Does Higher Education Affect Health and Health Behaviors?: Evidence from a Regression Discontinuity Design

This paper examines the causal effect of higher education on health related outcomes. To address the endogeneity in educational attainment, we exploit the educational reform in Korea which has increased the opportunity to engage in college education for affected cohorts. Using the regression discontinuity design, we do not find supportive evidence for health return of higher education. Moreover, we find that higher education has limited causal effects on health behaviors such as smoking and drinking. The limited effect might be a result of universal health care system in Korea which provides health insurance for practically all individuals

Extracellular Vesicles Derived from Hypoxic Human Mesenchymal Stem Cells Attenuate GSK3β Expression via miRNA-26a in an Ischemia-Reperfusion Injury Model.

PURPOSE: Bioactive molecules critical to intracellular signaling are contained in extracellular vesicles (EVs) and have cardioprotective effects in ischemia/reperfusion (IR) injured hearts. This study investigated the mechanism of the cardioprotective effects of EVs derived from hypoxia-preconditioned human mesenchymal stem cells (MSCs). MATERIALS AND METHODS: EV solutions (0.4 μg/μL) derived from normoxia-preconditioned MSCs (EV(NM)) and hypoxia-preconditioned MSCs (EV(HM)) were delivered in a rat IR injury model. Successful EV delivery was confirmed by the detection of PKH26 staining in hearts from EV-treated rats. RESULTS: EV(HM) significantly reduced infarct size (24±2% vs. 8±1%, p<0.001), and diminished arrhythmias by recovering electrical conduction, I(Na) current, and Cx43 expression. EV(HM) also reversed reductions in Wnt1 and β-catenin levels and increases in GSK3β induced after IR injury. miRNA-26a was significantly increased in EV(HM), compared with EV(NM), in real-time PCR. Finally, in in vitro experiments, hypoxia-induced increases in GSK3β expression were significantly reduced by the overexpression of miRNA-26a. CONCLUSION: EV(HM) reduced IR injury by suppressing GSK3β expression via miRNA-26a and increased Cx43 expression. These findings suggest that the beneficial effect of EVHM is related with Wnt signaling pathway

Non-destructive assessment of cannabis quality during drying process using hyperspectral imaging and machine learning

Cannabis sativa L. is an industrially valuable plant known for its cannabinoids, such as cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC), renowned for its therapeutic and psychoactive properties. Despite its significance, the cannabis industry has encountered difficulties in guaranteeing consistent product quality throughout the drying process. Hyperspectral imaging (HSI), combined with advanced machine learning technology, has been used to predict phytochemicals that presents a promising solution for maintaining cannabis quality control. We examined the dynamic changes in cannabinoid compositions under diverse drying conditions and developed a non-destructive method to appraise the quality of cannabis flowers using HSI and machine learning. Even when the relative weight and water content remained constant throughout the drying process, drying conditions significantly influenced the levels of CBD, THC, and their precursors. These results emphasize the importance of determining the exact drying endpoint. To develop HSI-based models for predicting cannabis quality indicators, including dryness, precursor conversion of CBD and THC, and CBD : THC ratio, we employed various spectral preprocessing methods and machine learning algorithms, including logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and Gaussian naïve Bayes (GNB). The LR model demonstrated the highest accuracy at 94.7–99.7% when used in conjunction with spectral pre-processing techniques such as multiplicative scatter correction (MSC) or Savitzky–Golay filter. We propose that the HSI-based model holds the potential to serve as a valuable tool for monitoring cannabinoid composition and determining optimal drying endpoint. This tool offers the means to achieve uniform cannabis quality and optimize the drying process in the industry

Post-intervention Status in Patients With Refractory Myasthenia Gravis Treated With Eculizumab During REGAIN and Its Open-Label Extension

OBJECTIVE: To evaluate whether eculizumab helps patients with anti-acetylcholine receptor-positive (AChR+) refractory generalized myasthenia gravis (gMG) achieve the Myasthenia Gravis Foundation of America (MGFA) post-intervention status of minimal manifestations (MM), we assessed patients' status throughout REGAIN (Safety and Efficacy of Eculizumab in AChR+ Refractory Generalized Myasthenia Gravis) and its open-label extension. METHODS: Patients who completed the REGAIN randomized controlled trial and continued into the open-label extension were included in this tertiary endpoint analysis. Patients were assessed for the MGFA post-intervention status of improved, unchanged, worse, MM, and pharmacologic remission at defined time points during REGAIN and through week 130 of the open-label study. RESULTS: A total of 117 patients completed REGAIN and continued into the open-label study (eculizumab/eculizumab: 56; placebo/eculizumab: 61). At week 26 of REGAIN, more eculizumab-treated patients than placebo-treated patients achieved a status of improved (60.7% vs 41.7%) or MM (25.0% vs 13.3%; common OR: 2.3; 95% CI: 1.1-4.5). After 130 weeks of eculizumab treatment, 88.0% of patients achieved improved status and 57.3% of patients achieved MM status. The safety profile of eculizumab was consistent with its known profile and no new safety signals were detected. CONCLUSION: Eculizumab led to rapid and sustained achievement of MM in patients with AChR+ refractory gMG. These findings support the use of eculizumab in this previously difficult-to-treat patient population. CLINICALTRIALSGOV IDENTIFIER: REGAIN, NCT01997229; REGAIN open-label extension, NCT02301624. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that, after 26 weeks of eculizumab treatment, 25.0% of adults with AChR+ refractory gMG achieved MM, compared with 13.3% who received placebo

Minimal Symptom Expression' in Patients With Acetylcholine Receptor Antibody-Positive Refractory Generalized Myasthenia Gravis Treated With Eculizumab

The efficacy and tolerability of eculizumab were assessed in REGAIN, a 26-week, phase 3, randomized, double-blind, placebo-controlled study in anti-acetylcholine receptor antibody-positive (AChR+) refractory generalized myasthenia gravis (gMG), and its open-label extension

<i>LazyFrog</i>: Advancing Security and Efficiency in Commercial Wireless Charging with Adaptive Frequency Hopping

With the proliferation of electronic devices and electricity-based mobility solutions, the significance of wireless power transfer technology has increased substantially. However, ensuring secure and reliable power transmission to authorized users remains a significant challenge. Addressing this complex issue requires an integrated approach that balances efficiency, stability, and security considerations. While current efforts primarily focus on improving charging efficiency and user convenience, integrating robust security measures into wireless charging infrastructure is challenging due to its inherently open nature and susceptibility to external interference. Technical advancements are required to strengthen the security of the wireless charging infrastructure; however, these should be balanced with power loss management. This study tackles two core issues: the increasing hardware requirements for billing system authentication protocols and the interception of wireless charging signals by unauthorized users, leading to power theft and subsequent losses. To address these challenges, we propose a mechanism termed “LazyFrog”. This mechanism dynamically adjusts the frequency hopping schedule, activating frequency changes only in response to detected threats during remote charging or upon identifying unauthorized access attempts. The proposed mechanism compares the expected power reception at the device with the actual power supplied by the charging station, enabling the detection of abnormal power losses. By minimizing unnecessary frequency changes and optimizing energy consumption, LazyFrog reduces hardware requirements. Moreover, we have implemented a relative distance estimation mechanism to facilitate efficient power transfer as wireless devices move within the charging environment. With these features, LazyFrog demonstrates a secure, flexible, and energy-efficient wireless charging system ready for practical application

Development of a Food Literacy Assessment Tool for Healthy, Joyful, and Sustainable Diet in South Korea

Background: Food literacy (FL) is important as the ability to consider the unique aspects of food in our lives, society, and environment. The main objectives of this study were as follows: (1) to revisit the definition of FL, considering the cultural, relational, and ecological aspects that were often neglected in previous research, and (2) to develop a measurement tool for adults. Methods: Expert workshops, the Delphi survey, the test–retest survey, and one-on-one interviews were conducted. The content validity ratio was calculated from the Delphi survey. The correlation coefficient of each item was measured twice, and the Cronbach’s alpha was calculated. Results: This study proposed a new definition of FL, including future-oriented values, and suggested three main domains with 33 items: (1) 14 questions in nutrition and safety FL (Cronbach’s α = 0.877, average correlation coefficient = 0.70), (2) 8 questions in cultural and relational FL (Cronbach’s α = 0.705, average correlation coefficient = 0.71), and (3) 11 questions in socio-ecological FL (Cronbach’s α = 0.737, average correlation coefficient = 0.61). Conclusions: This newly developed questionnaire should be tested in different populations; however, this questionnaire can be a basis for measuring and improving FL for healthy, joyful, and sustainable diets for adults

Voxel-wise adversarial semi-supervised learning for medical image segmentation

Background and Objective: Semi-supervised learning for medical image segmentation is an important area of research for alleviating the huge cost associated with the construction of reliable large-scale annotations in the medical domain. Recent semi-supervised approaches have demonstrated promising results by employing consistency regularization, pseudo-labeling techniques, and adversarial learning. These methods primarily attempt to learn the distribution of labeled and unlabeled data by enforcing consistency in the predictions or embedding context. However, previous approaches have focused only on local discrepancy minimization or context relations across single classes.Methods: In this paper, we introduce a novel adversarial learning-based semi-supervised segmentation method that effectively embeds both local and global features from multiple hidden layers and learns context relations between multiple classes. Our voxel-wise adversarial learning method utilizes a voxel-wise feature discriminator, which considers multilayer voxel-wise features (involving both local and global features) as an input by embedding class-specific voxel-wise feature distribution. Furthermore, our previous representation learning method is improved by overcoming information loss and learning stability problems, which enables rich representations of labeled data.Result: In the experiments, we used the Left Atrial Segmentation Challenge dataset and the Abdominal Multi -Organ dataset to prove the effectiveness of our method in both single class and multiclass segmentation. The experimental results demonstrate that our method outperforms current best-performing state-of-the-art semi-supervised learning approaches. Our proposed adversarial learning-based semi-supervised segmentation method successfully leveraged unlabeled data to improve the network performance by 2% in Dice score coefficient for multi-organ dataset.Conclusion: We compare our approach to a wide range of medical datasets, and showed our method can be adapted to embed class-specific features. Furthermore, visual interpretation of the feature space demonstrates that our proposed method enables a well-distributed and separated feature space from both labeled and unlabeled data, which improves the overall prediction results.Y