Explore the vast range of titles available.

Depression and neurocognitive disorder continue to be the major neuropsychiatric disorders affecting persons with HIV (PWH). The prevalence of major depressive disorder is two to fourfold higher among PWH than the general population (∼6.7%). Prevalence estimates of neurocognitive disorder among PWH range from 25 to over 47% – depending upon the definition used (which is currently evolving), the size of the test battery employed, and the demographic and HIV disease characteristics of the participants included, such as age range and sex distribution. Both major depressive disorder and neurocognitive disorder also result in substantial morbidity and premature mortality. However, though anticipated to be relatively common, the comorbidity of these two disorders in PWH has not been formally studied. This is partly due to the clinical overlap of the neurocognitive symptoms of these two disorders. Both also share neurobehavioral aspects — particularly apathy — as well as an increased risk for non-adherence to antiretroviral therapy. Shared pathophysiological mechanisms potentially explain these intersecting phenotypes, including neuroinflammatory, vascular, and microbiomic, as well as neuroendocrine/neurotransmitter dynamic mechanisms. Treatment of either disorder affects the other with respect to symptom reduction as well as medication toxicity. We present a unified model for the comorbidity based upon deficits in dopaminergic transmission that occur in both major depressive disorder and HIV-associated neurocognitive disorder. Specific treatments for the comorbidity that decrease neuroinflammation and/or restore associated deficits in dopaminergic transmission may be indicated and merit study.

Africa is projected to add one billion urban residents by 2050. Yet developing sustainable solutions to tackle the host of challenges posed by rapid urban population growth is stymied by a lack municipality-level population data across the continent. To fill this gap, we intersect volunteered urban settlement data from OpenStreetMap with five synthetic gridded population datasets to estimate the how Africa's urban population is distributed among over 4750 individual urban settlements across Africa. We assess how urban settlement distributions changed from 2000 to 2015 within and between countries and across moisture zones. To this end, we construct urban settlement Lorenz curves to calculate change in Gini coefficients and test the degree to which Africa's urban settlements distributions fit power law distributions exhibited by Zipf's law. Our results reveal that 77%-85% of urban settlements in Africa have fewer than 100 000 people and that at least 50% of Africa's urban population live in urban settlements with fewer than 1 million residents. Across almost all African countries, the distribution of urban population shifted towards larger cities between 2000 and 2015. However, in arid regions, our results indicate that small- and medium-sized urban settlements are absorbing a greater share of urban population growth compared to large urban settlements. While our urban population estimates vary across gridded population datasets and differ from United Nations estimates, this is the first paper to measure urban population across Africa using a consistent methodology to identify urban settlement populations. Unlike UN urban population data for Africa, our results can readily be incorporated with geolocated environmental, public health, and economic data to support efforts to monitor United Nations Sustainable Development Goals related to urban sustainability, poverty reduction, and food security across Africa's ever-growing urban settlements.

Objectives To evaluate the effect of hepatic metastatic lesion size on inter-reader reproducibility of CT-based 2D radiomics imaging features. Methods Computerized tomography (CT) scans of 59 liver metastases from 34 patients with colorectal cancer were evaluated. Image segmentation was performed manually by three readers blinded to each other’s results. For each radiomics feature, we created two datasets by sorting measurements according to size, i.e., (i) from the smallest to the largest lesion and (ii) from the largest to the smallest lesion. The Lin concordance correlation coefficient (CCC) was employed to analyze the reproducibility of radiomics features. In particular, the CCC was computed as a function of a number of elements in the dataset, by gradually adding lesions from each sorted dataset. To evaluate the effect of lesion size, we analyzed the difference between these two functions thus assessing the contribution of small and large lesions into the reproducibility of radiomics features. Results Inter-reader reproducibility of CT-based 2D radiomics features assessed using Lin’s CCC demonstrates tumor-size dependence. For example, the Lin CCC for GLCM contrast equals 0.88 (95% C.I. 0.84 to 0.92, p  < 0.003) and could change by an additional + / − 0.06 depending on the presence of large or small lesions. Conclusions Groups of “large” and “small” lesions show different inter-reader reproducibility. The inter-reader reproducibility from the mixed group consisting of “large” and “small” lesions depends on the lesion-size distribution and can vary widely. This finding could partially explain variability in reproducibility of radiomics features in the literature. Key Points • Groups of “large” and “small” lesions show different inter-reader reproducibility. • The inter-reader reproducibility from the mixed group consisting of “large” and “small” lesions depends on the lesion-size distribution.

Bicuspid aortic valve (BAV) disease is associated with increased risk of aortopathy. In addition to current intervention guidelines, BAV mediated changes in aortic 3D hemodynamics have been considered as risk stratification measures. We aimed to evaluate the association of 4D flow cardiovascular magnetic resonance (CMR) derived voxel-wise aortic reverse flow with aortic dilation and to investigate the role of aortic valve regurgitation (AR) and stenosis (AS) on reverse flow in systole and diastole. 510 patients with BAV (52 ± 14 years) and 120 patients with trileaflet aortic valve (TAV) (61 ± 11 years) and mid-ascending aorta diameter (MAAD) > 35 mm who underwent CMR including 4D flow CMR were retrospectively included. An age and sex-matched healthy control cohort (n = 25, 49 ± 12 years) was selected. Voxel-wise reverse flow was calculated in the aorta and quantified by the mean reverse flow in the ascending aorta (AAo) during systole and diastole. BAV patients without AS and AR demonstrated significantly increased systolic and diastolic reverse flow (222% and 13% increases respectively, p < 0.01) compared to healthy controls and also had significantly increased systolic reverse flow compared to TAV patients with aortic dilation (79% increase, p < 0.01). In patients with isolated AR, systolic and diastolic AAo reverse flow increased significantly with AR severity (c = − 83.2 and c = − 205.6, p < 0.001). In patients with isolated AS, AS severity was associated with an increase in both systolic (c = − 253.1, p < 0.001) and diastolic (c = − 87.0, p = 0.02) AAo reverse flow. Right and left/right and non-coronary fusion phenotype showed elevated systolic reverse flow (> 17% increase, p < 0.01). Right and non-coronary fusion phenotype showed decreased diastolic reverse flow (> 27% decrease, p < 0.01). MAAD was an independent predictor of systolic (p < 0.001), but not diastolic, reverse flow (p > 0.1). 4D flow CMR derived reverse flow associated with BAV was successfully captured even in the absence of AR or AS and in comparison to TAV patients with aortic dilation. Diastolic AAo reverse flow increased with AR severity while AS severity strongly correlated with increased systolic reverse flow in the AAo. Additionally, increasing MAAD was independently associated with increasing systolic AAo reverse flow. Thus, systolic AAo reverse flow may be a valuable metric for evaluating disease severity in future longitudinal outcome studies.

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience.

Background Quantitative evaluation of mitral regurgitation (MR) in hypertrophic cardiomyopathy (HCM) by cardiovascular magnetic resonance (CMR) relies on an indirect volumetric calculation. The aim of this study was to directly assess and quantify MR jets in patients with HCM using 4D flow CMR jet tracking in comparison to standard-of-care CMR indirect volumetric method. Methods This retrospective study included patients with HCM undergoing 4D flow CMR. By the indirect volumetric method from CMR, MR volume was quantified as left ventricular stroke volume minus forward aortic volume. By 4D flow CMR direct jet tracking, multiplanar reformatted planes were positioned in the peak velocity of the MR jet during systole to calculate through-plane regurgitant flow. MR severity was collected for agreement analysis from a clinical echocardiograms performed within 1 month of CMR. Inter-method and inter-observer agreement were assessed by intraclass correlation coefficient (ICC), Bland–Altman analysis, and Cohen’s kappa. Results Thirty-seven patients with HCM were included. Direct jet tracking demonstrated good inter-method agreement of MR volume compared to the indirect volumetric method (ICC = 0.80, p = 0.004) and fair agreement of MR severity (kappa = 0.27, p = 0.03). Direct jet tracking showed higher agreement with echocardiography (kappa = 0.35, p = 0.04) than indirect volumetric method (kappa = 0.16, p = 0.35). Inter-observer reproducibility of indirect volumetric method components revealed the lowest reproducibility in end-systolic volume (ICC = 0.69, p = 0.15). Indirect volumetric method showed good agreement of MR volume (ICC = 0.80, p = 0.003) and fair agreement of MR severity (kappa = 0.38, p < 0.001). Direct jet tracking demonstrated (1) excellent inter-observer reproducibility of MR volume (ICC = 0.97, p < 0.001) and MR severity (kappa = 0.84, p < 0.001) and (2) excellent intra-observer reproducibility of MR volume (ICC = 0.98, p < 0.001) and MR severity (kappa = 0.88, p < 0.001). Conclusions Quantifying MR and assessing MR severity by indirect volumetric method in HCM patients has limited inter-observer reproducibility. 4D flow CMR jet tracking is a potential alternative technique to directly quantify and assess MR severity with excellent inter- and intra-observer reproducibility and higher agreement with echocardiography in this population.

Native T1, extracellular volume fraction (ECV), and late gadolinium enhancement (LGE) characterize myocardial tissue and relate to patient prognosis in a variety of diseases, including pulmonary hypertension. The purpose of this study was to evaluate if left ventricle (LV) fibrosis measurements have prognostic value for cardiac outcomes in pulmonary hypertension subgroups. 54 patients with suspected pulmonary hypertension underwent right-heart catheterization and were classified into pulmonary hypertension subgroups: pre-capillary component (PreCompPH) and isolated post-capillary (IpcPH). Cardiac magnetic resonance imaging (MRI) scans were performed with the acquisition of balanced cine steady-state free precession, native T1, and LGE pulse sequences to measure cardiac volumes and myocardial fibrosis. Associations between cardiac events and cardiac MRI measurements were analyzed within PreCompPH and IpcPH patients. IpcPH: LV native T1 was higher in patients who experienced a cardiac event within two years vs. those who did not. In patients with LV native T1 > 1050 ms, the rate of cardiac events was higher. ECV and quantitative LGE did not differ between groups. PreCompPH: native T1, ECV, and quantitative/qualitative LGE did not differ between patients who experienced a cardiac event within two years vs. those who did not. LV native T1 may have potential value for forecasting cardiac events in IpcPH, but not in PreCompPH, patients.

In swine models, there are well-established protocols for creating a closed-chest myocardial infarction (MI) as well as protocols for characterization of cardiac function with cardiac magnetic resonance (CMR). This methods manuscript outlines a novel technique in CMR data acquisition utilizing smart-signal gradient recalled echo (GRE)-based array sequences in a free-breathing swine heart failure model allowing for both high spatial and temporal resolution imaging. Nine male Yucatan mini swine weighing 48.7 ± 1.6 kg at 58.2 ± 3.1 weeks old underwent the outlined imaging protocol before and 1-month after undergoing closed chest left anterior descending coronary artery (LAD) occlusion/reperfusion. The left ventricular ejection fraction (LVEF) at baseline was 59.3 ± 2.4% and decreased to 48.1 ± 3.7% 1-month post MI (P = 0.029). The average end-diastolic volume (EDV) at baseline was 55.2 ± 1.7 ml and increased to 74.2 ± 4.2 ml at 1-month post MI (P = 0.001). The resulting images from this novel technique and post-imaging analysis are presented and discussed. In a Yucatan swine model of heart failure via closed chest left anterior descending coronary artery (LAD) occlusion/reperfusion, we found that CMR with GRE-based array sequences produced clinical-grade images with high spatial and temporal resolution in the free-breathing setting.

Cardiac magnetic resonance imaging (MRI) is emerging as an alternative to right heart catheterization for the evaluation of pulmonary hypertension (PH) patients. The aim of this study was to compare cardiac MRI-derived left ventricle fibrosis indices between pre-capillary PH (PrePH) and isolated post-capillary PH (IpcPH) patients and assess their associations with measures of ventricle function. Global and segmental late gadolinium enhancement (LGE), longitudinal relaxation time (native T1) maps, and extracellular volume fraction (ECV) were compared among healthy controls (N = 25; 37% female; 52 ± 13 years), PH patients (N = 48; 60% female; 60 ± 14 years), and PH subgroups (PrePH: N = 29; 65% female; 55 ± 12 years, IpcPH: N = 19; 53% female; 66 ± 13 years). Cardiac cine measured ejection fraction, end diastolic, and end systolic volumes and were assessed for correlations with fibrosis. LGE mural location was qualitatively assessed on a segmental basis for all subjects. PrePH patients had elevated (apical-, mid-antero-, and mid-infero) septal left ventricle native T1 values (1080 ± 74 ms, 1077 ± 39 ms, and 1082 ± 47 ms) compared to IpcPH patients (1028 ± 53 ms, 1046 ± 36 ms, 1051 ± 44 ms) (p < 0.05). PrePH had a higher amount of insertional point LGE (69%) and LGE patterns characteristic of non-vascular fibrosis (77%) compared to IpcPH (37% and 46%, respectively) (p < 0.05; p < 0.05). Assessment of global LGE, native T1, and ECV burdens did not show a statistically significant difference between PrePH (1.9 ± 2.7%, 1056.2 ± 36.3 ms, 31.2 ± 3.7%) and IpcPH (2.7 ± 2.7%, 1042.4 ± 28.1 ms, 30.7 ± 4.7%) (p = 0.102; p = 0.229 p = 0.756). Global native T1 and ECV were higher in patients (1050.9 ± 33.8 and 31.0 ± 4.1%) than controls (28.2 ± 3.7% and 1012.9 ± 29.4 ms) (p < 0.05). Cardiac MRI-based tissue characterization may augment understanding of cardiac involvement and become a tool to facilitate PH patient classification.