Factors Influencing Skin Aging and the Important Role of Estrogens and Selective Estrogen Receptor Modulators (SERMs)

Development of an epigenetic clock to predict visual age progression of human skin

Aging is a complex process characterized by the gradual decline of physiological functions, leading to increased vulnerability to age-related diseases and reduced quality of life. Alterations in DNA methylation (DNAm) patterns have emerged as a fundamental characteristic of aged human skin, closely linked to the development of the well-known skin aging phenotype. These changes have been correlated with dysregulated gene expression and impaired tissue functionality. In particular, the skin, with its visible manifestations of aging, provides a unique model to study the aging process. Despite the importance of epigenetic age clocks in estimating biological age based on the correlation between methylation patterns and chronological age, a second-generation epigenetic age clock, which correlates DNAm patterns with a particular phenotype, specifically tailored to skin tissue is still lacking. In light of this gap, we aimed to develop a novel second-generation epigenetic age clock explicitly designed for skin tissue to facilitate a deeper understanding of the factors contributing to individual variations in age progression. To achieve this, we used methylation patterns from more than 370 female volunteers and developed the first skin-specific second-generation epigenetic age clock that accurately predicts the skin aging phenotype represented by wrinkle grade, visual facial age, and visual age progression, respectively. We then validated the performance of our clocks on independent datasets and demonstrated their broad applicability. In addition, we integrated gene expression and methylation data from independent studies to identify potential pathways contributing to skin age progression. Our results demonstrate that our epigenetic age clock, VisAgeX, specifically predicting visual age progression, not only captures known biological pathways associated with skin aging, but also adds novel pathways associated with skin aging.

An extensive review on phenolic compounds and their potential estrogenic properties on skin physiology

Polyphenolic compounds constitute a diverse group of natural components commonly occurring in various plant species, known for their potential to exert both beneficial and detrimental effects. Additionally, these polyphenols have also been implicated as endocrine-disrupting (ED) chemicals, raising concerns about their widespread use in the cosmetics industry. In this comprehensive review, we focus on the body of literature pertaining to the estrogenic properties of ED chemicals, with a particular emphasis on the interaction of isoflavones with estrogen receptors. Within this review, we aim to elucidate the multifaceted roles and effects of polyphenols on the skin, exploring their potential benefits as well as their capacity to act as ED agents. By delving into this intricate subject matter, we intend to provoke thoughtful consideration, effectively opening a Pandora's box of questions for the reader to ponder. Ultimately, we invite the reader to contemplate whether polyphenols should be regarded as friends or foes in the realm of skincare and endocrine disruption.

Identification of types of wound bed tissue as a percentage and total wound area by planimetry in neuropathic and venous ulcers

BACKGROUND

Neuropathic and venous leg ulcers are chronic wounds associated with devitalized tissue and recurrent infection. Management should be guided by accurate tissue assessment, including the use of planimetry, which provides tissue types as a percentage of the total wound bed surface area.

OBJECTIVE

This innovative study aimed to assess and identify the wound bed tissues, as a percentage, of neuropathic and venous ulcers using digital planimetry, providing support to nurses optimize the management of necrotic tissues and, consequently, to avoid wound infection.

METHODS

This cross-sectional study enrolled 24 patients with chronic wounds who were assessed from January to March 2021 at the Wound Outpatients Clinic. The wound photographs were analyzed using Image J 1.53e and a smartphone with WoundDoc Plus® 2.8.2 via digital planimetry. Statistical analyses were performed using the binomial test, t-test, and Mann-Whitney.

RESULTS

Median wound areas (p=0.3263) did not differ between the group with 2 or 3 risk factors for delayed healing (Md: 31.7) and the group with up to 1 risk factor (Md: 5.3). A low exudate level was associated with the up-to-1-risk-factor-for-delayed-healing group (p=0.0405), while a medium level was associated with the two-or-three-risk-factor group (p=0.0247). A heat map displayed the tissue percentages in the wound bed. In the group with 2 or 3 risk factors for delayed healing, 91.7% (11/12) had less than 70% granulation tissue, which was the primary factor for this group (p<0.0001). Additionally, 66.7% (8/12) of patients with 2 or 3 risk factors for delayed healing exhibited discolored and/or dark red granulation tissue as the primary factor (p=0.0130).

CONCLUSION

This novel identification of wound area and tissue types as a percentage, using digital planimetry, can play a crucial role in assisting nurses in decision-making related to the appropriate management of devitalized tissues. Furthermore, this measurements may facilitate the conducting of virtual wound consultations and offer valuable support in the development of protocols aimed at preventing infection and biofilm formation in the wound bed.

In Silico Prediction of Human Organ Toxicity via Artificial Intelligence Methods

Unpredicted human organ level toxicity remains one of the major reasons for drug clinical failure. There is a critical need for cost-efficient strategies in the early stages of drug development for human toxicity assessment. At present, artificial intelligence methods are popularly regarded as a promising solution in chemical toxicology. Thus, we provided comprehensive in silico prediction models for eight significant human organ level toxicity end points using machine learning, deep learning, and transfer learning algorithms. In this work, our results showed that the graph-based deep learning approach was generally better than the conventional machine learning models, and good performances were observed for most of the human organ level toxicity end points in this study. In addition, we found that the transfer learning algorithm could improve model performance for skin sensitization end point using source domain of in vivo acute toxicity data and in vitro data of the Tox21 project. It can be concluded that our models can provide useful guidance for the rapid identification of the compounds with human organ level toxicity for drug discovery.