Animal models of human diseases are critically necessary for developing an in-depth knowledge of disease development and progression. In addition, animal models are vital to the development of potential treatments or even cures for human diseases. Pigs are exceptional models as their size, physiology, and genetics are closer to that of humans than rodents. In this review, we discuss the use of pigs in human translational research and the evolving technology that has increased the efficiency of genetically engineering pigs. With the emergence of the clustered, regularly interspaced, short palindromic repeat (CRISPR)/CRISPR-associated (Cas) protein 9 system technology, the cost and time it takes to genetically engineer pigs has markedly decreased. We will also discuss the use of another meganuclease, the transcription activator-like effector nucleases , to produce pigs with severe combined immunodeficiency by developing targeted modifications of the recombination activating gene 2 (RAG2).RAG2mutant pigs may become excellent animals to facilitate the development of xenotransplantation, regenerative medicine, and tumor biology. The use of pig biomedical models is vital for furthering the knowledge of, and for treating human, diseases.
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http://dx.doi.org/10.1177/0192623315613160 | DOI Listing |
Hum Exp Toxicol
January 2025
Department of Gynecology and Obstetrics, Fuyong People's Hospital, Shenzhen, China.
Gestational diabetes mellitus (GDM) is a metabolic disorder that arises during pregnancy and heightens the risk of placental dysplasia. Ginsenoside Re (Re) may stabilize insulin and glucagon to regulate glucose levels, which may improve diabetes-associated diseases. This study aims to investigate the mechanism of Re in high glucose (HG)-induced apoptosis of trophoblasts through endoplasmic reticulum stress (ERS)-related protein CHOP/GADD153.
View Article and Find Full Text PDFMAGMA
January 2025
Aix Marseille Univ, CNRS, CRMBM, Marseille, France.
Objective: Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness in this field. However, the impact of reducing neural network complexity remains unexplored in the FF quantification in individual muscles.
View Article and Find Full Text PDFCurr Atheroscler Rep
January 2025
Nantes Université, CHU Nantes, CNRS, Inserm, l'institut du thorax, F-44000, Nantes, France.
Purpose Of Review: While lipid-lowering therapies demonstrate efficacy, many patients still contend with significant residual risk of atherosclerotic cardiovascular diseases (ASCVD). The intestine plays a pivotal role in regulating circulating lipoproteins levels, thereby exerting influence on ASCVD pathogenesis. This review underscores recent genetic findings from the last six years that delineate new biological pathways and actors in the intestine which regulate lipid-related ASCVD risk.
View Article and Find Full Text PDFAppl Biochem Biotechnol
January 2025
Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, 575018, Karnataka, India.
Gymnostachyum febrifugum, a less-known ethnomedicinal plant from the Western Ghats of India, is used to treat various diseases and serves as an antioxidant and antibacterial herb. The present study aims to profile the cytotoxic phytochemicals in G. febrifugum roots using GC-MS/MS, in vitro confirmation of cytotoxic potential against breast cancer and an in silico study to understand the mechanism of action.
View Article and Find Full Text PDFArch Orthop Trauma Surg
January 2025
Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, 4710-057, Portugal.
Introduction: Total joint arthroplasties generally achieve good outcomes, but chronic pain and disability are a significant burden after these interventions. Acknowledging relevant risk factors can inform preventive strategies. This study aimed to identify chronic pain profiles 6 months after arthroplasty using the ICD-11 (International Classification of Diseases) classification and to find pre and postsurgical predictors of these profiles.
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