Introduction: Pretreatment neurocognitive function may predict the treatment response to low-dose ketamine infusion in patients with treatment-resistant depression (TRD). However, the association between working memory function at baseline and the antidepressant efficacy of ketamine infusion remains unclear.
Methods: A total of 71 patients with TRD were randomized to one of three treatment groups: 0.5 mg/kg ketamine, 0.2 mg/kg ketamine, or normal saline. Depressive symptoms were measured using the 17-item Hamilton Depression Rating Scale (HDRS) at baseline and after treatment. Cognitive function was evaluated using working memory and go-no-go tasks at baseline.
Results: A generalized linear model with adjustments for demographic characteristics, treatment groups, and total HDRS scores at baseline revealed only a significant effect of working memory function (correct responses and omissions) on the changes in depressive symptoms measured by HDRS at baseline (F=12.862, p<0.05). Correlation analysis further showed a negative relationship (r=0.519, p=0.027) between pretreatment working memory function and changes in HDRS scores in the 0.5 mg/kg ketamine group.
Discussion: An inverse relationship between pretreatment working memory function and treatment response to ketamine infusion may confirm that low-dose ketamine infusion is beneficial and should be reserved for patients with TRD.
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In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance.
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The Internet of Things (IoT) has emerged as a crucial element in everyday life. The IoT environment is currently facing significant security concerns due to the numerous problems related to its architecture and supporting technology. In order to guarantee the complete security of the IoT, it is important to deal with these challenges.
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School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China.
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