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Lymphadenopathy is associated with lymph node abnormal size or consistency due to many causes. We employed the deep convolutional neural network ResNet-34 to detect and classify CT images from patients with abdominal lymphadenopathy and healthy controls. We created a single database containing 1400 source CT images for patients with abdominal lymphadenopathy (n = 700) and healthy controls (n = 700).

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The emerging field of quantum life science combines principles from quantum physics and biology to study fundamental life processes at the molecular level. Quantum mechanics, which describes the properties of small particles, can help explain how quantum phenomena such as tunnelling, superposition, and entanglement may play a role in biological systems. However, capturing these effects in living systems is a formidable challenge, as it involves dealing with dissipation and decoherence caused by the surrounding environment.

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Importance: Nelonemdaz selectively antagonizes the 2B subunit of the N-methyl-d-aspartate glutamate receptor and scavenges free radical species.

Objective: To evaluate whether nelonemdaz enhances the clinical outcomes of patients with acute ischemic stroke undergoing emergent reperfusion therapy.

Design, Setting, And Participants: This multicenter double-blind placebo-controlled randomized phase 3 trial (December 25, 2021, to June 30, 2023, in South Korea) recruited patients with acute ischemic stroke who met the following criteria: National Institutes of Health Stroke Scale score greater than or equal to 8, Alberta Stroke Program Early Computed Tomography score greater than or equal to 4, and endovascular thrombectomy within 12 hours after stroke onset.

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Background: Breast cancer remains a significant global health challenge, requiring innovative therapeutic strategies. In silico methods, which leverage computational tools, offer a promising pathway for vaccine development. These methods facilitate antigen identification, epitope prediction, immune response modelling, and vaccine optimization, accelerating the design process.

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Objective: This study aimed to develop a simple machine-learning model incorporating lymph node metastasis status with F-18 Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) and clinical information for predicting regional lymph node metastasis in patients with colon cancer.

Methods: This retrospective study included 193 patients diagnosed with colon cancer between January 2014 and December 2017. All patients underwent F-18 FDG PET/CT and blood test before surgery.

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