Using Landsat satellite data to support pesticide exposure assessment in California

<p>Abstract</p> <p>Background</p> <p>The recent U.S. Geological Survey policy offering Landsat satellite data at no cost provides researchers new opportunities to explore relationships between environment and health. The purpose of this study was to examine the potential for using Landsat satellite data to support pesticide exposure assessment in California.</p> <p>Methods and Results</p> <p>We collected a dense time series of 24 Landsat 5 and 7 images spanning the year 2000 for an agricultural region in Fresno County. We intersected the Landsat time series with the California Department of Water Resources (CDWR) land use map and selected field samples to define the phenological characteristics of 17 major crop types or crop groups. We found the frequent overpass of Landsat enabled detection of crop field conditions (e.g., bare soil, vegetated) over most of the year. However, images were limited during the winter months due to cloud cover. Many samples designated as single-cropped in the CDWR map had phenological patterns that represented multi-cropped or non-cropped fields, indicating they may have been misclassified.</p> <p>Conclusions</p> <p>We found the combination of Landsat 5 and 7 image data would clearly benefit pesticide exposure assessment in this region by 1) providing information on crop field conditions at or near the time when pesticides are applied, and 2) providing information for validating the CDWR map. The Landsat image time-series was useful for identifying idle, single-, and multi-cropped fields. Landsat data will be limited during the winter months due to cloud cover, and for years prior to the Landsat 7 launch (1999) when only one satellite was operational at any given time. We suggest additional research to determine the feasibility of integrating CDWR land use maps and Landsat data to derive crop maps in locations and time periods where maps are not available, which will allow for substantial improvements to chemical exposure estimation.</p

Enhanced Expression of Interstitial Collagenase, Stromelysin-1, and Urokinase Plasminogen Activator in Lesions of Dermatitis Herpetiformis

Because dermatitis herpetiformis is characterized by neutrophilic inflammation and destructive changes in the basement membrane zone, we studied the in situ expression of interstitial collagenase and stromelysin-1 in 11 lesions. A prominent signal for collagenase mRNA was consistently detected in the basal keratinocytes of rete ridges surrounding the neutrophilic abscesses in 10 of 11 lesions, and the expression was independent of the age of the lesion and the migratory state of the basal keratinocytes. Expression of stromelysin-1 was detected in seven of 11 lesions and co-localized with collagenase. No expression of the 92-kDa gelatinase mRNA or matrilysin protein was found in the vicinity of neutrophilic accumulations or the damaged basement membrane. Urokinase-type plasminogen activator mRNA was found in basal keratinocytes in seven of nine samples. Collagenase, stromelysin-1, and urokinase-type plasminogen activator were not expressed in normal-appearing skin of patients with dermatitis herpetiformis. Our results suggest that in lesions of dermatitis herpetiformis, collagenase and stromelysin-1 may be induced in basal keratinocytes by neutrophil cytokines or by altered cell-matrix interactions through contact of keratinocytes with the matrix due to damaged basement membrane. Stromelysin-1, in particular, may contribute to formation of blisters by degrading basement membrane components

Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors

Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001) between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel-based modeling approach offers practical benefits for probing novel insights into the mode of action of investigational compounds, and for the identification of new target selectivities for drug repurposing applications

The chemopreventive polyphenol Curcumin prevents hematogenous breast cancer metastases in immunodeficient mice

Dissemination of metastatic cells probably occurs long before diagnosis of the primary tumor. Metastasis during early phases of carcinogenesis in high risk patients is therefore a potential prevention target. The plant polyphenol Curcumin has been proposed for dietary prevention of cancer. We therefore examined its effects on the human breast cancer cell line MDA-MB-231 in vitro and in a mouse metastasis model. Curcumin strongly induces apoptosis in MDA- MB- 231 cells in correlation with reduced activation of the survival pathway NF kappa B, as a consequence of diminished I kappa B and p65 phosphorylation. Curcumin also reduces the expression of major matrix metalloproteinases (MMPs) due to reduced NF kappa B activity and transcriptional downregulation of AP-1. NF kappa B/p65 silencing is sufficient to downregulate c-jun and MMP expression. Reduced NF kappa B/AP-1 activity and MMP expression lead to diminished invasion through a reconstituted basement membrane and to a significantly lower number of lung metastases in immunodeficient mice after intercardiac injection of 231 cells (p=0.0035). 68% of Curcumin treated but only 17% of untreated animals showed no or very few lung metastases, most likely as a consequence of down-regulation of NF kappa B/AP-1 dependent MMP expression and direct apoptotic effects on circulating tumor cells but not on established metastases. Dietary chemoprevention of metastases appears therefore feasible. Copyright (c) 2007 S. Karger AG, Basel