As the outermost layer of the body, the skin has many functions to maintain constant conditions in response to variable external factors. Despite these functions, the skin may be altered by various environmental factors such as temperature and humidity. In particular, skin barrier recovery is reduced when relative humidity is high. 1 According to Tsukahara et al, 2 in dry environments, skin elasticity is decreased and wrinkles form easily. Several studies have reported that sudden changes in humidity can cause abnormalities in the skin

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Objective Lip skin dryness and chapping are major concerns related to lip skin care in many populations. The distinctive features of lip skin, such as the low water‐holding capacity and weak skin barrier, are strongly associated with these problems; however, few studies have examined lip skin characteristics and the mechanisms underlying these issues. This study was conducted to identify the biophysical properties of dry lip skin and molecular targets affecting lip skin physiology. Methods Skin hydration, transepidermal water loss and lip skin scaling were evaluated in 40 female subjects. Skin scaling was assessed as a percentage area divided into five categories (G0, G1, G2, G3 and G4) according to the thickness level of tape‐stripped corneocytes. The activities and amounts of proteases, cathepsin D and bleomycin hydrolase were measured as markers for the desquamation process and skin hydration, respectively. Results Skin hydration showed a significantly positive correlation with the percentage area of evenly thin corneocytes (G0) and negative correlations with the percentage areas of slightly thick to severely thick corneocytes (G1‐G4). The corneocyte unevenness ratio (CUR) was calculated by dividing the sum of the G1, G2, G3 and G4 values with the G0 value. The CUR was significantly negatively correlated with skin hydration, suggesting that CUR is a new parameter representing the severity of lip scaling. Subjects with lower hydration and higher CUR had higher bleomycin hydrolase activity and lower cathepsin D activity, respectively, than subjects with higher hydration and lower CUR. Conclusion Our study revealed a correlation between lip skin hydration and severity of lip scaling and verified the association of protease activity with the hydration and chapping state of lip skin. These observations provide a basis for further studies of the persistent problem of lip skin dryness and chapping.

Background The accumulation of advanced glycation end products has been proposed as a causative agent of skin aging, but there are no conventional devices for quantifying advanced glycation end‐product accumulation in facial skin. Aims This study aimed to develop a convenient and accurate in situ advanced glycation end‐product measurement system for the human face. Methods We developed a facial glycation imaging system, which consisted of illumination (white light‐emitting diode, ultraviolet light‐emitting diode) and image acquisition modules to capture face images. Advanced glycation end product–related autofluorescence and total skin reflectance were calculated to obtain the skin glycation index using an image analysis algorithm. Correlations between the skin glycation index and facial skin elasticity and age were examined in 36 healthy Korean women. Results The facial glycation imaging system was validated against a volar forearm skin autofluorescence measurement device, that is, the AGE Reader mu, with forearm skin glycation index (R = 0.64, P < .01). Cheek elasticity was negatively correlated with cheek skin glycation index (R = −0.56, R = −0.57, and R = −0.61, P < .01 for R2, R5, and R7, respectively). Age was significantly correlated with forearm skin glycation index (R = 0.44, P < .01) and cheek skin glycation index (R = 0.48, P < .01). Conclusion We successfully developed a novel in situ facial skin glycation index measurement device. Our convenient and accurate system enables in situ skin glycation index monitoring for skin aging studies such as those on anti‐glycation cosmetics.

Human skin is constantly exposed to exogenous substances and microbial pathogens. 1 To prevent infection by invading pathogens and commensal bacteria, human skin provides a chemical barrier through antimicrobial peptides (AMPs) such as human β-defensin (hBD)-1 and -2. 2 AMPs are important evolutionarily conserved innate host defense mechanisms in many organisms. hBD-1 and -2 are cationic low-molecular-weight cysteine-rich AMPs that are expressed in epithelial tissues. 3,4 These AMPs are produced in epidermal keratinocytes and form a chemical barrier against the invasion of pathogens, mainly during skin barrier repair, with further studies on the expression of AMPs actively underway. 5 To measure AMP concentration on human skin, both the skin rinsing and tape stripping (TS) methods have been well established. 6,7 Both methods involve the collection of skin-derived AMPs by rinsing

Background Skin pores are structural features of the skin, which tend to change as the skin ages. Since previous studies measured pores two‐dimensionally, precise measurements using three‐dimensional imaging were needed to comprehensively understand skin pores. This study aimed to determine the patterns behind the changes in skin pores during one's lifetime and to identify new characteristics of the pores in aged. Materials and Methods Skin surface profiles were measured three‐dimensionally from the cheeks of 101 Korean women from February to March 2020 to analyze the exact state of their pores. The researchers performed K‐means clustering to classify the skin pores, and topographical features of pores were analyzed as well. Statistical analyses were performed to verify the differences in the skin pore characteristics among clusters and the correlation between clusters and ages. Results Skin pores were classified into five groups based on size, density, and elongation. The skin conditions of the cluster groups were well correlated with aging, despite excluding age as a factor in pore classification. Adjacent skin pores tend to connect in the elderly. Conclusion Skin pores become larger and longer over time. Skin pores connect together in the elderly, which might be related to wrinkle formation. This phenomenon strongly suggests skin pores as a characteristic of aging skin and as a potential target for anti‐aging treatment.

We report a prediction model for sunscreen sun protection factor (SPF) and protection grade of ultraviolet (UV) A (PA) based on machine learning. We illustrate with real clinical test results of UV protection ability of sunscreen for SPF and PA. With approximately 2200 individual clinical results for both SPF and PA level detection, individually, we were able to see that active ingredient information can provide accurate SPF and PA prediction rates through machine learning. Furthermore, we included four new factors—presence of pigment, concentration of pigment grade titanium dioxide, type of formulation and type of product—as additional information for the prediction model and were able to see increased prediction rates as results.

Background Skin assessment methodologies have focused mainly on intuitive aging characteristics, including facial wrinkles and pigmented spots, and usually adopt pattern recognition algorithms. Recently, distinct methods of interpreting skin aging, such as the detection of facial landmarks and age prediction using machine learning techniques, have been conducted. Materials and Methods We defined two indices that represent the severity of facial aging. The first index was the ratio of the bizygomatic distance and bigonial distance. The second index was the ratio of the degrees of the near mandible. The indices extracted from two‐dimensional frontal face images were intended to show the deformation of the facial skin downward with aging progress. To validate whether these proposed indicators can represent facial aging, we conducted correlation tests with age and facial skin characteristics and performed association tests between the indices and facial skin characteristics, adjusted for age. Results The indices showed strong correlations with age (r = 0.557 and 0.464, respectively) and facial skin characteristics. Although there were correlations between the indices and facial skin features, the associations between the indices and facial skin characteristics adjusted for age were weak or not significant. This suggests that the newly developed indices are appropriate for evaluating facial skin aging and distinct from typical measurements. Conclusion We suggest two novel indices for evaluating facial aging based on frontal face images. The indices exhibited strong correlations with age and representative facial skin characteristics. The newly developed values can be differentiated indicators of facial aging compared with general skin features.