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Thermal barrier coatings (TBCs) are essential to the reliable high-temperature operation of gas turbines and engines. They comprise a ceramic top coat (TC), a metallic bond coat (BC), and a superalloy substrate. Metallic BCs are common, but they require a minimal difference in the coefficient of thermal expansion (CTE) between the TC and substrate. Among ceramic materials, MAX phases have high CTE. This study reports our use of ab initio calculations to assess the suitability of MAX phases as TBCs. We model Nb-based MAX phases with 211 and 312 structures that have Al or Si at the A sites and C or N at the X sites. We use the quasi-harmonic approximation to calculate the Young's moduli and CTEs of the materials with respect to temperature. The Nb2SiN MAX phase appears as the most suitable for use as a BC between various ceramic TCs and an Inconel-718 substrate. It is predicted to be effective in relieving thermal stresses due to its high CTE of 10.882 × 10−⁶ K−1 at 1273 K. The results indicate that carbide MAX phases with high Young's modulus and low CTE should be used with caution. They may not accommodate thermal stresses as effectively, potentially leading to material failure or reduced performance. Overall, our study indicates the potential of Nb-based MAX structures for use as BCs.

BackgroundAccurate hospital length of stay (LoS) prediction enables efficient resource management. Conventional LoS prediction models with limited covariates and nonstandardized data have limited reproducibility when applied to the general population. ObjectiveIn this study, we developed and validated a machine learning (ML)–based LoS prediction model for planned admissions using the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). MethodsRetrospective patient-level prediction models used electronic health record (EHR) data converted to the OMOP CDM (version 5.3) from Seoul National University Bundang Hospital (SNUBH) in South Korea. The study included 137,437 hospital admission episodes between January 2016 and December 2020. Covariates from the patient, condition occurrence, medication, observation, measurement, procedure, and visit occurrence tables were included in the analysis. To perform feature selection, we applied Lasso regularization in the logistic regression. The primary outcome was an LoS of 7 days or longer, while the secondary outcome was an LoS of 3 days or longer. The prediction models were developed using 6 ML algorithms, with the training and test set split in a 7:3 ratio. The performance of each model was evaluated based on the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Shapley Additive Explanations (SHAP) analysis measured feature importance, while calibration plots assessed the reliability of the prediction models. External validation of the developed models occurred at an independent institution, the Seoul National University Hospital. ResultsThe final sample included 129,938 patient entry events in the planned admissions. The Extreme Gradient Boosting (XGB) model achieved the best performance in binary classification for predicting an LoS of 7 days or longer, with an AUROC of 0.891 (95% CI 0.887-0.894) and an AUPRC of 0.819 (95% CI 0.813-0.826) on the internal test set. The Light Gradient Boosting (LGB) model performed the best in the multiclassification for predicting an LoS of 3 days or more, with an AUROC of 0.901 (95% CI 0.898-0.904) and an AUPRC of 0.770 (95% CI 0.762-0.779). The most important features contributing to the models were the operation performed, frequency of previous outpatient visits, patient admission department, age, and day of admission. The RF model showed robust performance in the external validation set, achieving an AUROC of 0.804 (95% CI 0.802-0.807). ConclusionsThe use of the OMOP CDM in predicting hospital LoS for planned admissions demonstrates promising predictive capabilities for stays of varying durations. It underscores the advantage of standardized data in achieving reproducible results. This approach should serve as a model for enhancing operational efficiency and patient care coordination across health care settings.

BackgroundMaternal mortality remains a persistent challenge in low- and middle-income countries, where evidence-based interventions of obstetric triage and prehospital communication remain sparse. There is limited implementation evidence for technology-based approaches to improve obstetric care in such contexts. Liberia struggles with maternal mortality, particularly in rural areas where deaths are attributable to delays from absent triage and interfacility communication. We implemented a Mobile Obstetric Referral Emergency System (MORES) in rural Bong County to improve prehospital transfer, health worker attentiveness, and patient care for critical obstetric patients. MORES consisted of triage training and a 2-way, templated WhatsApp communication system to reduce delays among patients transferred from rural health facilities (RHF) to hospitals. ObjectiveThis study aimed to examine MORES implementation outcomes of usability, fidelity, effectiveness, sustainability, and scalability, as well as additional impacts on the wider health system. MethodsA structured case study design interview was developed by Liberian and US experts in obstetric triage. Participants included 62 frontline obstetric health providers including midwives (38/62, 61%), nurses (20/62, 32%), physicians assistants (3/62, 5%), and physicians (1/62, 2%) from 19 RHFs and 2 district hospitals who had used MORES for 1 year. Individual interviews were conducted on MORES implementation outcomes, transcribed, and analyzed in NVivo (version 12; Lumivero) with a team-based coding methodology. Content analysis with a deductive approach examined implementation outcomes of usability, fidelity, effectiveness, sustainability, and scalability, while an inductive approach categorized the unanticipated impacts of MORES on the wider health system. ResultsFour domains were identified regarding MORES implementation: Usability and Fidelity, Effectiveness, Sustainability and Scalability, and Health System Impact. All participants perceived MORES to have high usability and fidelity, as the triage and messaging system was implemented as intended for critical obstetric patients (62/62, 100%). For effectiveness, MORES accomplished its intended aims by improving prehospital transfer (57/62, 92%), increasing health worker attentiveness (39/62, 63%), and contributing to improved patient care (34/62, 55%). MORES was perceived as sustainable and scalable (62/62, 100%), particularly if technological barriers (21/62, 34%) and staff training (19/62, 31%) were addressed. MORES impacted the wider health system in unanticipated ways including improved coordination and accountability (55/62, 89%), feedback mechanisms for hospitals and RHFs (48/62, 77%), interprofessional teamwork (21/62, 34%), longitudinal follow-up care (20/62, 32%), creating a record of care delays (17/62, 27%), and electronic health record infrastructure (13/62, 21%). ConclusionsMORES was perceived to have high usability, fidelity, effectiveness, sustainability, and scalability by frontline obstetric providers in rural Liberia. MORES accomplished the intended aims of improving prehospital transfer, increasing health worker attentiveness, and contributing to improved patient care. Additionally, MORES strengthened the health system through 6 domains which impacted individual and system levels. Future studies should quantitatively evaluate delay and morbidity reductions and strategies for scaling MORES.

Summary: Many types of neurons exhibit a daily rhythm of intrinsic excitability. Here, we present a protocol for assessing circadian regulation of dentate granule cell excitability using a mouse model for conditional knockout of the molecular clock protein BMAL1. We describe steps for obtaining healthy oblique horizontal slices that contain the hippocampus and measuring intrinsic excitability and synaptic potentials by combining whole-cell patch-clamp recordings and perforant-path electric stimulation. We then detail procedures for validating single-cell genetic deletion of Bmal1 by immunohistochemistry.For complete details on the use and execution of this protocol, please refer to Gonzalez et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Abstract Distant metastasis, the leading cause of cancer death, is efficiently kept in check by immune surveillance. Studies have uncovered peripheral natural killer (NK) cells as key antimetastatic effectors and their dysregulation during metastasis. However, the molecular mechanism governing NK cell dysfunction links to metastasis remains elusive. Herein, MAP4K1 encoding HPK1 is aberrantly overexpressed in dysfunctional NK cells in the periphery and the metastatic site. Conditional HPK1 overexpression in NK cells suffices to exacerbate melanoma lung metastasis but not primary tumor growth. Conversely, MAP4K1‐deficient mice are resistant to metastasis and further protected by combined immune‐checkpoint inhibitors. Mechanistically, HPK1 restrains NK cell cytotoxicity and expansion via activating receptors. Likewise, HPK1 limits human NK cell activation and associates with melanoma NK cell dysfunction couples to TGF‐β1 and patient response to immune checkpoint therapy. Thus, HPK1 is an intracellular checkpoint controlling NK‐target cell responses, which is dysregulated and hijacked by tumors during metastatic progression.

BackgroundPre-transplant donor-specific anti-human leukocyte antigen antibody (HLA-DSA) is a recognized risk factor for acute antibody-mediated rejection (ABMR) and allograft failure. However, the clinical relevance of pre-transplant crossmatch (XM)-negative HLA-DSA remains unclear.MethodsWe investigated the effect of XM-negative HLA-DSA on post-transplant clinical outcomes using data from the Korean Organ Transplantation Registry (KOTRY). This study included 2019 living donor kidney transplant recipients from 40 transplant centers in South Korea: 237 with HLA-DSA and 1782 without HLA-DSA.ResultsABMR developed more frequently in patients with HLA-DSA than in those without (5.5% vs. 1.5%, p

Objectives: This study investigated the association between exacerbated economic hardship during the coronavirus disease 2019 (COVID-19) pandemic and changes in the health behaviors of Korean adolescents. Methods: We analyzed data from the 2021 Korea Youth Risk Behavior Survey and included 44 908 students (22 823 boys and 22 085 girls) as study subjects. The dependent variables included changes in health behaviors (breakfast habits, physical activity, and alcohol use) that occurred during the COVID-19 pandemic. The aggravation of economic hardship by COVID-19 and the subjective economic status of the family were used as exposure variables. Multiple logistic regression analysis was utilized to calculate the prevalence odds ratios (PORs). Results: Severe exacerbation of a family’s economic hardship due to COVID-19 was negatively associated with the health behaviors of adolescents, including increased breakfast skipping (POR, 1.85; 95% confidence interval [CI], 1.55 to 2.21 for boys and POR, 1.56; 95% CI, 1.27 to 1.92 for girls) and decreased physical activity (POR, 1.37; 95% CI, 1.19 to 1.57 for boys and POR, 1.38; 95% CI, 1.19 to 1.60 for girls). These negative changes in health behaviors were further amplified when combined with a low subjective family economic status. Conclusions: The experience of worsening household hardship can lead to negative changes in health behavior among adolescents. It is crucial to implement measures that address the economic challenges that arise from stressful events such as COVID-19 and to strive to improve the lifestyles of adolescents under such circumstances.

Centella asiatica, a traditional herb, is widely recognized for its pharmacologically active components, such as asiaticoside, madecassoside, asiatic acid, and madecassic acid. These components render it a highly sought-after ingredient in various industries, including cosmetics and pharmaceuticals. This study aimed to enhance the production and activity of these pharmacological constituents of C. asiatica using the plant growth-promoting rhizobacterium Priestia megaterium HyangYak-01 during its cultivation. To achieve this goal, the researchers conducted field experiments, which revealed an increase in the production of pharmacologically active compounds in C. asiatica cultivated with a P. megaterium HyangYak-01 culture solution. Additionally, quadrupole time-of-flight mass spectrometry (Q-TOF MS) confirmed that the composition ratios of the C. asiatica extract treated with the P. megaterium HyangYak-01 culture solution differed from those of the untreated control and type strain-treated groups. Skin cell experiments indicated that the C. asiatica extract treated with the P. megaterium HyangYak-01 culture solution exhibited greater skin barrier improvement and less pronounced inflammatory responses than those from plants grown without the bacterial culture solution. This study demonstrates that microbial treatment during plant cultivation can beneficially influence the production of pharmacological constituents, suggesting a valuable approach toward enhancing the therapeutic properties of plants.

As three-dimensional integrated circuit (3D-IC) chip technology advances, thermal management has become increasingly important because of increasing heat flux from thermal stacking. Micro-pin fin-embedded cooling has emerged as a promising solution for 3D-ICs, offering better thermal and hydraulic performance than conventional microchannel heat sinks. It is also easy to integrate into existing 3D-IC structures, such as through-silicon vias between stacks. The utilization of two-phase flow in micro-pin fins further enhances temperature uniformity and improves the heat transfer coefficient by leveraging latent heat. Nevertheless, predicting thermal performance in micro-pin fin heat sinks under boiling conditions remains challenging owing to intricate geometric shapes and diverse operating conditions. The present lack of correlation or theoretical models poses a significant obstacle. To address this problem, our study employed a Multimodal machine-learning (ML) approach, combining image data capturing boiling patterns of two-phase flow and information about geometric shape and operating conditions, to predict heat transfer characteristics in micro-pin fin heat sinks. We utilized experimental data comprising 155 types of boiling heat transfer data with the dielectric fluid FC-72 in two micro-fin shapes directly etched on Si. Four ML algorithms (XGBoost, LightGBM, Multilayer perceptron (MLP), and Multimodal ML) were employed to predict thermal performance. The correlation coefficient analysis before learning revealed the influence of each type of measurement data on the heater surface temperature during two-phase flow. Prediction accuracy was measured using mean absolute percent error (MAPE), and the results were compared in terms of maximum and average temperature depending on the characteristics of each ML algorithm. Overall, the Multimodal approach demonstrated superior capability in predicting temperature distributions with spatial details, surpassing conventional decision-tree algorithms and MLP in performance. When trained with boiling images, the Multimodal ML model achieved remarkable precision, evidenced by a MAPE of 1.81% for the maximum temperature and 0.84% for the average temperature, highlighting its exceptional accuracy in mapping the heated surface temperature profile. By contrast, the traditional MLP model, which lacked training on boiling images, showed diminished accuracy, with a MAPE of 2.54% for the maximum temperature and 1.77% for the average temperature, indicating a comparative shortfall against the Multimodal model results.

Minerals is required small amounts among various nutrients, but it has a significant impact on sow longevity and reproduction performance. This study was carried out to see the beneficial effects of marine-derived Ca-Mg complex on the reproductive performance of sows during four-parity periods. Seventy-two gilts ([Yorkshire × Landrace] × Duroc), with an average body weight of 181 kg, were randomly allocated to three groups; CON (basal diet), 0.3LC (CON - MgO - 0.3% limestone + 0.4% Ca-Mg complex), and 0.7LC (CON - MgO - 0.7% limestone + 0.4% Ca-Mg complex). During parity 3 and 4, the expression level of SCD gene was lower in the umbilical cord of piglets born to 0.3LC and 0.7LC sows compared with the CON sows. During parity 2, 3 and 4, SLC2A2 and FABP4 gene expressions were higher in the umbilical cord of piglets born to 0.7LC sows and the placenta of sows from 0.3LC groups, respectively. Ca-Mg complex increased (p < 0.05) Ca and Mg concentrations in sows and their piglets’ serum as well as in colostrum regardless of parities. The serum vitamin D concentration was higher (p < 0.05) in their first parity, whereas serum prolactin and estrogen concentrations were higher (p < 0.05) during the fourth and third parity, respectively. The growth hormone concentrations were higher (p < 0.05) in the piglets born to sows during the first and second parity. The fat and immunoglobulin A (IgA) concentrations in colostrum were higher (p < 0.05) during the third and fourth parity, respectively. A reduction (p < 0.05) in salivary cortisol, epinephrine, and norepinephrine concentrations was observed in 0.3LC and 0.7LC sow groups compared with CON after farrowing regardless of parity, however before farrowing, a reduction in norepinephrine was observed. Before farrowing, the epinephrine and norepinephrine concentrations were higher (p < 0.05) during the first and second parity. After farrowing, the concentration of these hormones was higher during the second parity. Taken together, sows’ parity and dietary Ca-Mg complex supplementation influenced serum metabolites, colostrum nutrients, stress hormones as well as the gene expressions related to lipid and glucose metabolism.

The effects of interfacial energy on precipitation behavior and physical properties of Cu–Ni–Si–(Co) alloys were investigated. The Cu–1.3Ni–0.3Si and Cu–1.3Ni–0.6Si–1.0Co alloys (in weight %) were prepared by combined cold-rolling and aging processes. The addition of Co promoted the formation of (Ni,Co)2Si by replacing Ni with Co in Ni2Si. Ab initio calculations demonstrated that the small addition of Co considerably reduced the interfacial energy between the Cu matrix and (Ni,Co)2Si, which effectively decreased the activation energy for precipitation. As a result, the precipitation of fine rod-shaped (Ni,Co)2Si was accelerated during the aging process and the number density of the precipitates increased up to 9.0 × 1010 cm−2. The nano-scale (Ni,Co)2Si precipitates with an average size of 13.5 nm strengthened the alloy properly through the Orowan mechanism without much loss of electrical conductivity. The maximum hardness and electrical conductivity were 220 Hv and 51 % International Annealed Copper Standard, respectively, for the Cu–1.3Ni–0.6Si–1.0Co alloy aged for 4 h at 420 °C.

Abstract Background The emigration of physicians from low- and middle-income countries (LMICs) to high-income countries (HICs), colloquially referred to as the “brain drain”, has been a topic of discussion in global health spheres for years. With the call to decolonize global health in mind, and considering that West Africa, as a region, is a main source of physicians emigrating to HICs, this rapid review aims to synthesize the reasons for, and implications of, the brain drain, as well as recommendations to mitigate physician emigration from West African countries to HICs. Methods A literature search was conducted on PubMed, EMBASE and The Cochrane Library. Main inclusion criteria were the inclusion of West African trained physicians' perspectives, the reasons and implications of physician emigration, and recommendations for management. Data on the study design, reasons for the brain drain, implications of brain drain, and proposed solutions to manage physician emigration were extracted using a structured template. The Hawker Tool was used as a risk of bias assessment tool to evaluate the included articles. Results A total of 17 articles were included in the final review. Reasons for physician emigration include poor working conditions and remuneration, limited career opportunities, low standards of living, and sociopolitical unrest. Implications of physician emigration include exacerbation of low physician to population ratios, and weakened healthcare systems. Recommendations include development of international policies that limit HICs’ recruitment from LMICs, avenues for HICs to compensate LMICs, collaborations investing in mutual medical education, and incorporation of virtual or short-term consultation services for physicians working in HICs to provide care for patients in LMICs. Conclusions The medical brain drain is a global health equity issue requiring the collaboration of LMICs and HICs in implementing possible solutions. Future studies should examine policies and innovative methods to involve both HICs and LMICs to manage the brain drain.

Background Existing models have performed poorly when predicting mortality for patients undergoing venovenous extracorporeal membrane oxygenation (VV-ECMO). This study aimed to develop and validate a machine learning (ML)-based prediction model to predict 90-day mortality in patients undergoing VV-ECMO.Methods This study included 368 patients with acute respiratory failure undergoing VV-ECMO from 16 tertiary hospitals across South Korea between 2012 and 2015. The primary outcome was the 90-day mortality after ECMO initiation. The inputs included all available features (n=51) and those from the electronic health record (EHR) systems without preprocessing (n=40). The discriminatory strengths of ML models were evaluated in both internal and external validation sets. The models were compared with conventional models, such as respiratory ECMO survival prediction (RESP) and predicting death for severe acute respiratory distress syndrome on VV-ECMO (PRESERVE).Results Extreme gradient boosting (XGB) (areas under the receiver operating characteristic curve, AUROC 0.82, 95% CI (0.73 to 0.89)) and light gradient boosting (AUROC 0.81 (95% CI 0.71 to 0.88)) models achieved the highest performance using EHR’s and all other available features. The developed models had higher AUROCs (95% CI 0.76 to 0.82) than those of RESP (AUROC 0.66 (95% CI 0.56 to 0.76)) and PRESERVE (AUROC 0.71 (95% CI 0.61 to 0.81)). Additionally, we achieved an AUROC (0.75) for 90-day mortality in external validation in the case of the XGB model, which was higher than that of RESP (0.70) and PRESERVE (0.67) in the same validation dataset.Conclusions ML prediction models outperformed previous mortality risk models. This model may be used to identify patients who are unlikely to benefit from VV-ECMO therapy during patient selection.

In this study, the effects of organic acid-soaking (malic, citric, tartaric, and succinic acid) and sonication on the formation of flavor and α-dicarbonyl compounds in Robusta (C. canephora syn. Coffea robusta) green beans were investigated. A total of 20 volatile compounds were identified in Robusta coffee. Furfural and 5-methyl furfural, two dominant volatile compounds in Arabica coffee, increased after organic acid pretreatment. In Robusta coffee processed from 3% malic acid-soaked coffee beans, furfural and 5-methyl furfural increased by 90.99% and 24.92%, respectively, compared to the control. In Robusta coffee processed from 3% malic acid-sonicated (280 W, 1 h) coffee beans, furfural and 5-methyl furfural increased by 236.03% and 114.77%, respectively. α-Dicarbonyls (glyoxal, methylglyoxal, and diacetyl) were significantly (p

This dataset provides detailed profiles of bacterial and fungal communities associated with flowers (anthosphere) of 12 different plant species collected from remote and secluded locations characterized by a flourishing and diverse plant ecosystem. In total, 144 flower samples were collected from 12 different wild plants. Bacterial 16S rRNA and fungal ITS genes obtained using the Illumina Miseq approach were used to describe the anthosphere. Metadata and raw sequences obtained in this study are available from the National Center for Biotechnology Information (BioProject ID: PRJNA983070). Amplicon Sequence Variants (ASVs) of bacteria and fungi were analyzed using the DADA2 pipeline. After quality filtering, trimming, and removing the chimeric sequences, 2076 bacterial and 2152 fungal ASVs were identified in the anthosphere. Burkholderiales and Enterobacterales in bacteria, and Pleosporales in fungi were the predominant groups in the anthosphere regardless of the plant species. Among the twelve different plant species, Forsythia koreana exhibited the highest abundance of both bacterial and fungal groups. This dataset represents a detailed exploration of the anthosphere in the most abundant and commonly observed plant species in South Korea, and provides new insights into the microbial communities and interactions of the anthosphere.

BackgroundMethylphenidate is an effective first-line treatment for attention-deficit/hyperactivity disorder (ADHD). However, many adverse effects of methylphenidate have been recorded from randomized clinical trials and patient-reported outcomes, but it is difficult to determine abuse from them. In the context of COVID-19, it is important to determine how drug use evaluation, as well as misuse of drugs, have been affected by the pandemic. As people share their reasons for using medication, patient sentiments, and the effects of medicine on social networking services (SNSs), the application of machine learning and SNS data can be a method to overcome the limitations. Proper machine learning models could be evaluated to validate the effects of the COVID-19 pandemic on drug use. ObjectiveTo analyze the effect of the COVID-19 pandemic on the use of methylphenidate, this study analyzed the adverse effects and nonmedical use of methylphenidate and evaluated the change in frequency of nonmedical use based on SNS data before and after the outbreak of COVID-19. Moreover, the performance of 4 machine learning models for classifying methylphenidate use based on SNS data was compared. MethodsIn this cross-sectional study, SNS data on methylphenidate from Twitter, Facebook, and Instagram from January 2019 to December 2020 were collected. The frequency of adverse effects, nonmedical use, and drug use before and after the COVID-19 pandemic were compared and analyzed. Interrupted time series analysis about the frequency and trends of nonmedical use of methylphenidate was conducted for 24 months from January 2019 to December 2020. Using the labeled training data set and features, the following 4 machine learning models were built using the data, and their performance was evaluated using F-1 scores: naïve Bayes classifier, random forest, support vector machine, and long short-term memory. ResultsThis study collected 146,352 data points and detected that 4.3% (6340/146,352) were firsthand experience data. Psychiatric problems (521/1683, 31%) had the highest frequency among the adverse effects. The highest frequency of nonmedical use was for studies or work (741/2016, 36.8%). While the frequency of nonmedical use before and after the outbreak of COVID-19 has been similar (odds ratio [OR] 1.02 95% CI 0.91-1.15), its trend has changed significantly due to the pandemic (95% CI 2.36-22.20). Among the machine learning models, RF had the highest performance of 0.75. ConclusionsThe trend of nonmedical use of methylphenidate has changed significantly due to the COVID-19 pandemic. Among the machine learning models using SNS data to analyze the adverse effects and nonmedical use of methylphenidate, the random forest model had the highest performance.

This study examined forest policy agendas developed in international policy-making process by analyzing international forest policy documents from 2001 to 2022 with power, perception, potency, and proximity. The forest policy agendas consistently addressed in the documents were agroforestry, biodiversity, climate change, certification, desertification, deforestation, forest landscape restoration, illegal logging and trade, non-timber forest products, sustainable forest management, traditional knowledge, governance, participation, partnerships, forest tenure, forest fire, forest disease, and community-based forest management. The emerging agendas since 2011 were ecosystem services, reducing emissions from deforestation and forest degradation plus (REDD+), resilience, urban forests, green economy/bioeconomy, and COVID-19. The changes in international forest policy discourse with long-standing and emerging agendas over time showed three characteristics: policy coherence by the power of international environmental conventions; expansion of forest policy targets and areas by perception and proximity of urban forests; and innovative approaches to resilience and bioeconomy by potency and perception. Therefore, this study offers new insights into the creation and transitions of forest policy agendas in the international forest policy discourse.

Background While the medical contributors to maternal mortality are well known, the contextual contributors are less known and understudied. Liberia has one of the highest maternal mortality rates in sub-Saharan Africa, with recent increases in maternal deaths in rural Bong County. The purpose of this study was to better classify the contextual factors leading up to maternal deaths and to develop a list of recommendations to prevent future similar deaths.Methods A retrospective mixed method study was conducted examining 35 maternal deaths in Bong County, Liberia using verbal autopsy reports from 2019. An interdisciplinary death audit team reviewed and analysed the maternal deaths to determine the contextual causes of the maternal death.Findings This study identified three contextual causes: limited resources (materials, transportation, facility, staff), inadequate skills and knowledge (staff, community, family, patient), and ineffective communication (between providers, between health facilities and hospitals, and between provider and patient/family). Of these, inadequate patient education (54.28%), inadequate staff education and training (51.42%), ineffective communication between health facilities and hospitals (31.42%), and inadequate materials (28.57%) were most frequently mentioned.Conclusion Maternal mortality remains an issue in Bong County, Liberia, due to addressable contextual causes. Interventions to ameliorate these preventable deaths include ensuring availability of resources and transportation through improved supply chain and health system accountability. Recurring training should be provided to healthcare workers which involves husbands, families and communities. Innovative means for providers and facilities to communicate clearly and consistently should also be prioritised to prevent future maternal deaths in Bong County, Liberia.

Abstract Background Food insecurity is a global health challenge exacerbated by COVID-19. In Liberia, two-thirds of pregnant women are anemic, one-third of children are stunted, and 70% of households experienced food insecurity due to COVID-19. Edible insects are a nutritious, environmentally responsible, and cost-effective dietary supplement used throughout sub-Saharan Africa. Rearing palm weevil insects at maternity waiting homes (MWHs)—residential dwellings near hospitals where pregnant women await childbirth and receive postpartum services—could serve as a nutritious supplement for expectant mothers in Liberia and provide an income generating activity for MWHs. Methods Following a one-day training, sixteen participants established palm weevil rearing sites at four MWHs in Liberia. Pre- and post-knowledge scores were assessed immediately prior to and following training. Pre-and post-knowledge scores were analyzed using paired t-test. Participants tracked two palm weevil rearing cycles (four months), using harvest amounts, turnover, barriers to implementation, and income generated as metrics. The number of women attending MWHs was recorded throughout the study period (July-December 2020). Results Sixteen participants from four MWHs completed the training and two rearing cycles (four months) successfully. All participants showed statistically significant increases in knowledge scores following the one-day workshop with a pre-test score of 2.31 and post-test score of 7.75 out of 10 (p

Abstract This study aimed to develop a model to predict the 5-year risk of developing end-stage renal disease (ESRD) in patients with type 2 diabetes mellitus (T2DM) using machine learning (ML). It also aimed to implement the developed algorithms into electronic medical records (EMR) system using Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR). The final dataset used for modeling included 19,159 patients. The medical data were engineered to generate various types of features that were input into the various ML classifiers. The classifier with the best performance was XGBoost, with an area under the receiver operator characteristics curve (AUROC) of 0.95 and area under the precision recall curve (AUPRC) of 0.79 using three-fold cross-validation, compared to other models such as logistic regression, random forest, and support vector machine (AUROC range, 0.929–0.943; AUPRC 0.765–0.792). Serum creatinine, serum albumin, the urine albumin-to-creatinine ratio, Charlson comorbidity index, estimated GFR, and medication days of insulin were features that were ranked high for the ESRD risk prediction. The algorithm was implemented in the EMR system using HL7 FHIR through an ML-dedicated server that preprocessed unstructured data and trained updated data.

Introduction: Physician emigration (the 'brain drain') and sociopolitical unrest significantly contribute to the instability of many low- and middle-income countries’ healthcare systems. However, limited literature captures the 'locally driven' and 'context specific' suggestions to promote and sustain these health systems’ resilience. Thus, the purpose of this study is to 1) understand the effects of physician emigration and sociopolitical unrest on Nigeria’s healthcare system, and to 2) synthesize solutions suggested by Nigeria-trained physicians in the form of a resilience framework. Methods: An anonymous online survey was conducted among Nigeria-trained physicians. Respondents were recruited using convenience and snowball sampling methods via a 'WhatsApp' group for Nigeria-trained doctors. Quantitative data were analyzed using 'Stata 17' and qualitative themes were coded by two independent researchers. Results: The final sample included 49 Nigeria-trained physicians—35 physicians practicing in Nigeria and 14 emigrated physicians. All of the physicians currently practicing in Nigeria have considered emigrating, with 79% of them having concrete plans to emigrate in the next five years. Among emigrated physicians, factors such as remuneration (92%) and socioeconomic state of the country (92%) contributed to their decision to emigrate. Suggestions to enhance health system resilience fell into six broad themes: 1) policy and politics, 2) funding and resources, 3) organization and structure, 4) training and education, 5) research and primary health, and 6) health for peace initiatives. Conclusions: The healthcare system is currently unstable and vulnerable due to physician emigration and sociopolitical unrest. To develop and implement solutions to mitigate these issues, capturing the locally trained physicians’ perspectives are critical. While each country’s healthcare system is unique, countries with similar strains can adapt this model for resilience building. Future studies should focus on adapting the model in different countries with policy-level action points.

Summary: The central circadian regulator within the suprachiasmatic nucleus transmits time of day information by a diurnal spiking rhythm driven by molecular clock genes controlling membrane excitability. Most brain regions, including the hippocampus, harbor similar intrinsic circadian transcriptional machinery, but whether these molecular programs generate oscillations of membrane properties is unclear. Here, we show that intrinsic excitability of mouse dentate granule neurons exhibits a 24-h oscillation that controls spiking probability. Diurnal changes in excitability are mediated by antiphase G-protein regulation of potassium and sodium currents that reduce excitability during the Light phase. Disruption of the circadian transcriptional machinery by conditional deletion of Bmal1 enhances excitability selectively during the Light phase by removing G-protein regulation. These results reveal that circadian transcriptional machinery regulates intrinsic excitability by coordinated regulation of ion channels by G-protein signaling, highlighting a potential novel mechanism of cell-autonomous oscillations.

IntroductionThe objective of this study was to investigate the effects of dietary supplementation of tributyrin and anise mixture (TA) on growth performance, apparent nutrient digestibility, fecal noxious gas emission, fecal score, jejunal villus height, hematology parameters, and fecal microbiota of weaned pigs.MethodsA total of 150 21-day-old crossbred weaned pigs [(Landrace × Yorkshire) × Duroc] were used in a randomized complete block design experiment. All pigs were randomly assigned to 3 groups based on the initial body weight (6.19 ± 0.29 kg). Each group had 10 replicate pens with 5 pigs (three barrows and two gilts) per pen. The experimental period was 42 days and consisted of 3 phases (phase 1, days 1–7; phase 2, days 8–21; phase 3, days 22–42). Dietary treatments were based on a corn-soybean meal-basal diet and supplemented with 0.000, 0.075, or 0.150% TA.Results and discussionWe found that dietary supplementation of graded levels of TA linearly improved body weight, body weight gain, average daily feed intake, and feed efficiency (P < 0.05). TA supplementation also had positive effects on apparent dry matter, crude protein, and energy digestibility (P < 0.05) and jejunal villus height (P < 0.05). The emission of ammonia from feces decreased linearly with the dose of TA increased (P < 0.05). Moreover, TA supplementation was capable to regulate the fecal microbiota diversity, manifesting in a linearly increased Chao1 index and observed species and a linearly decreased Pielou's index (P < 0.05). The abundance of Lactobacillus reuteri, Lactobacillus amylovorus, Clostridium butyricum were increased, while the abundance of Prevotella copri was decreased, by treatment (P < 0.05). Therefore, we speculated that TA supplementation would improve growth performance and reduce fecal ammonia emission through improving nutrient digestibility, which was attributed to the increase of jejunal villus height and the regulation of fecal microbiota.

Centella asiatica is a traditional herbaceous plant with numerous beneficial effects, widely known for its medicinal and cosmetic applications. Maximizing its growth can lead to beneficial effects, by focusing on the use of its active compounds. The use of plant growth-promoting rhizobacteria (PGPR) is known to be an alternative to chemical fertilizers. In this study, we used the PGPR Priestia megaterium HY-01 to increase the yield of C. asiatica. In vitro assays showed that HY-01 exhibited plant growth-promoting activities (IAA production, denitrification, phosphate solubilization, and urease activity). Genomic analyses also showed that the strain has plant growth-promoting-related genes that corroborate with the different PGP activities found in the assays. This strain was subsequently used in field experiments to test its effectiveness on the growth of C. asiatica. After four months of application, leaf and root samples were collected to measure the plant growth rate. Moreover, we checked the rhizosphere microbiome between the treated and non-treated plots. Our results suggest that treatment with Hyang-yak-01 not only improved the growth of C. asiatica (leaf length, leaf weight, leaf width, root length, root width, and chlorophyll content) but also influenced the rhizosphere microbiome. Biodiversity was higher in the treated group, and the bacterial composition was also different from the control group.

Background: Maternal mortality continues to disproportionately affect low- and middle-income countries, including Liberia. Though the relationship between obstetric triage systems and improved maternal outcomes is well documented, standardized triage protocols are lacking in rural Liberia. Mobile health interventions are a promising method to triage obstetric patients. Objectives: This study explores the acceptability of a WhatsApp Triage, Referral, and Transfer (WAT-RT) system among Liberian midwives and community health assistants. Methods: Individual interviews and focus group discussions were conducted among midwives (n = 18) and community health assistants (n = 112). Interviews were designed to understand the current referral system in rural Liberia, how a WAT-RT System can address referral limitations, and the acceptability of the WAT-RT System. Data were audio recorded, transcribed, and translated into English. Data analysis was conducted via NVivo12 with independent and cooperative techniques among multiple researchers. Findings: The current referral system is not standardized with limitations including a lack of triage protocols, transportation difficulties, and inconsistent communication of patient information, which could be addressed by a WAT-RT System. The acceptability for the WAT-RT System was high. Facilitators to implementation included utilizing a pre-existing communication and referral infrastructure, access and competency surrounding mobile phones, and increased opportunities for training and inter-provider collaboration. Barriers included disproportionate phone access between midwives and community health assistants, network reliability, and a lack of data standards. Recommendations for successful implementation included centralizing phone financing and standardizing triage protocols. Conclusions: The WAT-RT System demonstrated high acceptability among frontline health care providers in rural Liberia. Barriers to program success could be reasonably addressed with simple interventions and planning. Multiple benefits included addressing care delays for obstetric patients, promoting bidirectional provider communication, and increasing the quality of obstetric triage. Future studies should focus on piloting the WAT-RT System among this population and recruiting other key stakeholders to determine intervention feasibility.

For the first time, we succeeded in manufacturing a 2-hydroxyethyl cellulose (HEC)-based composite membrane with improved thermal stability, for use as a battery separator, coating a HEC polymer solution to a polypropylene (PP) support and using a vacuum-assisted process. A HEC polymer solution was prepared by utilizing HEC and lactic acid (LA) as a plasticizer. A vacuum-assisted process was used to move ethanol, which a mobile phase to permeate a plasticized region in the HEC polymer side for pore formation. The pores formed with uniform nano sizes, and areas in which some large pores formed were observed. The thermal stability of the composites was measured using TGA. The thermal decomposition temperatures were measured at about 250 °C for the neat HEC, about 210 °C for the HEC/LA film, and about 335 °C for the HEC/LA/PP membrane before the process. After the vacuum-assisted process, the first and second thermal decomposition were observed at about 360 °C and 450 °C, respectively. The HEC/LA/PP membrane after the process showed greater thermal stability than before the process. This means that the adhesion between the HEC polymer and the PP support was created through the rearrangement of the HEC chain, as LA escaped after the process, and it was seen indirectly that the mechanical strength was enhanced. In particular, the surface of the membrane was observed by SEM to investigate whether the HEC penetrated into the PP to block its pores, and whether the HEC region collapsed. Furthermore, the interaction of the HEC chain with the additives and the rearrangement of the HEC was confirmed using FT-IR. As a result, we demonstrated that the mechanical strength and thermal stability of the manufactured HEC/LA/PP membrane were enhanced.

In this study, furan analysis was conducted on dried red pepper powder treated by three cooking methods (boiling, roasting, and frying). A total of 144 samples were prepared and their furan levels were analysed using automated solid-phase micro-extraction gas chromatography-mass spectrometry. The furan concentration in boiled soup ranged from 1.26 to 4.65 ng/g, and from 7.37 to 27.68 ng/g for boiled red pepper samples. For the roasting method, a furan concentration between 6.66 and 761.37 ng/g was detected. For the frying method, the furan level of edible oils ranged from 3.93 to 125.88 ng/g, and a concentration ranging from 4.88 to 234.52 ng/g was detected for the fried red pepper samples. The cooking method using edible oil obtained a higher furan concentration than the water-based method. Samples using corn germ oil (linoleic acid-rich oil) obtained the highest furan concentration among the four edible oils. In all cooking methods, the higher the heating temperature and time, the higher the furan concentration detected. A kinetic study was conducted using the roasting model system and the apparent activation energy was 60.5 kJ/mol. The results of this study could be useful as a database for furan concentration in dried red pepper powder according to various cooking methods.

Border regions face challenges managing natural resources, which include forests, wildlife, air, and rivers. This study aims to provide an overview of research on various natural resource conflicts and cooperation in borderlands worldwide, considering the five spheres of the Earth system, namely the atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere. The type, scope, and place of natural resource conflicts in the borderlands were examined and key findings for clarifying the conflicts, cooperation, and geographical characteristics were derived using a systematic review methodology. The results indicate that over the last two decades, the conflicts over the hydrosphere are the most dominant. In the following order, conflicts over the biosphere have been frequently dealt with in transboundary areas. In Africa, dams (the anthroposphere) related to the hydrosphere especially influence and cause conflicts as well as cooperation for benefit-sharing among riparian countries. In North America, governance along the transboundary areas has been developed. “In Asia, several neighboring countries are linked through various types of associations ranging from multilateral organizations to sub-national administrations in order to effectively manage the long and wide-ranging natural resources that exist beyond the borders of the countries”. In Europe, numerous protected areas related to the biosphere have been designated. Therefore, this research helps better understand transboundary conflicts based on natural resources and could contribute to designing natural resource management strategies or models in borderlands.

Transposable elements (TEs) are classified into two classes according to their mobilization mechanism. Compared to DNA transposons that move by the “cut and paste” mechanism, retrotransposons mobilize via the “copy and paste” method. They have been an essential research topic because some of the active elements, such as Long interspersed element 1 (LINE-1), Alu, and SVA elements, have contributed to the genetic diversity of primates beyond humans. In addition, they can cause genetic disorders by altering gene expression and generating structural variations (SVs). The development and rapid technological advances in next-generation sequencing (NGS) have led to new perspectives on detecting retrotransposon-mediated SVs, especially insertions. Moreover, various computational methods have been developed based on NGS data to precisely detect the insertions and deletions in the human genome. Therefore, this review discusses details about the recently studied and utilized NGS technologies and the effective computational approaches for discovering retrotransposons through it. The final part covers a diverse range of computational methods for detecting retrotransposon insertions with human NGS data. This review will give researchers insights into understanding the TEs and how to investigate them and find connections with research interests.