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This study examines the identity and characteristics of modern Korean Buddhist education through an analysis of the religious studies lecture notes of a student from the Buddhist Central Seminary (Pulgyo Chungang Hangnim, 佛敎中央學林), preserved at Songgwang-sa Temple. Established in 1915 and operating until 1919, the seminary introduced a significant shift from traditional scripture-centered monastic education to a modern academic system. Western and Japanese academic traditions, religious studies, philosophy, and the general educational system influenced its curriculum. The lecture notes provide insight into the adoption of modern academic disciplines within Korean Buddhist education, revealing the influence of Japanese religious studies and Western comparative religion. They also demonstrate the possibility of early introduction of religious studies as an educational field in Korea. The seminary played a dual role as a hub for national education and reflection of the colonial context, embodying the complexities of nationalism and colonial influence during Japanese occupation. This study underscores the need for further scholarly exploration to understand the multifaceted nature of modern Korean Buddhist education and its unique role within the broader historical context of East Asian Buddhist history.

In the post-pandemic era, the uncertain global market and rising social-environmental issues drive organizations to adapt their supply chain strategies to more dynamic, flexible models, leveraging advanced technologies like AI, big data analytics, and decision support systems. This review paper aims to examine the current research on AI-integrated technologies in sustainable supply chain management (SSCM) to inform future research directions. We adopted bibliometric and text analysis, targeting 170 articles published between 2004 and 2023 from the Scopus database following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. We confirm that AI-integrated technologies have demonstrated the capability to enable SSCM across various sectors. We generated ten future research topics using the Latent Dirichlet Allocation (LDA) method and proposed 20 propositions. The results show that AI-integrated technologies in supply chain processes primarily address sustainability, focusing on environmental and economic issues. However, there is still a technological gap in tackling social issues like working conditions and fair dealing. Thus, we proposed a dynamic framework of AI in SSCM to help researchers and practitioners synthesize AI-integrated technologies in SSCM and optimize their supply chain models in future directions.

This study was conducted to identify and compare the effects of two education programs for infection control―a simulation using standardized patients and a peer role-play―on standard precaution knowledge, standard precaution awareness, infection-related anxiety, and infection control performance. This study used a nonequivalent control group pretest-posttest design. A total of 62 undergraduate nursing students in their 3rd year participated in the study, and were assigned to the experimental and control groups, accordingly. The infection control education program was developed based on the analysis, design, development, implementation, and evaluation model. The program for the experimental group included lectures, skills training, simulation using standardized patients, and debriefing, while the control group participated in the usual infection control education, consisting of lectures, skills training, and peer tutoring practices. Both groups exhibited statistically significant increases in knowledge, awareness of standard precaution, and infection control performance after the intervention. Infection-related anxiety and infection control performance were significantly higher in the simulation using a standardized patient group. Both education programs influenced compliance with the standard precaution for infection control. The results of this study contribute to the evidence regarding effective educational methods to improve infection control.

Background Drug-induced liver injury (DILI) is a critical issue in drug development because DILI causes failures in clinical trials and the withdrawal of approved drugs from the market. There have been many attempts to predict the risk of DILI based on in vivo and in silico identification of hepatotoxic compounds. In the current study, we propose the in silico prediction model predicting DILI using weighted molecular fingerprints. Results In this study, we used 881 bits of molecular fingerprint and used as features describing presence or absence of each substructure of compounds. Then, the Bayesian probability of each substructure was calculated and labeled (positive or negative for DILI), and a weighted fingerprint was determined from the ratio of DILI-positive to DILI-negative probability values. Using weighted fingerprint features, the prediction models were trained and evaluated with the Random Forest (RF) and Support Vector Machine (SVM) algorithms. The constructed models yielded accuracies of 73.8% and 72.6%, AUCs of 0.791 and 0.768 in cross-validation. In independent tests, models achieved accuracies of 60.1% and 61.1% for RF and SVM, respectively. The results validated that weighted features helped increase overall performance of prediction models. The constructed models were further applied to the prediction of natural compounds in herbs to identify DILI potential, and 13,996 unique herbal compounds were predicted as DILI-positive with the SVM model. Conclusions The prediction models with weighted features increased the performance compared to non-weighted models. Moreover, we predicted the DILI potential of herbs with the best performed model, and the prediction results suggest that many herbal compounds could have potential to be DILI. We can thus infer that taking natural products without detailed references about the relevant pathways may be dangerous. Considering the frequency of use of compounds in natural herbs and their increased application in drug development, DILI labeling would be very important.

Adverse drug-drug interaction (DDI) is a major concern to polypharmacy due to its unexpected adverse side effects and must be identified at an early stage of drug discovery and development. Many computational methods have been proposed for this purpose, but most require specific types of information, or they have less concern in interpretation on underlying genes. We propose a deep learning-based framework for DDI prediction with drug-induced gene expression signatures so that the model can provide the expression level of interpretability for DDIs. The model engineers dynamic drug features using a gating mechanism that mimics the co-administration effects by imposing attention to genes. Also, each side-effect is projected into a latent space through translating embedding. As a result, the model achieved an AUC of 0.889 and an AUPR of 0.915 in unseen interaction prediction, which is competitively very accurate and outperforms other state-of-the-art methods. Furthermore, it can predict potential DDIs with new compounds not used in training. In conclusion, using drug-induced gene expression signatures followed by gating and translating embedding can increase DDI prediction accuracy while providing model interpretability. The source code is available on GitHub ( https://github.com/GIST-CSBL/DeSIDE-DDI ).

Cooling in buildings is vital to human well-being but inevitability consumes significant energy, adding pressure on achieving carbon neutrality. Thermally superinsulating aerogels are promising to isolate the heat for more energy-efficient cooling. However, most aerogels tend to absorb the sunlight for unwanted solar heat gain, and it is challenging to scale up the aerogel fabrication while maintaining consistent properties. Herein, we develop a thermally insulating, solar-reflective anisotropic cooling aerogel panel containing in-plane aligned pores with engineered pore walls using boron nitride nanosheets by an additive freeze-casting technique. The additive freeze-casting offers highly controllable and cumulative freezing dynamics for fabricating decimeter-scale aerogel panels with consistent in-plane pore alignments. The unique anisotropic thermo-optical properties of the nanosheets combined with in-plane pore channels enable the anisotropic cooling aerogel to deliver an ultralow out-of-plane thermal conductivity of 16.9 mW m −1 K −1 and a high solar reflectance of 97%. The excellent dual functionalities allow the anisotropic cooling aerogel to minimize both parasitic and solar heat gains when used as cooling panels under direct sunlight, achieving an up to 7 °C lower interior temperature than commercial silica aerogels. This work offers a new paradigm for the bottom-up fabrication of scalable anisotropic aerogels towards practical energy-efficient cooling applications. Scaling up anisotropic freeze-casting processes can be challenging due to the temperature gradient farther from the cold source. Here, authors report an additive freeze-casting technique able to produce large-scale aerogel panels and demonstrate it towards practical passive cooling applications.

Background. The advent of macrolides has led to therapeutic advances in the treatment of Mycobacterium avium complex lung disease (MAC-LD). The aim of this study was to elucidate the treatment outcomes of macrolide-containing regimens. Methods. We performed a systematic review and meta-analysis of published studies reporting treatment outcomes of macrolide-containing regimens for MAC-LD using the Medline, Embase, and Cochrane Library databases through 31 July 2016. The rates of treatment success, default from treatment, and adverse events of macrolide-containing regimens were assessed. Treatment success was defined as either 12 months of sustained culture negativity while on therapy or achievement of culture conversion and completion of the planned treatment without relapse. Results. In total, 16 studies involving 1462 patients were included. The rate of treatment success was 60.0% (95% confidence interval [CI], 55.1%–64.8%). The proportion of patients who defaulted from the treatment was 16.0% (95% CI, 12.3%–19.7%). When a thrice-weekly dosing schedule was available, the default rate was 12.0% (95% CI, 8.9%–15.0%). Adverse events necessitating treatment discontinuation or dosage modification of macrolides were observed in 6.4% of patients (95%, CI, 3.2%–9.5%), and decreased auditory acuity was the most common adverse event. Conclusions. Treatment outcomes of macrolide-containing regimens are relatively poor in terms of both the treatment success and default rates. The default rate could be reduced if a thrice-weekly dosing schedule is available. Clinicians should be aware of decreased auditory function as the most common adverse event associated with macrolide-containing regimens.

HighlightsTwo functionally different anisotropic layers are rationally assembled for highly selective and stretchable multidirectional strain sensors.Concurrently excellent selectivity, sensitivity, stretchability, and linearity up to 100% strain is demonstrated for the first time in a multidirectional strain sensor.A novel stepwise crack propagation mechanism is proposed to enable high stretchability and linearity. Flexible multidirectional strain sensors are crucial to accurately determining the complex strain states involved in emerging sensing applications. Although considerable efforts have been made to construct anisotropic structures for improved selective sensing capabilities, existing anisotropic sensors suffer from a trade-off between high sensitivity and high stretchability with acceptable linearity. Here, an ultrasensitive, highly selective multidirectional sensor is developed by rational design of functionally different anisotropic layers. The bilayer sensor consists of an aligned carbon nanotube (CNT) array assembled on top of a periodically wrinkled and cracked CNT–graphene oxide film. The transversely aligned CNT layer bridge the underlying longitudinal microcracks to effectively discourage their propagation even when highly stretched, leading to superior sensitivity with a gauge factor of 287.6 across a broad linear working range of up to 100% strain. The wrinkles generated through a pre-straining/releasing routine in the direction transverse to CNT alignment is responsible for exceptional selectivity of 6.3, to the benefit of accurate detection of loading directions by the multidirectional sensor. This work proposes a unique approach to leveraging the inherent merits of two cross-influential anisotropic structures to resolve the trade-off among sensitivity, selectivity, and stretchability, demonstrating promising applications in full-range, multi-axis human motion detection for wearable electronics and smart robotics.

Background: The emergence of more than 40 new infectious diseases since the 1980s has emerged as a serious global health concern, many of which are zoonotic. In response, many international organizations, including the US Centers for Disease Control and Prevention (CDC), the World Health Organization (WHO), and the European Center for Disease Prevention and Control (ECDC), have developed strategies to combat these health threats. The need for rapid vaccine development has been highlighted by Coronavirus disease 2019 (COVID-19), and mRNA technology has shown promise as a platform. While the acceleration of vaccine development has been successful, concerns have been raised about the technical limits, safety, supply, and distribution of vaccines. Objective: This study analyzes the status of vaccine platform development in global pandemics and explores ways to respond to future pandemic crises through an overview of the roles of international organizations and their support programs. It examines the key roles and partnerships of international organizations such as the World Health Organization (WHO), vaccine research and development expertise of the Coalition for Epidemic Preparedness Innovations (CEPI), control of the vaccine supply chain and distribution by the Global Alliance for Vaccines and Immunization (GAVI), and technology transfer capabilities of the International Vaccine Institute (IVI) in supporting the development, production, and supply of vaccine platform technologies for pandemic priority diseases announced by WHO and CEPI and analyzes their vaccine support programs and policies to identify effective ways to rapidly respond to future pandemics caused by emerging infectious diseases. Methods: This study focused on vaccine platform technology and the key roles of international organizations in the pandemic crisis. Literature data on vaccine platform development was collected, compared, and analyzed through national and international literature data search sites, referring to articles, journals, research reports, publications, books, guidelines, clinical trial data, and related reports. In addition, the websites of international vaccine support organizations, such as WHO, CEPI, GAVI, and IVI, were used to examine vaccine support projects, initiatives, and collaborations through literature reviews and case study methods. Results: The COVID-19 pandemic brought focus on the necessity for developing innovative vaccine platforms. Despite initial concerns, the swift integration of cutting-edge development technologies, mass production capabilities, and global collaboration have made messenger RNA (mRNA) vaccines a game-changing technology. As a result of the successful application of novel vaccine platforms, it is important to address the remaining challenges, including technical limits, safety concerns, and equitable global distribution. To achieve this, it is essential to review the regulatory, policy, and support initiatives that have been implemented in response to the COVID-19 pandemic, with particular emphasis on the key stages of vaccine development, production, and distribution, to prepare for future pandemics. An analysis of the status of vaccine development for priority pandemic diseases implies the need for balanced vaccine platform development. Also, international organizations such as WHO, CEPI, GAVI, and IVI play key roles in pandemic preparedness and the development and distribution of preventive vaccines. These organizations collaborated to improve accessibility to vaccines, strengthen the global response to infectious diseases, and address global health issues. The COVID-19 pandemic response demonstrates how the synergistic collaboration of WHO’s standardized guidelines, CEPI’s vaccine research and development expertise, GAVI’s control of the vaccine supply chain and distribution, and IVI’s technology transfer capabilities can be united to create a successful process for vaccine development and distribution. Conclusions: In preparation for future pandemics, a balanced vaccine platform development is essential. It should include a balanced investment in both novel technologies such as mRNA and viral vector-based vaccines and traditional platforms. The goal is to develop vaccine platform technologies that can be applied to emerging infectious diseases efficiently and increase manufacturing and distribution capabilities for future pandemics. Moreover, international vaccine support organizations should play key roles in setting the direction of global networking and preparing for international vaccine support programs to address the limitations of previous pandemic responses. As a result, by transforming future pandemic threats from unpredictable crises to surmountable challenges, it is expected to strengthen global health systems and reduce the social and economic burden of emerging infectious diseases in the long term.

The contribution of caregivers to self-care for chronically ill patients is important for improving patient outcomes. The Caregiver Contribution to Self-Care Chronic Illness Inventory (CC-SC-CII) has been used to assess caregivers’ contributions to three distinct aspects of self-care (maintenance, monitoring, and management) globally. This study aimed to examine the psychometrics of the Korean version of the CC-SC-CII with 230 family caregivers (mean age = 49.8 years, 70% women) of patients with chronic illness. We demonstrated that the CC-SC-CII-Korean has good reliability with acceptable internal consistency and construct validity for all three factors using confirmatory factor analysis. The CC-SC-CII-Korean is a reliable and valid instrument to measure the contributions of Korean caregivers to the self-care of patients with chronic illnesses.