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The New Age of Cell-Free DNA in Pulmonary Medicine
Chest. 2025 Sep;168(3):581-583. doi: 10.1016/j.chest.2025.04.023.
NO ABSTRACT
PMID:40935546 | DOI:10.1016/j.chest.2025.04.023
Why the Oracle-OpenAI deal caught Wall Street by surprise
Fluctuating DNA methylation tracks cancer evolution at clinical scale
Nature, Published online: 10 September 2025; doi:10.1038/s41586-025-09374-4
Cancer evolutionary dynamics are quantitatively inferred using a method, EVOFLUx, applied to fluctuating DNA methylation.AI chatbots are already biasing research — we must establish guidelines for their use now
Nature, Published online: 09 September 2025; doi:10.1038/d41586-025-02810-5
The academic community has looked at how artificial-intelligence tools help researchers to write papers, but not how they distort the literature scientists choose to cite.Single-cell multiome and spatial profiling reveals pancreas cell type-specific gene regulatory programs of type 1 diabetes progression
Sci Adv. 2025 Sep 12;11(37):eady0080. doi: 10.1126/sciadv.ady0080. Epub 2025 Sep 10.
ABSTRACT
Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here, we performed single-nucleus multiomics and spatial transcriptomics in up to 32 nondiabetic (ND), autoantibody-positive (AAB+), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine subtypes. β, Acinar, and other cell types, and related cellular niches, had altered abundance and gene activity in T1D progression, including distinct pathways altered in AAB+ compared to T1D. We identified epigenomic drivers of gene activity in T1D and AAB+ which, combined with genetic association, revealed causal pathways of T1D risk including antigen presentation in β cells. Last, single-cell and spatial profiles together revealed widespread changes in cell-cell signaling in T1D including signals affecting β cell regulation. Overall, these results revealed drivers of T1D in the pancreas, which form the basis for therapeutic targets for disease prevention.
PMID:40929272 | PMC:PMC12422192 | DOI:10.1126/sciadv.ady0080
Organoids in Genetic Disorders: from Disease Modeling to Translational Applications
Stem Cell Rev Rep. 2025 Sep 11. doi: 10.1007/s12015-025-10973-x. Online ahead of print.
ABSTRACT
The emergence of organoid models has significantly bridged the gap between traditional cell cultures/animal models and authentic human disease states, particularly for genetic disorders, where their inherent genetic fidelity enables more biologically relevant research directions and enhances translational validity. This review systematically analyzes established organoid models of genetic diseases across organs (e.g., brain, eye, kidney, lung, and heart), highlighting their pivotal roles in identifying novel pathogenic genes, elucidating disease mechanisms, and advancing therapeutic strategies such as drug screening platforms, gene-editing therapies, and organ transplantation strategies. Furthermore, we critically address current limitations-including challenges in recapitulating complex pathologies and scaling production-while underscoring their potential for personalized medicine through multi-omics integration and bioengineering innovations. Although the scope of "genetic diseases" is broad, this synthesis focuses on disorders with well-defined inheritance patterns, such as monogenic disorders, copy number variations (CNVs), and aneuploidies. Despite covering only a subset of these conditions, this review aims to provide researchers with a comprehensive overview of the field, emphasizing how organoid-based approaches could accelerate both mechanistic discoveries and clinical translation in genetic disease research.
PMID:40931310 | DOI:10.1007/s12015-025-10973-x