Publications by authors named "S Maryam Sadeghi"

Background: In the present study, we aimed to evaluate the effects of medroxyprogesterone on hospital short clinical outcomes and ABG parameters in patients with chronic obstructive pulmonary disease (COPD) exacerbation under treatments with noninvasive ventilation (NIV) treated with progesterone 15 mg in comparison with placebo.

Materials And Methods: This is a double-blinded clinical trial that was performed in 2020-2021 in Isfahan, Iran, on 60 patients with COPD exacerbation that require NIV. All patients received short-acting beta-agonists, short-acting anticholinergics, systemic corticosteroids, and NIV.

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This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique for breast tumor segmentation based on a modified UNet architecture. To improve segmentation accuracy, the model integrates attention mechanisms, such as the Convolutional Block Attention Module (CBAM) and Non-Local Attention, with advanced encoder architectures, including ResNet, DenseNet, and EfficientNet. These attention mechanisms enable the model to focus more effectively on relevant tumor areas, resulting in significant performance improvements.

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The peptidoglycan biosynthetic pathway involves a series of enzymatic reactions in which UDP-N-acetylglucosamine-enolpyruvate reductase (MurB) plays a crucial role in catalyzing the conversion of UDP-N-acetylglucosamine-enolpyruvate (UNAGEP) to UDP-N-acetylmuramic acid. This reaction relies on NADPH and FAD and, since MurB is not found in eukaryotes, it is an attractive target for the development of antimicrobials. MurB from Brucella ovis, the causative agent of brucellosis in sheep, is characterized here.

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Subjective feelings are thought to arise from conceptual and bodily states. We examine whether the valence of feelings may also be decoded directly from objective ecological statistics of the visual environment. We train a visual valence (VV) machine learning model of low-level image statistics on nearly 8000 emotionally charged photographs.

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The health of a watershed is a crucial aspect that involves evaluating ecological, hydrological, and human factors to determine its overall well-being and functionality. Hydrological components are key parts of a watershed. Therefore, the present study aims to assess Watershed Hydrological Health (WHH) using the Pressure-State-Response (PSR) framework and to compare WHH priorities derived from Multi-Criteria Decision Making (MCDM) approaches in the Gorganroud Watershed, Iran.

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