RESUMEN
Pandemics such as COVID-19 have exposed global inequalities in essential health care. Here, we proposed a novel analytics of nucleic acid amplification tests (NAATs) by combining paper microfluidics with deep learning and cloud computing. Real-time amplifications of synthesized SARS-CoV-2 RNA templates were performed in paper devices. Information pertained to on-chip reactions in time-series format were transmitted to cloud server on which deep learning (DL) models were preloaded for data analysis. DL models enable prediction of NAAT results using partly gathered real-time fluorescence data. Using information provided by the G-channel, accurate prediction can be made as early as 9 min, a 78% reduction from the conventional 40 min mark. Reaction dynamics hidden in amplification curves were effectively leveraged. Positive and negative samples can be unbiasedly and automatically distinguished. Practical utility of the approach was validated by cross-platform study using clinical datasets. Predicted clinical accuracy, sensitivity and specificity were 98.6%, 97.6% and 99.1%. Not only the approach reduced the need for the use of bulky apparatus, but also provided intelligent, distributable and robotic insights for NAAT analysis. It set a novel paradigm for analyzing NAATs, and can be combined with the most cutting-edge technologies in fields of biosensor, artificial intelligence and cloud computing to facilitate fundamental and clinical research.Copyright © 2023 Elsevier Ltd
RESUMEN
In the new situation of modernization, there is an influx of diverse social thinking. At the same time, coupled with the influence of COVID-19, which has swept the world, the ideological and psychological space of college students has been greatly impacted. In this context, the ideological and psychological health of college students is an important value for the education of college students. To be specific, as an important place for cultivating college students, colleges and universities should pay attention to students' thoughts and ideas from their hearts. In addition, colleges and universities should give full play to the role of educational psychology in colleges and universities and actively promote the synergistic development of all educational sectors in schools, so as to promote the realization of the goal of education in the new era. In recent years, a series of mental health problems such as anxiety, depression, low self-esteem, and interpersonal sensitivity have become frequent among college students, and some have even developed suicidal ideation. This has a very serious negative impact on individuals, families, and society. Therefore, if the mental health problems of college students can be detected early, the relevant school departments and counselors can provide timely and targeted help to such students. At the same time, these at-risk students can receive early treatment, thus reducing the harm. As a result, it is quite valuable to find an effective method to identify students with mental health problems. Traditionally, researchers have used questionnaires to survey students about their mental health. However, this approach has the disadvantage of being easily concealed and inefficient. In recent years, researchers have begun to use weblogs to identify students with mental health problems, but this approach still has shortcomings. First of all, they still use questionnaires to obtain labels. In addition, students' psychological activities may not only be reflected in their online behavior but also in their other daily behaviors. Big data in higher education plays a crucial role in analyzing and identifying students with psychological abnormalities. As a result, this research mainly extracts the behavioral characteristics of students by cleaning and transforming a large amount of disorganized student school data based on the educational data collected from school cards, academic affairs systems, access control systems, and related business systems. What is more, this study further analyzes the differences in behavioral characteristics between normal and abnormal students through hypothesis testing and finally establishes a model to identify abnormal students and evaluate the results.