Increasing evidence has demonstrated that amyloid-β peptide (Aβ), the hallmark of Alzheimer's disease (AD), evokes oxidative and inflammatory cascades, which ultimately lead to the death of neurons. ...The purpose of the present study is to demonstrate the effect of nobiletin, a representative compound of citrus peel, in preventive and therapeutic approaches against neuronal damage by exposure to Aβ
. Nobiletin significantly ameliorated Aβ
mediated cell death by restoring abnormal changes in intracellular oxidative stress, cell cycle, nuclear morphology, and activity of apoptotic caspase. Regarding anti-inflammatory responses, nobiletin significantly suppressed interleukin-1β, tumor necrosis factor-α, nitric oxide (NO), and prostaglandin E
production in response to Aβ stimulation. Moreover, nobiletin inhibited Aβ-stimulated inducible NO synthase and cyclooxygenase-2 expression, which was attributed to the blockade of nuclear factor-κB p65 and phosphorylation of its inhibitor, IκB-α. Interestingly, nobiletin decreased expression of c-Jun N-terminal kinase and p38 without affecting extracellular signal-regulated kinase 1/2 activation. Taken together, the novel data implicate nobiletin as a potential candidate for the prevention of AD through the inhibition of oxidative stress, apoptosis, and inflammation.
This study aimed to investigate the public's experience of online reservation of residual COVID-19 vaccines in an additional vaccination program. Online reservation was used to predict the ...vaccination rate. A sample of 620 participants completed the online survey between July and August 2021. About 38% of the participants made the online reservation. About 91% had a vaccination intention. Online reservations showed significant differences in their distribution according to age group, educational level, past flu shot experience, and COVID-19 vaccination intention. A negative experience was the most common response, which was mostly attributed to the difficulty in making an online reservation due to reservations being full. Positive experiences included updated information and notifications on the residual vaccines available, being able to choose a vaccination clinic, and the ease of making, changing, and canceling a reservation. About 72% reported the positive effect of residual vaccine usage on herd immunity. The results of this study suggest that when developing another online reservation program for vaccination, it is necessary to consider and address the negative experiences of the public with online reservations. The additional vaccinations may have resulted in an increased vaccination rate. Vaccination reservations can be used as an indicator to predict the actual vaccination rate and as a measure of a positive attitude toward COVID-19 vaccination.
On GitHub, one of the most successful services for software project hosting, labels have been used to represent various information and decisions about reported issues. However, previous studies on ...labels were limited to simple statistics or label recommendations for issue types. In this paper, we aim to provide a better understanding of labels and their usage in software development. We particularly focus on using multiple and custom labels on issues. To analyze label usage, we collected software project data and label usage information from GitHub. We then quantitatively investigated the performance of projects with multi-label features and qualitatively investigated the categories of multi-labels, and the usage of multi-labels based on these categories. Our analysis results show that multi-labels are common in the majority of software projects and that projects using multi-label features manage their issues more effectively. In addition, our analysis results reveal different types of information represented by labels, which are related to features, development, and issues. This study finds several facts that can be used for studies on issue management and thus that help develop labeling techniques to mitigate the burden of issue management.
Issues are evolving requirements in software engineering. They are the main factors that increase the cost of software evolution. To help developers manage issues, GitHub provides issue labeling ...mechanisms in issue management systems. However, manually labeling issue reports still requires considerable developer workload. To ease developers' burden, researchers have proposed automatically classifying issue reports. To improve the classification accuracy, researchers adopted deep learning techniques and pretrained models. However, pretrained models in the general domain such as RoBERTa have limitations in understanding the contexts of software engineering tasks. In this paper, we create a pretrained model, IssueBERT, with issue data to understand whether a domain-specific pretrained model could improve the accuracy of issue report classification. We also adopt and explore several pretrained models in the software engineering domain, namely, CodeBERT, BERTOverflow, and seBERT. We conduct a comparative experiment on these pretrained models to evaluate their performance in classifying issue reports. Our comparison results show that IssueBERT outperforms the other pretrained models. Noticeably, IssueBERT yields an average F1 score that is 1.74% higher than that of seBERT and 3.61% higher than that of RoBERTa, even though IssueBERT was pretrained with much less data than seBERT and RoBERTa.
Issue reports are software artifacts that often specify the changed requirements of software systems. As software systems evolve according to these changed requirements, issue reports have become the ...essential artifacts that should be covered by requirement traceability. While researchers have developed automatic approaches for establishing the traceability links of issue reports, no papers have surveyed these approaches. In this paper, we conduct a systematic literature review of issue-based requirement traceability. We searched for articles published in renowned conferences and journals in the software engineering field from 2011 to 2022. From 1,347 initial articles, we identified 40 relevant articles. We investigated four aspects of issue-based traceability: problems, artifact pairs, techniques, and evaluation targets. Our findings are as follows. First, the challenges of issue-based requirement traceability are relevant to accuracy, effort, support, information, and trustworthiness. Second, issue reports are linked to commits, source code, user reviews, and test cases. Third, the studies mainly adopted machine learning and information retrieval techniques to generate and recover trace links. Finally, the main evaluation targets were open-source projects, but open datasets were also provided.
This study aimed to investigate the association between Korean adults' electronic health literacy and active participation in health decision-making while communicating with healthcare providers. A ...total of 421 adults 18 years and older were recruited from a survey panel developed by a survey company and completed an online survey asking about individual characteristics and electronic health literacy. Data were analyzed using descriptive statistics, independent-samples t tests, analysis of variance, and binomial logistic regression analysis. In results, for every one-unit increase in electronic health literacy, the rate of active participation in health decision-making was likely to increase by 3.62 times compared with the rate of passive participation in health decision-making. It was also likely to increase in those with a college degree or higher, those who used both Web sites and YouTube, those who perceived online health information to be useful, those who actively participated as a patient, and those with a desire to actively participate as a patient. To encourage active participation in health-related situations or decision-making processes, educational programs or interventions to improve individuals' electronic health literacy are needed. Healthcare providers must ensure that individuals feel comfortable expressing their preferences, needs, and values.
Issue reports are valuable resources for the continuous maintenance and improvement of software. Managing issue reports requires a significant effort from developers. To address this problem, many ...researchers have proposed automated techniques for classifying issue reports. However, those techniques fall short of yielding reasonable classification accuracy. We notice that those techniques rely on text-based unimodal models. In this paper, we propose a novel multimodal model-based classification technique to use heterogeneous information in issue reports for issue classification. The proposed technique combines information from text, images, and code of issue reports. To evaluate the proposed technique, we conduct experiments with four different projects. The experiments compare the performance of the proposed technique with text-based unimodal models. Our experimental results show that the proposed technique achieves a 5.07% to 14.12% higher F1-score than the text-based unimodal models. Our findings demonstrate that utilizing heterogeneous data of issue reports helps improve the performance of issue classification.
During the maintenance phase of software development, bug reports provide important information for software developers. Developers share information, discuss bugs, and fix associated bugs through ...bug reports; however, bug reports often include complex and long discussions, and developers have difficulty obtaining the desired information. To address this issue, researchers proposed methods for summarizing bug reports; however, to select relevant sentences, existing methods rely solely on word frequencies or other factors that are dependent on the characteristics of a bug report, failing to produce high-quality summaries or resulting in limited applicability. In this paper, we propose a deep-learning-based bug report summarization method using sentence significance factors. When conducting experiments over a public dataset using believability, sentence-to-sentence cohesion, and topic association as sentence significance factors, the results show that our method outperforms the state-of-the-art method BugSum with respect to precision, recall, and F-score and that the application scope of the proposed method is wider than that of BugSum.
Oxidative stress, which is induced by reactive oxygen species (ROS), causes cellular damage which contributes to the pathogenesis of neurodegenerative diseases.
(MC), a traditional medicinal plant, ...is known to have a variety of health benefits, such as antidiabetic, anti-inflammatory, and antioxidant effects. However, it is unknown whether MC has protective effects against oxidative stress-induced neuronal cell death. The aim of this study was to investigate the potential action of MC on oxidative stress induced by H₂O₂. First, we tested whether the pretreatment of
ethanol extract (MCEE) attenuates H₂O₂-induced cell death in human neuroblastoma SK-N-MC cells. MCEE pretreatment significantly improved cell viability and apoptosis that deteriorated by H₂O₂. Further, MCEE ameliorated the imbalance between intracellular ROS production and removal through the enhancement of the intracellular antioxidant system. Intriguingly, the inhibition of apoptosis was followed by the blockage of mitochondria-dependent cell death cascades and suppression of the phosphorylation of the mitogen-activated protein kinase signaling (MAPKs) pathway by MCEE. Taken together, MCEE was shown to be effective in protecting against H₂O₂-induced cell death through its antioxidant and anti-apoptotic properties.
Electronic medical records have been adopted in Korean clinical settings for a few decades. However, there is a lack of studies on the quality, usefulness, and easy use of electronic medical records ...examined from the perspective of nurses. This study sought the effect of the system quality, information quality, and service quality of an electronic medical record on nurses' perceived usefulness and ease of use of the electronic medical record. A total of 278 nurses from four hospitals completed a self-administered questionnaire using a 5-point Likert scale. The data were analyzed using descriptive statistics and hierarchical multiple regression methods. The results showed that the system, information, and service quality of the electronic medical records explained 36.2% of the variance in perceived usefulness (F = 51.760, P < .001) and 43.4% of the variance in perceived ease of use (F = 70.019, P < .001). Thus, these three qualities were significant factors predicting perceived usefulness and ease of use. The close cooperation of stakeholders including administrators, healthcare providers including nurses, and researchers involved in clinical practice will improve electronic medical record quality leading to user-friendly electronic medical record implementation. This study provided statistical interpretations to help in understanding regression analysis.
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