Predicting response to non-selective beta-blockers with liver-spleen stiffness and heart rate in patients with liver cirrhosis and high-risk varices.

INTRODUCTION: Non-selective beta-blockers (NSBB) are used for primary prophylaxis in patients with liver cirrhosis and high-risk varices (HRVs). Assessing therapeutic response is challenging due to the invasive nature of hepatic venous pressure gradient (HVPG) measurement. This study aims to define a noninvasive machine-learning based approach to determine response to NSBB in patients with liver cirrhosis and HRVs. METHODS: We conducted a prospective study on a cohort of cirrhotic patients with documented HRVs receiving NSBB treatment. Patients were followed-up with clinical and elastography appointments at 3, 6, and 12 months after NSBB treatment initiation. NSBB response was defined as stationary or downstaging variceal grading at the 12-month esophagogastroduodenoscopy (EGD). In contrast, non-response was defined as upstaging variceal grading at the 12-month EGD or at least one variceal hemorrhage episode during the 12-month follow-up. We chose cut-off values for univariate and multivariate model with 100% specificity. RESULTS: According to least absolute shrinkage and selection operator (LASSO) regression, spleen stiffness (SS) and liver stiffness (LS) percentual decrease, along with changes in heart rate (HR) at 3 months were the most significant predictors of NSBB response. A decrease > 11.5% in SS, > 16.8% in LS, and > 25.3% in HR was associated with better prediction of clinical response to NSBB. SS percentual decrease showed the highest accuracy (86.4%) with high sensitivity (78.8%) when compared to LS and HR. The multivariate model incorporating SS, LS, and HR showed the highest discrimination and calibration metrics (AUROC = 0.96), with the optimal cut-off of 0.90 (sensitivity 94.2%, specificity 100%, PPV 95.7%, NPV 100%, accuracy 97.5%).

Single-cell analysis reveals inflammatory interactions driving macular degeneration.

Due to commonalities in pathophysiology, age-related macular degeneration (AMD) represents a uniquely accessible model to investigate therapies for neurodegenerative diseases, leading us to examine whether pathways of disease progression are shared across neurodegenerative conditions. Here we use single-nucleus RNA sequencing to profile lesions from 11 postmortem human retinas with age-related macular degeneration and 6 control retinas with no history of retinal disease. We create a machine-learning pipeline based on recent advances in data geometry and topology and identify activated glial populations enriched in the early phase of disease. Examining single-cell data from Alzheimer's disease and progressive multiple sclerosis with our pipeline, we find a similar glial activation profile enriched in the early phase of these neurodegenerative diseases. In late-stage age-related macular degeneration, we identify a microglia-to-astrocyte signaling axis mediated by interleukin-1ß which drives angiogenesis characteristic of disease pathogenesis. We validated this mechanism using in vitro and in vivo assays in mouse, identifying a possible new therapeutic target for AMD and possibly other neurodegenerative conditions. Thus, due to shared glial states, the retina provides a potential system for investigating therapeutic approaches in neurodegenerative diseases.

Geometric learning of functional brain network on the correlation manifold.

The correlation matrix is a typical representation of node interactions in functional brain network analysis. The analysis of the correlation matrix to characterize brain networks observed in several neuroimaging modalities has been conducted predominantly in the Euclidean space by assuming that pairwise interactions are mutually independent. One way to take account of all interactions in the network as a whole is to analyze the correlation matrix under some geometric structure. Recent studies have focused on the space of correlation matrices as a strict subset of symmetric positive definite (SPD) matrices, which form a unique mathematical structure known as the Riemannian manifold. However, mathematical operations of the correlation matrix under the SPD geometry may not necessarily be coherent (i.e., the structure of the correlation matrix may not be preserved), necessitating a post-hoc normalization. The contribution of the current paper is twofold: (1) to devise a set of inferential methods on the correlation manifold and (2) to demonstrate its applicability in functional network analysis. We present several algorithms on the correlation manifold, including measures of central tendency, cluster analysis, hypothesis testing, and low-dimensional embedding. Simulation and real data analysis support the application of the proposed framework for brain network analysis.

Re-visiting Riemannian geometry of symmetric positive definite matrices for the analysis of functional connectivity.

Common representations of functional networks of resting state fMRI time series, including covariance, precision, and cross-correlation matrices, belong to the family of symmetric positive definite (SPD) matrices forming a special mathematical structure called Riemannian manifold. Due to its geometric properties, the analysis and operation of functional connectivity matrices may well be performed on the Riemannian manifold of the SPD space. Analysis of functional networks on the SPD space takes account of all the pairwise interactions (edges) as a whole, which differs from the conventional rationale of considering edges as independent from each other. Despite its geometric characteristics, only a few studies have been conducted for functional network analysis on the SPD manifold and inference methods specialized for connectivity analysis on the SPD manifold are rarely found. The current study aims to show the significance of connectivity analysis on the SPD space and introduce inference algorithms on the SPD manifold, such as regression analysis of functional networks in association with behaviors, principal geodesic analysis, clustering, state transition analysis of dynamic functional networks and statistical tests for network equality on the SPD manifold. We applied the proposed methods to both simulated data and experimental resting state fMRI data from the human connectome project and argue the importance of analyzing functional networks under the SPD geometry. All the algorithms for numerical operations and inferences on the SPD manifold are implemented as a MATLAB library, called SPDtoolbox, for public use to expediate functional network analysis on the right geometry.