Biomarker and Translational Research in Oncology and Liver Diseases
A special issue of International Journal of Translational Medicine (ISSN 2673-8937).
Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 14309
Special Issue Editor
Interests: drug development; biomarker and translational research in oncology and liver diseases; cellular biology of cell death and epigenetics
Special Issue Information
Dear Colleagues,
Chronic liver diseases are a global medical burden. With the steep rise of non-alcoholic fatty liver disease (NAFLD) and subsequent steatohepatitis (NASH), the incidence and prevalence of fibrotic and end-stage liver disease and of hepatocellular carcinoma (HCC) are also expected to rise in the coming years. While some progress was made in identifying and validating diagnostic biomarkers, only a few advances have been achieved on predictive biomarkers that could also lead to innovative and novel treatment approaches. The focus on the inflammatory microenvironment in NASH, cirrhosis and HCC has further increased the complexity since novel approaches beyond T-cell targeting are maturing now. This also requires novel designs for clinical trials and the strong need to thoroughly characterize the disease and disease-modulating pathways in translational clinical and preclinical research and studies. It is important to highlight that translational studies go beyond the analysis of biomarkers but do also comprise bioinformatics, scheduling and clinical pharmacology applications.
In this Special Issue, we aim to summarize the current status and challenges of translational and biomarker research in preclinical and clinical research of liver diseases, including NAFLD, NASH, cirrhosis and HCC. We especially welcome papers focusing on artificial intelligence and translational approaches like modelling, molecular imaging or liquid biopsy technologies.
Prof. Dr. Matthias Ocker
Guest Editor
Manuscript Submission Information
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Keywords
- NAFLD
- NASH
- HCC
- cirrhosis
- fibrosis
- liver cancer
- biomarker
- artificial intelligence
- machine learning
- bioinformatics
- clinical pharmacology
- modelling