Interference and co-existence of staphylococci and Cutibacterium acnes within the healthy human skin microbiome.

Human skin is populated by trillions of microbes collectively called the skin microbiome. Staphylococcus epidermidis and Cutibacterium acnes are among the most abundant members of this ecosystem, with described roles in skin health and disease. However, knowledge regarding the health beneficial effects of these ubiquitous skin residents is still limited.

Macrophages Are Polarized toward an Inflammatory Phenotype by their Aged Microenvironment in the Human Skin.

Aging of the skin is accompanied by cellular as well as tissue environmental changes, ultimately reducing the ability of the tissue to regenerate and adequately respond to external stressors. Macrophages are important gatekeepers of tissue homeostasis, and it has been reported that their number and phenotype change during aging in a site-specific manner. How aging affects human skin macrophages and what implications this has for the aging process in the tissue are still not fully understood.

Single-Cell RNA Profiling of Human Skin Reveals Age-Related Loss of Dermal Sheath Cells and Their Contribution to a Juvenile Phenotype.

The dermal sheath (DS) is a population of mesenchyme-derived skin cells with emerging importance for skin homeostasis. The DS includes hair follicle dermal stem cells, which exhibit self-renewal and serve as bipotent progenitors of dermal papilla (DP) cells and DS cells. Upon aging, stem cells exhibit deficiencies in self-renewal and their number is reduced.

Evaluation of Immunomodulatory Responses and Changed Wound Healing in Type 2 Diabetes-A Study Exploiting Dermal Fibroblasts from Diabetic and Non-Diabetic Human Donors.

The dermis is the connective layer between the epidermis and subcutis and harbours nerve endings, glands, blood vessels, and hair follicles. The most abundant cell type is the fibroblast. Dermal fibroblasts have a versatile portfolio of functions within the dermis that correspond with different types of cells by either direct contact or by autocrine and paracrine signalling.

Modeling transcriptomic age using knowledge-primed artificial neural networks.

The development of 'age clocks', machine learning models predicting age from biological data, has been a major milestone in the search for reliable markers of biological age and has since become an invaluable tool in aging research. However, beyond their unquestionable utility, current clocks offer little insight into the molecular biological processes driving aging, and their inner workings often remain non-transparent. Here we propose a new type of age clock, one that couples predictivity with interpretability of the underlying biology, achieved through the incorporation of prior knowledge into the model design.