22 research outputs found
Stoichiometrically Controlled Immobilization of Multiple Enzymes on Magnetic Nanoparticles by the Magnetosome Display System for Efficient Cellulose Hydrolysis
The
immobilization of multiple cellulase complexes receiving attention
for use in the efficient hydrolysis of celluloses. In this study,
the magnetosome display system was employed for the preparation of
systems mimicking natural multiple cellulase complexes (cellulosomes)
on magnetic nanoparticles (MNPs). Initially, two fluorescent proteins,
namely, green fluorescent protein and mCherry, were immobilized on
MNPs. Fluorescence analysis revealed the close proximity of two different
proteins on the MNPs. Enzyme-linked immunosorbent assay analysis showed
that stoichiometrically equivalent amounts of the proteins were immobilized
on the MNPs. Next, endoglucanase (EG) and β-glucosidase (BG)
were immobilized on MNPs to give EG/BG-MNPs. The resulting MNPs were
applied for the hydrolysis of celluloses, with rapid hydrolysis of
carboxymethyl cellulose being observed. Furthermore, the fusion of
the cellulose-binding domain to EG/BG-MNPs promoted improved hydrolysis
activity against the insoluble cellulose. We could therefore conclude
that the magnetosome display system can expand the possibilities of
mimicking natural cellulosome organization on MNPs
Questions used for assessing depression status.
<p>Questions used for assessing depression status.</p
Association of elevated depressive symptoms with obesity indices/markers of insulin resistance.
<p>Association of elevated depressive symptoms with obesity indices/markers of insulin resistance.</p
Mean value of age and depression score in each quartile of obesity indices/markers of insulin resistance.
<p>Mean value of age and depression score in each quartile of obesity indices/markers of insulin resistance.</p
Biosynthesis of Thermoresponsive Magnetic Nanoparticles by Magnetosome Display System
Thermoresponsive
magnetic nanoparticles (MNPs) were synthesized
using a magnetosome display system. An elastin-like polypeptide decamer
of VPGVG (ELP<sub>10</sub>), which is hydrophobic above the transition
temperature (<i>T</i><sub>t</sub>) and can form an insoluble
aggregation, was immobilized on biogenic MNPs in the magnetotactic
bacterium, Magnetospirillum magneticum AMB-1. It was suggested that hydrophobicity of the MNP surface increased
at 60 °C compared with 20 °C by the immobilization of ELP<sub>10</sub>. Size distribution analysis indicated that the immobilization
of ELP<sub>10</sub> onto MNPs induced the increased hydrophobicity
with increasing temperatures up to 60 °C, promoting aggregation
of the particles by hydrophobic and magnetic interactions. These results
suggest that the acceleration of magnetic collection at 60 °C
was caused by particle aggregation promoted by hydrophobic interaction
between ELP-MNPs. Furthermore, the immobilization of ELP on MNPs gave
a quick magnetic collection at 60 °C by external magnetic field.
The thermoresponsive properties will further expand the utility of
biotechnological applications of biogenic MNPs
Subject characteristics according to overtime work hours.
<p>Data are adjusted for age and sex, and presented as mean ± standard error unless otherwise specified.</p><p>*P for trend was obtained from linear regression for continuous variables, or from logistic regression for categorical variables. by assigning 23, 62, 90, and 100 to categories of overtime work.</p>†<p>n = 33,807 in one company.</p>‡<p>Defined as ≥150 min per week.</p
Odds ratio (OR) and 95% confidence interval of diabetes<sup>*</sup> according to overtime work hours.
<p>Abbreviations: OR, odds ratio; Ref, reference.</p><p>*Defined as fasting glucose ≥126 mg/dL (7.0 mmol/l), HbA1c ≥6.5% (48 mmol/mol), or current use of anti-diabetic drug.</p>†<p><i>P</i> for quadratic trend obtained from multiple logistic regression analysis by assigning 23, 62, 90, and 100 to categories of overtime work.</p>‡<p>Model 1 adjusted for age (continuous), sex, and company in 4 companies (n = 40,861).</p>§<p>Model 2 adjusted for factors in model 1 and smoking status (never, past, or current) in 4 companies (n = 40,861).</p>||<p>Model 3 adjusted for factors in model 2 and body mass index (kg/m<sup>2</sup>, continuous) in 4 companies (n = 40,861).</p>¶<p>Model 1 adjusted for age (continuous) and sex in 1 company (n = 33,807).</p><p>**Model 2 adjusted for factors in model 1 plus smoking status (never, past, or current), body mass index (kg/m<sup>2</sup>, continuous), alcohol use (non-drinker, drinker consuming >0 to <23 g, 23 to <46 g, or ≥46 g of ethanol per day), family history of diabetes (yes or no), shift work (yes or no), department (field work or non-field work), and job position (high or low) in 1 company (n = 33,807).</p>††<p>Model 3 adjusted for factors in model 2 and sleep duration (<6 hours, 6 to <7 hours, or ≥7 hours per day) in 1 company (n = 33,807).</p>‡‡<p>Model 4 adjusted for factors in model 3 and leisure time physical activity (<150 min or ≥150 min per week) in 1 company (n = 33,807).</p
Odds ratio with 95% confidence interval of diabetes according to overtime work hours stratified by participant characteristics.
<p>Abbreviations: BMI, body mass index; Ref, reference.</p><p>*<i>P</i> for trend obtained from multiple logistic regression analysis by assigning 23, 62, 90, and 100 to categories of overtime work.</p>†<p>Adjusted for age (continuous), sex, company, smoking status (never, past, or current), and BMI (kg/m<sup>2</sup>, continuous) in 4 companies (n = 41,081).</p>‡<p>48 women in 1 company were excluded in this analysis due to no diabetic patients.</p>§<p>Adjusted for age (continuous), sex, company, smoking status (never, past, or current), BMI (kg/m<sup>2</sup>, continuous), alcohol use (non-drinker, drinker consuming >0 to <23 g, 23 to <46 g, or ≥46 g of ethanol per day), sleep duration (<6 hours, 6 to <7 hours, or ≥7 hours per day), physical activity (<150 min or ≥150 min per week), family history of diabetes (yes or no), shift work (yes or no), department (field work or non-field work), and job position (high or low) in 1 company (n = 33,807).</p
Hba1c, Blood Pressure, and Lipid Control in People with Diabetes: Japan Epidemiology Collaboration on Occupational Health Study
<div><p>Aims</p><p>The control of blood glucose levels, blood pressure (BP), and low-density lipoprotein cholesterol (LDL-C) levels reduces the risk of diabetes complications; however, data are scarce on control status of these factors among workers with diabetes. The present study aimed to estimate the prevalence of participants with diabetes who meet glycated hemoglobin (HbA1c), BP, and LDL-C recommendations, and to investigate correlates of poor glycemic control in a large working population in Japan.</p><p>Methods</p><p>The Japan Epidemiology Collaboration on Occupational Health (J-ECOH) Study is an ongoing cohort investigation, consisting mainly of employees in large manufacturing companies. We conducted a cross-sectional analysis of 3,070 employees with diabetes (2,854 men and 216 women) aged 20–69 years who attended periodic health examinations. BP was measured and recorded using different company protocols. Risk factor targets were defined using both American Diabetes Association (ADA) guidelines (HbA1c < 7.0%, BP < 140/90 mmHg, and LDL-C < 100 mg/dL) and Japan Diabetes Society (JDS) guidelines (HbA1c < 7.0%, BP < 130/80 mmHg, and LDL-C < 120 mg/dL). Logistic regression models were used to explore correlates of poor glycemic control (defined as HbA1c ≥ 8.0%).</p><p>Results</p><p>The percentages of participants who met ADA (and JDS) targets were 44.9% (44.9%) for HbA1c, 76.6% (36.3%) for BP, 27.1% (56.2%) for LDL-C, and 11.2% (10.8%) for simultaneous control of all three risk factors. Younger age, obesity, smoking, and uncontrolled dyslipidemia were associated with poor glycemic control. The adjusted odds ratio of poor glycemic control was 0.58 (95% confidence interval, 0.46–0.73) for participants with treated but uncontrolled hypertension, and 0.47 (0.33–0.66) for participants with treated and controlled hypertension, as compared with participants without hypertension. There was no significant difference in HbA1c levels between participants with treated but uncontrolled hypertension and those with treated and controlled hypertension.</p><p>Conclusion</p><p>Data from a large working population, predominantly composed of men, suggest that achievement of HbA1c, BP, and LDL-C targets was less than optimal, especially in younger participants. Uncontrolled dyslipidemia was associated with poor glycemic control. Participants not receiving antihypertensive treatment had higher HbA1c levels.</p></div