No abstract

Spaceflight has widespread effects on human performance, including on the ability to dual task. Here, we examine how a spaceflight analog comprising 30 days of head-down-tilt bed rest (HDBR) combined with 0.5% ambient CO2 (HDBR + CO2) influences performance and functional activity of the brain during single and dual tasking of a cognitive and a motor task. The addition of CO2 to HDBR is thought to better mimic the conditions aboard the International Space Station. Participants completed three tasks: (1) COUNT: counting the number of times an oddball stimulus was presented among distractors; (2) TAP: tapping one of two buttons in response to a visual cue; and (3) DUAL: performing both tasks concurrently. Eleven participants (six males) underwent functional MRI (fMRI) while performing these tasks at six time points: twice before HDBR + CO2, twice during HDBR + CO2, and twice after HDBR + CO2. Behavioral measures included reaction time, standard error of reaction time, and tapping accuracy during the TAP and DUAL tasks, and the dual task cost (DTCost) of each of these measures. We also quantified DTCost of fMRI brain activation. In our previous HDBR study of 13 participants (with atmospheric CO2), subjects experienced TAP accuracy improvements during bed rest, whereas TAP accuracy declined while in the current study of HDBR + CO2. In the HDBR + CO2 subjects, we identified a region in the superior frontal gyrus that showed decreased DTCost of brain activation while in HDBR + CO2, and recovered back to baseline levels before the completion of bed rest. Compared to HDBR alone, we found different patterns of brain activation change with HDBR + CO2. HDBR + CO2 subjects had increased DTCost in the middle temporal gyrus whereas HDBR subjects had decreased DTCost in the same area. Five of the HDBR + CO2 subjects developed signs of spaceflight-associated neuro-ocular syndrome (SANS). These subjects exhibited lower baseline dual task activation and higher slopes of change during HDBR + CO2 than subjects with no signs of SANS. Collectively, this pilot study provides insight into the additional and/or interactive effects of CO2 levels during HDBR, and information regarding the impacts of this spaceflight analog environment on the neural correlates of dual tasking.

BackgroundPreeclampsia (PE) is one of the leading factors in maternal and perinatal mortality and morbidity worldwide. The only cure for PE to date is to deliver the placenta and stop gestation. However, the timing of delivery among PE patients is essential to minimize the risk of severe maternal morbidities, and at the same time ensure the survival of the baby.MethodsIn this study, we constructed a series of deep learning-based models to predict the prognosis, or the time to delivery, since the initial diagnosis of PE using electronic health record (EHR) data. We extracted and processed 1578 pregnancies in Michigan Medicine at the University of Michigan in 2015-2021 as the discovery cohort. Using the Cox-nnet v2 algorithm, we built the baseline model with EHR information prior to diagnosis, as well as the full model including baseline information and lab testing results and vital signs at the time of diagnosis. We evaluated the models using the C-index and log-rank p-values in KM survival curves, using both 20% testing data of the Michigan cohort, as well as 1177 PE pregnancy EHR data from the Medical Center of the University of Florida.ResultsThe baseline prognosis model for time to delivery since PE diagnosis achieved C-index values of 0.75 and 0.72 on the training and testing set respectively. While the full model reached C-indices of 0.77 and 0.74 in the same training and testing sets. Both models performed better than their Cox-PH model counterparts. The seven most important features in the baseline model in descending order were diagnosis gestational age, severe PE, past PE, age, parity, gravidity, and uncomplicated diabetes. Meanwhile, 14 most important features were selected and interpreted in the full model, including diagnosis gestational age, parity, severe PE, past PE, features in lab tests (white blood cell, platelet, and red blood cell counts, AST value), min respiratory rate, and features measuring blood pressure (minimum, mean and standard deviation of systolic blood pressure, and maximum and standard deviation of diastolic blood pressure).ConclusionThe time to delivery predicting models provide clinicians valuable tools and options to quantify the delivery risks and make better decisions on the optimal delivery time of PE patients at the time of diagnosis. Implementation of these actionable models into PE clinical care practice is expected to significantly improve the management of PE patients.

Background Preeclampsia is a potentially fatal complication during pregnancy, characterized by high blood pressure and presence of proteins in the urine. Due to its complexity, prediction of preeclampsia onset is often difficult and inaccurate. Methods This study aims to create quantitative models to predict the onset gestational age of preeclampsia using electronic health records. We retrospectively collected 1178 preeclamptic pregnancy records from the University of Michigan Health System(UM) as the discovery cohort, and 881 records from the University of Florida Health System(UF) as the validation cohort. We constructed two Cox-proportional hazards models with Lasso regularization: one baseline model utilizing maternal and pregnancy characteristics, and the other full model with additional lab results, vital signs, and medications in the first 20 weeks of pregnancy. We built the models using 80% of the UM data and subsequently tested them on the remaining 20% UM data and validated with UF data. We further stratified the patients into high and low risk groups for preeclampsia onset risk assessment. Findings The baseline model reached C-indices of 0.64 and 0.61 in the 20% UM testing data and the UF validation data, respectively, while the full model increased these C-indices to 0.69 and 0.61 respectively. Both the baseline and full models contain five selective features, among which number of fetuses in the pregnancy, hypertension and parity are shared between the two models with similar hazard ratios. In the baseline model, history of complicated type II diabetes and a mood/anxiety disorder during the first 20 weeks of pregnancy were important. In the full model, maximum diastolic blood pressure in early pregnancy was the predominant feature. Interpretation Electronic health record data provide useful information to predict gestational age of preeclampsia onset. Stratification of the cohorts using five-predictor Cox-PH models provide clinicians with convenient tools to assess the patient onset time of preeclampsia. Funding This study was supported by grants through the NIEHS, NICHD, NIDDK, and NCATS.