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Digital Representation of Patients as Medical Digital Twins: Data-Centric Viewpoint.

JMIR Med Inform

January 2025

INSERM U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 30 Bd Jean Monnet, Nantes, 44093, France, 33 2 40 08 74 10.

Precision medicine involves a paradigm shift toward personalized data-driven clinical decisions. The concept of a medical "digital twin" has recently become popular to designate digital representations of patients as a support for a wide range of data science applications. However, the concept is ambiguous when it comes to practical implementations.

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Background: Comprehensive clinical data regarding factors influencing the individual disease course of patients with movement disorders treated with deep brain stimulation might help to better understand disease progression and to develop individualized treatment approaches.

Methods: The clinical core data set was developed by a multidisciplinary working group within the German transregional collaborative research network ReTune. The development followed standardized methodology comprising review of available evidence, a consensus process and performance of the first phase of the study.

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Advancing cancer therapy with custom-built alternating electric field devices.

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January 2025

School of Pharmacy, Biodiscovery Institute & Boots Science Building, University of Nottingham, Nottingham, NG7 2RD, UK.

Background: In glioblastoma (GBM) therapy research, tumour treating fields by the company Novocure™, have shown promise for increasing patient overall survival. When used with the chemotherapeutic agent temozolomide, they extend median survival by five months. However, there is a space to design alternative systems that will be amenable for wider use in current research.

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AiGPro: a multi-tasks model for profiling of GPCRs for agonist and antagonist.

J Cheminform

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School of Systems Biomedical Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, 06978, Seoul, Republic of Korea.

G protein-coupled receptors (GPCRs) play vital roles in various physiological processes, making them attractive drug discovery targets. Meanwhile, deep learning techniques have revolutionized drug discovery by facilitating efficient tools for expediting the identification and optimization of ligands. However, existing models for the GPCRs often focus on single-target or a small subset of GPCRs or employ binary classification, constraining their applicability for high throughput virtual screening.

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Background: Primary school students struggling with mental health are less likely than high school students to access mental health care, due to barriers such as mental health stigma and low mental health literacy among children and parents. The near universal reach of schools offers a potential avenue to increase access to mental health care through early identification. The potential risks of this approach also need to be understood.

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