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► Journal BrowserSpecial Issue "Fractals in Aging and Disease"
A special issue of Fractal and Fractional (ISSN 2504-3110). This special issue belongs to the section "Life Science, Biophysics".
Deadline for manuscript submissions: 28 February 2022.
Special Issue Editors
Interests: biocomplexity; chaos; fractals in aging and disease
Special Issue Information
Dear Colleagues,
One major concern in biomedicine is that of aging well. In fact, the elderly population is increasing worldwide because of extended life expectancy. Only a little percentage, however, is given to growing old with a good health status as most individuals experience pathological aging.
The variability observed in the senescent phenotype can be explained in the light of the theory of complexity by considering longevity as a “secondary product” of evolution of a dynamic nonlinear system. Individual specific gene–environment interactions define the senescent phenotype as normal aging, pathological aging, or successful aging.
In light of this holistic point of view, paradigms such as biocomplexity, chaos, and fractals, suggest new approaches and provide new tools to give insight into the search of good biomarkers that are able to discriminate between aging and disease(s), as well as between physiological and pathological aging—two main tasks that deal with aging well.
It has been documented that biocomplexity generally decreases with aging, while it can increase or decrease with pathology depending on the type of disease and/or the stage of its progression. Fractal analysis has been emerging as a suitable tool to quantify changes of complexity in biosystems.
This Special Issue welcomes any type of article (research article, review, short communication, commentary, etc.) in the field of biocomplexity, chaos, and fractality in biomedicine from molecular to whole population level with perspective application for aging well.
Topics that are invited for submission include (but are not limited to):
- Quantitative characterization of space and time biomedical signals (both structural and functional) by fractals in aging and/or disease;
- Fractal properties (fractal dimension, lacunarity, succolarity) of biomedical functions and structures as indices of aging processes and/or disease etiopathogenesis;
- Fractal and multifractal analyses in biomedical field as potential tools for early diagnosis of disease onset and progression and for treatment assessment.
Dr. Annamaria Zaia
Dr. Pierluigi Maponi
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fractal and Fractional is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- aging
- biocomplexity
- fractals in biomedicine
- disease
- biomedical image quantification
- biomathematical modeling
- biomarker