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Purpose: To investigate BMAL1 and BMP6 expressive differences in ovarian granulosa cells (OGCs) of patients with polycystic ovary syndrome (PCOS), explore regulatory relationship, assess their impacts on OGC proliferation and apoptosis, and analyze their correlations with ART outcomes of patients.

Methods: A clinical study selected 40 PCOS patients who underwent IVF/ICSI in our hospital from January to October 2022 and 39 controls with male or tubal factor infertility. RT-qPCR and Western blot assessed BMAL1 and BMP6 mRNA/protein levels.

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Background: In South Africa, one in five adolescents experience pregnancy and face heightened rates of interpersonal violence and mental health challenges. Yet, few interventions are tailored to them.

Methods: 28 pregnant adolescents reporting past year intimate partner violence and/or non-partner rape were purposively recruited in antenatal clinics in Johannesburg to attend a 6-session arts-based intervention, delivered by 4 graduate art therapy students alongside clinical supervision.

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Introduction: Diabetic retinopathy grading plays a vital role in the diagnosis and treatment of patients. In practice, this task mainly relies on manual inspection using human visual system. However, the human visual system-based screening process is labor-intensive, time-consuming, and error-prone.

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Introduction: Age-related macular degeneration (AMD) is a prevalent eye disease among middle-aged and older adults. AMD leaves the patient with irreversible deteriorating vision, which profoundly impacts their daily lives and psychosocial well-being. Given the limited studies addressing the psychosocial needs of adults with AMD and, in particular, using an expressive arts-based intervention (EXABI) as an intervention, this study aims to investigate the effectiveness of such an intervention in enhancing the psychosocial well-being of adults with AMD.

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A discrete convolutional network for entity relation extraction.

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January 2025

State Key Laboratory of Public Big Data, Guizhou University, 550025, China; Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Guizhou University, 550025, China; College of Computer Science and Technology, Guizhou University, 550025, China. Electronic address:

Relation extraction independently verifies all entity pairs in a sentence to identify predefined relationships between named entities. Because these entity pairs share the same contextual features of a sentence, they lead to a complicated semantic structure. To distinguish semantic expressions between relation instances, manually designed rules or elaborate deep architectures are usually applied to learn task-relevant representations.

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