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Personalized Medicine is a novel medical practice that uses an individual's genetic profile to guide decisions made regarding the prevention, diagnosis, and treatment of disease. Knowledge of a patient's genetic profile is crucial to support doctors in selecting the proper therapy and administer it using the correct dose or regimen. Personalized Medicine is a great opportunity to turn the "one size fits all" approach to diagnostics, therapy, and prevention, into an individualized approach. In this paper we analyze the most recent achievements and regulatory challenges in Personalized Medicine and the role that research infrastructures can play in advancing its development.
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http://dx.doi.org/10.1177/03936155231179981 | DOI Listing |
Genome Biol
September 2025
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
Background: Soil salinization represents a critical global challenge to agricultural productivity, profoundly impacting crop yields and threatening food security. Plant salt-responsive is complex and dynamic, making it challenging to fully elucidate salt tolerance mechanism and leading to gaps in our understanding of how plants adapt to and mitigate salt stress.
Results: Here, we conduct high-resolution time-series transcriptomic and metabolomic profiling of the extremely salt-tolerant maize inbred line, HLZY, and the salt-sensitive elite line, JI853.
Stem Cell Rev Rep
September 2025
Department of Medical Genetics and Prenatal Diagnostics, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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.
View Article and Find Full Text PDFGeroscience
September 2025
Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
This study aims to investigate the predictive value of combined phenotypic age and phenotypic age acceleration (PhenoAgeAccel) for benign prostatic hyperplasia (BPH) and develop a machine learning-based risk prediction model to inform precision prevention and clinical management strategies. The study analyzed data from 784 male participants in the US National Health and Nutrition Examination Survey (NHANES, 2001-2008). Phenotypic age was derived from chronological age and nine serum biomarkers.
View Article and Find Full Text PDFClin Transl Oncol
September 2025
Department of Radiation Oncology, Vithas La Milagrosa University Hospital, Madrid, 28010, Spain.
This narrative review analyzes current evidence comparing single-session and two-session approaches in Stereotactic Body Radiation Therapy (SBRT) and high-dose-rate (HDR) brachytherapy for localized prostate cancer. These ultra-hypofractionated strategies deliver high-precision ablative doses while minimizing exposure to normal tissues. SBRT regimens with fewer than five fractions show tumor control comparable to conventional treatments, offering reduced treatment burden and increased convenience.
View Article and Find Full Text PDFBariatric surgery is an effective treatment for morbid obesity, but patient outcomes differ greatly because of a variety of phenotypes, comorbidities, and postoperative adherence. In bariatric care, artificial intelligence (AI) and machine learning (ML) are becoming revolutionary tools because traditional predictive models based on BMI and demographic variables are unable to account for these complexities. To put it simply, AI is a branch of computer science that enables machines to perform tasks that typically require human intelligence.
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