Sep 2026· Korean Circulation Journal· 8 references
Inflammasome and immune disorders
Abstract
Numerous strategies have been studied to improve clinical outcomes after acute myocardial infarction (AMI), and the most firmly established is low-density lipoprotein (LDL) cholesterol-lowering therapy.Contemporary dyslipidemia guidelines therefore recommend intensive LDL-lowering after AMI, with an European Society of Cardiology/European Atherosclerosis Society and American College of Cardiology/American Heart Association goal of below 55 mg/dL together with at least a 50% reduction from baseline in very-high-risk patients.1)2) Yet despite aggressive LDL lowering, cardiovascular event continue to occur, and considerable effort is now directed at identifying and treating the residual risk that remains once lipid targets are met.Inflammation has long been implicated in the prognosis of patients with AMI.The inflammatory hypothesis of atherosclerosis moved from association to causation with the CANTOS trial, in which canakinumab-an interleukin (IL)-1β antagonist that lowers IL-6 and C-reactive protein (CRP) without altering lipids-reduced cardiovascular events.3) Low-dose colchicine subsequently reduced ischemic events after myocardial infarction (MI) and in chronic coronary disease, 4)5) whereas methotrexate, which does not lower IL-6 or CRP, was neutral (CIRT)-underscoring that benefit tracks specifically with the IL-6-CRP axis.6) More recently, however, the CLEAR-SYNERGY (OASIS-9) trial did not show a significant reduction in cardiovascular events with colchicine after MI, reminding us that the optimal agent, timing, and target population for anti-inflammatory therapy remain unsettled.7) In this context, the study by Hyun et al. 8) published in the current issue of the Korean Circulation Journal provides timely evidence on whether CRP adequately reflects residual risk after AMI.Among 661 such patients with serial high-sensitivity C-reactive protein (hsCRP) measurements, 19.5% had high residual inflammatory risk (RIR), defined as an hsCRP level >2 mg/L at 1 year.During a median follow-up of 5.3 years, high RIR was independently associated with a more than 4-fold higher risk of all-cause mortality (adjusted hazard ratio [HR], 3.41) and a 3-fold higher risk of major adverse cardiac and cerebrovascular event (adjusted HR, 3.08).Importantly, these associations remained consistent in the subgroup with LDL cholesterol <55 mg/dL.
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.