Chronic wound infections are frequently associated with bacterial biofilms, which significantly reduce the efficacy of antimicrobial treatments. Conventional in vitro evaluation methods, such as disc diffusion assays and static biofilm models, lack physiological relevance, whereas more advanced wound models are often costly, complex, and difficult to reproduce. In this study, we present a cost-effective and easily applicable in vitro wound model based on gelatin–alginate hydrogels for the evaluation of topical antimicrobial therapies. Hydrogels composed of gelatin type A and sodium alginate were integrated into a transwell system and crosslinked using microbial transglutaminase and calcium ions. This configuration enables passive diffusion of simulated wound fluid from the basolateral compartment, thereby mimicking key aspects of the wound environment. The model supports the growth of Pseudomonas aeruginosa biofilms within a three-dimensional matrix. The hydrogel system supported bacterial growth and microcolony formation during 48 h of incubation at 37 °C. The system was applied in both biofilm inhibition and eradication studies using tobramycin, ε-poly-L-Lysine and the commercially available wound gel Prontosan®. Viable bacteria were quantitatively recovered through hydrogel dissolution followed by serial dilution and count of colony-forming units. Overall, the developed model provides a simple and accessible platform for quantitative evaluation of antibiofilm treatments. While not intended to fully replicate the complexity of native tissue, this system offers a practical alternative to collagen-based models and helps bridge the gap between conventional in vitro assays and more advanced wound models.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
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
The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new an...
Marko Ikonen, Petri Kettunen, Nilay V. Oza et al.· EUROMICRO Conference on Soft...· 67 citations· ⚡9
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 18, 2026
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.