonsei University and Mellor–Yamada–Janjic), no cumulus and multi‐scale Kain‐Fritsch, two land surface models (Noah and Noah‐MP), and two microphysics schemes (Thompson and Milbrandt), were conducted and compared. Compared with gauge observations, all PPSs simulate mean daily precipitation with mean relative errors (MREs) of 27.7%–53.6%. Besides, spatial correlation coefficients (SCCs) between simulated and observed mean daily precipitation range from 0.56 to 0.71. For simulations of T2, all PPSs perform similarly well, even though the mean cold biases are up to about 3°C. Meanwhile, all PPSs exhibit acceptable performance in simulating spatial distributions of snow depth, snow cover, and snowfall amount, with SCCs of 0.37–0.65 between simulations and observations. However, the WRF simulations significantly overestimate snow depth (∼0.4 cm mean error) and snowfall amount (MREs >372%). The Milbrandt scheme slightly outperforms the other PPSs in simulating snow‐related variable magnitudes. Due to their inaccurate temperature and airflow modeling over the lake surface and its surroundings, none of the WRF simulations well reproduce the characteristics that more snow occurs over the lake and downwind area. Overall, this study provides a useful reference for future convection‐permitting climate modeling of snow or other extreme events when using the WRF model in the TP and other alpine regions. Plain Language Summary: Snow falls frequently in cold seasons, especially in alpine regions. When there is a lake, a very interesting snowfall phenomenon named lake‐effect snow may happen, that is, more snow occurred over the lake and downwind areas. However, the lake‐effect snow caused by large lakes over the Tibetan Plateau (TP) may induce snow disaster events. Thus, conducting reliable simulations of lake‐effect snow events are essential for understanding the mechanism of these particular events over the TP. This study investigated the performance of the numerical model in simulating precipitation, 2 m air temperature (T2), snowfall, and lake‐effect snow (October 4–8, 2018) over the TP. The results show that the simulated precipitation and T2 perform reasonably well, although wet and cold biases are observed. However, the investigated numerical model fails to reproduce the characteristics of this event well, which may be due to the inaccurate temperature and airflow modeling over the lake surface and its surroundings. Continued improvement is needed for future modeling. Hence, this study provides some suggestions for future numerical modeling of lake‐effect snow or other snow events over the TP and other alpine regions. Key Points: All simulations of the Weather Research Forecasting model can reasonably capture the spatial distributions of precipitation, 2 m air temperature, and snow‐related variablesThe Milbrandt scheme slightly outperforms the other physical parameterization schemes (PPSs) in simulating the magnitudes of the snow‐related variablesNone of the PPSs reproduce the characteristics of lake‐effect snow well due to their inaccurate temperature and airflow modeling [ABSTRACT FROM AUTHOR]

Background: Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Tumor marker (TM) detection can indicate the existence and growth of a tumor and has therefore been used extensively for diagnosing LC. Here, we conducted a bibliometric analysis to examine TM-related publica- tions for LC diagnosis to illustrate the current state and future trends of this field, as well as to identify additional promising TMs with high sensitivity. Methods: Publications regarding TMs in LC diagnosis were downloaded from the Web of Science Core Collection. CiteSpace was applied to perform a bibliometric analysis of journals, cocitation authors, keywords, and references related to this field. VOSviewer was used to generate concise diagrams about countries, institutions, authors, and keywords. Changes in the TM research frontier were analyzed through citation burst detection. Results: A total of 990 studies were analyzed in this work. The collaboration network analysis revealed that the People's Republic of China, Yonsei University, and Molina R were the most productive country, institution, and scholar, respectively. Additionally, Molina R was the author with the most citations. The National Natural Science Foundation of China was the largest funding source. "Carcinoembryonic antigen (CEA) as tumor marker in lung cancer" was the top reference with the most citations, Lung Cancer was the core journal, and "serum tumor marker" experienced a citation burst over the past 5 years. Conclusion: This bibliometric analysis of TMs in LC diagnosis presents the current trends and frontiers in this field. We summarized the research status of this field and the methods to improve the diagnostic efficacy of traditional serum TMs, as well as provided new directions and ideas for improving the LC clinical detection rate. Priority should be given to the transformation of computer-assisted diagnostic technology for clinical applications. In addition, circulating tumor cells, exosomes, and microRNAs were the current most cutting-edge TMs. [ABSTRACT FROM AUTHOR]

Background: Emergence delirium is an early postoperative behavior change in pediatric patients, posing risks to patient safety and leading to prolonged hospital stays and increased medical costs. As a result, the research on emergence delirium has grown substantially. This study aims to identify the most influential literature, trends, and topics in emergence delirium research, as well as to quantify the fundamental data of academic publications on this topic.Methods: We searched for articles on emergence delirium in the Science Citation Index Expanded databases, covering the period from 2002 to 2022. Bibliographic information, including countries, institutions, journals, authorships, references, and keywords, was collected for further analysis.Results: A total of 739 articles on emergence delirium published between 2002 and 2022 were collected. China emerged as the most prolific publisher in this field, accounting for over 30% of all articles (226 publications), followed by the United States (n = 143) and South Korea (n = 92). The top three productive journals were Pediatric anesthesia (n=78, IF=2.129), Anesthesia and Analgesia (n=28, IF=6.627), and BMC Anesthesiology (n=28, IF=2.583). Yonsei University was the most active institution, with 22 publications related to emergence delirium. Among authors, Kin, Hee-Soo (n = 9) published the most articles in this field, followed by Yao, Yusheng (n = 7), Lee, Ji-Hyun (n = 7). The prominent topics in emergence delirium research during the past two decades were "children", "emergence delirium" and "propofol".Conclusion: Through bibliometric analysis, this study provides a comprehensive overview of the trends and developments in the field of emergence delirium over the past two decades. The results demonstrate a significant growth in emergence delirium research worldwide, with China leading in the number of publications. Despite the wealth of literature on strategies for preventing and managing emergence delirium in clinical settings, further basic research is needed to elucidate the underlying mechanisms of emergence delirium. [ABSTRACT FROM AUTHOR]

This study analyzes the annual reports of CMB in order to examine CMB’s assistance of Korea. CMB originally assisted medical education in China, and it turned to assist Asia with changes in the international situation. This paper examines three periods spanning from 1953 to 1980 when Korea received CMB assist. The first period was from 1953 to 1962, when Korea received help with material resources that were lacking after the Korean War. The second period was from 1963 to 1972 during which the scale of assistance further expanded. Additionally, Seoul National University began to have human resources with the necessary support for education and research with the assistance from CMB. The third period was from 1973 to 1980, when the CMB newly established the overall direction of aid, the contents of assistance for Korea also changed. Throughout this period, Korean medicine was able to lay the foundation for independence, and public health, including community medicine, came to be considered as an important aspect of society.

This study aimed to evaluate the performance of the WRF-Chem model in estimating the amount of NO2 and O3 in the Tehran region during recent summers (2019 to 2021). First, by investigating the NO2 and O3 concentrations (including hourly and daily averages) from the Air Quality Control stations in Tehran city, the days of maximum ozone concentration in summer were selected for which run the Weather Research and Forecasting Chemistry (WRF-Chem) model. The simulations were performed for 36-hours, and the first 12 hours were considered spin-up times. The simulations were conducted using two nests with 30 and 10 km resolutions, respectively, so that the second domain covered the Tehran region. Also, in the model settings, 35 levels were considered in the vertical direction, and the pressure at the highest level was 50 hPa. In these simulations, the Global Forecast System (GFS) data with a spatial resolution of 0.5 degrees and 6-hours time step was used as the initial and boundary conditions. Physical parameterization schemes that performed well in previous studies in simulating atmospheric pollutants dispersion were used in the model configuration. The Rapid Radiation Transfer Model (RRTM) and the Goddard Shortwave schemes were also used to simulate the long-and short-wave radiation, respectively. The Monin-Obukhov scheme was used to simulate surface layer fluxes and the Yonsei University (YSU) PBL scheme was also used to simulate boundary layer fluxes. The land surface fluxes were obtained from the NOAH model. In addition, the Grell and Devenyi ensemble scheme was used to parameterize moist convection and the WRF-Single-Moment-Microphysics 5-class scheme was used to parameterize microphysical processes. The RADM2 chemical mechanism was also used in this configuration. The PREP-CHEM-SRC emissions preprocessor (version 1.5) was used to produce anthropogenic, biogenic and biomass burning gridded emission over the user-specified simulation domain. The global emission data comes from RETRO and GOCART background emission data. Anthropogenic emissions of greenhouse gases and air pollutants including CO, NH3, NOx, SO2, NMVOC and CH4, were derived from the EDGAR_HTAP v5.0 Emissions Database with 0.1 horizontal resolution. Primary anthropogenic aerosol emissions of BC, OC and DMS from GOCART model databases were also used. Biogenic emissions were calculated using the MEGAN model. Also, 3BEM fire emissions which are prepared using PREP-CHEM-SRC are used. Evaluation of the results of WRF-Chem model simulations was performed by two methods of horizontal distribution and station evaluation. The evaluation results of the simulated horizontal distribution of NO2 and the one is taken from the OMI satellite data showed that considering the slight spatial displacement in the model results, the WRF-Chem model had good performance in simulating the maximum surface NO2 in all cases. Considering the spatial distribution of O3 in days with maximum ozone pollution, the model has simulated areas of maximum ozone in the Tehran. However, in most cases, no comment can be made on the accuracy of the simulated maximum areas because of the not precisely coinciding of the results maps with satellite transits. The station evaluation showed an overestimate of ozone concentration and a high underestimate of NO2 concentration by the WRF-Chem model. [ABSTRACT FROM AUTHOR]

Planetary boundary layer (PBL) schemes parameterize unresolved turbulent mixing within the PBL and free troposphere (FT). Previous studies reported that precipitation simulation over the Amazon in South America is quite sensitive to PBL schemes and the exact relationship between the turbulent mixing and precipitation processes is, however, not disentangled. In this study, regional climate simulations over the Amazon in January–February 2019 are examined at process level to understand the precipitation sensitivity to PBL scheme. The focus is on two PBL schemes, the Yonsei University (YSU) scheme, and the asymmetric convective model v2 (ACM2) scheme, which show the largest difference in the simulated precipitation. During daytime, while the FT clouds simulated by YSU dissipate, clouds simulated by ACM2 maintain because of enhanced moisture supply due to the enhanced vertical moisture relay transport process: (a) vertical mixing within PBL transports surface moisture to the PBL top, and (b) FT mixing feeds the moisture into the FT cloud deck. Due to the thick cloud deck over Amazon simulated by ACM2, surface radiative heating is reduced and consequently the convective available potential energy is reduced. As a result, precipitation is weaker from ACM2. Two key parameters dictating the vertical mixing are identified, p, an exponent determining boundary layer mixing and λ, a scale dictating FT mixing. Sensitivity simulations with altered p, λ, and other treatments within YSU and ACM2 confirm the precipitation sensitivity. The FT mixing in the presence of clouds appears most critical to explain the sensitivity between YSU and ACM2. Plain Language Summary: Predictions of weather and climate in terms of clouds and precipitation over the Amazon in South America are quite uncertain. This uncertainty has been largely attributed to errors in the planetary boundary layer (PBL) scheme, which represents turbulent mixing. A lack of understanding of the relationship between turbulence, clouds, and precipitation processes prevents us from improving PBL representation in models to achieve better weather and climate simulations. This study disentangles the turbulence/clouds/precipitation relationship, and identifies the root cause of model errors in PBL schemes using regional climate simulations over the Amazon. Two PBL schemes, the Yonsei University (YSU) scheme, and the asymmetric convective model v2 (ACM2) scheme, are examined, which show the largest difference in the simulated precipitation. The main difference between the two PBL schemes is the dissipation (YSU) or maintenance (ACM2) of clouds during daytime above the boundary layer, which modulates surface heating and consequently precipitation. The maintenance of a thick cloud deck over the Amazon in ACM2, is caused by enhanced vertical transport of moisture from the surface to above the boundary layer. Such an improved understanding of the turbulence/clouds/precipitation relationship allow us to propose potential solutions to improve PBL schemes in weather and climate models. Key Points: Disentangle turbulence/cloud/precipitation processes over Amazon and reveal root cause for sensitivity to planetary boundary layer (PBL) schemes using the Weather Research and Forecasting modelFree troposphere (FT) mixing becomes prominent in the presence of clouds, which in turn supports maintenance of the FT clouds that would otherwise dissipateStronger vertical moisture relay transport in asymmetric convective model v2 (ACM2) PBL scheme supports thicker FT clouds, leading to reduced heating and precipitation [ABSTRACT FROM AUTHOR]

Left ventricular hypertrophy is a significant independent risk factor for all-cause mortality and morbidity, and an accurate diagnosis at an early stage of heart change is clinically significant. Electrocardiography is the most convenient, economical, and non-invasive method for screening in primary care. However, the coincidence rate of the actual left ventricular hypertrophy and diagnostic findings was low, consequently increasing the interest in algorithms using big data and deep learning. We attempted to diagnose left ventricular hypertrophy using big data and deep learning algorithms, and aimed to confirm its diagnostic power according to the differences between males and females. This retrospective study used electrocardiographs obtained at Yonsei University Wonju Severance Christian Hospital, Wonju, Korea, from October 2010 to February 2020. Binary classification was performed for primary screening for left ventricular hypertrophy. Three datasets were used for the experiment: the male, female, and entire dataset. A cutoff for binary classification was defined as the meaningful as a screening test (<132 g/m2 vs. ≥132 g/m2, <109 g/m2 vs. ≥109 g/m2). Six types of input were used for the classification tasks. We attempted to determine whether electrocardiography had predictive power for left ventricular hypertrophy diagnosis. For the entire dataset, the model achieved an area under the receiver operating characteristic (AUROC) curve of 0.836 (95% CI, 0.833–838) with a sensitivity of 78.37% (95% CI, 76.79–79.95). For the male dataset, the AUROC was 0.826 (95% CI, 0.822–830) with a sensitivity of 76.73% (95% CI, 75.14–78.33). For the female dataset, the AUROC was 0.772 (95% CI, 0.769–775) with a sensitivity of 72.90% (95% CI, 70.33–75.46). Our model confirmed that left ventricular hypertrophy can be classified to some extent using electrocardiography, demographics, and electrocardiography features. In particular, a learning environment that considered gender differences was constructed. Consequently, the difference in diagnostic power between men and women was confirmed. Our model will help patients with suspected left ventricular hypertrophy to undergo screening tests at a low cost. In addition, our research and attempts will show the expected effect that gender-consideration approaches can help with various currently proposed diagnostic methods. [ABSTRACT FROM AUTHOR]

Background. Physiological conduction system pacing has attracted attention to overcome the dyssynchrony problems of conventional right ventricular pacing (RVP). Left bundle branch area pacing (LBBAP), which complements short combing of His bundle pacing (HBP), has emerged and has proven its efficiency and safety. In addition, initial experiences of LBBAP were mainly using lumen-less pacing lead, and the feasibility of stylet-driven pacing lead (SDL) was also established. The purpose of this study is to evaluate the learning curve for LBBAP using SDL. Methods. The study enrolled 265 patients who underwent LBBAP or RVP performed by operators without previous LBBAP experience at Yonsei University Severance Hospital in Korea between December 2020 and October 2021. LBBAP was performed using SDL with an extendable helix. The learning curve was evaluated by analyzing fluoroscopy and procedure times. And, before and after reaching the learning curve, we evaluated how much the time required for the LBBAP differed from the time required for the RVP. Results. LBBAP was successful in 50 of 50 (100.0%) patients left bundle branch pacing was successful in 49 of 50 (98.0%). In 50 patients who underwent LBBAP, mean fluoroscopy and procedural times were 15.1 ± 13.5 minutes and 59.9 ± 24.8 minutes, respectively. The plateau of fluoroscopy time reached in the 25th case and the plateau of procedure time reached in the 24th case. Conclusion. During the initial experience with LBBAP, fluoroscopy and procedural times improved with increasing operator experience. For operators who were experienced in cardiac pacemaker implantation, the steepest part of the learning curve was over the first 24-25 cases. It is shorter than the previously reported learning curves of HBP. [ABSTRACT FROM AUTHOR]