“Caracterización de los sistemas de producción de ovinos de pelo en el suroeste del departamento de Matagalpa 2010”

Con el objetivo de caracterizar los sistemas de producción de ovinos de pelo en el territorio suroeste del departamento de Matagalpa 2010. (Sébaco, Ciudad Darío, San Isidro y Matagalpa). Se aplicó una encuesta a 103 productores que poseen ovinos de pelo, la muestra se definió aleatoriamente utilizando la ecuación planteada por Scheaffer (1987), se utilizó la técnica de muestreo de bola de nieve, planteada por Frey et al (2000). Esta investigación permitió conocer las debilidades y oportunidades en los sistemas de explotación de esta especie promisoria para la zona seca del país. Con los resultados obtenidos de las encuestas se procedió ha elaborar una base de datos en el programa SPSS versión 11.5 en español. Encontrando un predominio del sexo femenino como titulares de las explotaciones ovinas, 58.3% cursó educación primaria, el 98% de las explotaciones cuentan con raza pelibuey, el 100% de las explotaciones realizan destete y monta de forma natural, una media de mortalidad de corderos de 1, alimentan a las ovejas con potrero sin división (81.6%), se suministra pasto de corte, pastoreo, leguminosas y se suplementa sal común 49.5%, aplican vacunas contra ántrax y pierna negra (63.1%), desparasitaciones internas y externas (66%), ambos con una frecuencia de 2 veces al año, en el manejo productivo no se lleva control en la actividad ovina (100%), los equipo e instalaciones son rústicas, los corrales ovinos el son elaborados con alambre y/o madera, techado con plástico y/o zinc (49.51%), en cuanto a asistencia técnica el 58.3% ha recibid

Subgroup analysis of ^$the adjusted association between <i>GSTM1-GSTT1</i> null genotype and HCC risk.

<p>M: model of meta-analysis; R: random-effects model; F: fixed-effects model. <i>P_H</i>: <i>P</i> value of heterogeneity test. <i>P_E</i>: <i>P</i> value of Egger’s test. <i>P_OR</i>: <i>P</i><0.001 replace the <i>P</i> = 0.000 and the <i>P</i> less than 0.001. ^$: adjusted association (after omitting 3 articles <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057043#pone.0057043-Bian2" target="_blank">[30]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057043#pone.0057043-Yu3" target="_blank">[37]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057043#pone.0057043-Deng1" target="_blank">[44]</a>).</p

Subgroup analysis of the association between <i>GSTM1</i> null genotype and HCC risk.

<p>M: model of meta-analysis; R: random-effects model; F: fixed-effects model. <i>P_H</i>: <i>P</i> value of heterogeneity test. <i>P_E</i>: <i>P</i> value of Egger’s test. <i>P_OR</i>: <i>P</i><0.001 replace <i>P</i> = 0.000 and <i>P</i> less than 0.001. @: <i>P</i> values could not be calculated.</p

Subgroup analysis of ^$the adjusted association between <i>GSTT1</i> null genotype and HCC risk.

<p>M: model of meta-analysis; R: random-effects model; F: fixed-effects model. <i>P_H</i>: <i>P</i> value of heterogeneity test. <i>P_E</i>: <i>P</i> value of Egger’ test. <i>P_OR</i>: <i>P</i><0.001 replace the <i>P</i> = 0.000 and the <i>P</i> less than 0.001.</p>$<p>adjusted association (after omitting 3 articles <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057043#pone.0057043-Bian2" target="_blank">[30]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057043#pone.0057043-Sun1" target="_blank">[34]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057043#pone.0057043-Ma2" target="_blank">[42]</a>).</p

Association between <i>GSTM1</i> null genotype and HCC risk analyzed by forest plot of meta-analysis.

<p>The forest plots of pooled OR with 95% CI (Null genotype <i>vs.</i> Present genotype; OR = 1.47, 95% CI: 1.21 to 1.79; Random-effects model, <i>P</i><0.001).</p

Association between <i>GSTM1-GSTT1</i> dual-null genotype and HCC risk analyzed by forest plot of meta-analysis.

<p>The forest plots of pooled OR with 95% CI (Dual-null genotype <i>vs.</i> Present genotype; OR = 1.79, 95% CI: 1.26 to 2.53; Random-effects model, <i>P</i><0.001).</p

Characteristics of the studies related with the effects of <i>GSTs</i> genetic polymorphisms and HCC risk.

<p>ALL: HCC cases were confirmed by pathologic diagnosis; PARTIAL: part of HCC cases were confirmed by pathologic diagnosis; NA: relative data were not available in original studies;</p>*<p>Articles published in English;</p>̂<p>Articles published in Chinese.</p>§<p>McGlynn et al. did not show genotype frequency of cases and controls, but presented OR with 95% CI;</p><p>Southeast regions in China mainland include Hebei, Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, and Guangxi. Central regions in China mainland include Hunan and Henan.</p

Flow chart of study selection.

<p>Flow chart of study selection.</p

Galbraith plot of association between <i>GST</i> polymorphisms and HCC risk.

<p>Each figure represents a unique article in this meta-analysis. The figures outside the three lines are spotted as the outliers and the possible sources of heterogeneity in the analysis pooled of total available studies. (A) Galbraith plot identifies the outliers from 26 studies about <i>GSTM1</i> polymorphisms and HCC risk. (B) Galbraith plot identifies the outliers from 21 studies about <i>GSTT1</i> polymorphisms and HCC risk. (C) Galbraith plot identifies the outliers from 12 studies about <i>GSTM1-GSTT1</i> polymorphisms and HCC risk.</p

Subgroup analysis of the association between <i>GSTM1-GSTT1</i> null genotype and HCC risk.

<p>M: model of meta-analysis; R: random-effects model; F: fixed-effects model. <i>P_H</i>: <i>P</i> value of heterogeneity test. <i>P_E</i>: <i>P</i> value of Egger’s test. <i>P_OR</i>: <i>P</i><0.001 replace the <i>P</i> = 0.000 and the <i>P</i> less than 0.001. @: <i>P</i> values could not be calculated.</p