Effect of encapsulation on hygroscopic, structural, physico-thermal and in vitro release properties of DPP-IV inhibitory peptides-rich sodium caseinate hydrolysates
Considering the increasing worldwide prevalence of Type 2 diabetes mellitus (T2DM), utilizing milk protein-derived bioactive peptides offers a promising and sustainable solution to manage hyperglycemia. However, formulating functional foods rich in Dipeptidyl peptidase-IV (DPP-IV) inhibitory peptides is hindered by their poor physicochemical stability and high hygroscopicity. To overcome these limitations, this study specifically aimed to encapsulate a DPP-IV inhibitory peptide-rich sodium caseinate hydrolysate (SCH) within the gum Arabic and resistant maltodextrin polysaccharide matrix using spray drying. The hygroscopic profiles of SCH (without encapsulation) and the resulting encapsulated powder (EP) were evaluated comparatively. Structural and thermal transitions were mapped using Scanning Electron Microscopy (SEM), Differential Scanning Calorimetry (DSC), Thermogravimetric Analysis (TGA), Fourier Transform Infrared (FTIR) spectroscopy, and X-ray Diffraction (XRD). FTIR and SEM analyses demonstrated that encapsulation had adequately protected the peptides within the matrix. DSC revealed that encapsulation has increased the glass transition temperatures (Tg) from 52.89 °C (SCH) to 89.66 °C (EP). XRD patterns demonstrated improvement in crystallinity after encapsulation. Moisture sorption analyses indicated that EP was effectively predicted by the Halsey, Kuhn, and GAB models at 25 and 35 °C and exhibited type-II sigmoid isotherm. Further, EP shows ∼6-fold higher DPP-IV inhibition than SCH in the intestinal phase. The findings demonstrated that encapsulation has markedly reduced hygroscopicity and insolubility index (by 0.6 mL), enhanced dispersibility (by 5.6%), of DPP-IV inhibitory peptide-rich sodium caseinate hydrolysates, besides offering better protection during gastro-intestinal transit. This could help to develop a shelf-stable, DPP-IV inhibitory peptides-rich sodium caseinate hydrolysate powder for better management of T2DM.
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.