RESUMO
Hypoxic-ischemic encephalopathy (HIE) secondary to perinatal asphyxia occurs when the brain does not receive enough oxygen and blood. A surrogate marker for "intact survival" is necessary for the successful management of HIE. The severity of HIE can be classified based on clinical presentation, including the presence of seizures, using a clinical classification scale called Sarnat staging; however, Sarnat staging is subjective, and the score changes over time. Furthermore, seizures are difficult to detect clinically and are associated with a poor prognosis. Therefore, a tool for continuous monitoring on the cot side is necessary, for example, an electroencephalogram (EEG) that noninvasively measures the electrical activity of the brain from the scalp. Then, multimodal brain imaging, when combined with functional near-infrared spectroscopy (fNIRS), can capture the neurovascular coupling (NVC) status. In this study, we first tested the feasibility of a low-cost EEG-fNIRS imaging system to differentiate between normal, hypoxic, and ictal states in a perinatal ovine hypoxia model. Here, the objective was to evaluate a portable cot-side device and perform autoregressive with extra input (ARX) modeling to capture the perinatal ovine brain states during a simulated HIE injury. So, ARX parameters were tested with a linear classifier using a single differential channel EEG, with varying states of tissue oxygenation detected using fNIRS, to label simulated HIE states in the ovine model. Then, we showed the technical feasibility of the low-cost EEG-fNIRS device and ARX modeling with support vector machine classification for a human HIE case series with and without sepsis. The classifier trained with the ovine hypoxia data labeled ten severe HIE human cases (with and without sepsis) as the "hypoxia" group and the four moderate HIE human cases as the "control" group. Furthermore, we showed the feasibility of experimental modal analysis (EMA) based on the ARX model to investigate the NVC dynamics using EEG-fNIRS joint-imaging data that differentiated six severe HIE human cases without sepsis from four severe HIE human cases with sepsis. In conclusion, our study showed the technical feasibility of EEG-fNIRS imaging, ARX modeling of NVC for HIE classification, and EMA that may provide a biomarker of sepsis effects on the NVC in HIE.
RESUMO
This study assessed the relationship between solitary confinement and post-traumatic stress disorder (PTSD) symptoms in a cohort of recently released former prisoners. The cross-sectional design utilized baseline data from the Transitions Clinic Network, a multi-site prospective longitudinal cohort study of post-incarceration medical care. Our main independent variable was self-reported solitary confinement during the participants' most recent incarceration; the dependent variable was the presence of PTSD symptoms determined by primary care (PC)-PTSD screening when participants initiated primary care in the community. We used multivariable logistic regression to adjust for potential confounders, such as prior mental health conditions, age, and gender. Among 119 participants, 43% had a history of solitary confinement and 28% screened positive for PTSD symptoms. Those who reported a history of solitary confinement were more likely to report PTSD symptoms than those without solitary confinement (43 vs. 16%, p < 0.01). In multivariable logistic regression, a history of solitary confinement (OR = 3.93, 95% CI 1.57-9.83) and chronic mental health conditions (OR = 4.04, 95% CI 1.52-10.68) were significantly associated with a positive PTSD screen after adjustment for the potential confounders. Experiencing solitary confinement was significantly associated with PTSD symptoms among individuals accessing primary care following release from prison. Larger studies should confirm these findings.