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Introduction: The core objective of this study was to precisely locate metastatic lymph nodes, identify potential areas in nasopharyngeal carcinoma patients that may not require radiotherapy, and propose a hypothesis for reduced target volume radiotherapy on the basis of these findings. Ultimately, we reassessed the differences in dosimetry of organs at risk (OARs) between reduced target volume (reduced CTV2) radiotherapy and standard radiotherapy.

Methods And Materials: A total of 209 patients participated in the study.

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Background: Innovation in crop establishment is crucial for wheat productivity in drought-prone climates. Seedling establishment, the first stage of crop productivity, relies heavily on root and coleoptile system architecture for effective soil water and nutrient acquisition, particularly in regions practicing deep planting. Root phenotyping methods that quickly determine coleoptile lengths are vital for breeding studies.

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Background: Rex rabbit is famous for its silky and soft fur coat, a characteristic predominantly attributed to its hair follicles. Numerous studies have confirmed the crucial roles of mRNAs and non-coding RNAs (ncRNAs) in regulating key cellular processes such as cell proliferation, differentiation, apoptosis and immunity. However, their involvement in the regulation of the hair cycle in Rex rabbits remains unknown.

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Recently, 3-D porous architecture of the composites play a key role in cell proliferation, bone regeneration, and anticancer activities. The osteoinductive and osteoconductive properties of β-TCP allow for the complete repair of numerous bone defects. Herein, β-TCP was synthesized by wet chemical precipitation route, and their 3-D porous composites with HBO and Cu nanoparticles were prepared by the solid-state reaction method with improved mechanical and biological performances.

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An automatic cervical cell classification model based on improved DenseNet121.

Sci Rep

January 2025

Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.

The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.

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