Despite their promise, lipid nanoparticle gene delivery systems have repeatedly failed clinical trials and struggle to achieve efficient, localized transfection in target tissues. The majority of endocytosed nanoparticles are degraded before nucleic acid release, and an inability to track particle distribution in vivo prevents validation of successful delivery. Alternatively, nanobubbles (NBs) are lipid-shelled, gas-core preclinical ultrasound contrast agents and stimuli-responsive drug delivery vehicles. Under varying acoustic pressures, NBs expand, contract, and burst, releasing cargo in an externally controlled, site-specific manner while scattering unique echoes for simultaneous ultrasound visualization. Here, we introduce a cationic nanobubble (CNB) formulation with a +42.3 mV zeta potential, 265 nm diameter, and 2.43×10 11 NBs/mL concentration. CNBs produce stable ultrasound contrast, electrostatically load plasmid DNA onto their surface, and internalize into >99% of human prostate cancer cells within 15 minutes in vitro . CNBs remain brightly echogenic intracellularly and induce sonication-dependent expression of green fluorescent protein (GFP). In vivo , CNBs generate contrast in mouse livers for 50 minutes after intravenous administration. Therapeutic ultrasound stimulation over the liver causes a sharp reduction in ultrasound contrast, visualizing localized cavitation in the target organ and inducing a 2.5-fold increase in anti-GFP mean fluorescence intensity relative to the untransfected control. Importantly, no GFP expression is observed without ultrasound stimulation, supporting a mechanism for selective and site-specific gene delivery. This study presents a highly stable CNB capable of efficient DNA loading and ultrasound-dependent gene expression. These results provide a foundation for the future development of CNB platforms to advance image-guided, ultrasound-triggered gene therapy.
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.· Journal of Systems and Softw...· 111 citations· ⚡8
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.· Journal of Systems and Softw...· 78 citations· ⚡6
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· SSE@SIGSOFT FSE· 56 citations· ⚡4
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· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
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 sequence constraints.
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.