Selective serotonin reuptake inhibitors (SSRIs) have been reported to increase cognitive performance in some clinical studies of Alzheimer's disease (AD). However, there is a lack of evidence supporting the efficacy of SSRIs as cognition enhancers in AD, and the role of SSRIs as a treatment for AD remains largely unclear. Here, we characterized the impact of fluoxetine (FLX), a well-known SSRI, on neurons in the dentate gyrus (DG) and in CA1 and CA3 of the hippocampus of middle-aged (16 to 17 months old) APPswe/PSEN1dE9 (APP/PS1) transgenic AD model mice. We found that intraperitoneal (i.p.) injection of FLX (10 mg/kg/day) for 5 weeks effectively alleviated the impairment of spatial learning ability in middle-aged APP/PS1 mice as evaluated using the Morris water maze. More importantly, the number of neurons in the hippocampal DG was significantly increased by FLX. Additionally, FLX reduced the deposition of beta amyloid, inhibited GSK-3β activity and increased the level of β-catenin in middle-aged APP/PS1 mice. Collectively, the results of this study indicate that FLX delayed the progression of neuronal loss in the hippocampal DG in middle-aged AD mice, and this effect may underlie the FLX-induced improvement in learning ability. FLX may therefore serve as a promising therapeutic drug for AD.
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http://dx.doi.org/10.18632/oncotarget.15398 | DOI Listing |
World J Surg Oncol
January 2025
Department of Hepatobiliary and Pancreas, Affiliated Hospital of Qingdao University, NO.1677 Wutaishan Road, Qingdao, Shandong Province, 266555, China.
Background: With the rising diagnostic rate of gallbladder polypoid lesions (GPLs), differentiating benign cholesterol polyps from gallbladder adenomas with a higher preoperative malignancy risk is crucial. This study aimed to establish a preoperative prediction model capable of accurately distinguishing between gallbladder adenomas and cholesterol polyps using machine learning algorithms.
Materials And Methods: We retrospectively analysed the patients' clinical baseline data, serological indicators, and ultrasound imaging data.
BMC Med
January 2025
Department of Nuclear Medicine, West China Hospital, Sichuan University, Guoxue Alley, Address: No.37, Chengdu City, Sichuan, 610041, China.
Background: This study aimed to construct a radiomics-based imaging biomarker for the non-invasive identification of transformed follicular lymphoma (t-FL) using PET/CT images.
Methods: A total of 784 follicular lymphoma (FL), diffuse large B-cell lymphoma, and t-FL patients from 5 independent medical centers were included. The unsupervised EMFusion method was applied to fuse PET and CT images.
Sci Rep
January 2025
School of Computer Science and Technology, Donghua University, Shanghai, 201620, China.
Extracting high-order abstract patterns from complex high-dimensional data forms the foundation of human cognitive abilities. Abstract visual reasoning involves identifying abstract patterns embedded within composite images, considered a core competency of machine intelligence. Traditional neuro-symbolic methods often infer unknown objects through data fitting, without fully exploring the abstract patterns within composite images and the sequential sensitivity of visual sequences.
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January 2025
Department of Urology, Vanderbilt University Medical Center, Nashville, USA.
Recent advancements of large language models (LLMs) like generative pre-trained transformer 4 (GPT-4) have generated significant interest among the scientific community. Yet, the potential of these models to be utilized in clinical settings remains largely unexplored. In this study, we investigated the abilities of multiple LLMs and traditional machine learning models to analyze emergency department (ED) reports and determine if the corresponding visits were due to symptomatic kidney stones.
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January 2025
Rashpetco Company, Cairo, Egypt.
This study presents a comprehensive workflow to detect low seismic amplitude gas fields in hydrocarbon exploration projects, focusing on the West Delta Deep Marine (WDDM) concession, offshore Egypt. The workflow integrates seismic spectral decomposition and machine learning algorithms to identify subtle anomalies, including low seismic amplitude gas sand and background amplitude water sand. Spectral decomposition helps delineate the fairway boundaries and structural features, while Amplitude Versus Offset (AVO) analysis is used to validate gas sand anomalies.
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