Mechanisms and Models of Comorbidities in Cancer Progression and Treatments
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".
Deadline for manuscript submissions: closed (1 September 2023) | Viewed by 1787
Special Issue Editors
Interests: cancer progression and treatment; inflammation; immune response; comorbidity; digital twins; computational disease modelling; mathematical modelling of diseases
Interests: mathematical models of evolutionary processes; cancer evolution; resistance evolution; infection dynamics
2. Mathematics and Physics (IMFUFA), Department of Science and Environment, Roskilde University, 4000 Roskilde, Denmark
Interests: mathematical modeling of hematopoiesis during health and disease and the coupling to the immune system; vortex dynamics
Special Issue Information
Dear Colleagues,
Recently, there have been an increasing focus on the role of inflammation in cancer progression and treatment. Most cancer is accompanied by an immune response and inflammation is known to be a trigger of and an accelerator for most cancer progression. Such inflammation may be imposed by environmental and lifestyle factors (stress, pollution, or smoking), bacterial or virus infections (HPV, COVID-19), or secondary diseases, such as bowel diseases (Morbus Crohn, arthritis). Thus, cancers, inflammation, and comorbidities constitute a severe disease complication triangle (DCT), which is considered vital.
The approach to study the couplings of the DCT is manyfold, however, a combined effort between clinicians, engineers, applied mathematicians, and many more disciplines are considered necessary to obtain a significant better understanding of the DCT problem.
In this Special Issue, we welcome new ideas, approaches, and methods for studying DCT preferable but not exclusively on physiological mechanisms using in silico methods.
Prof. Dr. Johnny T. Ottesen
Prof. Dr. Dominik Wodarz
Dr. Morten Andersen
Guest Editor
Manuscript Submission Information
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Keywords
- cancer progression and treatment
- inflammation
- immune response
- comorbidity
- digital twins
- computational disease modelling
- mathematical modelling of diseases
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