Skip to content

Tissue-specific responses to hexavalent chromium and acenaphthene co-exposure in zebrafish.

Sep 2026 · Comparative biochemistry and physiology. Toxicology & pharmacology : CBP · pp. 110691 · 0 citations · 43 references
Medicine

Abstract

Hexavalent chromium [Cr(VI)] and acenaphthene (Ace) can co-occur in industrially affected aquatic environments, but their effects across target organs under co-exposure remain poorly characterized. Adult male zebrafish were exposed for 28 d to environmentally relevant or high sublethal concentrations of Cr(VI) and Ace, individually and in combination. Histopathological changes, oxidative-stress biomarkers, TUNEL staining, and the expression of inflammation- and apoptosis-related markers were assessed in brain and heart tissues. At the highest co-exposure concentrations, brain malondialdehyde increased by 113.22%, whereas glutathione levels and superoxide dismutase and catalase activities decreased by 27.89%, 21.25%, and 52.25%, respectively. High-dose Cr(VI) alone reduced cardiac glutathione levels and superoxide dismutase and catalase activities by 23.75%, 21.31%, and 14.00%, respectively. Several co-exposure groups showed greater oxidative, inflammatory, and apoptotic responses than the corresponding single-pollutant groups. At the highest co-exposure concentrations, cardiac IL-10 mRNA and protein abundance both decreased by more than 50%, whereas TUNEL-positive signals increased 11 to 14-fold in the brain and heart. Changes in Bax, Bcl-2, Caspase-3, Caspase-9, and p53 expression were consistent with the possible involvement of mitochondrial apoptosis. Overall, response magnitudes differed between tissues: the heart showed comparatively pronounced responses to high-dose Cr(VI), whereas the brain was more responsive to high-dose Ace for several endpoints. These findings support multi-organ assessment when evaluating the potential effects of Cr(VI) and Ace co-exposure in aquatic organisms.

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.