Antioxidant, Cytotoxic, Anti-Glycation, and Anti-Tyrosinase Compounds from the Leaves of Uvaria Siamensis.

A new oxidized heptene, 7-benzoyloxy-4-hydroxy-1-ethoxy-2E,4Z-heptadiene-1,6-dione, namely siamheptene A (1), together with eight known compounds (2-9), were isolated from the leaves of Uvaria siamensis. Their structures were elucidated by detailed analysis of spectroscopic (IR, 1D and 2D NMR) and mass spectrometric data. Compound 9 is reported for the first time from Uvaria genus. Siamheptene A was evaluated for cytotoxicity against HeLa (cervical cancer cells), A549 (lung cancer cells), and Vero cells using the MTT assay and screened for antibacterial activities. In addition, the isolated compounds (1-7, and 9) were investigated for their antioxidant (DPPH, FRAP and ABTS+ assays), anti-glycation, and anti-tyrosinase properties. Based on our results, compound 1 had mild cytotoxicity against Hela and A549 cancer cell lines, with IC50 ranging from 31.09 to 31.67 µg/mL. Compound 1 also showed antioxidant activities in all tasted assays. However, it showed no detectable activity (>128 µg/mL) against various bacterial strains, and it has no inhibitory effects on tyrosinase enzymes. Among of all tested compounds, chrysin (5), showcased highest anti-glycation and anti-tyrosinase activities. This comprehensive analysis provides highlighting the potential of 1 as a lead compound for further structural modification and development of cytotoxic or antioxidant agents.

Human skin type classification using image processing and deep learning approaches.

Cosmetics consumers need to be aware of their skin type before purchasing products. Identifying skin types can be challenging, especially when they vary from oily to dry in different areas, with skin specialist providing more accurate results. In recent years, artificial intelligence and machine learning have been utilized across various fields, including medicine, to assist in identifying and predicting situations. This study developed a skin type classification model using a Convolutional Neural Networks (CNN) deep learning algorithms. The dataset consisted of normal, oily, and dry skin images, with 112 images for normal skin, 120 images for oily skin, and 97 images for dry skin. Image quality was enhanced using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique, with data augmentation by rotation applied to increase dataset variety, resulting in a total of 1,316 images. CNN architectures including MobileNet-V2, EfficientNet-V2, InceptionV2, and ResNet-V1 were optimized and evaluated. Findings showed that the EfficientNet-V2 architecture performed the best, achieving an accuracy of 91.55% with average loss of 22.74%. To further improve the model, hyperparameter tuning was conducted, resulting in an accuracy of 94.57% and a loss of 13.77%. The Model performance was validated using 10-fold cross-validation and tested on unseen data, achieving an accuracy of 89.70% with a loss of 21.68%.

Antioxidant, Anti-Tyrosinase, and Anti-Skin Pathogenic Bacterial Activities and Phytochemical Compositions of Corn Silk Extracts, and Stability of Corn Silk Facial Cream Product.

Zea mays L. Poaceae stigma (corn silk, CS) is a byproduct of agricultural waste and is used as a traditional herb in many countries. CS is rich in chemical compounds known to benefit human health and is also a remedy for infectious diseases and has anti-proliferative effects on human cancer cell lines. In the present study, CS extract has been evaluated for its antioxidant, antibacterial, and anti-tyrosinase activities and its phytochemical composition. The higher total phenolic and flavonoid contents were found in the ethanolic extract of corn silk (CSA), at 28.27 ± 0.86 mg gallic acid equivalent/g extract and 4.71 ± 0.79 mg quercetin equivalent/g extract, respectively. Moreover, the antioxidant content of CSA was found at 5.22 ± 0.87 and 13.20 ± 0.42 mg gallic acid equivalent/g extract using DPPH and reducing power assays. Furthermore, the ethanolic extract of corn silk showed tyrosinase inhibition with an IC50 value of 12.45 µg/mL. The bacterial growth inhibition of CSA was tested using agar disc diffusion and broth dilution assays against Cutibacterium acnes and Staphylococcus epidermidis. It was found that CSA inhibited C. acnes and S. epidermidis with an inhibition zone of 11.7 ± 1.2 and 9.3 ± 0.6 mm, respectively. Moreover, the CSA showed MIC/MBC of 15.625 mg/mL against C. acnes. The following phytochemical compounds were detected in CSA: cardiac glycosides; n-hexadecanoic acid; hexadecanoic acid, ethyl ester; oleic acid; and 9,12-octadecadienoic acid, ethyl ester. After the corn silk cream product was formulated, the product demonstrated stability without phase separation. This research is beneficial for promoting effective ways to use agricultural waste while utilizing the antioxidant, anti-tyrosinase, and antibacterial activities of corn silk. Moreover, the use of technology and innovation to obtain high-value CS extract will benefit the development of commercial cosmetic products by providing safe, natural, and quality ingredients to the consumer.