Nov 2026· Neurology(R) neuroimmunology & neuroinflammation· Vol 13 6, pp.
e200655
· 0 citations· 26 references
Medicine
TL;DR
Underlying tissue destruction in Definite vs Probable PRLs in people with progressive MS is assessed using quantitative MRI to assess underlying tissue destruction in PRLs in people with progressive MS.
Abstract
Background
AND
Objectives
Paramagnetic rim lesions (PRLs) in multiple sclerosis (MS) indicate chronic, innate immune-mediated inflammation contributing to disease progression. Rim disappearance or attenuation is interpreted as a treatment response; however, it remains uncertain whether reduced susceptibility signal-likely reflecting decreased iron within the rim compartment-represents true resolution of chronic inflammation, natural lesion aging, and/or advanced tissue degeneration. Cross-sectionally, PRLs show variable rim conspicuity, enabling classification as Definite or Probable. We used quantitative MRI (qMRI) to assess underlying tissue destruction in Definite vs Probable PRLs in people with progressive MS (PwPMS).
Methods
One hundred six PwPMS (34 primary progressive [PP], 72 secondary progressive; age 56 ± 10 years; median Expanded Disability Status Scale [EDSS] 5.5; 44% on disease-modifying therapy) underwent 3D-EPI and multishell diffusion MRI. PRLs (N = 155) were classified as Definite (N = 114) or Probable (N = 41). Prior clinical MRI scans estimated lesion age using an accelerated failure time model accounting for censoring. Whole-lesion, rim, and core regions were segmented; lesion size, quantitative susceptibility mapping, diffusion tensor imaging, Neurite Orientation Dispersions Density Imaging metrics, and T1 signal intensity were compared after multiple comparison correction.
Results
In total, 52% of PwPMS had ≥1 PRL; only 33% had Definite PRLs. Probable PRLs were more frequent in secondary vs PP MS (24% vs 9%, p = 0.05). Higher PRL counts were associated with younger age (p < 0.001), and PRLs were more often Definite in younger PwPMS (p < 0.0004). Participants with only Definite PRLs had shorter disease duration than those with ≥1 Probable PRL or no PRLs, and Probable PRLs were significantly older. Definite PRLs showed higher rim susceptibility (22 vs 8 ppb, p < 0.001); Probable PRLs demonstrated greater tissue disruption at qMRI-higher mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and FISO and lower neurite density index (NDI), and larger lesion size (all p < 0.001).
Discussion
In progressive MS, PRLs with lower rim susceptibility are more likely classified as Probable but may represent older lesions with greater tissue damage. Because chronic active lesions do not require iron pathologically and rims can fade over time, rim disappearance at MRI cannot be interpreted unequivocally as resolving inflammation and may reflect natural lesion evolution. Rim persistence may be a more informative marker of ongoing compartmentalized inflammation than rim disappearance is of its resolution.
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 angle and explores waste in the Kanban-driven software development project context. A preliminary research model is presented for helping the consequent replication of the study. The results from the empirical analysis suggest Kanban can be an effective method in visualizing and organizing the current work, but does not prevent waste from creeping in, although the overall project outcome may be successful.
Marko Ikonen, Petri Kettunen, Nilay V. Oza et al.· EUROMICRO Conference on Soft...· 67 citations· ⚡9
Related blog posts
Microsoft Research Blog· microsoft.comSep 21, 2026
Custom-made molecules are advancing medicine, materials, and agriculture, but producing them is slow and expensive. A new Nature paper highlights RetroChimera, a predictive model that helps accelerate chemical synthesis, helping researchers explore a wide range of molecules. The post Improving synthesis prediction of small molecules at scale with RetroChimera appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 14, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
AI may appear weightless, but every model depends on physical infrastructure. To understand responsible AI, we need to look beyond algorithms and consider the entire lifecycle of the hardware behind them. The post Responsible AI Must Consider Its Afterlife appeared first on GPT-Lab.