Posterior circulation collaterals as predictors of outcome in basilar artery occlusion: a sub-analysis of the BASICS randomized trial

Introduction and purpose: Basilar artery occlusion (BAO) is still one of the most devastating neurological conditions associated with high morbidity and mortality. In the present study, we aimed to assess the role of posterior circulation collaterals as predictors of outcome in the BASICS trial and to compare two grading systems (BATMAN score and PC-CS) in terms of prognostic value. Methods: We performed a sub-analysis of the BASICS trial. Baseline clinical and imaging variables were analyzed. For the imaging analysis, baseline CT and CTA were analyzed by a central core lab. Only those patients with good or moderate quality of baseline CTA and with confirmed BAO were included. Multivariable binary logistic regression analysis was used to test the independent association of clinical and imaging characteristics with a favorable outcome at 3 months (defined as a modified Rankin Score of ≤3). ROC curve analysis was used to assess and compare accuracy between the two collateral grading systems. Results: The mean age was 67.0 (±12.5) years, 196 (65.3%) patients were males and the median NIHSS was 21.5 (IQR 11–35). Median NCCT pc-ASPECTS was 10 (IQR10-10) and median collateral scores for BATMAN and PC-CS were 8 (IQR 7–9) and 7 (IQR 6–8) respectively. Collateral scores were associated with favorable outcome at 3 months for both BATMAN and PC-CS but only with a modest accuracy on ROC curve analysis (AUC 0.62, 95% CI [0.55–0.69] and 0.67, 95% CI [0.60–0.74] respectively). Age (OR 0.97, 95% CI [0.95–1.00]), NIHSS (OR 0.91, 95% CI [0.89–0.94]) and collateral score (PC-CS – OR 1.2495% CI [1.02–1.51]) were independently associated with clinical outcome. Conclusion: The two collateral grading systems presented modest prognostic accuracy. Only the PC-CS was independently associated with a favorable outcome at 3 months

Posterior circulation collaterals as predictors of outcome in basilar artery occlusion: a sub-analysis of the BASICS randomized trial

Introduction and purposeBasilar artery occlusion (BAO) is still one of the most devastating neurological conditions associated with high morbidity and mortality. In the present study, we aimed to assess the role of posterior circulation collaterals as predictors of outcome in the BASICS trial and to compare two grading systems (BATMAN score and PC-CS) in terms of prognostic value.MethodsWe performed a sub-analysis of the BASICS trial. Baseline clinical and imaging variables were analyzed. For the imaging analysis, baseline CT and CTA were analyzed by a central core lab. Only those patients with good or moderate quality of baseline CTA and with confirmed BAO were included. Multivariable binary logistic regression analysis was used to test the independent association of clinical and imaging characteristics with a favorable outcome at 3 months (defined as a modified Rankin Score of ≤3). ROC curve analysis was used to assess and compare accuracy between the two collateral grading systems.ResultsThe mean age was 67.0 (±12.5) years, 196 (65.3%) patients were males and the median NIHSS was 21.5 (IQR 11–35). Median NCCT pc-ASPECTS was 10 (IQR10-10) and median collateral scores for BATMAN and PC-CS were 8 (IQR 7–9) and 7 (IQR 6–8) respectively. Collateral scores were associated with favorable outcome at 3 months for both BATMAN and PC-CS but only with a modest accuracy on ROC curve analysis (AUC 0.62, 95% CI [0.55–0.69] and 0.67, 95% CI [0.60–0.74] respectively). Age (OR 0.97, 95% CI [0.95–1.00]), NIHSS (OR 0.91, 95% CI [0.89–0.94]) and collateral score (PC-CS – OR 1.2495% CI [1.02–1.51]) were independently associated with clinical outcome.ConclusionThe two collateral grading systems presented modest prognostic accuracy. Only the PC-CS was independently associated with a favorable outcome at 3 months

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Vitamin A, vitamin E, iron and zinc status in a cohort of HIV-infected mothers and their uninfected infants

Introduction We hypothesized that nutritional deficiency would be common in a cohort of postpartum, human immunodeficiency virus (HIV)-infected women and their infants. Methods Weight and height, as well as blood concentrations of retinol, α-tocopherol, ferritin, hemoglobin, and zinc, were measured in mothers after delivery and in their infants at birth and at 6-12 weeks and six months of age. Retinol and α-tocopherol levels were quantified by high performance liquid chromatography, and zinc levels were measured by atomic absorption spectrophotometry. The maternal body mass index during pregnancy was adjusted for gestational age (adjBMI). Results Among the 97 women 19.6% were underweight. Laboratory abnormalities were most frequently observed for the hemoglobin (46.4%), zinc (41.1%), retinol (12.5%) and ferritin (6.5%) levels. Five percent of the women had mean corpuscular hemoglobin concentrations < 31g/dL. The most common deficiency in the infants was α-tocopherol (81%) at birth; however, only 18.5% of infants had deficient levels at six months of age. Large percentages of infants had zinc (36.8%) and retinol (29.5%) deficiencies at birth; however, these percentages decreased to 17.5% and 18.5%, respectively, by six months of age. No associations between infant micronutrient deficiencies and either the maternal adjBMI category or maternal micronutrient deficiencies were found. Conclusions Micronutrient deficiencies were common in HIV-infected women and their infants. Micronutrient deficiencies were less prevalent in the infants at six months of age. Neither underweight women nor their infants at birth were at increased risk for micronutrient deficiencies

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost