Autonomous real-time control for membrane capacitive deionization.

Artificial intelligence has been employed to simulate and optimize the performance of membrane capacitive deionization (MCDI), an emerging ion separation process. However, a real-time control for optimal MCDI operation has not been investigated yet. In this study, we aimed to develop a reinforcement learning (RL)-based control model and investigate the model to find an energy-efficient MCDI operation strategy. To fulfill the objectives, we established three long-short term memory models to predict applied voltage, outflow pH, and outflow electrical conductivity. Also, four RL agents were trained to minimize outflow concentration and energy consumption simultaneously. Consequently, actor-critic (A2C) and proximal policy optimization (PPO2) achieved the ion separation goal (<0.8 mS/cm) as they determined the electrical current and pump speed to be low. Particularly, A2C kept the parameters consistent in charging MCDI, which caused lower energy consumption (0.0128 kWh/m3) than PPO2 (0.0363 kWh/m3). To understand the decision-making process of A2C, the Shapley additive explanation based on the decision tree model estimated the influence of input parameters on the control parameters. The results of this study demonstrate the feasibility of RL-based controls in MCDI operations. Thus, we expect that the RL-based control model can improve further and enhance the efficiency of water treatment technologies.

Decoupling ion concentrations from effluent conductivity profiles in capacitive and battery electrode deionizations using an artificial intelligence model.

Owing to its simplicity of measurement, effluent conductivity is one of the most studied factors in evaluations of desalination performance based on the ion concentrations in various ion adsorption processes such as capacitive deionization (CDI) or battery electrode deionization (BDI). However, this simple conversion from effluent conductivity to ion concentration is often incorrect, thereby necessitating a more congruent method for performing real-time measurements of effluent ion concentrations. In this study, a random forest (RF)-based artificial intelligence (AI) model was developed to address this shortcoming. The proposed RF model showed an excellent prediction accuracy when it was first validated in predicting the effluent conductivity for both CDI (R2 = 0.86) and BDI (R2 = 0.95) data. Moreover, the RF model successfully predicted the concentration of each ion (Na⁺, K⁺, Ca2⁺, and Cl⁻) from the conductivity values. The accuracy of the ion concentration prediction was even higher than that of the effluent conductivity prediction, likely owing to the linear correlation between the input and output variables of the dataset. The effect of the sampling interval was also evaluated for conductivity and ion concentrations, and there was no significant difference up to sampling intervals of <80 s based on the error value of the model. These findings suggest that an RF model can be used to predict ion concentrations in CDI/BDI, which may be used as core indicators in evaluating desalination performance.

Artificial neural network modeling for the oxidation kinetics of divalent manganese ions during chlorination and the role of arsenite ions in the binary/ternary systems.

This study investigated the coexistence and contamination of manganese (Mn(II)) and arsenite (As(III)) in groundwater and examined their oxidation behavior under different equilibrating parameters, including varying pH, bicarbonate (HCO3-) concentrations, and sodium hypochlorite (NaClO) oxidant concentrations. Results showed that if the molar ratio of NaClO: As(III) was >1, the oxidation of As(III) could be achieved within a minute with an extremely high oxidation rate of 99.7 %. In the binary system, the removal of As(III) prevailed over Mn(II). The As(III) oxidation efficiency increased from 59.8 ± 0.6 % to 70.8 ± 1.9 % when pH rose from 5.7 to 8.0. The oxidation reaction between As(III) and NaClO releases H+ ions, decreasing the pH from 6.77 to 6.19 and reducing the removal efficiency of Mn(II). The presence of HCO3- reduced the oxidation rate of Mn(II) from 63.2 % to 13.9 % within four hours. Instead, the final oxidation rate of Mn(II) increased from 68.1 % to 87.7 %. This increase can be attributed to HCO3- ions competing with the free Mn(II) for the adsorption sites on the sediments, inhibiting the formation of H+. Moreover, kinetic studies revealed that the oxidation reaction between Mn(II) and NaClO followed first-order kinetics based on their R2 values. The significant factors affecting the Mn(II) oxidation efficiency were the initial concentration of NaClO and pH. Applying an artificial neural network (ANN) model for data analysis proved to be an effective tool for predicting Mn(II) oxidation rates under different experimental conditions. The actual Mn(II) oxidation data and the predicted values obtained from the ANN model showed significant consistency. The training and validation data sets yielded R2 values of 0.995 and 0.992, respectively. Moreover, the ANN model highlights the importance of pH and NaClO concentrations in influencing the oxidation rate of Mn(II).

Toxic dinoflagellate Centrodinium punctatum (Cleve) F.J.R. Taylor: An examination on the responses in growth and toxin contents to drastic changes of temperature and salinity.

To understand environmental effects affecting paralytic shellfish toxin production of Centrodinium punctatum, this study examined the growth responses, and toxin contents and profiles of a C. punctatum culture exposed to drastic changes of temperature (5-30 °C) and salinity (15-40). C. punctatum grew over a temperature range of 15-25 °C, with an optimum of 20 °C., and over a salinity range of 25-40, with optimum salinities of 30-35. This suggests that C. punctatum prefers relatively warm waters and an oceanic habitat for its growth and can adapt to significant changes of salinity levels. When C. punctatum was cultivated at different temperature and salinity levels, the PST profile included four major analogs (STX, neoSTX, GTX1 and GTX4, constituted >80 % of the profile), while low amounts of doSTX and traces of dc-STX and dc-GTX2 were also observed. Interestingly, though overall toxin contents did not change significantly with temperature, increases in the proportion of STX, and decreases in proportions in GTX1 and GTX4 were observed with higher temperatures. Salinity did not affect either toxin contents or profile from 25 to 35. However, the total toxin content dropped to approximately half at salinity 40, suggesting this salinity may induce metabolic changes in C. punctatum.

Curcumol metabolized by rat liver S9 fraction and orally administered in mouse suppressed the proliferation of colon cancer in vitro and in vivo.

Following 3R (reduction, refinement, and replacement) principles, we employed the rat liver S9 fraction to mimic liver metabolism of curcumol having high in vitro IC50 on cancer cells. In HCT116 and HT29 colon cancer cells, the metabolites of curcumol by S9 fraction exerted more enhanced activity in inducing cell cycle arrest and apoptosis via regulating the expression of cyclin D1, CDK1, p21, PARP and Bcl-2 than curcumol. In addition, oral administration of curcumol at 4 mg/kg BW significantly suppressed the development of colon tumor induced by azoxymethane/dextran sulfate sodium, and induced cell cycle arrest and apoptosis in tumor tissues. In mass analysis, curcumenol and curzerene were identified as the metabolites of curcumol by S9 fraction metabolism. Taken together, curcumol metabolites showed the enhanced suppressive effect on colon cancer, suggesting that S9 fraction can be considered as simple, fast, and bio-mimicking platform for the screening of chemical libraries on different chronic diseases.

Cycloastragenol inhibits adipogenesis and fat accumulation in vitro and in vivo through activating Hedgehog signaling.

In this study, we investigated the effect of cycloastragenol (CAG), a triterpenoid isolated from Astragalus membranaceus roots, on regulating the adipogenesis and fat accumulation in vitro and in vivo. During the adipogenesis of 3T3-L1 cells, CAG inhibited lipid accumulation and the expression of key adipogenic factors, proliferator-activated receptor γ (PPARγ) and CCAAT enhancer binding protein α (C/EBPα) and increased the expression of Gli1, a key mediator in Hedgehog (Hh) signaling. In HFD-induced animal experiment, CAG significantly reduced body weight gain without affecting brown fat weight. In addition, CAG regulated the expression of PPARγ, C/EBPα, and Gli1 in visceral white adipose tissue (vWAT). We also confirmed the inhibitory effect of CAG on specifically targeting white adipose tissue (WAT) formation in stromal vascular fraction (SVF) cell differentiation. Taken together, these results suggest that CAG may be a potent phytochemical preventing adipogenesis and obesity via Hh signaling. Supplementary Information: The online version contains supplementary material available at 10.1007/s10068-023-01403-0.

Correction to: 1,3,5,8­Tetrahydroxyxanthone suppressed adipogenesis via activating Hedgehog signaling in 3T3­L1 adipocytes.

[This corrects the article DOI: 10.1007/s10068-022-01130-y.].

Near-surface termination of upward-propagating strike-slip ruptures on the Yangsan Fault, Korea.

We present a new example of the termination of strike-slip paleoearthquake ruptures in near-surface regions on the Yangsan Fault, Korea, based on multi-scale structural observations. Paleoearthquake ruptures occur mostly along the boundary between the inherited fault core and damage zone (N10-20°E/> 75°SE). The ruptures propagated upward to the shallow subsurface along a < 3-cm-wide specific slip zone with dextral-slip sense, along which the deformation mechanism is characterized mainly by granular flow in near-surface region. The ruptures either reach the surface or are terminated in unconsolidated sediment below the surface. In the latter case, the rupture splays show westward bifurcation, and their geometry and kinematics show a change to NNW-strike with low-angle dip and dextral-reverse oblique-slip sense in the strata. We suggest that the upward termination of the contractional strike-slip ruptures is controlled by the inherited fault geometry that is unfavorable with respect to the stress field (ENE-WSW σHmax) at basement depths in terms of movement on the fault, and the lack of extension of the fault into shallow subsurface; a depth-dependent change in stress from σHmax > σv > σHmin to σHmax > σHmin > σv at depth of a ~ 200 m; and the physical properties of unconsolidated sediment, which have low inter-granular cohesion, resulting in distributed deformation.

Perilla oil and α-linolenic acid ameliorated thrombosis in rats induced by collagen and epinephrine.

Perilla frutescens is an annual herbaceous plant widely cultivated for oil production in China, Japan, and Korea. In this study, we investigated the effect of perilla oil (PO) on thrombosis induced by collagen and epinephrine (CE) in rats. The oral administration of PO significantly increased prothrombin time (PT) and activated partial thromboplastin time (aPTT) in the blood plasma and inhibited the expression of cells adhesion markers (CAMs) such as intercellular CAM-1 (ICAM-1), vascular CAM (VCAM-1), E-selectin and P-selectin in the aorta tissue. Furthermore, pulmonary occlusion induced by CE in rats was suppressed by PO. α-Linolenic acid (ALA) was quantified at 60.14 ± 2.50 g/100 g of PO, and its oral administration at the same concentration with that in PO exerted the similar effect on PT, aPTT, ICAM-1, VCAM-1, E-selectin and P-selectin in CE-induced thrombosis rats. Taken together, PO and ALA significantly ameliorated thrombosis by regulating CAMs.

Bloom development of toxic dinoflagellate Alexandrium catenella (Group I) in Jinhae-Masan Bay, Korea: Germination strategy of resting cysts in relation to temperature and salinity.

To better understand the role of resting cysts in the outbreak of paralytic shellfish poisoning and bloom dynamics in Jinhae-Masan Bay, Korea, this study investigated the germination features of ellipsoidal Alexandrium cysts isolated from sediments collected in winter and summer under different combinations of temperature and salinity. Morphology and phylogeny of germling cells revealed that the ellipsoidal Alexandrium cysts belong to Alexandrium catenella (Group I). The cysts could germinate across a wide range of temperature (5-25 °C) with germination success within 5 days, indicating that continuous seeding for the maintenance of vegetative cells in the water column may occur through the year without an endogenous clock to regulate germination timing. In addition, the cyst germination of A. catenella (Group I) was not controlled by seasonal salinity changes. Based on the results, this study provides a schematic scenario of the bloom development of A. catenella (Group I) in Jinhae-Masan Bay, Korea.

An open-source deep learning model for predicting effluent concentration in capacitive deionization.

To effectively evaluate the performance of capacitive deionization (CDI), an electrochemical ion separation technology, it is necessary to accurately estimate the number of ions removed (effluent concentration) according to energy consumption. Herein, we propose and evaluate a deep learning model for predicting the effluent concentration of a CDI process. The developed deep learning model exhibited excellent prediction accuracy for both constant current and constant voltage modes (R2 ≥ 0.968), and the accuracy increased with the data size. This model was based on the open-source language, Python, and the code has since been distributed with proper instructions for general use. Owing to the nature of the data-oriented deep learning model, the findings of this study are not only applicable to conventional CDI but also to various types of CDI (membrane CDI, flow CDI, faradaic CDI, etc.). Therefore, by referring to the examples shown in this study, we hope that this open-source deep learning code will be widely used in CDI research.

Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction.

Recent advances in machine learning (ML) have revolutionized an extensive range of research and industry fields by successfully addressing intricate problems that cannot be resolved with conventional approaches. However, low interpretability and incompatibility make it challenging to apply ML to complicated bioprocesses, which rely on the delicate metabolic interplay among living cells. This overview attempts to delineate ML applications to bioprocess from different perspectives, and their inherent limitations (i.e., uncertainties in prediction) were then discussed with unique attempts to supplement the ML models. A clear classification can be made depending on the purpose of the ML (supervised vs unsupervised) per application, as well as on their system boundaries (engineered vs natural). Although a limited number of hybrid approaches with meaningful outcomes (e.g., improved accuracy) are available, there is still a need to further enhance the interpretability, compatibility, and user-friendliness of ML models.

Automation of membrane capacitive deionization process using reinforcement learning.

Capacitive deionization (CDI) is an alternative desalination technology that uses electrochemical ion separation. Although several attempts have been made to maximize the energy efficiency and productivity of CDI with conventional control methods, it is difficult to optimize the CDI processes because of the complex correlation between the operational conditions and the composition of feed water. To address these challenges, we applied deep reinforcement learning (DRL) to automatically control the membrane capacitive deionization (MCDI) process, which is one of the representative CDI processes, to accomplish high energy efficiency while desalinating water. In the DRL model, the numerical model is combined as the environment that provides states according to the actions. The feed water conditions, that is, the input state of the DRL, were assumed to have a random salt concentration and constant foulant concentration. The model was constructed to minimize energy consumption and maximize desalted water volume per cycle. After training of 1,000 episodes, the DRL model achieved a 22.07% reduction in specific energy consumption (from 0.054 to 0.042 kWh m-3) and 11.60% increase in water desalted water volume per cycle (from 1.96×10-5 to 2.19×10-5 m3), achieving the desired degree of desalination, compared to the first episode. This improved performance was because the trained model selected the optimized operating conditions of current, voltage, and the number and intensity of flushing. Furthermore, it was possible to train the model depending on demand by modifying the reward function of the DRL model. The fundamental principle described in this study for applying the DRL model in MCDI operations can be the cornerstone of a fully automated water desalination process.

Deep reinforcement learning in an ultrafiltration system: Optimizing operating pressure and chemical cleaning conditions.

Enhancing engineering efficiency and reducing operating costs are permanent subjects that face all engineers over the world. To effectively improve the performance of filtration systems, it is necessary to determine an optimal operating condition beyond conventional methods of periodic and empirical operation. Herein, this paper proposes an effective approach to finding an optimal operating strategy using deep reinforcement learning (DRL), particularly for an ultrafiltration (UF) system. Deep learning was developed to represent the UF system utilizing a long-short term memory and provided an environment for DRL. DRL was designed to control three actions; operating pressure, cleaning time, and cleaning concentration. Ultimately, DRL proposed the UF system to actively change the operating pressure and cleaning conditions over time toward better water productivity and operating efficiency. DRL denoted ∼20.9% of specific energy consumption can be reduced by increasing average water flux (39.5-43.7 L m-2 h-1) and reducing operating pressure (0.617-0.540 bar). Moreover, the optimal action of DRL was reasonable to achieve better performance beyond the conventional operation. Crucially, this study demonstrated that due to the nature of DRL, the approach is tractable for engineering systems that have structurally complex relationships among operating conditions and resultants.

Survey of Korean pediatrician's perceptions of barriers to and improvements in breastfeeding.

BACKGROUND: Although the benefits of breastfeeding are broadly acknowledged with the efforts of the government and several medical societies, the rate of exclusive breastfeeding through 6 months is lower in Korea than in developed countries. PURPOSE: This study aimed to investigate pediatricians' perceptions of breastfeeding barriers and the current breastfeeding counseling environment and propose government policies to encourage breastfeeding in Korea. METHODS: Fourteen survey questions were developed during meetings of Korean Society of Breastfeeding Medicine experts. The Korean Pediatric Society emailed a structured questionnaire to domestic pediatricians registered as official members of the Korean Pediatric Society on May 4, 2021, and June 3, 2021. This study examined the survey responses received from 168 pediatricians. RESULTS: The 168 respondents included 62 professors, 53 paid doctors, and 53 private physicians. Breastfeeding was recommended by 146 Korean pediatricians (86.9%). However, only 99 responders (59%) currently provide breastfeeding counseling in hospitals. Most respondents stated providing less than 15 minutes of breastfeeding counseling time in the clinic. Moreover, 89.88% of the respondents responded that they would participate in breastfeeding counseling education if an appropriate breastfeeding counseling program was newly established. CONCLUSION: This study showed that, although Korean pediatricians had a positive attitude toward breastfeeding, limited counseling was provided for parents. Along with policy support to improve the medical environment through the establishment of an appropriate breastfeeding counseling program, high-quality counseling and an increased breastfeeding rate are expected.

Garcinone C Suppresses Tumorsphere Formation and Invasiveness by Hedgehog/Gli1 Signaling in Colorectal Cancer Stem-like Cells.

Hyperactivation of hedgehog signaling occurs in colorectal cancer stem-like cells (CSCs), a rare subpopulation, potentially involved in metastasis, chemotherapy resistance, and cancer relapse. Garcinone C, a xanthone isolated from mangosteen (Garcinia mangostana), suppresses colorectal cancer in vivo and in vitro by inhibiting Gli1-dependent noncanonical hedgehog signaling. Herein, we investigated the effect of garcinone C on cancer stemness and invasiveness in colorectal cancer; Gli1 was noted as pivotal in maintaining stemness and invasiveness in HCT116 and HT29 CSCs. Garcinone C inhibited the proliferation and self-renewal of HCT116 and HT29 CSCs. Colon cancer stemness markers such as CD44, CD133, ALDH1, and Nanog were significantly decreased by garcinone C. Computational studies showed that garcinone C showed a high affinity with the Gli1 protein ZF domain by forming hydrogen bonds with amino acid residues of ASP244, ARG223, and ASP216. Besides, MG132 blocked the effects of garcinone C on Gli1. Thus, garcinone C suppressed colorectal CSCs by binding to Gli1 and enhancing its degradation. MMP2 and MMP9 levels, invasive-related markers, were increased in HCT116 CSCs but decreased by garcinone C. E-cadherin level was reduced in HCT116 CSCs, while the presence of garcinone C was restored. Garcinone C inhibited the proliferation and invasiveness of colorectal CSCs by targeting Gli1-dependent Hh signaling. Garcinone C may be a potent natural agent against colorectal cancer relapse.

Survey on the Status of Breastfeeding in Korean Medical Institution Workers.

BACKGROUND: Human breast milk is essential and provides irreplaceable nutrients for early humans. However, breastfeeding is not easy for various reasons in medical institution environments. Therefore, in order to improve the breastfeeding environment, we investigated the difficult reality of breastfeeding through questionnaire responses from medical institution workers. METHODS: A survey was conducted among 179 medical institution workers with experience in childbirth within the last five years. The survey results of 175 people were analyzed, with incoherent answers excluded. RESULTS: Of the 175 people surveyed, a total of 108 people (61.7%) worked during the day, and 33 people (18.9%) worked in three shifts. Among 133 mothers who stayed with their babies in the same nursing room, 111 (93.3%) kept breastfeeding for more than a month, but among those who stayed apart, only 10 (71.4%) continued breastfeeding for more than a month (P = 0.024). Ninety-five (88.0%) of daytime workers, 32 (94.1%) two-shift workers, and 33 (100%) three-shift workers continued breastfeeding for more than a month (P = 0.026). Workers in general hospitals tended to breastfeed for significantly longer than those that worked in tertiary hospitals (P = 0.003). A difference was also noted between occupation categories (P = 0.019), but a more significant difference was found in the comparison between nurses and doctors (P = 0.012). Longer breastfeeding periods were noted when mothers worked three shifts (P = 0.037). Depending on the period planned for breastfeeding prior to childbirth, the actual breastfeeding maintenance period after birth showed a significant difference (P = 0.002). Of 112 mothers who responded to the question regarding difficulties in breastfeeding after returning to work, 87 (77.7%) mentioned a lack of time caused by being busy at work, 82 (73.2%) mentioned the need for places and appropriate circumstances. CONCLUSION: In medical institutions, it is recommended that environmental improvements in medical institutions, the implementation of supporting policies, and the provision of specialized education on breastfeeding are necessary to promote breastfeeding.

Breastfeeding and vitamin D.

The recent re-emergence of vitamin D deficiency (VDD) and rickets among breastfed infants without adequate sunlight exposure and vitamin D supplementation has been reported worldwide. Breastfed infants are particularly vulnerable to VDD because of the low vitamin D content of breast milk, restricted sunlight exposure, increased pollution, and limited natural dietary sources of vitamin D. The prevalence of VDD in breastfed infants differs vastly between studies and nations at 0.6%-91.1%. The recommended intake of vitamin D for lactating mothers to optimize their overall vitamin D status and, consequently, of their breast milk is 200-2,000 IU/day, indicating a lack of consensus. Some studies have suggested that maternal high-dose vitamin D supplementation (up to 6,400 IU/day) can be used as an alternate strategy to direct infant supplementation. However, concern persists about the safety of maternal high-dose vitamin D supplementation. Direct infant supplementation is the currently available option to support vitamin D status in breastfed infants. The recommended dose for vitamin D supplementation in breastfed infants according to various societies and organizations worldwide is 200-1,200 IU/day. Most international guidelines recommend that exclusively or partially breastfed infants be supplemented with 400 IU/day of vitamin D during their first year of life. However, domestic studies on the status and guidelines for vitamin D in breastfed infants are insufficient. This review summarizes the prevalence of VDD in breastfed infants, vitamin D content of breast milk, and current guidelines for vitamin D supplementation of lactating mothers and infants to prevent VDD in breastfed infants.

Survey on the Status of Breastfeeding in Korean Medical Institution Workers

Background@#Human breast milk is essential and provides irreplaceable nutrients for early humans. However, breastfeeding is not easy for various reasons in medical institution environments. Therefore, in order to improve the breastfeeding environment, we investigated the difficult reality of breastfeeding through questionnaire responses from medical institution workers. @*Methods@#A survey was conducted among 179 medical institution workers with experience in childbirth within the last five years. The survey results of 175 people were analyzed, with incoherent answers excluded. @*Results@#Of the 175 people surveyed, a total of 108 people (61.7%) worked during the day, and 33 people (18.9%) worked in three shifts. Among 133 mothers who stayed with their babies in the same nursing room, 111 (93.3%) kept breastfeeding for more than a month, but among those who stayed apart, only 10 (71.4%) continued breastfeeding for more than a month (P = 0.024). Ninety-five (88.0%) of daytime workers, 32 (94.1%) two-shift workers, and 33 (100%) three-shift workers continued breastfeeding for more than a month (P = 0.026). Workers in general hospitals tended to breastfeed for significantly longer than those that worked in tertiary hospitals (P = 0.003). A difference was also noted between occupation categories (P = 0.019), but a more significant difference was found in the comparison between nurses and doctors (P = 0.012). Longer breastfeeding periods were noted when mothers worked three shifts (P = 0.037). Depending on the period planned for breastfeeding prior to childbirth, the actual breastfeeding maintenance period after birth showed a significant difference (P = 0.002). Of 112 mothers who responded to the question regarding difficulties in breastfeeding after returning to work, 87 (77.7%) mentioned a lack of time caused by being busy at work, 82 (73.2%) mentioned the need for places and appropriate circumstances. @*Conclusion@#In medical institutions, it is recommended that environmental improvements in medical institutions, the implementation of supporting policies, and the provision of specialized education on breastfeeding are necessary to promote breastfeeding.

A Multi-Center Educational Research Regarding Breastfeeding for Pediatrics Residents in Korea

Purpose@#Pediatricians have a significant responsibility to educate mothers about the importance of breastfeeding. However, there have been minimal efforts in the courses of resident training in Korea. The purpose of this study is to evaluate the change in knowledge and attitude before and after a 4-week breastfeeding educational intervention among multicenter residents. @*Methods@#Prospective interventional educational research was designed for residents at eight training hospitals in Korea. Institutional reviews were obtained in each hospital. The education curriculum consisted of 14 courses regarding breastfeeding theory and practice. These materials were used to teach pediatric residents for 4 weeks. Knowledge-based tests were administered before the course, and re-tests were administered after the course using different test items of similar levels. Test scores and survey responses were compared before and after the intervention. @*Results@#A total of 73 residents (1st year 20, 2nd year 23, 3rd year 16, and 4th year residents 14) from eight training hospitals completed the intervention. Their average age was 30.3±2.9 years, 17 (23.3%) were male, 22 (30.1%) were married, and eight had more than one child of their own. The mean pre-test score was 61.8±13.4 and the mean post-test score was 78.3±7.5 (P<0.001). The inter-grade difference in the score was significant in the pre-test (P=0.005), but not significant in the post-test (P=0.155). There were more responses of obtaining confidence after the intervention (P<0.001). @*Conclusion@#In our study, pediatric residents showed improvement in their knowledge and confidence level after 4 weeks of the breastfeeding curriculum. This will provide a basis for future policymaking in the training of pediatric residents regarding breastfeeding in Korea.