New Advances in Metabolic Imaging
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Global Health".
Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 329
Special Issue Editor
Interests: metabolic imaging; confocal microscopy; artificial intelligence; machine-learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The study of alterations in metabolic pathways for the assessment of various clinical situations is the focus of metabolic imaging, a discipline that, by combining molecular biology with in vivo imaging techniques, allows the visualization of endogenous molecules and supramolecular properties, thus providing a true window on all metabolic processes essential for cell survival. Since metabolic pathways are characterized by a strong interconnection, any perturbation in one node of metabolism causes a global response, and it is, therefore, reasonable to hypothesize that the onset of any pathology may be associated with a specific metabolic fingerprint, which can both provide a potential biomarker for the early detection of the disease, as well as a powerful target for therapeutic approaches. In this perspective, targeting and measuring metabolic alterations noninvasively constitutes a potent method for facilitating diagnosis and assessing therapy response, thus making advances in metabolic imaging crucial to holistically understand the metabolism of living systems and to bridge in vitro models and clinical applications.
I would like to invite you to contribute papers addressing the aforementioned related aspects for this Special Issue of the International Journal of Environmental Research and Public Health (IJERPH), which aims to bring together the newest and latest advances that have been made in the field of metabolic imaging, in terms of both image acquisition and data analysis, with a particular interest in AI-driven approaches for the prediction and the establishment of predictive models that can provide support in the early diagnosis as well as in the long-term management of metabolic-related diseases.
Dr. Giada Bianchetti
Guest Editor
Manuscript Submission Information
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Keywords
- metabolic imaging
- metabolic modeling
- diabetes
- tumors
- imaging techniques
- image analysis
- machine-learning
- artificial intelligence
- predictive models
- translational medicine
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