Significant progress has been made in disease progression modelling, with crucial implications for understanding and managing chronic, progressive diseases, such as neurodegenerative diseases. One prominent framework is Subtype and Stage Inference (SuStaIn), an event-based machine learning model that characterizes disease progression and can reveal spatiotemporal and phenotypic heterogeneity in chronic diseases using only cross-sectional data. SuStaIn has demonstrated success across neurodegenerative, psychiatric, and pulmonary diseases. However, a key limitation of SuStaIn is its assumption of monotonic disease progression, which may be biologically implausible and clinically restrictive, particularly for diseases that commonly involve remission and recovery (e.g., psychiatric disorders). To examine the impact of this assumption, this proof‑of‑concept study systematically evaluated SuStaIn’s performance on non‑monotonic data. We generated both monotonic and bidirectional ground-truth disease progression datasets. The bidirectional datasets were further manipulated across sample sizes, numbers of biomarkers, and subtype proportions. Model performance was evaluated using Kendall’s tau to compare the inferred event sequences with the ground-truth progression. Results show that SuStaIn performs significantly worse on datasets with bidirectional underlying disease progression than on baseline monotonic datasets. While SuStaIn reliably captures the number of subtypes and their proportions, it fails to accurately model progression within subtypes that contain switch biomarkers. Moreover, increasing the sample size and the number of biomarkers does not substantially improve performance under bidirectional conditions, in contrast to monotonic progressions. These findings provide empirical evidence that SuStaIn’s monotonic assumption limits its ability to model bidirectional disease processes, as in many psychiatric disorders. More broadly, this work deepens the understanding of SuStaIn as an event-based model and informs future methodological extensions to relax the monotonic progression constraint.
IL-37 is a member of the IL-1 cytokine family and functions as a broad anti-inflammatory mediator through suppression of pro-inflammatory cytokines and modulation of immune signaling pathways. Uniquely, IL-37 signals through both extracellular receptor-mediated mechanisms (IL-18R1 and IL-1R8 (SIGIRR)) and intracellular pathways (SMAD3-mediated nuclear translocation) that broadly reduce inflammatory pathway activation. The protective effects of IL-37 in EAE occur through its extracellular function, specifically binding IL-1R8, thereby inhibiting pro-inflammatory signaling cascades such as NF-κB, MAP kinases, and mTOR pathways. IL-37 has been studied in cardiovascular endothelial cells, where it inhibits atherosclerosis, reduced endothelial apoptosis, and protects against coronary endothelial damage, suggesting therapeutic potential in inflammatory endothelial pathologies. However, the expression and function of IL-37 in human brain endothelial cells remains poorly characterized. These cells are implicated in Multiple sclerosis (MS), a chronic neuroinflammatory disease affecting around 2.9 million people worldwide. The disease results from immune cell infiltration across the blood-brain barrier (BBB), which disrupts neuronal signalling by promoting inflammation, demyelination, gliosis and neuroaxonal degeneration. Transgenic mouse studies that introduced IL-37 in experimental autoimmune encephalomyelitis (EAE), the standard animal model for MS, demonstrated significantly reduced clinical scores and decreased demyelination. In addition, IL-37 expression reduced immune cell accumulation in the spinal cord and shifted cytokine profiles toward a more anti-inflammatory phenotype.Because human brain endothelial cells regulate immune trafficking and inflammatory signaling at the BBB and are associated withMS pathology, this project aims to investigate IL-37 signaling in human brain endothelial cells and determine whether this pathway contributes to immune regulation at the BBB. Understanding IL-37 in this context may provide insight into mechanisms that limit neuroinflammation in MS and identify potential therapeutic targets.
Punctate white matter lesions (PWMLs) are common in preterm neonates and linked to adverse neurodevelopmental outcomes. Conventional identification relies on subjective visual contrast on MRI. We propose a global, training-free z-score normalization framework for automatic PWML detection, enabling standardized, interpretable segmentation across modalities without machine learning or subject-specific calibration. We analyzed 44 neonates from the dHCP dataset, comprising 1069 axial slices from T1w, T2w, and mean diffusivity (MD) maps. Reference masks were generated by consensus among four trained raters. Slices within each modality were standardized to a global z-score space, allowing comparable intensity distributions. For each modality, a detection threshold of z-score was swept from -3 to 3 standard deviations to construct receiver operating characteristic (ROC) curves. Lesion–white matter contrast ratios (CR) were also computed. Inter-rater reliability (Cohen’s k) reached 0.88 for T1w, 0.77 for T2w, and 0.73 for MD. PWML–white matter contrast differences were statistically significant (p < 0.01). Contrast magnitude was greatest for T1w (CR = 0.57 ± 0.19), compared to T2w (-0.18 ± 0.08) and MD (-0.14 ± 0.12). ROC analysis revealed that T1w outperformed the other modalities, achieving an AUC of 0.98 at an optimal threshold of 0.84 (True Positive Rate = 0.90, False Positive Rate = 0.06). In contrast, T2w and MD achieved lower AUCs of 0.84 and 0.67, respectively. Global z-score normalization yields a robust, fully automatic lesion detection method. This interpretable baseline achieves high sensitivity with minimal false positives and provides a reproducible benchmark for evaluating deep-learning or quantitative MRI pipelines.
In order to properly function, the immune system must achieve a fine balance between attacking foreign invaders while avoiding damage to healthy (self) tissue. One way it maintains this balance is through the induction of T cell anergy. Anergy is a key tolerance mechanism that renders self-reactive T cells hyporesponsive, thereby preventing the development of autoimmune diseases. T cell anergy is typically induced under steady state conditions when antigen recognition occurs without co-stimulation, such as in the absence of inflammation. Regulatory T (Treg) cell-mediated suppression of self-reactive T cells may contribute to anergy induction, but their specific role remains poorly defined. In this study, we investigate how antigen-specific Tregs influence the generation and accumulation of anergic CD8 T cells in vivo. To test this, we generated Tregs ex vivo from OVA-specific OT-II CD4 T cells. We injected OVA-specific OT-I CD8 T cells with or without OT-II Tregs into mice expressing OVA as a model self-antigen. After fourteen days, we observed an increase in the proportion of OT-I T cells in the spleen of mice co-adoptively transferred with OT-II Tregs as compared to mice injected with OT-I T cells alone. We confirmed, via ex vivo re-stimulation, that OT-I T cells induced in the presence of OT-II Tregs remained anergic. Interestingly, single-cell RNA-sequencing revealed that levels of antigen-specific Tregs correlate with transcriptional heterogeneity among anergic CD8 T cells in vivo. These findings suggest that antigen-specific Tregs promote the accumulation of anergic CD8 T cells. This work contributes to a broader effort to define mechanisms of peripheral tolerance, providing a foundation for their potential use in therapeutic interventions to prevent autoimmune disease.
