Metalloproteinase-Mediated, Context-Dependent Function of Amphiregulin and HB-EGF in Human Keratinocytes and Skin

Human keratinocytes (KCs) express multiple EGF receptor (EGFR) ligands; however, their functions in specific cellular contexts remain largely undefined. To address this issue, first we measured mRNA and protein levels for multiple EGFR ligands in KCs and skin. Amphiregulin (AREG) was by far the most abundant EGFR ligand in cultured KCs, with >19 times more mRNA and >7.5 times more shed protein than any other family member. EGFR ligand expression in normal skin was low (<8‰ of RPLP0/36B4); however, HB-EGF and AREG mRNAs were strongly induced in human skin organ culture. KC migration in scratch wound assays was highly metalloproteinase (MP)- and EGFR dependent, and was markedly inhibited by EGFR ligand antibodies. However, lentivirus-mediated expression of soluble HB-EGF, but not soluble AREG, strongly enhanced KC migration, even in the presence of MP inhibitors. Lysophosphatidic acid (LPA)-induced ERK phosphorylation was also strongly EGFR and MP dependent and markedly inhibited by neutralization of HB-EGF. In contrast, autocrine KC proliferation and ERK phosphorylation were selectively blocked by neutralization of AREG. These data show that distinct EGFR ligands stimulate KC behavior in different cellular contexts, and in an MP-dependent fashion

Domains of Chronic Stress, Lifestyle Factors, and Allostatic Load in Middle-Aged Mexican-American Women

Abstract available at publisher's website

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Assessment of female-specific SPECT parameters for prediction of cardiac outcomes in women with suspected Ischemia

Thesis (Ph. D.)--University of Rochester. School of Medicine and Dentistry. Dept. of Community and Preventive Medicine, 2009.Purpose. Evidence suggests that the value of scintigraphic variables best used to diagnose, treat, or predict cardiac events may differ according to gender. The purpose of this study was to develop female-specific diagnostic values for function and perfusion variables generated from Single Photon Emission Computed Tomography (SPECT) using logistic regression models, and to compare the diagnostic accuracy of these values for predicting cardiac death (CD) to literature-based mixed-gender and female-specific diagnostic values. Other goals of this study were to: (1) determine the association between SPECT parameters and CD and acute non-fatal myocardial infarction (ANFMI) after adjusting for clinical covariates over a five year follow-up period; and (2) construct a statistical model to simplify risk prediction in female populations evaluated for suspected ischemia. Methods. This retrospective cohort study included 1,811 women with suspected ischemia who had an initial SPECT scan between June 2000 – December 2005 at Strong Memorial Hospital (SMH). Women were excluded if they had a history of heart disease, were <18 years old, or did not consent to the study. Clinical, demographic, and scintigraphic information was obtained from the Nuclear Cardiology Database System (NCDS), medical records, SMH billing data, and the Clinical Information System (CIS) database. Cardiac death was defined as ICD-9 code: 410-411; 414-417; 425; 427-428; 430-438; 440-448, and was obtained from the National Center for Health Statistics (NCHS), National Death Index (NDI). ANFMI outcomes were identified from Strong Memorial, Rochester General, and Park Ridge hospital billing data and included ICD-9 codes: 410, 429.7. Receiver Operating Characteristic (ROC) curves were developed to determine female-specific diagnostic cut-points for function and perfusion variables, left ventricular ejection fraction (LVEF) and summed stress score (SSS), which were the scintigraphic variables most predictive of the primary outcome, cardiac death. A Cox Proportional Hazards model was derived using the most sensitive values of LVEF and SSS to assess their association with cardiac outcomes after adjusting for clinical covariates. Results. The incidence (95% CI) of CD over the course of follow-up was 13.9% (10.5-17.3); the incidence of ANFMI was 3.9% (2.1-5.7). Maximizing the combination of sensitivity and specificity to predict cardiac death resulted in a LVEF cut-off of 2. Comparisons of sensitivity among 1,731 women with complete information on independent and dependent variables of interest showed that we were able to increase the sensitivity of predicting cardiac death from 26% in the mixed-gender model, and 38% in the literature-based female-specific model, to 59% using the new cut-off value for LVEF. The new cut-off value for SSS provided negligible improvements in sensitivity of predicting cardiac death. We created a modified Framingham Risk Score (mFRS) that was included as a baseline covariate to illustrate that scintigraphic variables provided incremental predictive information beyond clinical variables. After controlling for confounders mFRS and stress type, risk of cardiac death was 2.3 times greater among women with an LVEF 58% (p = 0.003) during the 5 year follow-up period. SSS was no longer significant after controlling for LVEF and clinical covariates for prediction of CD. A small number of events limited our ability to accurately predict ANFMI. The prognostic model, limited to age and LVEF, accurately predicted the observed risk of cardiac death. Conclusions. Our new diagnostic cut-off value for LVEF substantially increased the sensitivity of cardiac death prediction among our population of women with suspected ischemia compared with traditionally used mixed-gender cut-off values and literature based-female specific values. While our prognostic model accurately predicted the risk of cardiac death, it may be too simplistic for clinical use. Results from this study suggest that a female-specific, SPECT-derived, LVEF value of <58% provides incremental value beyond routinely collected clinical variables for the prediction of cardiac death. Use of this cut-off value for cardiac event prediction should be externally validated in a new population of women with suspected ischemia referred to SPECT imaging