Changes in the geometry and robustness of diffusion tensor imaging networks: Secondary analysis from a randomized controlled trial of young autistic children receiving an umbilical cord blood infusion

Stem Cell Secretions as a Potential Therapeutic Agent for Autism Spectrum Disorder: A Narrative Review

Autism spectrum disorder (ASD) is a neurodevelopmental illness characterized by impaired social interaction and restricted repetitive behaviors or interests. The rising prevalence of ASD diagnosis has triggered a surge in research into investigating the underlying neuropathological processes and finding new therapeutic approaches. ASD is characterized by neuroinflammation and dysregulation of neuro-immune cross-talk, which suggests that stem cell treatment might be a potential therapeutic approach. The beneficial and restorative effects of stem cells are mainly due to their paracrine activity, in which stem cells generate and release extracellular vesicles such as exosomes and distinct secreted non-vesicle soluble proteins, including, growth factors, chemokines, cytokines, and immunomodulatory molecules referred to as the Secretome. In this paper, we reviewed the existing research exploring the therapeutic potential of stem cell secretome focusing on their role in addressing ASD pathology. Furthermore, we proposed a comprehensive mechanism of action for stem cell secretions, encompassing the broader secretome as well as the specific contribution of exosomes, in alleviating ASD neuropathology. Across the reviewed studies, exosomes and secreted soluble factors of the transplanted stem cell demonstrate a potential efficacy in ameliorating autistic-like behaviors. The proposed mechanism of action involves the modulation of signaling pathways implicated in neuroinflammation, angiogenesis, cellular apoptosis, and immunomodulation.

Gene interaction network analysis in multiple myeloma detects complex immune dysregulation associated with shorter survival

The plasma cell cancer multiple myeloma (MM) varies significantly in genomic characteristics, response to therapy, and long-term prognosis. To investigate global interactions in MM, we combined a known protein interaction network with a large clinically annotated MM dataset. We hypothesized that an unbiased network analysis method based on large-scale similarities in gene expression, copy number aberration, and protein interactions may provide novel biological insights. Applying a novel measure of network robustness, Ollivier-Ricci Curvature, we examined patterns in the RNA-Seq gene expression and CNA data and how they relate to clinical outcomes. Hierarchical clustering using ORC differentiated high-risk subtypes with low progression free survival. Differential gene expression analysis defined 118 genes with significantly aberrant expression. These genes, while not previously associated with MM, were associated with DNA repair, apoptosis, and the immune system. Univariate analysis identified 8/118 to be prognostic genes; all associated with the immune system. A network topology analysis identified both hub and bridge genes which connect known genes of biological significance of MM. Taken together, gene interaction network analysis in MM uses a novel method of global assessment to demonstrate complex immune dysregulation associated with shorter survival.

Discrete Ricci curvatures capture age-related changes in human brain functional connectivity networks

Introduction

Geometry-inspired notions of discrete Ricci curvature have been successfully used as markers of disrupted brain connectivity in neuropsychiatric disorders, but their ability to characterize age-related changes in functional connectivity is unexplored.

Methods

We apply Forman-Ricci curvature and Ollivier-Ricci curvature to compare functional connectivity networks of healthy young and older subjects from the Max Planck Institute Leipzig Study for Mind-Body-Emotion Interactions (MPI-LEMON) dataset (N = 225).

Results

We found that both Forman-Ricci curvature and Ollivier-Ricci curvature can capture whole-brain and region-level age-related differences in functional connectivity. Meta-analysis decoding demonstrated that those brain regions with age-related curvature differences were associated with cognitive domains known to manifest age-related changes-movement, affective processing, and somatosensory processing. Moreover, the curvature values of some brain regions showing age-related differences exhibited correlations with behavioral scores of affective processing. Finally, we found an overlap between brain regions showing age-related curvature differences and those brain regions whose non-invasive stimulation resulted in improved movement performance in older adults.

Discussion

Our results suggest that both Forman-Ricci curvature and Ollivier-Ricci curvature correctly identify brain regions that are known to be functionally or clinically relevant. Our results add to a growing body of evidence demonstrating the sensitivity of discrete Ricci curvature measures to changes in the organization of functional connectivity networks, both in health and disease.