Artificial intelligence in medical genomics
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Optimising the mainstreaming of renal genomics: Complementing empirical and theoretical strategies for implementation
To identify and develop complementary implementation strategies that support nephrologists in mainstreaming renal genomic testing. Interviews were conducted with individuals nominated as ‘genomics champions’ and ‘embedded genomics experts’ as part of a mainstreaming project to identify initial barriers and investigate empirical strategies for delivering the project at initial stage. Data were mapped onto implementation science framework to identify complementary theoretical strategies. Interviews with 14 genomics champions and embedded genomics experts (genetic counsellors, nephrologists, renal nurses), identified 34 barriers to incorporating genomic testing into routine care, e.g., lack of long-term multidisciplinary team support and role clarity. In total, 25 empirical implementation strategies were identified such as creating new clinical teams. Using the Consolidated Framework for Implementation Research, 10 complementary theoretical implementation strategies were identified. Our study presents a novel approach complementing empirical strategies with theoretical strategies to support nephrologists in incorporating genomic testing into routine practice. Complementary strategies can potentially address barriers and inform future studies when mainstreaming renal genomics. This process underscored the need for integrating collaborative efforts among health professionals, patients, implementation scientists and the health system to overcome identified challenges to mainstream genomic testing. Future research should explore the applicability of these strategies to support mainstreaming genomic testing in different clinical settings.
Responsive DNA artificial cells for contact and behavior regulation of mammalian cells
Artificial cells have emerged as synthetic entities designed to mimic the functionalities of natural cells, but their interactive ability with mammalian cells remains challenging. Herein, we develop a generalizable and modular strategy to engineer DNA-empowered stimulable artificial cells designated to regulate mammalian cells (STARM) via synthetic contact-dependent communication. Constructed through temperature-controlled DNA self-assembly involving liquid-liquid phase separation (LLPS), STARMs feature organized all-DNA cytoplasm-mimic and membrane-mimic compartments. These compartments can integrate functional nucleic acid (FNA) modules and light-responsive gold nanorods (AuNRs) to establish a programmable sense-and-respond mechanism to specific stimuli, such as light or ions, orchestrating diverse biological functions, including tissue formation and cellular signaling. By combining two designer STARMs into a dual-channel system, we achieve orthogonally regulated cellular signaling in multicellular communities. Ultimately, the in vivo therapeutic efficacy of STARM in light-guided muscle regeneration in living animals demonstrates the promising potential of smart artificial cells in regenerative medicine.
Modelling and design of transcriptional enhancers
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Artificial intelligence in clinical genetics
Artificial intelligence (AI) has been growing more powerful and accessible, and will increasingly impact many areas, including virtually all aspects of medicine and biomedical research. This review focuses on previous, current, and especially emerging applications of AI in clinical genetics. Topics covered include a brief explanation of different general categories of AI, including machine learning, deep learning, and generative AI. After introductory explanations and examples, the review discusses AI in clinical genetics in three main categories: clinical diagnostics; management and therapeutics; clinical support. The review concludes with short, medium, and long-term predictions about the ways that AI may affect the field of clinical genetics. Overall, while the precise speed at which AI will continue to change clinical genetics is unclear, as are the overall ramifications for patients, families, clinicians, researchers, and others, it is likely that AI will result in dramatic evolution in clinical genetics. It will be important for all those involved in clinical genetics to prepare accordingly in order to minimize the risks and maximize benefits related to the use of AI in the field.
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