Preeclampsia is a multifactorial pathology with negative outcomes in affected patients in both the peripartum and postpartum period. Black patients in the United States, when compared to their White ...and Hispanic counterparts, have higher rates of preeclampsia. This article aims to review the current literature to investigate how race, social determinants of health, and genetic profiles influence the prevalence and outcomes of patients with preeclampsia. Published studies utilized in this review were identified through PubMed using authors' topic knowledge and a focused search through a Medline search strategy. These articles were thoroughly reviewed to explore the contributing biosocial factors, genes/biomarkers, as well as negative outcomes associated with disparate rates of preeclampsia. Increased rates of contributing comorbidities, including hypertension and obesity, which are largely associated with low access to care in Black patient populations lead to disparate rates of preeclampsia in this population. Limited research shows an association between increased rate of preeclampsia in Black patients and specific APOL1, HLA-G, and PP13 gene polymorphisms as well as factor V Leiden mutations. Further research is required to understand the use of certain biomarkers in predicting preeclampsia within racial populations. Understanding contributing biosocial factors and identifying genes that may predispose high-risk populations may help to address the disparate rates of preeclampsia in Black patients as described in this review. Further research is required to understand if serum, placental, or urine biomarkers may be used to predict individuals at risk of developing preeclampsia in pregnancy. KEY POINTS: · Prevalence of preeclampsia in the U.S. is higher in Black patients compared to other racial groups.. · Patients with preeclampsia are at risk for poorer health outcomes both during and after delivery.. · Limited research suggests specific biomarkers or gene polymorphisms contribute to this difference; however, explanations for this disparity are multifactorial and further investigation is necessary..
With improvements to both scan quality and facial recognition software, there is an increased risk of participants being identified by a 3D render of their structural neuroimaging scans, even when ...all other personal information has been removed. To prevent this, facial features should be removed before data are shared or openly released, but while there are several publicly available software algorithms to do this, there has been no comprehensive review of their accuracy within the general population. To address this, we tested multiple algorithms on 300 scans from three neuroscience research projects, funded in part by the Ontario Brain Institute, to cover a wide range of ages (3-85 years) and multiple patient cohorts. While skull stripping is more thorough at removing identifiable features, we focused mainly on defacing software, as skull stripping also removes potentially useful information, which may be required for future analyses. We tested six publicly available algorithms (afni_refacer, deepdefacer, mri_deface, mridefacer, pydeface, quickshear), with one skull stripper (FreeSurfer) included for comparison. Accuracy was measured through a pass/fail system with two criteria; one, that all facial features had been removed and two, that no brain tissue was removed in the process. A subset of defaced scans were also run through several preprocessing pipelines to ensure that none of the algorithms would alter the resulting outputs. We found that the success rates varied strongly between defacers, with afni_refacer (89%) and pydeface (83%) having the highest rates, overall. In both cases, the primary source of failure came from a single dataset that the defacer appeared to struggle with - the youngest cohort (3-20 years) for afni_refacer and the oldest (44-85 years) for pydeface, demonstrating that defacer performance not only depends on the data provided, but that this effect varies between algorithms. While there were some very minor differences between the preprocessing results for defaced and original scans, none of these were significant and were within the range of variation between using different NIfTI converters, or using raw DICOM files.
PURPOSEMagnetic resonance imaging (MRI) scanner-specific geometric distortions may contribute to scanner induced variability and decrease volumetric measurement precision for multi-site studies. The ...purpose of this study was to determine whether geometric distortion correction increases the precision of brain volumetric measurements in a multi-site multi-scanner study. METHODSGeometric distortion variation was quantified over a one-year period at 10 sites using the distortion fields estimated from monthly 3D T1-weighted MRI geometrical phantom scans. The variability of volume and distance measurements were quantified using synthetic volumes and a standard quantitative MRI (qMRI) phantom. The effects of geometric distortion corrections on MRI derived volumetric measurements of the human brain were assessed in two subjects scanned on each of the 10 MRI scanners and in 150 subjects with cerebrovascaular disease (CVD) acquired across imaging sites. RESULTSGeometric distortions were found to vary substantially between different MRI scanners but were relatively stable on each scanner over a one-year interval. Geometric distortions varied spatially, increasing in severity with distance from the magnet isocenter. In measurements made with the qMRI phantom, the geometric distortion correction decreased the standard deviation of volumetric assessments by 35% and distance measurements by 42%. The average coefficient of variance decreased by 16% in gray matter and white matter volume estimates in the two subjects scanned on the 10 MRI scanners. CONCLUSIONGeometric distortion correction using an up-to-date correction field is recommended to increase precision in volumetric measurements made from MRI images.
Background and purpose
The pathophysiology of Parkinson's disease (PD) negatively affects brain network connectivity, and in the presence of brain white matter hyperintensities (WMHs) cognitive and ...motor impairments seem to be aggravated. However, the role of WMHs in predicting accelerating symptom worsening remains controversial. The objective was to investigate whether location and segmental brain WMH burden at baseline predict cognitive and motor declines in PD after 2 years.
Methods
Ninety‐eight older adults followed longitudinally from Ontario Neurodegenerative Diseases Research Initiative with PD of 3–8 years in duration were included. Percentages of WMH volumes at baseline were calculated by location (deep and periventricular) and by brain region (frontal, temporal, parietal, occipital lobes and basal ganglia + thalamus). Cognitive and motor changes were assessed from baseline to 2‐year follow‐up. Specifically, global cognition, attention, executive function, memory, visuospatial abilities and language were assessed as were motor symptoms evaluated using the Movement Disorder Society Unified Parkinson's Disease Rating Scale Part III, spatial–temporal gait variables, Freezing of Gait Questionnaire and Activities Specific Balance Confidence Scale.
Results
Regression analysis adjusted for potential confounders showed that total and periventricular WMHs at baseline predicted decline in global cognition (p < 0.05). Also, total WMH burden predicted the decline of executive function (p < 0.05). Occipital WMH volumes also predicted decline in global cognition, visuomotor attention and visuospatial memory declines (p < 0.05). WMH volumes at baseline did not predict motor decline.
Conclusion
White matter hyperintensity burden at baseline predicted cognitive but not motor decline in early to mid‐stage PD. The motor decline observed after 2 years in these older adults with PD is probably related to the primary neurodegenerative process than comorbid white matter pathology.
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