Decentralized diagnostics are vital for infectious disease control, yet current point-of-care tools remain largely one-dimensional, failing to capture the multidimensional profiles required for precision management. Here, we show SID-WAVES (Smartphone-Integrated Diagnostics with Wax-encoded Amplified and Versatile Evaluation System), an intelligent, multimodal microfluidic platform. Integrating low-melting-point phase-change and dual-siphon valves with RPA–CRISPR/Cas12a nucleic acid detection and protein immunoassays, the platform enables automated, one-step, sample-to-answer operation via an AI-assisted smartphone readout that reduces operator-dependent variability. Spatial-temporal-signal encoding supports multiplexed, multi-sample, and multimodal analysis, achieving attomolar-level sensitivity (10−18 M) and >95% diagnostic accuracy across 225 clinical and self-collected molecular assays, demonstrating robust usability. For proteins, a triple-amplification strategy and hierarchical capture enhance sensitivity 125-fold, improving accuracy from 87.6% to 100% in 54 simulated samples. Functioning as a reconfigurable analytical framework where panels are changed by swapping targets and probes, this work helps mitigate the Sensitivity–Multiplexing–Accessibility trade-off, reframing decentralized diagnostics from single-target screening to multi-dimensional health profiling. Decentralized diagnostics has the potential to improve health equity as well as support a more rapid response to disease outbreaks. Here authors present SID-WAVES, a multimodal microfluidic platform which allows ‘sample-in, results-out’ detection of both nucleic acid and protein biomarkers.
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.