METHODS: The reflectance spectra of the left forearms of participants were analysed by the pigmentation analysis system to determine their skin parameters--pheomelanin and eumelanin concentrations, melanosome volume fraction, and epidermal thickness. The determined skin parameters are then inputted into the realistic skin model (RSM) of the Advanced Systems Analyses Program (asap®) to generate the ground truth reflectance spectra for the given skin parameters to validate the system.
RESULTS: The developed pigmentation analysis system is found to be accurate with a spectral error of 0.0163 ± 0.009 between measured reflectance and the reflectance output of the analysis system and RSM. The regression analysis shows a strong linear relationship (R(2) = 0.994) indicating good precision. The relative concentrations of pheomelanin (38.23 ± 15.04) and eumelanin (1.68 ± 0.91) analysed by the system gives a ratio of pheomelanin to eumelanin of 0.048 ± 0.029; this value is consistent with previously reported figure of 0.049.
CONCLUSION: The proposed pigmentation analysis system is able to determine melanin types and their relative concentrations. It has the potential to assess the efficacy of the skin-whitening and pigmentation treatments objectively in a non-invasive manner.
METHODS: Text ads from Google searches in eight countries (Bahamas, Germany, India, Malaysia, Mexico, South Africa, United Arab Emirates, and United States) were collected in 2022, totaling 1,974 prepolicy and 3,262 post-policy ads, and analyzed in 2023. A gold standard database was established by two coders who labeled 707 ads, which trained five natural language processing models to label the ads, covering content and target demographics. The descriptive statistics and multivariable logistic models were applied to analyze content before versus after policy implementation, both globally and by country.
RESULTS: Vertex AI emerged as the best natural language processing model with the highest F1 score of 0.87. There were significant decreases from pre- to post-policy implementation in the prevalence of labels of "Racial or Ethnic Identification" and "Ingredients: Natural" by 47% and 66%, respectively. Notable differences were identified from pre- to post-policy implementation in India, Mexico, and Germany.
CONCLUSIONS: The study observed changes in skin-lightening product advertisement labels from pre- to post-policy implementation, both globally and within countries. Considering the influence of digital advertising on colorist norms, assessing digital ad policy changes is crucial for public health surveillance. This study presents a computational method to help monitor digital platform policies for consumer product advertisements that affect public health.
METHODS: In this cross-sectional study, we performed a broad assessment of forty-one signs of skin ageing and characterised the phenotypes that constituted key components of skin ageing. We also explored the interrelationship among forty-one skin ageing phenotypes using Spearman's Correlation and Principal Component Analysis.
RESULTS: We analysed our study population, which is composed of 3281 ethnic Chinese participants from the Singapore/Malaysia Cross-sectional Genetics Epidemiology Study (SMCGES). The first ten principal components cumulatively explain 46.88% of the variance of skin ageing phenotypes in our study population. We discovered that the commonly discussed forms of skin ageing (i.e., wrinkles, pigmentation, and photo-ageing) only accounted for a small portion (24.39%) of the variance of all skin ageing phenotypes in our study population. Telangiectasia, a poor lip fullness, a lighter skin colour, xerosis, ephelides (freckles), ptosis of eyelids (droopy eyelids), eyebags, and a low eyebrow positioning were other key components of skin ageing, accounting for a further 22.49% of the variance of skin ageing phenotypes in our study population. We found that each of these ten skin ageing phenotypes characterises a key and important aspect of skin ageing. In this broad assessment of skin ageing, we first described the prevalence of forty-one signs of skin ageing and then characterised in detail both the prevalence and severity distribution of ten key skin ageing phenotypes.
CONCLUSIONS: We presented clear evidence that skin ageing is much more than just wrinkles, pigmentation and photo-ageing. The addition of telangiectasia, poor lip fullness, a lighter skin colour, xerosis, ephelides, ptosis of eyelids, eyebags, and a low eyebrow positioning added more dimensions to skin ageing phenotype presentations.
METHODS: The study investigated the impact of characterized UC-MSC-sEVs on various aspects including the proliferation, migration, antioxidant activity, and ECM gene expression of human dermal fibroblasts (HDF). Additionally, the effects of UC-MSC-sEVs on the proliferation, melanin content, and tyrosinase (TYR) activity of human melanoma cells (MNT-1) were examined. Furthermore, ex vivo models were employed to evaluate the skin permeation of PKH26-labelled UC-MSC-sEVs.
RESULTS: The findings indicated that a high concentration of UC-MSC-sEVs positively influenced the proliferation of HDF. However, no changes in cell migration rate were observed. While the expressions of collagen type 1 and type 3 remained unaffected by UC-MSC-sEVs treatment, there were dose-dependent increases in the gene expressions of fibronectin, matrix metallopeptidase (MMP) 1, and MMP 3. Furthermore, UC-MSC-sEVs treatment did not impact the antioxidative superoxide dismutase (SOD) expression in HDF. Although UC-MSC-sEVs did not alter the proliferation of MNT-1 cells, it did result in a dose-dependent reduction in melanin synthesis without affecting TYR activity. However, when it was applied topically, UC-MSC-sEVs failed to penetrate the skin barrier and remained localized within the stratum corneum layer even after 18 hours.
CONCLUSION: These results highlight the potential of UC-MSC-sEVs in stimulating HDF proliferation, regulating ECM synthesis, and reducing melanin production. This demonstrates the promising application of UC-MSC-sEVs in medical aesthetics for benefits such as scar reduction, skin rejuvenation, and skin lightening.
METHODS: A cross-sectional study was performed. MED was determined using a handheld device with wavelength of 311 nm. Vitiligo severity was measured using the Vitiligo Area Scoring Index (VASI) while disease activity was assessed by the Vitiligo Disease Activity (VIDA). Sun exposure was quantified by the Sun Exposure Index (SEI).
RESULTS: A total of 36 patients (mean age of 49.06 ± 21.29 years old) participated with 12 patients from each skin phototype. MEDs of skin affected by vitiligo were 255 ± 120.3, 280 ± 93.4, and 525 ± 226.1 mJ/cm2 for phototypes III, IV, and V respectively. The MEDs of unaffected skin were 465 ± 120.3, 580 ± 123.6, and 1150 ± 116.8 mJ/cm2 for phototypes III, IV, and V respectively. The MEDs for skin affected by vitiligo were 45%, 52%, and 54% less than the MEDs of unaffected skin for phototypes III, IV, and V respectively. The differences in MEDs between vitiligo-affected skin and unaffected skin were significant for all skin phototypes (p skin with vitiligo negatively correlated with vitiligo severity, r = -0.426, p = 0.01. There were no significant correlations between MED and VIDA, disease duration, or SEI.
CONCLUSION: MED of skin with vitiligo was much lower than unaffected skin, and MEDs between skin phototypes III, IV, and V differed despite similar depigmentation. MED was not influenced by vitiligo disease activity, duration, or sun exposure.