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Background And Objectives: Mitochondrial disorders are multiorgan disorders resulting in significant morbidity and mortality. We aimed to characterize death-associated factors in an international cohort of deceased individuals with mitochondrial disorders.

Methods: This cross-sectional multicenter observational study used data provided by 26 mitochondrial disease centers from 8 countries from January 2022 to March 2023.

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Potential trend of regenerative treatment for type I diabetes has been introduced for more than a decade. However, the technologies regarding insulin-producing cell (IPC) production and transplantation are still being developed. Here, we propose the potential IPC production protocol employing mouse gingival fibroblast-derived induced pluripotent stem cells (mGF-iPSCs) as a resource and the pre-clinical approved subcutaneous IPC transplantation platform for further clinical confirmation study.

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Personalized cancer drug treatment is emerging as a frontier issue in modern medical research. Considering the genomic differences among cancer patients, determining the most effective drug treatment plan is a complex and crucial task. In response to these challenges, this study introduces the Adaptive Sparse Graph Contrastive Learning Network (ASGCL), an innovative approach to unraveling latent interactions in the complex context of cancer cell lines and drugs.

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Background: Ankle sprain is a common clinical disease, which has the highest incidence rate among joint and ligament injuries. And acute ankle sprains can easily develop into chronic ankle instability, thereby increasing the difficulty of treatment. The current clinical guidelines for post-acute ankle sprains are still controversial.

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Entity-enhanced BERT for medical specialty prediction based on clinical questionnaire data.

PLoS One

January 2025

School of Industrial and Management Engineering, Korea University, Seongbuk-gu, Seoul, Republic of Korea.

A medical specialty prediction system for remote diagnosis can reduce the unexpected costs incurred by first-visit patients who visit the wrong hospital department for their symptoms. To develop medical specialty prediction systems, several researchers have explored clinical predictive models using real medical text data. Medical text data include large amounts of information regarding patients, which increases the sequence length.

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