Summary of drug therapy to treat cognitive impairment-induced obstructive sleep apnea

Obstructive sleep apnea (OSA) is a severe sleep disorder associated with intermittent hypoxia and sleep fragmentation. Cognitive impairment is a signifi- cant and common OSA complication often described in such patients. The most commonly utilized methods in clinical OSA treatment are oral appliances and continuous positive airway pressure (CPAP). However, the current therapeutic methods for improving cognitive function could not achieve the expected efficacy in same patients. Therefore, further understanding the molecular mechanism behind cognitive dysfunction in OSA disease will provide new treatment methods and targets. This review briefly summarized the clinical manifestations of cognitive impairment in OSA disease. Moreover, the pathophysiological molecular mechanism of OSA was outlined. Our study concluded that both SF and IH could induce cognitive impairment by multiple signaling pathways, such as oxidative stress activation, inflammation, and apoptosis. However, there is a lack of effective drug therapy for cognitive impairment in OSA. Finally, the therapeutic potential of some novel compounds and herbal medicine was evaluated on attenuating cognitive impairment based on certain preclinical studies

Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning

The retrieval phase is a vital component in recommendation systems, requiring the model to be effective and efficient. Recently, generative retrieval has become an emerging paradigm for document retrieval, showing notable performance. These methods enjoy merits like being end-to-end differentiable, suggesting their viability in recommendation. However, these methods fall short in efficiency and effectiveness for large-scale recommendations. To obtain efficiency and effectiveness, this paper introduces a generative retrieval framework, namely SEATER, which learns SEmAntic Tree-structured item identifiERs via contrastive learning. Specifically, we employ an encoder-decoder model to extract user interests from historical behaviors and retrieve candidates via tree-structured item identifiers. SEATER devises a balanced k-ary tree structure of item identifiers, allocating semantic space to each token individually. This strategy maintains semantic consistency within the same level, while distinct levels correlate to varying semantic granularities. This structure also maintains consistent and fast inference speed for all items. Considering the tree structure, SEATER learns identifier tokens' semantics, hierarchical relationships, and inter-token dependencies. To achieve this, we incorporate two contrastive learning tasks with the generation task to optimize both the model and identifiers. The infoNCE loss aligns the token embeddings based on their hierarchical positions. The triplet loss ranks similar identifiers in desired orders. In this way, SEATER achieves both efficiency and effectiveness. Extensive experiments on three public datasets and an industrial dataset have demonstrated that SEATER outperforms state-of-the-art models significantly.Comment: 8 main pages, 3 pages for appendi