Expression of cellulase genes in <i>X</i>. <i>citri</i> subsp. <i>citri</i>.

<p>(A) RT-PCR analysis of gene transcription in <i>X</i>. <i>citri</i> subsp. <i>citri</i> culturing in nutrient broth medium (NB) and growing in citrus. M, DL2000 marker. (B) Detection of BglC3 and EngXCA secretion using immunoblotting. Protein samples were analyzed by SDS-PAGE and immunoblotted using anti-c-Myc antibodies. TE, Total protein extract; SE, Culture supernatant.</p

Bacterial growth in citrus tissue.

<p>Leaf planchets were cut from each infiltration area at 2-d intervals post inoculation to analyze bacterial growth. The values shown are means of three technical repeats with standard deviations.</p

Ectopic expression of nine cellulases in <i>Escherichia coli</i> BL21(DE3).

<p>(A) Degradation halos on Luria-Bertani plates supplemented with 0.5% carboxymethyl cellulose and 1.0 mM isopropyl-β-D-thiogalactopyranoside. (B) Diameters of the degradation halos. The tests were repeated three times; the data in the figure are the mean values. Two asterisks in the data column indicated significant differences at <i>P</i> = 0.01 by Student’s <i>t</i> test.</p

Factors associated with the awareness, treatment and control of hypertension in two surveys, 2001 and 2010.

<p>*** P<0.001;</p><p>** P<0.01;</p><p>* P<0.05</p><p>Factors associated with the awareness, treatment and control of hypertension in two surveys, 2001 and 2010.</p

Characteristics of the subjects who completed the surveys in 2001 and 2010.

<p>Characteristics of the subjects who completed the surveys in 2001 and 2010.</p

Age-specific prevalence of pre-hypertension and the prevalence, awareness, treatment and control of hypertension.

<p>Fig 1 shows the age-specific prevalence of (A). prehypertension (B). prevalence (C). awareness (D). treatment and (E). control of hypertension.</p

Increasing Prevalence of Metabolic Syndrome in a Chinese Elderly Population: 2001–2010

<div><p>Objective</p><p>The information on the changes of prevalence of MetS in China is limited. Our objective was to assess a 10-year’s change of the prevalence of MetS in a Chinese elderly population between 2001 and 2010.</p><p>Methods</p><p>We conducted two cross-sectional surveys in a representative sample of elderly population aged 60 to 95 years in Beijing in 2001 and 2010 respectively. MetS was defined according to the 2009 harmonizing definition.</p><p>Results</p><p>A total of 2,334 participants (943 male, 1,391 female) in 2001 and 2,102 participants (848 male, 1,254 female) in 2010 completed the survey. The prevalence of MetS was 50.4% (95%CI: 48.4%–52.4%) in 2001 and 58.1% (95%CI: 56.0%–60.2%) in 2010. The absolute change of prevalence of MetS was 7.7% over the 10-year’s period (p<0.001). The syndrome was more common in female than male in both survey years. Among the five components, hypertriglyceridemia and low HDL-C had increased most, with an increase of 14.8% (from 29.4% to 44.2%) and 9.9% (from 28.3% to 38.2%) respectively. The adjusted ORs of MetS for CHD, stroke and CVD were 1.67(95%CI: 1.39–1.99), 1.50(95%CI: 1.19–1.88) and 1.70(95%CI: 1.43–2.01) respectively in 2001, and were 1.74(95%CI: 1.40–2.17), 1.25(95%CI: 0.95–1.63) and 1.52(95%CI: 1.25–1.86) respectively in 2010.</p><p>Conclusion</p><p>The prevalence of MetS is high and increasing rapidly in this Chinese elderly population. Participants with Mets and its individual components are at significantly elevated ORs for CVD. Urgent public health actions are needed to control MetS and its components, especially for dislipidemia.</p></div

Age-adjusted and Gender-specific Prevalence of MetS in the Participants of Two Surveys at 2001 and 2010.

<p>Age-adjusted and Gender-specific Prevalence of MetS in the Participants of Two Surveys at 2001 and 2010.</p

Gender-specific Prevalence of MetS for Participants Included in Both Two Surveys.

△<p>Paired χ2 test is used (McNemar method) to calculate P value.</p

OR and 95% CI of CVD for MetS and its individual components.

<p>Model 1: adjusting for gender, age, education, marital status;</p><p>Model 2: adjusting for alcohol drinking, smoking, physical exercise, family histories of CVD and the 4 variables in model 1.</p