Three-Dimensional Bioprinted Skin Microrelief and Its Role in Skin Aging

Skin aging is a complex physiological process, in which cells and the extracellular matrix (ECM) interreact, which leads to a change in the mechanical properties of skin, which in turn affects the cell secretion and ECM deposition. The natural skin microrelief that exists from birth has rarely been taken into account when evaluating skin aging, apart from the common knowledge that microreliefs might serve as the starting point or initialize micro-wrinkles. In fact, microrelief itself also changes with aging. Does the microrelief have other, better uses? In this paper, owing to the fast-developing 3D printing technology, skin wrinkles with microrelief of different age groups were successfully manufactured using the Digital light processing (DLP) technology. The mechanical properties of skin samples with and without microrelief were tested. It was found that microrelief has a big impact on the elastic modulus of skin samples. In order to explore the role of microrelief in skin aging, the wrinkle formation was numerically analyzed. The microrelief models of different age groups were created using the modified Voronoi algorithm for the first time, which offers fast and flexible mesh formation. We found that skin microrelief plays an important role in regulating the modulus of the epidermis, which is the dominant factor in wrinkle formation. The wrinkle length and depth were also analyzed numerically for the first time, owing to the additional dimension offered by microrelief. The results showed that wrinkles are mainly caused by the modulus change of the epidermis in the aging process, and compared with the dermis, the hypodermis is irrelevant to wrinkling. Hereby, we developed a hypothesis that microrelief makes the skin adaptive to the mechanical property changes from aging by adjusting its shape and size. The native-like skin samples with microrelief might shed a light on the mechanism of wrinkling and also help with understanding the complex physiological processes associated with human skin.

Objective and quantitative measurement of skin micro-relief by image analysis and application in age-dependent changes

BACKGROUND

Skin micro-relief has been researched by a variety of devices and methods, which usually are expensive or complicated. On the other hand, skin micro-relief relates to quite a few parameters, and it is hard to evaluate all of them at the same time. In the study, all parameters related to skin micro-relief are extracted and evaluated by image analysis.

MATERIALS AND METHODS

Skin micro-relief evaluation was divided into four aspects: (a) Tamura features method was used to evaluate skin surface. (b) Morphological transform was applied to extract skin pores. (c) Watershed transform was applied to extract skin furrows. (d) labeling operation was used to evaluate the number, area and average area of skin closed polygons. Then, cheek images from 163 healthy Japanese females (0-70 years old) are analyzed to explore the age-dependent changes.

RESULTS

Most parameters increased as age went on with significant differences, such as skin surface coarseness, contrast, skin pore number, area, average area, skin furrow width, skin closed polygon area and skin closed polygon average area. Skin coarseness has a strong correlation with pore area.

CONCLUSION

The method proposed in the study provided a comprehensive and effective assessment of skin micro-relief.

Skin Aging Estimation Scheme Based on Lifestyle and Dermoscopy Image Analysis

Besides genetic characteristics, people also undergo a process of skin aging under the influence of diverse factors such as sun exposure, food intake, sleeping patterns, and drinking habits, which are closely related to their personal lifestyle. So far, many studies have been conducted to analyze skin conditions quantitatively. However, to describe the current skin condition or predict future skin aging effectively, we need to understand the correlation between skin aging and lifestyle. In this study, we first demonstrate how to trace people’s skin condition accurately using scale-invariant feature transform and the color histogram intersection method. Then, we show how to estimate skin texture aging depending on the lifestyle by considering various features from face, neck, and hand dermoscopy images. Lastly, we describe how to predict future skin conditions in terms of skin texture features. Based on the Pearson correlation, we describe the correlation between skin aging and lifestyle, and estimate skin aging according to lifestyle using the polynomial regression and support vector regression models. We evaluate the performance of our proposed scheme through various experiments.