Occupational exposure and markers of genetic damage, systemic inflammation and lung function: a Danish cross-sectional study among air force personnel

Air force ground crew personnel are potentially exposed to fuels and lubricants, as raw materials, vapours and combustion exhaust emissions, during operation and maintenance of aircrafts. This study investigated exposure levels and biomarkers of effects for employees at a Danish air force military base. We enrolled self-reported healthy and non-smoking employees (n = 79) and grouped them by exposure based on job function, considered to be potentially exposed (aircraft engineers, crew chiefs, fuel operators and munition specialists) or as reference group with minimal occupational exposure (avionics and office workers). We measured exposure levels to polycyclic aromatic hydrocarbons (PAHs) and organophosphate esters (OPEs) by silicone bands and skin wipes (PAHs only) as well as urinary excretion of PAH metabolites (OH-PAHs). Additionally, we assessed exposure levels of ultrafine particles (UFPs) in the breathing zone for specific job functions. As biomarkers of effect, we assessed lung function, plasma levels of acute phase inflammatory markers, and genetic damage levels in peripheral blood cells. Exposure levels of total PAHs, OPEs and OH-PAHs did not differ between exposure groups or job functions, with low correlations between PAHs in different matrices. Among the measured job functions, the UFP levels were higher for the crew chiefs. The exposure level of the PAH fluorene was significantly higher for the exposed group than the reference group (15.9 +/- 23.7 ng/g per 24 h vs 5.28 +/- 7.87 ng/g per 24 h, p = 0.007), as was the OPE triphenyl phosphate (305 +/- 606 vs 19.7 +/- 33.8 ng/g per 24 h, p = 0.011). The OPE tris(1, 3-dichlor-2-propyl)phosphate had a higher mean in the exposed group (60.7 +/- 135 ng/g per 24 h) compared to the reference group (8.89 +/- 15.7 ng/g per 24 h) but did not reach significance. No evidence of effects for biomarkers of systemic inflammation, genetic damage or lung function was found. Overall, our biomonitoring study show limited evidence of occupational exposure of air force ground crew personnel to UFPs, PAHs and OPEs. Furthermore, the OH-PAHs and the assessed biomarkers of early biological effects did not differ between exposed and reference groups

The apicomplexan plastid and its evolution

Protistan species belonging to the phylum Apicomplexa have a non-photosynthetic secondary plastid—the apicoplast. Although its tiny genome and even the entire nuclear genome has been sequenced for several organisms bearing the organelle, the reason for its existence remains largely obscure. Some of the functions of the apicoplast, including housekeeping ones, are significantly different from those of other plastids, possibly due to the organelle’s unique symbiotic origin

Optimal foraging and community structure: implications for a guild of generalist grassland herbivores

A particular linear programming model is constructed to predict the diets of each of 14 species of generalist herbivores at the National Bison Range, Montana. The herbivores have body masses ranging over seven orders of magnitude and belonging to two major taxa: insects and mammals. The linear programming model has three feeding constraints: digestive capacity, feeding time and energy requirements. A foraging strategy that maximizes daily energy intake agrees very well with the observed diets. Body size appears to be an underlying determinant of the foraging parameters leading to diet selection. Species that possess digestive capacity and feeding time constraints which approach each other in magnitude have the most generalized diets. The degree that the linear programming models change their diet predictions with a given percent change in parameter values (sensitivity) may reflect the observed ability of the species to vary their diets. In particular, the species which show the most diet variability are those whose diets tend to be balanced between monocots and dicots. The community-ecological parameters of herbivore body-size ranges and species number can possibly be related to foraging behavior.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47765/1/442_2004_Article_BF00377109.pd

Application of machine learning methodology to assess the performance of DIABETIMSS program for patients with type 2 diabetes in family medicine clinics in Mexico

BACKGROUND: The study aimed to assess the performance of a multidisciplinary-team diabetes care program called DIABETIMSS on glycemic control of type 2 diabetes (T2D) patients, by using available observational patient data and machine-learning-based targeted learning methods. METHODS: We analyzed electronic health records and laboratory databases from the year 2012 to 2016 of T2D patients from six family medicine clinics (FMCs) delivering the DIABETIMSS program, and five FMCs providing routine care. All FMCs belong to the Mexican Institute of Social Security and are in Mexico City and the State of Mexico. The primary outcome was glycemic control. The study covariates included: patient sex, age, anthropometric data, history of glycemic control, diabetic complications and comorbidity. We measured the effects of DIABETIMSS program through 1) simple unadjusted mean differences; 2) adjusted via standard logistic regression and 3) adjusted via targeted machine learning. We treated the data as a serial cross-sectional study, conducted a standard principal components analysis to explore the distribution of covariates among clinics, and performed regression tree on data transformed to use the prediction model to identify patient sub-groups in whom the program was most successful. To explore the robustness of the machine learning approaches, we conducted a set of simulations and the sensitivity analysis with process-of-care indicators as possible confounders. RESULTS: The study included 78,894 T2D patients, from which 37,767patients received care through DIABETIMSS. The impact of DIABETIMSS ranged, among clinics, from 2 to 8% improvement in glycemic control, with an overall (pooled) estimate of 5% improvement. T2D patients with fewer complications have more significant benefit from DIABETIMSS than those with more complications. At the FMCs delivering the conventional model the predicted impacts were like what was observed empirically in the DIABETIMSS clinics. The sensitivity analysis did not change the overall estimate average across clinics. CONCLUSIONS: DIABETIMSS program had a small, but significant increase in glycemic control. The use of machine learning methods yields both population-level effects and pinpoints the sub-groups of patients the program benefits the most. These methods exploit the potential of routine observational patient data within complex healthcare systems to inform decision-makers