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Psychological stress refers to the load or oppression that people’s thoughts, feelings, and other inner processes bear, as well as the emotional shifts brought on by the school, work, society, everyday life, interpersonal connections, and other things. It can trigger people’s worry and other negative feelings, making them mentally dejected and frustrated, as well as raise people’s spirits to cheer up and meet stimuli and difficulties. College students are in their 20s, and they are energetic, have extreme mood swings, and are prone to mental problems. As a distinct group in the social development trend, college students are influenced by the learning and growth environment, and their understanding of the world, values, and outlook on life is maintained at the theoretical level, lacking practical thinking and experience, making it difficult to adapt in a short period of time. Excessive psychological strain is caused by new events and new contradicting conditions, which interfere with normal living and learning. This study employs a deep learning model to test and assess the psychological stress in college students, with the goal of addressing the varied psychological stresses that college students are prone to. The deep learning model employed in this paper is based on the classic ResNet50 network model, which compresses its network structure, lowering the computational cost of ResNet50 network model training and increasing the network’s efficiency. To boost processing performance and save storage and computational resources, we trained a network with few parameters, a small model, and high precision. The findings of the investigation can help college officials prevent and recognize problems in students early on. It actively builds a good home-school cooperation mechanism and enhances the students’ ability to cope with and solve stress through enhancing the students’ behavioral experience, so that students can form a good psychological stress coping thinking and behavior, while attaching importance to the cultivation of college students’ psychological quality.

People’s study, life, and work pressures have increased dramatically in recent years, as the pace of life has accelerated. Long-term high mental stress causes physiological secretion dysfunction and decreased immunity, resulting in a variety of diseases and even the risk of karoshi. Mental workload is another important factor that contributes to mental illness. Accurate assessment of mental workload is a method of effectively coping with physical and mental illness. Traditional physiological indicator measurement is inaccurate because changes caused by physiological indicators may not be solely due to psychological factors. As a result, based on the long short-term memory network, this paper proposes a mental workload evaluation model of multibranch LSTM with attention mechanism (LSTM). This model introduces an attention mechanism based on the classic LSTM network, which can screen the features differently, not only improving the utilization of effective information but also reducing calculation parameters and simplifying the model. This study employs a multibranch LSTM to improve the network’s generalization performance and stability. The basic idea behind multibranch LSTM is to train the branch LSTM network model separately using a variety of data, resulting in a branch LSTM network with a specific structure for the input EEG data. Finally, a total prediction result is obtained by combining the prediction outputs of multiple branch LSTM networks, which is used as the final mental workload evaluation result. The experimental results show that the model used in this paper has higher accuracy and more stable evaluation results in assessing psychological load. This study has a certain amount of reference value.

Abstract Introduction DJ‐1 mutation is a causative reason for familial Parkinson's disease (PD). Leucine166Proline (L166P) and C106S are two important DJ‐1 mutations. In this study, we established hydrogen peroxide (H2O2) induced L166P and C106S DJ‐1‐transfected neuroblastoma (SH‐SY5Y) cellular models of PD and investigated the effects of Cistanche extracts and key bioactive compounds, including acteoside, echinacoside, caffeic acid, and Cistanche total glycosides on these two models. Methods After expressing FLAG‐tagged L166P and C106S DJ‐1 plasmids in Escherichia coli, the expressed plasmids were collected, treated with restriction enzyme, and identified using DNA electrophoresis. After purification, the L166P DJ‐1 and C106S DJ‐1 plasmids were separately transfected into SH‐SY5Y cells using liposomes. Transfected SH‐SY5Y cells were detected by western blotting and immunocytochemistry. Cell viability was determined using MTT assay. Results Both western blotting and immunocytochemistry showed that L166P and C106S DJ‐1 were highly expressed in the transfected SH‐SY5Y cells. MTT assays showed that transfection with L166P or C106S DJ‐1 reduced the viability of SH‐SY5Y cells exposed to H2O2, as compared to untransfected SH‐SY5Y cells. In addition, Cistanche extracts and key bioactive compounds, including acteoside, echinacoside, caffeic acid, and Cistanche total glycosides, significantly inhibited the decreases of cell viability caused by H2O2 in L166P and C106S DJ‐1‐transfected SH‐SY5Y cells. Conclusions These findings suggest that we successfully established sensitive and stable H2O2 induced L166P DJ‐1‐ and C106S DJ‐1‐transfected SH‐SY5Y cell models of PD and Cistanche extracts may thus be useful for treating PD.