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Lipid-protein complexes are the basis of pulmonary surfactants covering the respiratory surface and mediating gas exchange in lungs. Cardiolipin is a mitochondrial lipid overexpressed in mammalian lungs infected by bacterial pneumonia. In addition, increased oxygen supply (hyperoxia) is a pathological factor also critical in bacterial pneumonia. In this paper we fabricate a micrometer-size graphene-based sensor to measure oxygen permeation through pulmonary membranes. Combining oxygen sensing, X-ray scattering, and Atomic Force Microscopy, we show that mammalian pulmonary membranes suffer a structural transformation induced by cardiolipin. We observe that cardiolipin promotes the formation of periodic protein–free inter–membrane contacts with rhombohedral symmetry. Membrane contacts, or stalks, promote a significant increase in oxygen gas permeation which may bear significance for alveoli gas exchange imbalance in pneumonia. Changes in the pulmonary membrane, caused by bacterial infection, form part of the pathology of pneumonia. Here, the authors report on a graphene-based oxygen sensor which is used along with X-ray diffraction and AFM to measure the structural changes and changes in oxygen permeability of pulmonary membranes associated with bacterial pneumonia.

Background: Carbapenem-resistant Enterobacterales (CRE) are significant healthcare-associated pathogens. Liver transplant (LT) recipients are particularly vulnerable to CRE acquisition due to frequent hospitalizations, extensive antibiotic exposure, and prolonged stays in intensive care units. This study aimed to develop and evaluate prediction models for CRE acquisition in LT recipients at a hospital where more than 500 LT surgeries are performed annually. Method: This case-control study retrospectively analyzed the electronic medical records of 1,250 adult LT recipients (250 CRE-positive and 1,000 CRE-negative cases) at a 2,768-bed tertiary hospital in Seoul, Korea, from February 2020 to February 2024. Data imbalance was addressed using the synthetic minority over-sampling technique, and missing values were handled through median imputation and k-nearest neighbor imputation methods. Prediction models were developed using logistic regression, random forest, and extreme gradient boosting (XGBoost) algorithms, with optimal models selected through 5-fold cross-validation and recursive feature elimination. Model interpretability was enhanced using Shapley additive explanations and partial dependence plot analyses. Result: Of the CRE isolates, 94% were carbapenemase-producing Enterobacterales, with Klebsiella pneumoniae comprising 55.7% of all CRE isolates. Univariate analysis revealed significant differences between groups in LT month (June-September, p<.001), mechanical ventilation over 72 hours (p=.002), and model for end-stage liver disease (MELD) score (p=.041). The XGBoost model, selected as the final model, demonstrated strong specificity (0.848) and a high negative predictive value (NPV 0.830) for identifying non-carriers, although its overall predictive power was limited. Features used in the XGBoost model included LT month, third-generation cephalosporins, and the presence of hepatocellular carcinoma, all of which showed a positive correlation with CRE acquisition. In contrast, mechanical ventilation over 72 hours and living donor LT exhibited negative correlations. Viral hepatitis and body mass index were included in the model, but their impact on CRE acquisition risk remained unclear. Notably, the negative association of mechanical ventilation contrasts with findings from previous studies, highlighting the need for further investigation. Conclusion: This study demonstrates the clinical relevance of machine learning models in predicting CRE acquisition among LT recipients. The XGBoost model showed high specificity and NPV, indicating its potential to effectively identify low-risk patients. Future studies could benefit from adopting prospective, multicenter designs to clarify causal relationships and improve model performance.

The changes in the mechanical integrity of the cervix during pregnancy have implications for a successful delivery. Cervical collagens are known to remodel extensively in mice with progressing gestation leading to a soft cervix at term. During this process, mature crosslinked collagens are hypothesized to be replaced with immature less crosslinked collagens to facilitate cervical softening and ripening. To determine the mechanical role of collagen crosslinks during normal mouse cervical remodeling, tensile load-to-break tests were conducted for the following time points: nonpregnant (NP), gestation day (d) 6, 12, 15, 18 and 24 hr postpartum (PP) of the 19-day gestation period. Immature crosslinks (HLNL and DHLNL) and mature crosslinks (DPD and PYD) were measured using ultra performance liquid chromatography-electrospray ionization tandem mass spectrometry (UPLC-ESI-MS/MS). There were no significant changes in the total immature crosslink density (HLNL+DHLNL mol per collagen mol) throughout normal mouse gestation (range: 0.31-0.49). Total mature crosslink density (PYD+DPD mol per collagen mol) decreased significantly in early softening from d6 to d15 (d6: 0.17, d12: 0.097, d15: 0.026) and did not decrease with further gestation. The maturity ratio (total mature to total immature crosslinks) significantly decreased in early softening from d6 to d15 (d6: 0.2, d15: 0.074). All of the measured crosslinks correlated significantly with a measure of tissue stiffness and strength, with the exception of the immature crosslink HLNL. This data provides quantitative evidence to support the hypothesis that as mature crosslinked collagens decline, they are replaced by immature collagens to facilitate increased tissue compliance in the early softening period from d6 to d15.

Bulk nanobubbles, measuring less than 200 nm in water, have shown their salient properties in promoting growth in various species of plants and orthodox seeds, and as potential drug-delivery carriers in medicine. Studies of recalcitrant seeds have reported markedly increased germination rates with gibberellin treatment; however, neither the mechanism promoting germination nor the implication for food safety is well elucidated. In our study, recalcitrant wasabi ( Eutrema japonicum ) seeds treated with bulk oxygen nanobubbles (BONB) containing K + , Na + , and Cl − (BONB-KNaCl) showed significantly accelerated germination. As germination progressed, 99% of K + ions in the BONB-KNaCl medium were absorbed by the seeds, whereas Ca 2+ ions were released. These results suggest that the germination mechanism involves the action of K + channels for migration of K + ions down their concentration gradient and Ca 2+ pumps for the movement of Ca 2+ ions, the first potential discovery in germination promotion in recalcitrant seeds using nutrient solutions with BONB-KNaCl.

This paper presents an energy-optimized electronic performance tracking system (EPTS) device for analyzing the athletic movements of football players. We first develop a tiny battery-operated wearable device that can be attached to the backside of field players. In order to analyze the strategic performance, the proposed wearable EPTS device utilizes the GNSS-based positioning solution, the IMU-based movement sensing system, and the real-time data acquisition protocol. As the life-time of the EPTS device is in general limited due to the energy-hungry GNSS sensing operations, for the energy-efficient solution extending the operating time, in this work, we newly develop the advanced optimization methods that can reduce the number of GNSS accesses without degrading the data quality. The proposed method basically identifies football activities during the match time, and the sampling rate of the GNSS module is dynamically relaxed when the player performs static movements. A novel deep convolution neural network (DCNN) is newly developed to provide the accurate classification of human activities, and various compression techniques are applied to reduce the model size of the DCNN algorithm, allowing the on-device DCNN processing even at the memory-limited EPTS device. Experimental results show that the proposed DCNN-assisted sensing control can reduce the active power by 28%, consequently extending the life-time of the EPTS device more than 1.3 times.

With the advent of an aging society, the lack of physical activity has become a major concern, leading to various age-related diseases. To prevent such issues, research on wearable robots aimed at improving gait has been actively pursued. Among them, exoskeleton robots, a widely used approach, require an accurate understanding of the user’s gait cycle for effective control. Various studies have explored gait cycle detection and prediction methods depending on the type of gait robot platform and the use of sensors. However, a major challenge in gait cycle prediction algorithms remains the issue of nonlinear predictive trajectories. In the study, a robotic hip exoskeleton (RHE) was utilized to implement an enhanced gait phase estimation (GPE) algorithm integrated with a muscle support system. Participants were divided into two groups (Group A and Group B) based on their initial gait performance, and the effectiveness of gait rehabilitation training was evaluated. The results showed that in the 10-meter walk test (10MWT), walking time decreased by approximately 5% in Group A and 27% in Group B. In the 6-minute walk test (6MinWT), walking distance increased by approximately 1% in Group A and 14% in Group B. Group B, which had lower initial gait performance, showed a greater gait performance improvement rate compared to Group A, which had higher initial gait performance. Through the gait performance results of the two groups, the applicability of the GPE based muscle support algorithm was confirmed.

Students’ reasoning and decision making on complex socioscientific issues are critical for developing scientific literacy for 21st century citizenship. By incorporating a scenario-based approach, this study aims to understand the complexity of students’ decision making on environmental issues: forests and land use. To help students grasp the context of these issues, we developed scenarios reflecting their experiences and understanding of forests within local communities. Through scenario-based surveys, students in Grade 5–6 science classrooms were encouraged to explore diverse stakeholders’ perspectives and articulate their decisions regarding the scenarios. Additionally, students in focus groups participated in semi-structured discussions and interviews. The data collected from the surveys and students’ dialogues were thematically analyzed. The study found that students prioritized environmental concerns, demonstrated skepticism toward politicians’ perspectives, and emphasized righteousness in their decision making. These findings suggest that a holistic approach is essential to engage students’ diverse perspectives in socioscientific and environmental problem solving. However, this also highlights the ongoing challenge of disciplinary boundaries within school curricula and pedagogical practices in science classrooms.

Glaucoma is a leading eye disease, causing vision loss by gradually affecting peripheral vision if left untreated. Current diagnosis of glaucoma is performed by ophthalmologists, human experts who typically need to analyze different types of medical images generated by different types of medical equipment: fundus, Retinal Nerve Fiber Layer (RNFL), Optical Coherence Tomography (OCT) disc, OCT macula, perimetry, and/or perimetry deviation. Capturing and analyzing these medical images is labor intensive and time consuming. In this paper, we present a novel approach for glaucoma diagnosis and localization, only relying on fundus images that are analyzed by making use of state-of-the-art deep learning techniques. Specifically, our approach towards glaucoma diagnosis and localization leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), respectively. We built and evaluated different predictive models using a large set of fundus images, collected and labeled by ophthalmologists at Samsung Medical Center (SMC). Our experimental results demonstrate that our most effective predictive model is able to achieve a high diagnosis accuracy of 96%, as well as a high sensitivity of 96% and a high specificity of 100% for Dataset-Optic Disc (OD), a set of center-cropped fundus images highlighting the optic disc. Furthermore, we present Medinoid, a publicly-available prototype web application for computer-aided diagnosis and localization of glaucoma, integrating our most effective predictive model in its back-end.