RESUMO
ChatGPT, an artificial intelligence (AI)-driven language model engineered by OpenAI, has experienced a substantial upsurge in adoption within higher education due to its versatile applications and sophisticated capabilities. Although prevailing research on ChatGPT has predominantly concentrated on its technological aspects and pedagogical ramifications, a comprehensive understanding of students' perceptions and experiences regarding ChatGPT remains elusive. To address this gap, this study employed a peer interview methodology, conducting a thematic analysis of 106 first-year undergraduates and 81 first-year postgraduate students' perceptions from diverse disciplines at a comprehensive university in East China. The data analysis revealed that among the four factors examined-grade, age, gender, and major-grade emerged as the most influential determinant, followed by age and major. Postgraduate students demonstrated heightened awareness of the potential limitations of ChatGPT in addressing academic challenges and exhibited greater concern for security issues associated with its application. This research offers essential insights into students' perceptions and experiences with ChatGPT, emphasizing the importance of recognizing potential limitations and ethical concerns associated with ChatGPT usage. Additionally, the findings highlight ethical concerns, as students noted the importance of responsible data handling and academic integrity in ChatGPT usage, underscoring the need for ethical guidance in AI utilization. Moreover, further research is essential to optimize AI use in education, aiming to improve learning outcomes effectively.
RESUMO
Melanoma is the most aggressive type of skin cancer and has a high mortality rate once metastasis occurs. Hypoxia is a universal characteristic of the microenvironment of cancer and a driver of melanoma progression. In recent years, long noncoding RNAs (lncRNAs) have attracted widespread attention in oncology research. In this study, screening was performed and revealed seven hypoxia-related lncRNAs AC008687.3, AC009495.1, AC245128.3, AL512363.1, LINC00518, LINC02416 and MCCC1-AS1 as predictive biomarkers. A predictive risk model was constructed via univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Patients were grouped according to the model risk score, and Kaplan-Meier analysis was performed to compare survival between groups. Functional and pathway enrichment analyses were performed to compare gene set enrichment between groups. Moreover, a nomogram was constructed with the risk score as a variable. In both the training and validation sets, patients in the low-risk group had better overall survival than did those in the high-risk group (P<0.001). The 3-, 5- and 10-year area under the curve (AUC) values for the nomogram model were 0.821, 0.795 and 0.820, respectively. Analyses of immune checkpoints, immunotherapy response, drug sensitivity, and mutation landscape were also performed. The results suggested that the low-risk group had a better response to immunotherapeutic. In addition, the nomogram can effectively predict the prognosis and immunotherapy response of melanoma patients. The signature of seven hypoxia-related lncRNAs showed great potential value as an immunotherapy response biomarker, and these lncRNAs might be treatment targets for melanoma patients.