BackgroundAmyotrophic lateral sclerosis (ALS) is a progressive, fatal neurodegenerative disease with a lifetime risk of developing as 1 in 700. Despite many recent discoveries about the genetics of ALS, the etiology of sporadic ALS remains largely unknown with gene-environment interaction suspected as a driver. Water quality and the toxin beta methyl-amino-alanine produced by cyanobacteria are suspected environmental triggers. Our objective was to develop an eco-epidemiological modeling approach to characterize the spatial relationships between areas of higher than expected ALS incidence and lake water quality risk factors derived from satellite remote sensing as a surrogate marker of exposure.MethodsOur eco-epidemiological modeling approach began with implementing a spatial clustering analysis that was informed by local indicators of spatial autocorrelation to identify locations of normalized excess ALS counts at the census tract level across northern New England. Next, water quality data for all lakes over 6 hectares (n = 4,453) were generated using Landsat TM band ratio regression techniques calibrated with in situ lake sampling. Derived lake water quality risk maps included chlorophyll-a (Chl-a), Secchi depth (SD), and total nitrogen (TN). Finally, a spatially-aware logistic regression modeling approach was executed characterizing relationships between the derived lake water quality metrics and ALS hot spots.ResultsSeveral distinct ALS hot spots were identified across the region. Remotely sensed lake water quality indicators were successfully derived; adjusted R2 values ranged between 0.62-0.88 for these indicators based on out-of-sample validation. Map products derived from these indicators represent the first wall-to-wall metrics of lake water quality across the region. Logistic regression modeling of ALS case membership in localized hot spots across the region, i.e., census tracts with higher than expected ALS counts, showed the following: increasing average SD within a radius of 30 km corresponds with a decrease in the odds of belonging to an ALS hot spot by 59%; increasing average TN within a radius of 30 km and average Chl-a concentration within a radius of 10 km correspond with increased odds of belonging to an ALS hot spot by 167% and 4%, respectively.ConclusionsThe strengths of satellite remote sensing information can help overcome traditional field limitations and spatiotemporal data gaps to provide the public health community valuable exposure data. Geographic scale needs to be diligently considered when evaluating relationships among ecological processes, risk factors, and human health outcomes. Broadly, we found that poorer lake water quality was significantly associated with increased odds of belonging to an ALS cluster in the region. These findings support the hypothesis that sporadic ALS (sALS) can, in part, be triggered by environmental water-quality indicators and lake conditions that promote harmful algal blooms.

Despite the importance of neurological disorders associated with herpesviruses, the mechanism by which these viruses influence the central nervous system (CNS) has not been definitively established. Owing to the limitations of studying neuropathogenicity of human herpesviruses in their natural host, many aspects of their pathogenicity and immune response are studied in animal models. Here, we present an important model system that enables studying neuropathogenicity of herpesviruses in the natural host. Equine herpesvirus type 1 (EHV-1) is an alphaherpesvirus that causes a devastating neurological disease (EHV-1 myeloencephalopathy; EHM) in horses. Like other alphaherpesviruses, our understanding of virus neuropathogenicity in the natural host beyond the essential role of viraemia is limited. In particular, information on the role of different viral proteins for virus transfer to the spinal cord endothelium in vivo is lacking. In this study, the contribution of two viral proteins, DNA polymerase (ORF30) and glycoprotein D (gD), to the pathogenicity of EHM was addressed. Furthermore, different cellular immune markers, including alpha-interferon (IFN-α), gamma-interferon (IFN-γ), interleukin-10 (IL-10) and interleukin-1 beta (IL-1β), were identified to play a role during the course of the disease.

The number, size, and distribution of inland freshwater lakes present a challenge for traditional water-quality assessment due to the time, cost, and logistical constraints of field sampling and laboratory analyses. To overcome this challenge, Landsat imagery has been used as an effective tool to assess basic water-quality indicators, such as Secchi depth (SD), over a large region or to map more advanced lake attributes, such as cyanobacteria, for a single waterbody. The overarching objective of this research application was to evaluate Landsat Thematic Mapper (TM) for mapping nine water-quality metrics over a large region and to identify hot spots of potential risk. The second objective was to evaluate the addition of landscape pattern metrics to test potential improvements in mapping lake attributes and to understand drivers of lake water quality in this region. Field-level in situ water-quality measurements were collected across diverse lakes (n = 42) within the Lower Peninsula of Michigan. A multicriteria statistical approach was executed to map lake water quality that considered variable importance, model complexity, and uncertainty. Overall, band ratio radiance models performed well (R 2 = 0.65-0.81) for mapping SD, chlorophyll-a, green biovolume, total phosphorus (TP), and total nitrogen (TN) with weaker (R 2 = 0.37) ability to map total suspended solids (TSS) and cyanobacteria levels. In this application, Landsat TM and pattern metrics showed poor ability to accurately map non-purgable organic carbon (NPOC) and diatom biovolume, likely due to a combination of gaps in temporal overpass and field sampling and lack of signal sensitivity within broad spectral channels of Landsat TM. The composition and configuration of croplands, urban, and wetland patches across the landscape were found to be moderate predictors of lake water quality that can complement lake remote-sensing data. Of the 4071 lakes, over 4 ha in the Lower Peninsula, approximately two-thirds, were identified as mesotrophic (n = 2715). This application highlights how an operational tool might support lake decision-making or assessment protocols to identify hot spots of potential risk.

Precipitation in Kenya is highly variable and dominated by a variety of physical processes. Statistical studies of climate patterns have historically focused on application of ordinary least squares (OLS) regression to test hypotheses related to multiple predictive variables, perhaps in an attempt to better understand the physical mechanisms that drive precipitation, or on use of spatially explicit models, typically kriging-or spline-based analyses, for the purpose of improving predictions. Each of these approaches may be individually useful; however, they all possess limitations. OLS approaches have yielded biased results in the presence of spatially autocorrelated data. Kriging-and spline-based studies often focus on providing improved predictions rather than understanding. Here we use spatial regression, a method not commonly used in analysis of climate data, to assess the role of predictive variables while explicitly incorporating spatial autocorrelation in parameter estimation and hypothesis testing. This approach can yield a better understanding of relationships between precipitation and multiple predictive variables with improved statistical rigour. Using spatial regression, we show that topographic variables such as elevation and slope strongly influence rainfall during the 'long rains' and 'short rains', which are vital for Kenyan agriculture. Outside these seasons, we find that smaller (mesoscale) variations in elevation are statistically significant. Further, we demonstrate the shortcomings of automated selection procedures such as stepwise regression through the identification of spurious results due to confounding.

Objective:Individuals with diabetes who develop cancer have a worse 5-year overall survival rate and are more likely to develop an infection and/or be hospitalized when compared to those without diabetes. Patients with diabetes and cancer receiving chemotherapy have an increased risk for developing glycemic issues. The relationship between chemotherapy and glycemic control is not completely understood. The aim of this study was to explore the relationship between glycemic control, symptoms, physical and mental function, development of adverse events, and chemotherapy reductions or stoppages in adults with Type 2 diabetes (T2D) and cancer.Methods:A prospective 12-week longitudinal cohort study recruited 24 adults with T2D, solid tumor cancer, or lymphoma receiving outpatient intravenous chemotherapy. Eighteen individuals completed baseline data and were included in the analysis. A comparative case analysis was performed to analyze the results.Results:Potential predictors of occurrence of an adverse event include sex (relative risk [RR] = 1.5), treatment with insulin (RR = 2.17), years with diabetes (RR = 3.85), and baseline glycated hemoglobin (HbA1c) (odds ratio [OR] = 1.67). Baseline body mass index (BMI) (OR = 1.16) and HbA1c (OR = 1.61) were potentially predictive of a chemotherapy stoppage.Conclusions:Level of glycemic control at the time an individual begins treatment for cancer appears to contribute to the occurrence of an adverse event, developing an infection and/or being hospitalized during treatment, and the increased risk of having a chemotherapy reduction or stoppage. Clinicians working with patients receiving chemotherapy for a solid tumor cancer who have pre-existing diabetes, need to be aware of how the patients glycemic level at the start of treatment may impact successful treatment completion.