Search

The authors explored undergraduate business students' feelings and thoughts about themselves, other students, and their professors when mobile devices are used in the lecture hall for nonacademic purposes. Specifically, they investigated mobile device use during class time, and whether students support it, are indifferent toward it, or consider it to be an intrusion. Using the critical incident technique, the authors thematically categorized students' thoughts and feelings regarding mobile device usage in the classroom for nonacademic purposes. The results uncovered unique perspectives presented by the students that carry implications for students, professors and administrators.

Background. Functional near‐infrared spectroscopy (fNIRS) is being extensively explored as a potential primary screening tool for major depressive disorder (MDD) because of its portability, cost‐effectiveness, and low susceptibility to motion artifacts. However, the fNIRS‐based computer‐aided diagnosis (CAD) of MDD using deep learning methods has rarely been studied. In this study, we propose a novel deep learning framework based on a convolutional neural network (CNN) for the fNIRS‐based CAD of MDD with high accuracy. Materials and Methods. The fNIRS data of participants—48 patients with MDD and 68 healthy controls (HCs)—were obtained while they performed a Stroop task. The hemodynamic responses calculated from the preprocessed fNIRS data were used as inputs to the proposed CNN model with an ensemble CNN architecture, comprising three 1D depth‐wise convolutional layers specifically designed to reflect interhemispheric asymmetry in hemodynamic responses between patients with MDD and HCs, which is known to be a distinct characteristic in previous MDD studies. The performance of the proposed model was evaluated using a leave‐one‐subject‐out cross‐validation strategy and compared with those of conventional machine learning and CNN models. Results. The proposed model exhibited a high accuracy, sensitivity, and specificity of 84.48%, 83.33%, and 85.29%, respectively. The accuracies of conventional machine learning algorithms—shrinkage linear discriminator analysis, regularized support vector machine, EEGNet, and ShallowConvNet—were 73.28%, 74.14%, 62.93%, and 62.07%, respectively. Conclusions. In conclusion, the proposed deep learning model can differentiate between the patients with MDD and HCs more accurately than the conventional models, demonstrating its applicability in fNIRS‐based CAD systems. [ABSTRACT FROM AUTHOR]

Previous studies report low self-concept clarity in depression, but they are limited to nonclinical samples and self-report questionnaires. To comprehensively understand the relationship between self-concept clarity and depression, this study expanded the range of depression to include clinical samples and explored self-concept clarity through a behavioral task. In Study 1, the self-concept clarity scale was administered to 48 nonclinical and 53 clinical participants, and the quadratic model best explained the relationship. In Study 2, 24 undergraduates and 21 depressed patients completed the me/not-me task. Shorter reaction time and greater internal consistency of negative self-descriptive words were associated with higher depression severity. The results suggest that a certain and consistent negative self-view relates to more severe depression. [ABSTRACT FROM AUTHOR]

Natural killer cell‐derived extracellular vesicles (NK‐EVs) are candidate biotherapeutics against various cancers. However, standardised potency assays are necessary for a reliable assessment of NK‐EVs' cytotoxicity. This study aims to thoroughly evaluate a highly sensitive resazurin phenoxazine‐based cell viability potency assay (measurement of the cellular redox metabolism) for quantifying the cytotoxicity of NK‐EVs against leukaemia K562 cells (suspension model) and breast cancer MDA‐MB‐231 cells (adherent model) in vitro. The assay was evaluated based on common analytical parameters setforth by regulatory guidelines, including specificity, selectivity,accuracy, precision, linearity, range and stability. Our results revealed that this resazurin‐based cell viability potency assay reliably and reproducibly measured a dose‐response of NK‐EVs' cytotoxic activity against both cancer models. The assay showed precision with 5% and 20% variation for intra‐run and inter‐run variability. The assay signal showed specificity and selectivity of NK‐EVs against cancer target cells, as evidenced by the diminished viability of cancer cells following a 5‐hour treatment with NK‐EVs, without any detectable interference or background. The linearity analysis of target cancer cells revealed strong linearity for densities of 5000 K562 and 1000 MDA‐MB‐231 cells per test with a consistent range. Importantly, NK‐EVs' dose‐response for cytotoxicity showed a strong correlation (|ρ| ∼ 0.8) with the levels of known cytotoxic factors associated with the NK‐EVs' corona (FasL, GNLY, GzmB, PFN and IFN‐γ), thereby validating the accuracy of the assay. The assay also distinguished cytotoxicity changes in degraded NK‐EVs, indicating the ability of the assay to detect the potential loss of sample integrity. Compared to other commonly reported bioassays (i.e., flow cytometry, cell counting, lactate dehydrogenase release assay, DNA‐binding reporter assay and confluence assay), our results support this highly sensitive resazurin‐based viability potency assay as a high‐throughput and quantitative method for assessing NK‐EVs' cytotoxicity against both suspension and adherent cancer models for evaluating NK‐EVs' biotherapeutics. [ABSTRACT FROM AUTHOR]

Although social entrepreneurship has gained popularity and our understanding of social entrepreneurship has evolved, insufficient attention has been given to understand the behaviours of consumers, a key stakeholder of social entrepreneurship. Because consumers directly impact the performance of social enterprises, it is imperative to address this research gap. Relying on the theory of planned behaviour, we examine the relationship between consumers' intentions and behaviours when purchasing social enterprise products and services. Moreover, this study explores how consumers' sociodemographic characteristics, such as political ideology, trust towards social entrepreneurship, and household income, influence the relationship between consumers' intentions and behaviours. Based on a sample of randomly selected 972 Korean consumers, this study finds a consumer's intention is positively associated with behaviour, and the positive relationship between intention and action is strengthened when a consumer has a progressive political ideology and higher income level. [ABSTRACT FROM AUTHOR]

Objective The objective of this prospective study was to assess longitudinal variations in health-related quality of life (HR-QOL) in patients diagnosed with differentiated thyroid cancer (DTC) before and after thyroidectomy. Methods A cohort of 185 DTC patients who underwent thyroidectomy between January 2013 and December 2017 and who completed all necessary questionnaires was evaluated. Their HR-QOL was gauged using the University of Washington Quality of Life questionnaire (UW-QOL) and the City of Hope Quality of Life-Thyroid Version questionnaire (QOL-TV) both prior to surgery and at 3 months, 6 months, 1 year, 2 years, 3 years, and 5 years postoperatively. Results Out of 185 patients, 150 (81.1%) were female, with an average age of 48.7 ± 12.9 years. For both UW-QOL and QOL-TV, the total composite QOL scores notably declined from preoperative levels to 3 months postoperatively, then gradually improved over 5 years, ultimately exceeding preoperative scores. Factors such as total thyroidectomy, radioactive iodine (RAI) ablation, and postoperative hypoparathyroidism were associated with lower physical composite QOL scores. Patients who underwent remote-access thyroidectomy expressed significantly higher satisfaction with appearance compared with those who had conventional thyroidectomy. Mood and anxiety were major clinical concerns both before and after surgery, showing considerable improvement postoperatively. Conclusion For DTC patients, HR-QOL experienced a significant drop 3 months postsurgery, subsequently showing gradual improvement, surpassing preoperative QOL by 5 years. Factors contributing to improved physical QOL included the utilization of remote-access thyroidectomy, less extensive thyroidectomy, and the absence of RAI ablation and hypoparathyroidism. [ABSTRACT FROM AUTHOR]

Depending on the degree of fracture, pelvic fracture can be accompanied by vascular damage, and in severe cases, it may progress to hemorrhagic shock. Pelvic radiography can quickly diagnose pelvic fractures, and the Association for Osteosynthesis Foundation and Orthopedic Trauma Association (AO/OTA) classification system is useful for evaluating pelvic fracture instability. This study aimed to develop a radiomics-based machine-learning algorithm to quickly diagnose fractures on pelvic X-ray and classify their instability. data used were pelvic anteroposterior radiographs of 990 adults over 18 years of age diagnosed with pelvic fractures, and 200 normal subjects. A total of 93 features were extracted based on radiomics:18 first-order, 24 GLCM, 16 GLRLM, 16 GLSZM, 5 NGTDM, and 14 GLDM features. To improve the performance of machine learning, the feature selection methods RFE, SFS, LASSO, and Ridge were used, and the machine learning models used LR, SVM, RF, XGB, MLP, KNN, and LGBM. Performance measurement was evaluated by area under the curve (AUC) by analyzing the receiver operating characteristic curve. The machine learning model was trained based on the selected features using four feature-selection methods. When the RFE feature selection method was used, the average AUC was higher than that of the other methods. Among them, the combination with the machine learning model SVM showed the best performance, with an average AUC of 0.75±0.06. By obtaining a feature-importance graph for the combination of RFE and SVM, it is possible to identify features with high importance. The AO/OTA classification of normal pelvic rings and pelvic fractures on pelvic AP radiographs using a radiomics-based machine learning model showed the highest AUC when using the SVM classification combination. Further research on the radiomic features of each part of the pelvic bone constituting the pelvic ring is needed. [ABSTRACT FROM AUTHOR]

Water evaporation-driven energy harvesting is an emerging mechanism for contributing to green energy production with low cost. Herein, we developed polyacrylonitrile (PAN) nanofiber-based evaporation-driven electricity generators (PEEGs) to confirm the feasibility of utilizing electrospun PAN nanofiber mats in an evaporation-driven energy harvesting system. However, PAN nanofiber mats require a support substrate to enhance its durability and stability when it is applied to an evaporation-driven energy generator, which could have additional effects on generation performance. Accordingly, various support substrates, including fiberglass, copper, stainless mesh, and fabric screen, were applied to PEEGs and examined to understand their potential impacts on electrical generation outputs. As a result, the PAN nanofiber mats were successfully converted to a hydrophilic material for an evaporation-driven generator by dip-coating them in nanocarbon black (NCB) solution. Furthermore, specific electrokinetic performance trends were investigated and the peak electricity outputs of Voc were recorded to be 150.8, 6.5, 2.4, and 215.9 mV, and Isc outputs were recorded to be 143.8, 60.5, 103.8, and 121.4 μA, from PEEGs with fiberglass, copper, stainless mesh, and fabric screen substrates, respectively. Therefore, the implications of this study would provide further perspectives on the developing evaporation-induced electricity devices based on nanofiber materials. [ABSTRACT FROM AUTHOR]