Point-of-care breath test for biomarkers of active pulmonary tuberculosis

Rationale: Volatile organic compounds (VOCs) in breath provide biomarkers of tuberculosis (TB) because Mycobacterium tuberculosis manufactures VOC metabolites that are detectable in the breath of infected patients. Objectives: We evaluated breath VOC biomarkers in subjects with active pulmonary TB, using an internet-linked rapid point-of-care breath test. Methods: 279 subjects were studied at four centers in three countries, Philippines, UK, and India, and data was analyzed from 251 (130 active pulmonary TB, 121 controls). A point-of-care system collected and concentrated breath and air VOCs, and analyzed them with automated thermal desorption, gas chromatography, and surface acoustic wave detection. A breath test was completed in 6 min. Chromatograms were converted to a series of Kovats Index (KI) windows, and biomarkers of active pulmonary TB were identified by Monte Carlo analysis of KI window alveolar gradients (abundance in breath minus abundance in room air). Measurements and main results: Multiple Monte Carlo simulations identified eight KI windows as biomarkers with better than random performance. Four KI windows corresponded with KI values of VOCs previously identified as biomarkers of pulmonary TB and metabolic products of M. tuberculosis, principally derivatives of naphthalene, benzene and alkanes. A multivariate predictive algorithm identified active pulmonary TB with 80% accuracy (area under curve of receiver operating characteristic curve), sensitivity = 71.2%, and specificity = 72%. Accuracy increased to 84% in age-matched subgroups. In a population with 5% prevalence, the breath test would identify active pulmonary TB with 98% negative predictive value and 13% positive predictive value. Conclusions: A six-minute point-of-care breath test for volatile biomarkers accurately identified subjects with active pulmonary TB. © 2011 Elsevier Ltd. All rights reserved

Use of multidimensional item response theory methods for dementia prevalence prediction : an example using the Health and Retirement Survey and the Aging, Demographics, and Memory Study

Background Data sparsity is a major limitation to estimating national and global dementia burden. Surveys with full diagnostic evaluations of dementia prevalence are prohibitively resource-intensive in many settings. However, validation samples from nationally representative surveys allow for the development of algorithms for the prediction of dementia prevalence nationally. Methods Using cognitive testing data and data on functional limitations from Wave A (2001-2003) of the ADAMS study (n = 744) and the 2000 wave of the HRS study (n = 6358) we estimated a two-dimensional item response theory model to calculate cognition and function scores for all individuals over 70. Based on diagnostic information from the formal clinical adjudication in ADAMS, we fit a logistic regression model for the classification of dementia status using cognition and function scores and applied this algorithm to the full HRS sample to calculate dementia prevalence by age and sex. Results Our algorithm had a cross-validated predictive accuracy of 88% (86-90), and an area under the curve of 0.97 (0.97-0.98) in ADAMS. Prevalence was higher in females than males and increased over age, with a prevalence of 4% (3-4) in individuals 70-79, 11% (9-12) in individuals 80-89 years old, and 28% (22-35) in those 90 and older. Conclusions Our model had similar or better accuracy as compared to previously reviewed algorithms for the prediction of dementia prevalence in HRS, while utilizing more flexible methods. These methods could be more easily generalized and utilized to estimate dementia prevalence in other national surveys

Global, regional, and national burden of colorectal cancer and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Funding: F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia, I.P. (FCT), in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy i4HB; FCT/MCTES through the project UIDB/50006/2020. J Conde acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006.proofepub_ahead_of_prin

Mapping inequalities in exclusive breastfeeding in low- and middle-income countries, 2000–2018

Exclusive breastfeeding (EBF)—giving infants only breast-milk for the first 6 months of life—is a component of optimal breastfeeding practices effective in preventing child morbidity and mortality. EBF practices are known to vary by population and comparable subnational estimates of prevalence and progress across low- and middle-income countries (LMICs) are required for planning policy and interventions. Here we present a geospatial analysis of EBF prevalence estimates from 2000 to 2018 across 94 LMICs mapped to policy-relevant administrative units (for example, districts), quantify subnational inequalities and their changes over time, and estimate probabilities of meeting the World Health Organization’s Global Nutrition Target (WHO GNT) of ≥70% EBF prevalence by 2030. While six LMICs are projected to meet the WHO GNT of ≥70% EBF prevalence at a national scale, only three are predicted to meet the target in all their district-level units by 2030

Prevalence and Outcomes Associated with Vitamin D Deficiency among Indexed Hospitalizations with Cardiovascular Disease and Cerebrovascular Disorder—A Nationwide Study

Background: According to past studies, recovery and survival following severe vascular events such as acute myocardial infarction and stroke are negatively impacted by vitamin D deficiency. However, the national estimate on disability-related burden is unclear. We intend to evaluate the prevalence and outcomes of vitamin D deficiency (VDD) among patients with cardiovascular disease (CVD) and cerebrovascular disorder (CeVD). Methods: We performed a cross-sectional study on the Nationwide Inpatient Sample data (2016–2017) of adult (≥18 years) hospitalizations. We identified patients with a secondary diagnosis of VDD and a primary diagnosis of CVD and CeVD using the 9th revision of the International Classification of Diseases, clinical modification code (ICD-10-CM) codes. A univariate and mixed-effect multivariable survey logistic regression analysis was performed to evaluate the prevalence, disability, and discharge disposition of patients with CVD and CeVD in the presence of VDD. Results: Among 58,259,589 USA hospitalizations, 3.44%, 2.15%, 0.06%, 1.28%, 11.49%, 1.71%, 0.38%, 0.23%, and 0.08% had primary admission of IHD, acute MI, angina, AFib, CHF, AIS, TIA, ICeH, and SAH, respectively and 1.82% had VDD. The prevalence of hospitalizations due to CHF (14.66% vs. 11.43%), AIS (1.87% vs. 1.71%), and TIA (0.4% vs. 0.38%) was higher among VDD patients as compared with non-VDD patients (p < 0.0001). In a regression analysis, as compare with non-VDD patients, the VDD patients were associated with higher odds of discharge to non-home facilities with an admission diagnosis of CHF (aOR 1.08, 95% CI 1.07–1.09), IHD (aOR 1.24, 95% CI 1.21–1.28), acute MI (aOR 1.23, 95% CI 1.19–1.28), AFib (aOR 1.21, 95% CI 1.16–1.27), and TIA (aOR 1.19, 95% CI 1.11–1.28). VDD was associated with higher odds of severe or extreme disability among patients hospitalized with AIS (aOR 1.1, 95% CI 1.06–1.14), ICeH (aOR 1.22, 95% CI 1.08–1.38), TIA (aOR 1.36, 95% CI 1.25–1.47), IHD (aOR 1.37, 95% CI 1.33–1.41), acute MI (aOR 1.44, 95% CI 1.38–1.49), AFib (aOR 1.10, 95% CI 1.06–1.15), and CHF (aOR 1.03, 95% CI 1.02–1.05) as compared with non-VDD. Conclusions: CVD and CeVD in the presence of VDD increase the disability and discharge to non-home facilities among USA hospitalizations. Future studies should be planned to evaluate the effect of VDD replacement for improving outcomes

A Comprehensive Assessment of Vascular and Nonvascular Risk Factors Associated with Migraine

INTRODUCTION: Migraine is a chronic disabling neurological disease, with an estimated expense of $15-20 million/year. Several studies with a small number of patients have studied risk factors for migraine such as cardiovascular disorders, stroke, smoking, demographic, and genetic factors but this is the first comprehensive study for evaluation of vascular and nonvascular risk factors. It is important to evaluate all the risk factors that help to prevent the healthcare burden related to migraine. Methodology: We performed a retrospective cross-sectional analysis of the Nationwide Inpatient Sample (NIS) (years 2013-2014) in adult (\u3e18-years old) hospitalizations in the United States. Migraine patients were identified using ICD-9-CM code to determine the demographic characteristics, vascular, and nonvascular risk factors. Univariate analysis was performed using the chi-square test and a multivariate survey logistic regression analysis was performed to identify the prevalence of the risk factors and evaluate the odds of prevalence of risk factors amongst migraine patients compared to nonmigraine patients, respectively. RESULTS: On weighted analysis, after removing missing data of age, gender and race, from years 2013 to 2014, of the total 983,065 (1.74%) migraine patients were identified. We found that younger (median age 48-years vs. 60-years), female (82.1% vs. 58.5%; p CONCLUSION: In this study, we have identified significant risk factors for migraine hospitalizations. Early identification of these risk factors may improve the risk stratification in migraine patients

Approaching continuous variability modeling

This paper discusses about variability management achieved in two different domains, Software Product Line Engineering (SPL) and Runtime Adaptive Systems (RTA). Variability Management is an important key concept in Software Product Lines, which can be exploited to achieve efficient dynamic variants in Runtime adaptation (RTA). In our Research Paper, we compare the existing variability techniques followed in SPL and RTA and identify the commonalities and differences in these approaches. We present our results based on Systematic Literature Review (SLR) of runtime variability in Dynamic Software Product Line (DSPL) and RTA