Skip to content

A component-resolved in vitro skin barrier model for assessing nanoplastic retention and barrier susceptibility.

Yuxuan He Hui Huang Jing Yang Jia-Hui Zhu Chuan-Xin Ma Yu Shen
Sep 2026 · Toxicology in Vitro · pp. 106307 · 0 citations · 52 references
Medicine

Abstract

Human-relevant in vitro models are needed to assess dermal nanoplastic hazards, yet current skin penetration approaches often treat the stratum corneum as a compositionally uniform barrier. This limits mechanistic understanding of how barrier biochemistry relates to nanoplastic retention, particularly in skin states with altered lipid or protein organization. Here, we developed a component-resolved in vitro stratum corneum model to quantify interactions between 50 nm polystyrene nanoplastics and six major skin barrier constituents: ceramide, cholesterol, keratin, palmitic acid, proline, and phenylalanine. Component-specific operational retention indices (RAI) were determined using standardized gravity-driven flow experiments and high-resolution transmission electron microscopy, then integrated into a multi-phase penetration model. Retention differed markedly among components. Palmitic acid, representing free fatty acids, showed the highest operational retention, retaining approximately 26-fold more particles than keratin. Protein-rich and sterol-associated components displayed lower initial retention but greater time-dependent accumulation, an apparent trend that with only three time points cannot be assigned to a defined kinetic regime. Ex vivo two-photon imaging of porcine skin showed an apparent detectable fluorescence depth of 12.3 ± 2.1 μm; this was not used to calibrate or validate the dimensionless component-weighted score. The framework is exploratory and mechanistic, characterizing how stratum corneum constituents differ in operational retention of nanoplastics, providing a hazard-relevant basis for, rather than a validated prediction of, dermal penetration.

View source

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.