Aims: Although extensive investigation has revealed that an astrocyte-specific protein aquaporin-4 (AQP4) participates in regulating synaptic plasticity and memory, a functional relationship between AQP4 and learning processing has not been clearly established. This study was designed to test our hypothesis that AQP4 modulates the aversive motivation in Morris water maze (MWM).
Methods And Results: Using hidden platform training, we observed that AQP4 KO mice significantly decreased their swimming velocity compared with wild-type (WT) mice. To test for a relationship between velocities and escape motivation, we removed the platform and subjected a new group of mice similar to the session of hidden platform training. We found that KO mice exhibited a gradual reduction in swimming velocity, while WT mice did not alter their velocity. In the subsequent probe trial, KO mice after no platform training significantly decreased their mean velocity compared with those KO mice after hide platform training. However, all of KO mice were not impaired in their ability to locate a visible, cued escape platform.
Conclusions: Our findings, along with a previous report that AQP4 regulates memory consolidation, implicate a novel role for this glial protein in modulating the aversive motivation in spatial learning paradigm.
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http://dx.doi.org/10.1111/cns.12191 | DOI Listing |
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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Beijing Youcare Kechuang Pharmaceutical Technology Co., Ltd., Beijing 100176, China.
Human respiratory syncytial virus (RSV) remains a significant global health threat, particularly for vulnerable populations. Despite extensive research, effective antiviral therapies are still limited. To address this urgent need, we present AVP-GPT2, a deep-learning model that significantly outperforms its predecessor, AVP-GPT, in designing and screening antiviral peptides.
View Article and Find Full Text PDFPharmaceutics
January 2025
Department of Materials Science, Graduate School of Pure and Applied Sciences, University of Tsukuba, Tennoudai 1-1-1, Tsukuba 305-8573, Ibaraki, Japan.
Orally administered sorafenib has shown limited improvement in overall survival for non-small-cell lung cancer patients, likely due to poor pharmacokinetics and adverse effects, including gastrointestinal toxicity. To address these issues, we developed silica-containing antioxidant nanoparticles (siRNP) as a carrier to enhance the therapeutic efficacy of lipophilic sorafenib. Sorafenib was loaded into siRNP via dialysis (sora@siRNP).
View Article and Find Full Text PDFNutrients
January 2025
Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea.
Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture.
View Article and Find Full Text PDFNutrients
January 2025
Statistical Consulting Centre, National Institute for Applied Statistics Research Australia, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW 2522, Australia.
: Obesity remains a global health challenge. Many commercial online weight loss programs are available, and they have advantages in terms of scalability and access. Few of these programs have been evaluated for effectiveness in a real-world context.
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