This study provided a comprehensive evaluation of CHARMS™ skincare cosmetics in terms of their ability to improve skin tone, their antioxidant properties, and the presence of volatile organic compounds (VOCs) with potential skin benefits. Skincare efficacy has become a growing consumer concern, particularly regarding antioxidant activity and skin-lightening effects. Using a clinical trial on 20 female volunteers, the research revealed that CHARMS™ products significantly enhanced skin appearance, with a 3.23% improvement in skin lightness and a 5.75% reduction in skin redness.

 

Antioxidant analysis demonstrated that the serum and cleanser exhibited the strongest radical scavenging activities, while the moisturizer showed the highest total phenolic content, and the cleanser yielded the highest flavonoid content. These findings suggest that each product in the CHARMS™ line contributes differently to skin protection and rejuvenation. Furthermore, electronic nose gas chromatography (e-nose-GC) detected VOCs such as limonene and γ-terpinene, compounds known for their skin-lightening and antioxidant effects.

 

Together, these results highlight the scientific basis for the cosmetic benefits of CHARMS™ skincare products. The combination of natural ingredients, antioxidant activity, and the presence of VOCs supports their effectiveness in reducing oxidative stress, preventing pigmentation, and improving skin tone. The study also confirmed that CHARMS™ products complied with Malaysia’s Control of Cosmetic Products regulations, underscoring their safety and suitability for daily skincare routines.

 

Overall, this work not only validated the claims of CHARMS™ cosmetics but also provided an evidence-based perspective on how antioxidant-rich ingredients and bioactive VOCs synergistically promote skin tone improvement. The findings suggest promising applications of CHARMS™ in both cosmetic and dermatological contexts, meeting consumer demand for safe, effective, and scientifically proven skincare products.

Osaka Metropolitan University researchers developed an AR 3D food model to teach portion size estimation and conducted an intervention study to test its effectiveness.

Researchers from the Yong Loo Lin School of Medicine, National University of Singapore (NUS Medicine) and National Center for Nanoscience and Technology, the Chinese Academy of Sciences (CAS), have jointly developed an approach to significantly reduce the risk of cancer recurrence and metastasis after surgery, by targeting both bulk cancer cells and the elusive cancer stem cells (CSCs) responsible for relapse.

The role of the arts in narrating environmental stories, nighttime cooling in urban parks and more from ESA's journals.

In machine learning tasks for applications as diverse as facial recognition and movie recommendations, neural networks have been used extensively. To address the complexities of neural networks and issues such as overfitting, loss of generalization power, and excessive hardware cost, Qing et al. developed a general compression scheme based on automatically differentiable tensor networks that considerably reduces the number variational parameters needed while maintaining or improving the network’s performance. Their work was published on May 15, 2025, in an article titled “Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters” in Intelligent Computing, a Science Partner Journal.