University of Alabama at Birmingham

About: University of Alabama at Birmingham is a education organization based out in Birmingham, Alabama, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 38523 authors who have published 86775 publications receiving 3930642 citations. The organization is also known as: UAB & The University of Alabama at Birmingham.

Estimated global mortality associated with the first 12 months of 2009 pandemic influenza A H1N1 virus circulation: a modelling study

Abstract: Summary Background 18 500 laboratory-confirmed deaths caused by the 2009 pandemic influenza A H1N1 were reported worldwide for the period April, 2009, to August, 2010. This number is likely to be only a fraction of the true number of the deaths associated with 2009 pandemic influenza A H1N1. We aimed to estimate the global number of deaths during the first 12 months of virus circulation in each country. Methods We calculated crude respiratory mortality rates associated with the 2009 pandemic influenza A H1N1 strain by age (0–17 years, 18–64 years, and >64 years) using the cumulative (12 months) virus-associated symptomatic attack rates from 12 countries and symptomatic case fatality ratios (sCFR) from five high-income countries. To adjust crude mortality rates for differences between countries in risk of death from influenza, we developed a respiratory mortality multiplier equal to the ratio of the median lower respiratory tract infection mortality rate in each WHO region mortality stratum to the median in countries with very low mortality. We calculated cardiovascular disease mortality rates associated with 2009 pandemic influenza A H1N1 infection with the ratio of excess deaths from cardiovascular and respiratory diseases during the pandemic in five countries and multiplied these values by the crude respiratory disease mortality rate associated with the virus. Respiratory and cardiovascular mortality rates associated with 2009 pandemic influenza A H1N1 were multiplied by age to calculate the number of associated deaths. Findings We estimate that globally there were 201 200 respiratory deaths (range 105 700–395 600) with an additional 83 300 cardiovascular deaths (46 000–179 900) associated with 2009 pandemic influenza A H1N1. 80% of the respiratory and cardiovascular deaths were in people younger than 65 years and 51% occurred in southeast Asia and Africa. Interpretation Our estimate of respiratory and cardiovascular mortality associated with the 2009 pandemic influenza A H1N1 was 15 times higher than reported laboratory-confirmed deaths. Although no estimates of sCFRs were available from Africa and southeast Asia, a disproportionate number of estimated pandemic deaths might have occurred in these regions. Therefore, efforts to prevent influenza need to effectively target these regions in future pandemics. Funding None.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Effects of estrogen plus progestin on risk of fracture and bone mineral density: the Women's Health Initiative randomized trial.

Abstract: Context In the Women's Health Initiative trial of estrogen-plus-progestin therapy, women assigned to active treatment had fewer fractures. Objective To test the hypothesis that the relative risk reduction of estrogen plus progestin on fractures differs according to risk factors for fractures. Design, setting, and participants Randomized controlled trial (September 1993-July 2002) in which 16 608 postmenopausal women aged 50 to 79 years with an intact uterus at baseline were recruited at 40 US clinical centers and followed up for an average of 5.6 years. Intervention Women were randomly assigned to receive conjugated equine estrogen, 0.625 mg/d, plus medroxyprogesterone acetate, 2.5 mg/d, in 1 tablet (n = 8506) or placebo (n = 8102). Main outcome measures All confirmed osteoporotic fracture events that occurred from enrollment to discontinuation of the trial (July 7, 2002); bone mineral density (BMD), measured in a subset of women (n = 1024) at baseline and years 1 and 3; and a global index, developed to summarize the balance of risks and benefits to test whether the risk-benefit profile differed across tertiles of fracture risk. Results Seven hundred thirty-three women (8.6%) in the estrogen-plus-progestin group and 896 women (11.1%) in the placebo group experienced a fracture (hazard ratio [HR], 0.76; 95% confidence interval [CI], 0.69-0.83). The effect did not differ in women stratified by age, body mass index, smoking status, history of falls, personal and family history of fracture, total calcium intake, past use of hormone therapy, BMD, or summary fracture risk score. Total hip BMD increased 3.7% after 3 years of treatment with estrogen plus progestin compared with 0.14% in the placebo group (P Conclusions This study demonstrates that estrogen plus progestin increases BMD and reduces the risk of fracture in healthy postmenopausal women. The decreased risk of fracture attributed to estrogen plus progestin appeared to be present in all subgroups of women examined. When considering the effects of hormone therapy on other important disease outcomes in a global model, there was no net benefit, even in women considered to be at high risk of fracture.