Surface texture affects how surfaces are perceived. During contact between a finger and an object, mechanical stimuli propagate from the skin surface to the sites of our tactile mechanoreceptors. By changing the material, geometric and interfacial properties of these countersurfaces, product designers may modulate the elicited mechanical stimuli to deliver more functional and desirable sensations to consumers. We investigate the sliding contact between a finger and a variety of rigid sinusoidal surface textures using a numerical model. A parametric study was conducted in which the wavelength and amplitude of surface textures were varied whilst stimulation of the mechanoreceptors within the finger was recorded. It was found that the wavelength of the countersurface is more significant parameter than its amplitude. Particularly, when surface wavelengths were commensurate to that of the fingerprint, elevated and highly oscillatory strain energy density signals were experienced at each of the receptor sites. In these scenarios, increases in amplitude of the countersurface further elevate the mechano- stimulation, whilst this was substantially attenuated when the finger slides against non-commensurate surface textures. The obtained results can be used in conjunction with perceptive data to elicit or tune targeted sensations, for instance by designing specific surface textures for products and packaging, or by modifying the skin surface properties through cosmetic application.
Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public. Lay summaryProbabilistic risk assessment, initially from engineering, is applied in toxicology to understand chemical-related hazards and their consequences. In toxicology, uncertainties abound—unclear molecular events, varied proposed outcomes, and population-level assessments for issues like neurodevelopmental disorders. Establishing links between chemical exposures and diseases, especially rare events like birth defects, often demands extensive studies. Existing methods struggle with subtle effects or those affecting specific groups. Future risk assessments must address developmental disease origins, presenting challenges beyond current capabilities. The intricate nature of many toxicological processes, lack of consensus on mechanisms and outcomes, and the need for nuanced population-level assessments highlight the complexities in understanding and quantifying risks associated with chemical exposures in the field of toxicology.
Chinese Medical Association and L′Oréal Group jointly launched "China Skin & Hair Grant" from 2003 to 2018 to support Chinese dermatologists in skin and hair research. This program has not only helped improve the research capability of Chinese dermatologists, but also yielded abundant valuable Chinese population-based clinical and basic research results, and further enabled active academic communication through Chinese and international journals and conferences. This article summarizes main results of scalp- and hair-related research projects based on program records and publications.
Fondée sur la science, la politique RSE de L’Oréal est centrée sur des réalisations concrètes, des progrès incrémentaux et une vraie transformation de l’entreprise en profondeur. Elle repense ses produits, ses formules, ses packagings et ses usines – qui seront, par exemple, neutres en carbone dès 2025. Ce travail s’étend également à tout l’écosystème L’Oréal et embarque des acteurs aussi différents que ses fournisseurs, ses consommateurs et ses clients (grande distribution, chaînes de parfumerie, pharmacies, salons de coiffure).
