We used brain extracellular vesicle (EV) proteomics to identify protein responses to acute neuroinflammation and an EV-based microRNA intervention. Rats received vehicle, intracerebroventricular lipopolysaccharide (LPS), or LPS followed by an intravenous EV formulation containing anti-inflammatory microRNAs (miRNAs). Brains were collected 24 h after LPS or vehicle administration, and three independently processed animal-derived EV-enriched preparations per group were analyzed by label-free proteomics. Across all nine samples, 953 protein groups had complete normalized abundance measurements. Compared with vehicle controls, LPS exposure was associated with 227 exploratory protein changes, comprising 95 increases and 132 decreases at nominal P < 0.05 and an absolute log2 fold change >= log2(1.5). Increased proteins were enriched for translation-related annotations, whereas decreased proteins were enriched for plasma-membrane annotations. These LPS-associated changes did not remain significant after Benjamini-Hochberg correction at a false discovery rate (FDR) threshold of q < 0.05. Following treatment, 119 of the 227 candidates were closer to control abundance on the log2 scale; 23 candidates also showed nominal treatment-versus-LPS differences. Lamb2 and Ptgfrn were the two proteins that differed significantly between treated and LPS-only animals after FDR correction, identifying them as priorities for further validation. Nevertheless, 174 proteins differed significantly between treated animals and vehicle controls, and analysis across the measured proteins did not demonstrate an overall shift toward control values. Thus, the intervention was associated with specific protein responses, but treated animals retained substantial differences from controls. This study identifies candidate markers for investigating treatment responses in brain EV-enriched preparations and demonstrates the value of examining individual proteins alongside broader proteomic changes. Independent validation is needed to determine their functional relevance and distinguish miRNA-specific effects from contributions of the administered EV carrier.
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 seque...
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.