iPSC-derived reactive astrocytes from patients with multiple-sclerosis protect cocultured neurons in inflammatory conditions

Multiple sclerosis (MS) is the most common chronic inflammatory disease of the central nervous system (CNS). The individual course is highly variable with complete remission in some patients and relentless courses in others. We generated induced pluripotent stem cells (iPSCs) to investigate possible mechanisms in benign MS (BMS), compared to progressive MS (PMS). We differentiated neurons and astrocytes that were then stressed with inflammatory cytokines typically associated with MS. TNFα/IL-17A treatment increased neurite damage in MS neurons irrespective of clinical phenotypes. In contrast, TNFα/IL-17A-reactive BMS astrocytes cultured with healthy control (HC) neurons exhibited significantly decreased axonal damage, compared to PMS astrocytes. Accordingly, single cell transcriptomic analysis of BMS-astrocyte co-cultured neurons demonstrated upregulated pathways of neuronal resilience, namely these astrocytes revealed differential growth factor expression. Moreover, supernatants from BMS astrocyte-neuron co-cultures rescued TNFα/IL-17-induced neurite damage. This process was associated with the unique expression of the growth factors, LIF and TGF-β1, as induced by TNFα/IL-17 and JAK-STAT activation. Our findings highlight a potential therapeutic role of modulating astrocyte phenotypes that generate a neuroprotective milieu preventing permanent neuronal damage

Developing a Risk Model to Target High-Risk Preventive Interventions for Sexual Assault Victimization Among Female U.S. Army Soldiers

Sexual violence victimization is a significant problem among female U.S. military personnel. Preventive interventions for high-risk individuals might reduce prevalence but would require accurate targeting. We attempted to develop a targeting model for female Regular U.S. Army soldiers based on theoretically guided predictors abstracted from administrative data records. As administrative reports of sexual assault victimization are known to be incomplete, parallel machine learning models were developed to predict administratively recorded (in the population) and self-reported (in a representative survey) victimization. Capture–recapture methods were used to combine predictions across models. Key predictors included low status, crime involvement, and treated mental disorders. Area under the receiver operating characteristic curve was .83–.88. Between 33.7% and 63.2% of victimizations occurred among soldiers in the highest risk ventile (5%). This high concentration of risk suggests that the models could be useful in targeting preventive interventions, although final determination would require careful weighing of intervention costs, effectiveness, and competing risks