31 research outputs found

    Factors associated with spatial clustering of foot-and-mouth disease in Nepal

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    The purpose of this study was to quantify associations between hypothesized epidemiological factors and the spatial distribution of foot-and-mouth disease (FMD) in Nepal. Spatial clustering of reports of at least one FMD case by Village Development Committee (VDC) in 2004 was examined by use of the spatial scan statistic. A Bayesian Poisson multivariate regression model was used to quantify the association between the number of reports and 25 factors hypothesized to be associated with FMD risk. The spatial scan statistic identified (P < 0.01) two clusters of FMD reports. Large numbers of people, buffalo, and animal technicians in a district were associated with an elevated risk of a VDC reporting ≥1 FMD case. The knowledge of high-risk areas and factors associated with the risk of FMD in Nepal could be applied in future disease control programs

    A simulation model of intraherd transmission of foot and mouth disease with reference to disease spread before and after clinical diagnosis

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    Abstract. Intraherd transmission of foot and mouth disease virus (FMDV) was examined using a simulation model for a hypothetical 1,000-cow dairy, assuming clinical diagnosis was made when at least 1% (10 cows) or 5% (50 cows) had clinical signs of FMD, 1 index case cow, and transition state distributions for the latent, subclinically infectious, and clinically infectious periods of FMD calculated from published data. Estimates assumed for the number of animal-to-animal contacts (k) adequate for transmission ranged from 0.6 to 9.0 per hour (13.7-216.0 per day). A total of 40,000 iterations (5,000 for each scenario, assessing 4 adequate contact rates and 2 detection criteria) were run. The model predicted that FMD would not be diagnosed in the herd until 10.0-13.5 days after the index case cow had become infected, at which time between 65% and 97% of the cows (646-967 cows) to nearly 100% (978-996 cows) would already have become infected with the virus, if the number of cows showing clinical signs of FMD at the time of diagnosis were 10 or 50, respectively. At the time of diagnosis, the simulated number of infectious cattle varied substantially from 82-472 to 476-537 cows, depending on adequate contact rate and whether the diagnosis was made when 10 or 50 animals were showing clinical signs, respectively. The simulated number of infectious cows increased rapidly during the first few days after diagnosis. In the scenario where at least 10 cows showing clinical signs was necessary before a clinical diagnosis was made, each day after diagnosis, the number of infectious animals increased by nearly 100 to more than 200 cases per day up to day 5, assuming 0.57-9.0 animal-to-animal contacts per hour, respectively. Results obtained when it was assumed that at least 50 clinical cases were present at the time of diagnosis showed smaller relative increases because nearly one-half of the herd was projected to be infected at the time of diagnosis. From these results, it is clear that once an individual in a herd becomes infected with FMDV, herd infectivity is not static, rather it accelerates as would be expected as long as there are sufficient susceptible animals to sustain the increasing transmission rate, after which time the rate at which new infections occurs will diminish. Results indicate that biosecurity strategies aimed at minimizing both intraherd and interherd contact will be critical in minimizing the spread of FMD before the initial diagnosis is made. In addition, simulations suggest that very early clinical diagnosis of FMD and effective isolation or depopulation and disposal will be critical in limiting the number of infectious animals capable of transmitting the virus to other herds and thus in timely control of an epidemic. Early diagnosis will rely on early virus detection from animals in the preclinical phase of infection, rather than waiting for clinical signs to manifest in sufficient numbers to be noticed and to warrant investigation. Foot and mouth disease (FMD) is one of the most economically important livestock diseases in the world. In recent years, large-scale epidemics have been observed in Taiwan in 1997, 11 the UK in 2001

    The MafA transcription factor becomes essential to islet β-cells soon after birth

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    The large Maf transcription factors, MafA and MafB, are expressed with distinct spatial-temporal patterns in rodent islet cells. Analysis of Mafa(-/-) and pancreas-specific Mafa(∆panc) deletion mutant mice demonstrated a primary role for MafA in adult β-cell activity, different from the embryonic importance of MafB. Our interests here were to precisely define when MafA became functionally significant to β-cells, to determine how this was affected by the brief period of postnatal MafB production, and to identify genes regulated by MafA during this period. We found that islet cell organization, β-cell mass, and β-cell function were influenced by 3 weeks of age in Mafa(Δpanc) mice and compromised earlier in Mafa(Δpanc);Mafb(+/-) mice. A combination of genome-wide microarray profiling, electron microscopy, and metabolic assays were used to reveal mechanisms of MafA control. For example, β-cell replication was produced by actions on cyclin D2 regulation, while effects on granule docking affected first-phase insulin secretion. Moreover, notable differences in the genes regulated by embryonic MafB and postnatal MafA gene expression were found. These results not only clearly define why MafA is an essential transcriptional regulator of islet β-cells, but also why cell maturation involves coordinated actions with MafB

    Gene Targeting Implicates Cdc42 GTPase in GPVI and Non-GPVI Mediated Platelet Filopodia Formation, Secretion and Aggregation

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    Background: Cdc42 and Rac1, members of the Rho family of small GTPases, play critical roles in actin cytoskeleton regulation. We have shown previously that Rac1 is involved in regulation of platelet secretion and aggregation. However, the role of Cdc42 in platelet activation remains controversial. This study was undertaken to better understand the role of Cdc42 in platelet activation. Methodology/Principal Findings: We utilized the Mx-cre;Cdc42 lox/lox inducible mice with transient Cdc42 deletion to investigate the involvement of Cdc42 in platelet function. The Cdc42-deficient mice exhibited a significantly reduced platelet count than the matching Cdc42 +/+ mice. Platelets isolated from Cdc42 2/2, as compared to Cdc42 +/+, mice exhibited (a) diminished phosphorylation of PAK1/2, an effector molecule of Cdc42, (b) inhibition of filopodia formation on immobilized CRP or fibrinogen, (c) inhibition of CRP- or thrombin-induced secretion of ATP and release of P-selectin, (d) inhibition of CRP, collagen or thrombin induced platelet aggregation, and (e) minimal phosphorylation of Akt upon stimulation with CRP or thrombin. The bleeding times were significantly prolonged in Cdc42 2/2 mice compared with Cdc42 +/+ mice. Conclusion/Significance: Our data demonstrate that Cdc42 is required for platelet filopodia formation, secretion an

    Positional Cloning of a Type 2 Diabetes Quantitative Trait Locus; Tomosyn-2, a Negative Regulator of Insulin Secretion

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    We previously mapped a type 2 diabetes (T2D) locus on chromosome 16 (Chr 16) in an F2 intercross from the BTBR T (+) tf (BTBR) Lepob/ob and C57BL/6 (B6) Lepob/ob mouse strains. Introgression of BTBR Chr 16 into B6 mice resulted in a consomic mouse with reduced fasting plasma insulin and elevated glucose levels. We derived a panel of sub-congenic mice and narrowed the diabetes susceptibility locus to a 1.6 Mb region. Introgression of this 1.6 Mb fragment of the BTBR Chr 16 into lean B6 mice (B6.16BT36–38) replicated the phenotypes of the consomic mice. Pancreatic islets from the B6.16BT36–38 mice were defective in the second phase of the insulin secretion, suggesting that the 1.6 Mb region encodes a regulator of insulin secretion. Within this region, syntaxin-binding protein 5-like (Stxbp5l) or tomosyn-2 was the only gene with an expression difference and a non-synonymous coding single nucleotide polymorphism (SNP) between the B6 and BTBR alleles. Overexpression of the b-tomosyn-2 isoform in the pancreatic β-cell line, INS1 (832/13), resulted in an inhibition of insulin secretion in response to 3 mM 8-bromo cAMP at 7 mM glucose. In vitro binding experiments showed that tomosyn-2 binds recombinant syntaxin-1A and syntaxin-4, key proteins that are involved in insulin secretion via formation of the SNARE complex. The B6 form of tomosyn-2 is more susceptible to proteasomal degradation than the BTBR form, establishing a functional role for the coding SNP in tomosyn-2. We conclude that tomosyn-2 is the major gene responsible for the T2D Chr 16 quantitative trait locus (QTL) we mapped in our mouse cross. Our findings suggest that tomosyn-2 is a key negative regulator of insulin secretion

    Variation in the VP1 Gene of Foot-And-Mouth Disease Virus Serotype a Associated with Epidemiological Characteristics of Outbreaks in the 2001 Epizootic in Argentina

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    A mixed binomial Bayesian regression model was used to quantify the relation between nucleotide differences in the VP1 gene of Foot-and-mouth disease virus (FMDV) serotype A, and epidemiologic characteristics of the outbreaks from which the viruses were obtained between January and December 2001 in Argentina. An increase in the probability of different nucleotides between isolates was associated with a longer time between isolation dates, a greater distance between isolation locations, an increase in the difference between attack rates, and an increase in the difference in outbreak durations. The farther apart the outbreak herds were in the southerly and easterly directions, the greater the difference in nucleotide changes. The model accurately predicted genetic distances of isolates obtained in 2001 compared with a 2002 isolate (P < 0.01), which suggested that the predictive modeling approach applied in the present study may be useful in understanding the epidemiology of evolution of FMDV and in forensic analysis of disease epidemics.Instituto de BiotecnologíaFil: Perez, Andrés M. University of California, Davis. School of Veterinary Medicine. Department of Medicine and Epidemiology. Center for Animal Disease Modeling and Surveillance; Estados UnidosFil: Perez, Andrés M. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Konig, Guido Alberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; ArgentinaFil: Konig, Guido Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Spath, Ernesto Juan. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Thurmond, Mark C. University of California, Davis. School of Veterinary Medicine. Department of Medicine and Epidemiology. Center for Animal Disease Modeling and Surveillance; Estados Unido
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