Optical Non-Invasive Approaches to Diagnosis of Skin Diseases

A number of noninvasive approaches have been developed over the years to provide objective evaluation of the skin both in health and in disease. The advent of computers, as well as of lasers and photonics, has made it possible to develop additional techniques that were impossible a few years ago. These approaches provide the dermatologist with sensitive tools to measure the skin's condition in terms of physiologic parameters (e.g., color, erythema and pigmentation, induration, sebaceous and stratum corneum lipids, barrier function, etc.). Yet, a typical dermatologic diagnosis relies primarily on the trained eyes of the physician and to a lesser extent on information from other senses, such as touch and smell. The trained senses of the dermatologist backed by his/her brain form a powerful set of tools for evaluating the skin. The golden rule in diagnosis remains the histologic examination of a skin biopsy, a rather invasive method. These tools have served the profession well. The advent of ever faster and cheaper computers and of sensitive, inexpensive optical instrumentation of minimal dimensions provides the professional with the possibility of making objective measures of a number of skin parameters

Diversity of the Human Skin Microbiome Early in Life

Within days after birth, rapid surface colonization of infant skin coincides with significant functional changes. Gradual maturation of skin function, structure, and composition continues throughout the first years of life. Recent reports have revealed topographical and temporal variations in the adult skin microbiome. Here we address the question of how the human skin microbiome develops early in life. We show that the composition of cutaneous microbial communities evolves over the first year of life, showing increasing diversity with age. Although early colonization is dominated by Staphylococci, their significant decline contributes to increased population evenness by the end of the first year. Similar to what has been shown in adults, the composition of infant skin microflora appears to be site specific. In contrast to adults, we find that Firmicutes predominate on infant skin. Timely and proper establishment of healthy skin microbiome during this early period might have a pivotal role in denying access to potentially infectious microbes and could affect microbiome composition and stability extending into adulthood. Bacterial communities contribute to the establishment of cutaneous homeostasis and modulate inflammatory responses. Early microbial colonization is therefore expected to critically affect the development of the skin immune function

Resistance to Water Diffusion in the Stratum Corneum Is Depth-Dependent

<div><p>The stratum corneum (SC) provides a permeability barrier that limits the inflow and outflow of water. The permeability barrier is continuously and dynamically formed, maintained, and degraded along the depth, from the bottom to the top, of the SC. Naturally, its functioning and structure also change dynamically in a depth-dependent manner. While transepidermal water loss is typically used to assess the function of the SC barrier, it fails to provide any information about the dynamic mechanisms that are responsible for the depth-dependent characteristics of the permeability barrier. This paper aims to quantitatively characterize the depth-dependency of the permeability barrier using <i>in vivo</i> non-invasive measurement data for understanding the underlying mechanisms for barrier formation, maintenance, and degradation. As a framework to combine existing experimental data, we propose a mathematical model of the SC, consisting of multiple compartments, to explicitly address and investigate the depth-dependency of the SC permeability barrier. Using this mathematical model, we derive a measure of the water permeability barrier, i.e. resistance to water diffusion in the SC, from the measurement data on transepidermal water loss and water concentration profiles measured non-invasively by Raman spectroscopy. The derived resistance profiles effectively characterize the depth-dependency of the permeability barrier, with three distinct regions corresponding to formation, maintenance, and degradation of the barrier. Quantitative characterization of the obtained resistance profiles allows us to compare and evaluate the permeability barrier of skin with different morphology and physiology (infants vs adults, different skin sites, before and after application of oils) and elucidates differences in underlying mechanisms of processing barriers. The resistance profiles were further used to predict the spatial-temporal effects of skin treatments by <i>in silico</i> experiments, in terms of spatial-temporal dynamics of percutaneous water penetration.</p></div

Results of <i>in silico</i> absorption-desorption experiment with water applied topically for 10 seconds (time = 10–20 s).

<p>(<b>a</b>) Temporal dynamics of total water content (change from steady state) for adults; simulation results (solid line) and experimental data (diamonds) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117292#pone.0117292.ref017" target="_blank">17</a>]. (<b>b</b>) Spatial-temporal dynamics of water concentration before and 30 minutes after topical application of petrolatum. (<b>c</b>) Spatial-temporal increase in water concentration after application of petrolatum. Negative values (time = 10–20 s) and positive values (time = 20–50 s) respectively indicate slower absorption and desorption after application of petrolatum.</p

Resistance profiles (means) before and 30 minutes after topical application of different oils.

<p>(<b>a</b>) almond oil (<i>n</i> = 92), (<b>b</b>) jojoba oil (<i>n</i> = 98), (<b>c</b>) paraffin oil (<i>n</i> = 99), and (<b>d</b>) petrolatum (<i>n</i> = 86).</p

Comparison of permeability barrier for different types of skin.

<p>(<b>a, d</b>) Water concentration profiles (means +/- SD), (<b>b, e</b>) resistance profiles (means), and (<b>c, f</b>) quantitative indices for resistance profiles (means, with significance * p<0.05, ** p<0.01, *** p<0.001) for (<b>a-c</b>) volar forearm of 12 infants (3–12 months) and 12 adults (14–73 years) and (<b>d-f</b>) face (cheek), exposed arm (dorsal forearm), and protected (upper inner) arm of 20 adults (18–70 years).</p