Agglomeration, regional grants and firm location

We examine whether discretionary government grants influence the location ofnew plants, and how effective these incentives are in the presence of agglomeration andurbanisation externalities. We find evidence that regional industrial structure affects thelocation of new entrants. Firms in more agglomerated industries locate new plants near toothers in the same industry. Firms are also attracted to industrially diversified locations.Foreign multinationals locate new plants near to other foreign-owned plants in the sameindustry. Fiscal incentives in the form of grants are found to have some effect in attractingplants to specific geographic areas eligible for such aid. We examine whether discretionary government grants influence the location ofnew plants, and how effective these incentives are in the presence of agglomeration andurbanisation externalities. We find evidence that regional industrial structure affects thelocation of new entrants. Firms in more agglomerated industries locate new plants near toothers in the same industry. Firms are also attracted to industrially diversified locations.Foreign multinationals locate new plants near to other foreign-owned plants in the sameindustry. Fiscal incentives in the form of grants are found to have some effect in attractingplants to specific geographic areas eligible for such aid

Muscle fiber-type distribution predicts weight gain and unfavorable left ventricular geometry: a 19 year follow-up study

BACKGROUND: Skeletal muscle consists of type-I (slow-twitch) and type-II (fast-twitch) fibers, with proportions highly variable between individuals and mostly determined by genetic factors. Cross-sectional studies have associated low percentage of type-I fibers (type-I%) with many cardiovascular risk factors. METHODS: We investigated whether baseline type-I% predicts left ventricular (LV) structure and function at 19-year follow-up, and if so, which are the strongest mediating factors. At baseline in 1984 muscle fiber-type distribution (by actomyosin ATPase staining) was studied in 63 healthy men (aged 32–58 years). The follow-up in 2003 included echocardiography, measurement of obesity related variables, physical activity and blood pressure. RESULTS: In the 40 men not using cardiovascular drugs at follow-up, low type-I% predicted higher heart rate, blood pressure, and LV fractional shortening suggesting increased sympathetic tone. Low type-I% predicted smaller LV chamber diameters (P ≤ 0.009) and greater relative wall thickness (P = 0.034) without increase in LV mass (concentric remodeling). This was explained by the association of type-I% with obesity related variables. Type-I% was an independent predictor of follow-up body fat percentage, waist/hip ratio, weight gain in adulthood, and physical activity (in all P ≤ 0.001). After including these risk factors in the regression models, weight gain was the strongest predictor of LV geometry explaining 64% of the variation in LV end-diastolic diameter, 72% in end-systolic diameter, and 53% in relative wall thickness. CONCLUSION: Low type-I% predicts obesity and weight gain especially in the mid-abdomen, and consequently unfavourable LV geometry indicating increased cardiovascular risk

Intraventricular dyssynchrony in light chain amyloidosis: a new mechanism of systolic dysfunction assessed by 3-dimensional echocardiography

<p>Abstract</p> <p>Background</p> <p>Light chain amyloidosis (AL) is a rare but often fatal disease due to intractable heart failure. Amyloid deposition leads to diastolic dysfunction and often preserved ejection fraction. We hypothesize that AL is associated with regional systolic dyssynchrony. The aim is to compare left ventricular (LV) regional synchrony in AL subjects versus healthy controls using 16-segment dyssynchrony index measured from 3-dimension-al (3D) echocardiography.</p> <p>Methods</p> <p>Cardiac 3D echocardiography full volumes were acquired in 10 biopsy-proven AL subjects (60 ± 3 years, 5 females) and 10 healthy controls (52 ± 1 years, 5 females). The LV was subdivided into 16 segments and the time from end-diastole to the minimal systolic volume for each of the 16 segments was expressed as a percent of the cycle length. The standard deviations of these times provided a 16-segment dyssynchrony index (16-SD%). 16-SD% was compared between healthy and AL subjects.</p> <p>Results</p> <p>Left ventricular ejection fraction was comparable (control vs. AL: 62.4 ± 0.6 vs. 58.6 ± 2.8%, p = NS). 16-SD% was significantly higher in AL versus healthy subjects (5.93 ± 4.4 vs. 1.67 ± 0.87%, p = 0.003). 16-SD% correlated with left ventricular mass index (R 0.45, p = 0.04) but not to left ventricular ejection fraction.</p> <p>Conclusion</p> <p>Light chain amyloidosis is associated with left ventricular regional systolic dyssynchrony. Regional dyssynchrony may be an unrecognized mechanism of heart failure in AL subjects.</p

Differential Regulation of PDE5 Expression in Left and Right Ventricles of Feline Hypertrophy Models

Though long known to affect smooth muscle biology, recent studies indicate that phosphodiesterase 5 (PDE5) is also expressed in myocardium. Recognizing that the regulation of PDE5 in hypertrophy is not well understood, we assessed the response of PDE5 expression and the level of cGMP-dependent kinase I (cGKI) in the left and right ventricles of feline hypertrophy models.Using a cDNA library of feline aortic smooth muscle cells, we identified and cloned PDE5 cDNA for the first time in this species. The sequence shares 98% identity with its human orthologue at the amino acid level. E. coli expression of the cloned allele allowed selection of antibodies with appropriate specificity, facilitating the analysis of PDE5 expression in feline models created by selective proximal aortic (Ao) or pulmonary artery (PA) banding that resulted in hypertrophy of the left ventricle (LV) and right ventricle (RV), respectively. We demonstrated that PDE5 expression responded differentially with a decreased expression in the LV and an increased expression in the RV in the Ao-banded model. Similarly, in the PA-banded model, LV showed reduced expression while the RV expression was unaltered. In addition, the expression of cGKI was significantly decreased in the RV of Ao-banded group, correlating inversely with the increase in PDE5 expression.The differential regulation of PDE5 and cGKI expression suggests that the mechanisms involved in hypertrophy could be different in RV vs. LV. Reciprocal PDE5 and cGKI expression in the RV of Ao-banded model suggests functional significance for PDE5 up-regulation

Predicting environmental chemical factors associated with disease-related gene expression data

<p>Abstract</p> <p>Background</p> <p>Many common diseases arise from an interaction between environmental and genetic factors. Our knowledge regarding environment and gene interactions is growing, but frameworks to build an association between gene-environment interactions and disease using preexisting, publicly available data has been lacking. Integrating freely-available environment-gene interaction and disease phenotype data would allow hypothesis generation for potential environmental associations to disease.</p> <p>Methods</p> <p>We integrated publicly available disease-specific gene expression microarray data and curated chemical-gene interaction data to systematically predict environmental chemicals associated with disease. We derived chemical-gene signatures for 1,338 chemical/environmental chemicals from the Comparative Toxicogenomics Database (CTD). We associated these chemical-gene signatures with differentially expressed genes from datasets found in the Gene Expression Omnibus (GEO) through an enrichment test.</p> <p>Results</p> <p>We were able to verify our analytic method by accurately identifying chemicals applied to samples and cell lines. Furthermore, we were able to predict known and novel environmental associations with prostate, lung, and breast cancers, such as estradiol and bisphenol A.</p> <p>Conclusions</p> <p>We have developed a scalable and statistical method to identify possible environmental associations with disease using publicly available data and have validated some of the associations in the literature.</p

DreamTel; Diabetes risk evaluation and management tele-monitoring study protocol

<p>Abstract</p> <p>Background</p> <p>The rising prevalence of type 2 diabetes underlines the importance of secondary strategies for the prevention of target organ damage. While access to diabetes education centers and diabetes intensification management has been shown to improve blood glucose control, these services are not available to all that require them, particularly in rural and northern areas. The provision of these services through the Home Care team is an advance that can overcome these barriers. Transfer of blood glucose data electronically from the home to the health care provider may improve diabetes management.</p> <p>Methods and design</p> <p>The study population will consist of patients with type 2 diabetes with uncontrolled A1c levels living on reserve in the Battlefords region of Saskatchewan, Canada. This pilot study will take place over three phases. In the first phase over three months the impact of the introduction of the Bluetooth enabled glucose monitor will be assessed. In the second phase over three months, the development of guidelines based treatment algorithms for diabetes intensification will be completed. In the third phase lasting 18 months, study subjects will have diabetes intensification according to the algorithms developed.</p> <p>Discussion</p> <p>The first phase will determine if the use of the Bluetooth enabled blood glucose devices which can transmit results electronically will lead to changes in A1c levels. It will also determine the feasibility of recruiting subjects to use this technology. The rest of the Diabetes Risk Evaluation and Management Tele-monitoring (DreamTel) study will determine if the delivery of a diabetes intensification management program by the Home Care team supported by the Bluetooth enabled glucose meters leads to improvements in diabetes management.</p> <p>Trial Registration</p> <p>Protocol NCT00325624</p