An analysis associated with iconic language rise in 4 datasets

The clinical context for using blood eosinophil (EOS) counts as treatment-response biomarkers in asthma and chronic obstructive pulmonary disease (COPD) requires better understanding of EOS distributions and ranges. selleck We describe EOS distributions and ranges published in asthma, COPD, control (non-asthma/COPD) and general populations.
We conducted a comprehensive literature review and meta-analysis of observational studies (Jan 2008 to Nov 2018) that included EOS counts in asthma, severe asthma, COPD, control and general populations. Excluded studies had total sample sizes <200, EOS as inclusion criterion, hospitalised population only, exclusively paediatric participants.
Overall, 91 eligible studies were identified, most had total-population-level data available asthma (n=39 studies), severe asthma (n=12 studies), COPD (n=23 studies), control (n=7 studies), general populations (n=14 studies); some articles reported data for multiple populations. Reported EOS distributions were right-skewed (n=7 studieerity, should be considered during treatment decision making.
The diffusing capacity for carbon monoxide corrected for haemoglobin (DLCO
), measures gas movement across the alveolar-capillary interface. We hypothesised that DLCO
is a sensitive measure of injurious allograft processes disrupting this interface.
To determine the prognostic significance of the DLCO
trajectory on chronic lung allograft dysfunction (CLAD) and survival.
A retrospective analysis was conducted of all bilateral lung transplant recipients at a single centre, between Jan-1998 and Jan-2018, with ≥1 DLCO
measurements. Low baseline DLCO
was defined as the failure to achieve a DLCO
>75% predicted. Drops in DLCO
were defined as >15% below recent baseline.
1259/1492 lung transplant recipients were included. The median time to peak DLCO
was 354 (range 181-737) days and the mean %-predicted DLCO
was 80.2% (sd 21.2). Multivariable analysis demonstrated that low baseline DLCO
was significantly associated with death (HR 1.68, 95% CI 1.27-2.20, p<0.001). Low baseline DLCO
sfunction not captured by spirometry. There may be benefit in routine monitoring of DLCOcor after lung transplantation to identify patients at risk of poor outcomes.
Body mass index (BMI) is inversely associated with lung cancer risk, while residual confounding by smoking or weight change is controversial. Evidence on height and lung cancer is scarce.
We investigated the associations between anthropometrics, BMI, and height, and incidence of lung cancer among 92,098 study subjects (44,158 men and 47,940 women) in the Japan Public Health Center-based Prospective Study. Cox proportional hazards regression was performed with adjustment for potential confounders and by cancer subtypes and smoking status. Information on weight and height was self-reported at baseline, and validated using measured health check-up data.
During follow-up between 1990 and 2013 (average, 19.1 years), a total of 2,152 lung cancer cases were newly diagnosed. In a multivariate regression model, lower BMI was positively associated with overall lung cancer risk [<19 kg/m
HR = 1.48; 95% confidence interval (CI) = 1.18-1.85 and 19-22.9 kg/m
; HR = 1.19; 95% CI = 1.05-1.35;
= <0.001] in men. The risk estimate was also elevated for adenocarcinoma in the BMI <19 kg/m
category and for squamous cell carcinoma among men in the 19-22.9 kg/m
BMI category. An association was also observed between low BMI, weight decrease, and squamous cell carcinoma in women. No significant associations were observed for other weight categories, height, weight change and lung cancer, adenocarcinoma, squamous and small cell carcinoma.
Our prospective study suggests that lower BMI may be associated with an increased risk of smoking-related lung cancer in Japan, irrespective of gender.
This study highlights the association between lower BMI and the risk of lung cancer in men.
This study highlights the association between lower BMI and the risk of lung cancer in men.
Human papillomavirus (HPV) is the most common sexually transmitted infection within the United States (US). Despite clinical agreement on the effectiveness and widespread availability of the prophylactic HPV vaccine, vaccination coverage in the US is suboptimal and varies by geographic region and area-level variables. The goals of this article were to model the variation in vaccination rates among boys and girls within ZIP Codes in Virginia, determine whether neighborhood sociodemographic variables explain variation in HPV vaccination, and identify areas with significantly depressed vaccination coverage.
We used Bayesian hierarchical spatial regression models with statewide immunization registry data to consider the correlation in vaccination among boys and girls, as well as the spatial correlation in vaccination for each sex.
The results showed low vaccination coverage in our birth cohort (28.9% in girls and 23.8% in boys) relative to the national level (56.8% and 51.8%, respectively). Several area-level variables were significantly and positively associated with vaccination coverage, including population density, percentage of Hispanic population, and average number of vehicles. In addition, there were several areas of significantly lowered vaccination coverage, including predominantly rural ones, and overall large geographic disparities in HPV vaccination.
Determining the geospatial patterning and area-level factors associated with HPV vaccination within a prescribed geographic area helps to inform future planning efforts.
The results of this study will help inform future planning efforts for geographically targeted interventions and policies, as well as drive new research to implement clinical and community strategies to increase HPV vaccination.
The results of this study will help inform future planning efforts for geographically targeted interventions and policies, as well as drive new research to implement clinical and community strategies to increase HPV vaccination.The current resurgence of hydropower expansion toward tropical areas has been largely based on run-of-the-river (ROR) dams, which are claimed to have lower environmental impacts due to their smaller reservoirs. The Belo Monte dam was built in Eastern Amazonia and holds the largest installed capacity among ROR power plants worldwide. Here, we show that postdamming greenhouse gas (GHG) emissions in the Belo Monte area are up to three times higher than preimpoundment fluxes and equivalent to about 15 to 55 kg CO2eq MWh-1 Since per-area emissions in Amazonian reservoirs are significantly higher than global averages, reducing flooded areas and prioritizing the power density of hydropower plants seem to effectively reduce their carbon footprints. Nevertheless, total GHG emissions are substantial even from this leading-edge ROR power plant. This argues in favor of avoiding hydropower expansion in Amazonia regardless of the reservoir type.