Neurologic Complications of Poverty: the Associations Between Poverty as a Social Determinant of Health and Adverse Neurologic Outcomes.

PURPOSE OF REVIEW: Increasing attention has been paid in recent decades to social determinants of health as a risk factor for disease development and disease severity. While traditionally heart disease, family history, lipid profile, and tobacco use have all been associated with increased risk of neurological disease, numerous studies now show that the influence of poverty may be just as strong a risk factor. This study summarizes the recent literature on poverty as it contributes to neurological disease. RECENT FINDINGS: Children growing up in poverty have increased risk for cognitive deficits and behavioral disorders as reported by Noble et al. (Dev Sci. 9(6):642-54, 2006) and Farah et al. (Brain Res. 1110(1):166-74, 2006) as well as worse outcomes when it comes to epilepsy management and disease course as discussed by Camfield et al. (Epilepsia. 57(11):1826-33, 2016). In adulthood, as the number of social determinants of health increases, the incidence of stroke and severe stroke increases significantly as reported by Reshetnyak et al. (Stroke. 51:2445-53, 2020) as does exposure to neurologically significant infectious diseases and incidence of dementia as reported by Sumilo et al. (Rev Med Virol. 18(2):81-95, 2008) and Zuelsdorff et al. (Alzheimer's Dement. 6(1):e12039, 2020). Social determinants of health including poverty should be considered a risk factor for disease. More attention is needed from clinicians as well as from a public health perspective to address this disparity.

DNA Strand Breaks and Denaturation as Probes of Chemical Reactivity versus Thermal Effects of Atmospheric Pressure Plasma Jets.

An atmospheric pressure plasma jet (APPJ) is being advanced as an alternative radiation type that offers excellent efficacy in an array of medical applications against specific biological targets such as DNA. This work explores the possibility of implementing DNA and its damage as a probe for specific plasma diagnostics such as reactive plasma species formation and transient local heating. We analyzed both APPJ characteristics based on the detection of plasma-induced strand breaks and DNA denaturation. Further, we implemented a machine learning model based on artificial neural networks to predict the type and extent of DNA damage for a given combination of APPJ parameter values. This methodology is an important step toward deciphering and explaining the potential adverse effects of APPJ on biological samples of any prospective interest in medicine.