Cross-sectional study on the association between C-reactive protein to albumin ratio (CAR) and sarcopenia risk in patients with type 2 diabetes mellitus
To investigate the association of C-reactive protein to albumin ratio (CAR) with sarcopenia and to evaluate its discriminative performance in patients with type 2 diabetes mellitus (T2DM). We enrolled 265 patients with T2DM within this single-center, retrospective study. Sarcopenia was diagnosed according to the 2019 Asian Working Group for Sarcopenia (AWGS) criteria. CAR was calculated as CRP (mg/L) divided by albumin (g/L) and categorized into tertiles. Multivariable logistic regression was used to assess the independent association between CAR and sarcopenia. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminative performance. Sarcopenia was identified in 52 (19.6%) patients, who exhibited significantly higher CAR levels than those without sarcopenia (P < 0.05). CAR was negatively correlated with both appendicular skeletal muscle mass index (ASMI) and grip strength (P < 0.01). After adjusting for age, sex, BMI, diabetes duration, HbA1c, and eGFR, the highest CAR tertile was associated with a 3.12-fold increased risk of sarcopenia compared with the lowest tertile (OR = 3.12, 95% CI 1.08–9.02, P = 0.036). Furthermore, CAR demonstrated moderate predictive capacity for sarcopenia (AUC=0.768), which was superior to the neutrophil-to-lymphocyte ratio (AUC = 0.648). Elevated CAR is independently associated with increased odds of sarcopenia in T2DM. As a readily available and cost-effective composite marker of inflammation and nutrition, CAR may provide complementary information for sarcopenia risk stratification in 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 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.