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Assessment of MPOX infection-related knowledge levels, concerns, and associated factors: a community-based cross-sectional study.
BMC Public Health
January 2025
Department of Infectious Diseases and Clinical Microbiology, Faculty of Medicine, Dokuz Eylul University, İzmir, Türkiye.
Background: MPOX (Monkeypox) is a zoonotic disease of increasing global concern due to its re-emergence and potential for human-to-human transmission. Effective public health interventions rely on understanding socio-demographic determinants of knowledge and perceptions of the disease. This study aimed to investigate MPOX-related knowledge and concerns among a diverse sample in Türkiye, identifying key factors influencing knowledge levels.
View Article and Find Full Text PDFExtracting organs of interest from medical images based on convolutional neural network with auxiliary and refined constraints.
Sci Rep
January 2025
Department of Rehabilitation Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
Accurately extracting organs from medical images provides radiologist with more comprehensive evidences to clinical diagnose, which offers up a higher accuracy and efficiency. However, the key to achieving accurate segmentation lies in abundant clues for contour distinction, which has a high demand for the network architecture design and its practical training status. To this end, we design auxiliary and refined constraints to optimize the energy function by supplying additional guidance in training procedure, thus promoting model's ability to capture information.
View Article and Find Full Text PDFTraining data diversity enhances the basecalling of novel RNA modification-induced nanopore sequencing readouts.
Nat Commun
January 2025
Department of Pharmacy Practice and Science, University of Arizona, Tucson, AZ, USA.
Accurately basecalling sequence backbones in the presence of nucleotide modifications remains a substantial challenge in nanopore sequencing bioinformatics. It has been extensively demonstrated that state-of-the-art basecallers are less compatible with modification-induced sequencing signals. A precise basecalling, on the other hand, serves as the prerequisite for virtually all the downstream analyses.
View Article and Find Full Text PDFBiomedRAG: A retrieval augmented large language model for biomedicine.
J Biomed Inform
January 2025
Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA. Electronic address:
Retrieval-augmented generation (RAG) involves a solution by retrieving knowledge from an established database to enhance the performance of large language models (LLM). , these models retrieve information at the sentence or paragraph level, potentially introducing noise and affecting the generation quality. To address these issues, we propose a novel BiomedRAG framework that directly feeds automatically retrieved chunk-based documents into the LLM.
View Article and Find Full Text PDFHeterogeneous entity representation for medicinal synergy prediction.
Bioinformatics
January 2025
School of Data Science and Society, University of North Carolina at Chapel Hill, NC 27599, United States.
Motivation: Forecasting the synergistic effects of drug combinations facilitates drug discovery and development, especially regarding cancer therapeutics. While numerous computational methods have emerged, most of them fall short in fully modeling the relationships among clinical entities including drugs, cell lines, and diseases, which hampers their ability to generalize to drug combinations involving unseen drugs. These relationships are complex and multidimensional, requiring sophisticated modeling to capture nuanced interplay that can significantly influence therapeutic efficacy.
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