OBM Geriatrics

(ISSN 2638-1311)

OBM Geriatrics is an Open Access journal published quarterly online by LIDSEN Publishing Inc. The journal takes the premise that innovative approaches – including gene therapy, cell therapy, and epigenetic modulation – will result in clinical interventions that alter the fundamental pathology and the clinical course of age-related human diseases. We will give strong preference to papers that emphasize an alteration (or a potential alteration) in the fundamental disease course of Alzheimer’s disease, vascular aging diseases, osteoarthritis, osteoporosis, skin aging, immune senescence, and other age-related diseases.

Geriatric medicine is now entering a unique point in history, where the focus will no longer be on palliative, ameliorative, or social aspects of care for age-related disease, but will be capable of stopping, preventing, and reversing major disease constellations that have heretofore been entirely resistant to interventions based on “small molecular” pharmacological approaches. With the changing emphasis from genetic to epigenetic understandings of pathology (including telomere biology), with the use of gene delivery systems (including viral delivery systems), and with the use of cell-based therapies (including stem cell therapies), a fatalistic view of age-related disease is no longer a reasonable clinical default nor an appropriate clinical research paradigm.

Precedence will be given to papers describing fundamental interventions, including interventions that affect cell senescence, patterns of gene expression, telomere biology, stem cell biology, and other innovative, 21st century interventions, especially if the focus is on clinical applications, ongoing clinical trials, or animal trials preparatory to phase 1 human clinical trials.

Papers must be clear and concise, but detailed data is strongly encouraged. The journal publishes research articles, reviews, communications and technical notes. There is no restriction on the length of the papers and we encourage scientists to publish their results in as much detail as possible.

Archiving: full-text archived in CLOCKSS.

Rapid publication: manuscripts are undertaken in 12 days from acceptance to publication (median values for papers published in this journal in 2021, 1-2 days of FREE language polishing time is also included in this period). 

Current Issue: 2023  Archive: 2022 2021 2020 2019 2018 2017

Special Issue

Utilizing Big Data to Elucidate Skin Aging [Big Data in Skin Aging]

Submission Deadline: April 15, 2023 (Open) Submit Now

Guest Editor

Raya Khanin, PhD, Associate Professor

LifeNome Inc., New York, NY, USA

Website | E-Mail

Research Interests: Computational genomics; Translational bioinformatics; Precision medicine

About this Topic:

After several decades of studying skin aging, many molecular processes have been elucidated, but many still remain a mystery. Understanding processes underlying skin aging is important for various reasons. Firstly, skin aging is the most recognizable and often the most psychologically difficult aspect of aging. At the same time, skin aging is potentially also one of the most amenable to intervention, prevention and rejuvenation. Gigantic cosmetic, aesthetic and pharmaceutical industries focus on anti-aging therapies to reverse aging. Finally, skin aging can be regarded as an ideal model for studying tissue aging.

Wide acceptance of genomic technologies from DNA genotyping to RNA sequencing, metabolomics, proteome profiling and metagenomics, brought about a new era in Life Sciences, and Medicine in general. These technologies have been increasingly used to study skin aging, effect of the environment on skin conditions, and efficacy of ingredients. Moreover, an enormous number of images from professional to selfies enable integration of different data types to zoom into biology underlying facial and hair aging processes, leading to personalized or precision approaches in beauty.

The current issue solicits research studies that use biological high-throughput data, image analysis and other big data to (i) elucidate skin aging processes (ii) identify biomarkers of skin aging (iii) discover novel skincare ingredients, dermatology approaches, or novel applications (iv) determine genetic and other biological factors for different types of skin aging, and skincare ingredient efficacy and (v) paving the way to personalized anti-aging approaches.