Objective To investigate the prevalence rate and related factors of excessive daytime sleepiness (EDS) in the elderly. Methods Epworth Sleep Scales (ESS) was used to make a randomized questionnaire among the retired elderly. Results 1 000 questionnaires were released and totally 768 qualified answers were returned. The mean score of ESS was 4.22?0.10 and the score ≥8 was used as an abmormal value. The prevlence rate of EDS in the elderly group was 10.9%. Regression analysis was done based on ESS score as dependent variable, and age, sex, duration of sleep, difficulty in falling asleep, early wakeup, daytime sleepiness, habitual snoring and loud unstable snoring as independent variables. It was showed that daytime tiredness(r=1.458, P

Carotenoids have a range of diverse biological functions and actions, especially playing an important role in human health with provitamin A activity, anti-cancer activity, enhancing immune ability and so on. Human body can't synthesis carotenoids by itself and must absorb them from outside. However, carotenoid contents in many plant are very low, and many kinds of carotenoid are difficult to produce by chemical ways. With the elucidation of carotenoid biosynthetic pathway and cloning genes of relative enzymes from microorganisms and higher plants, it is possible to regulate carotenoid biosynthesis via genetic engineering. This article reviews gene cloning of carotenoid biosynthetic enzymes in microorganisms and higher plants, and advances in the studies of carotenoid production in heterologous microorganisms and crop plants using gene-manipulated carotenoid biosynthesis.
Candida albicans ; genetics ; Carotenoids ; biosynthesis ; Cloning, Molecular ; Escherichia coli ; genetics ; Genetic Engineering ; methods ; Plants ; genetics ; Saccharomyces cerevisiae ; genetics

Objective:To analyze the effects of different blood pressure variables and their variabilities on diabetic nephropathy(DN)in patients with type 2 diabetes.Methods:This prospective cohort study included 3 050 type 2 diabetic patients without DN at baseline from Lee′s clinic in Taiwan, China. The metabolic parameters of patients were regularly checked, and urine albumin creatinine ratio(UACR)were evaluated annually. The average follow-up period was 7 years(3-10 years). The means and standard deviations(SD)of systolic blood pressure(SBP), diastolic blood pressure(DBP), pulse pressure(PP), and mean arterial pressure(MAP)were calculated. According to whether SBP-Mean was higher or lower than 130 mmHg(1 mmHg=0.133 kPa) and SBP-SD was higher or lower than 11.06 mmHg(average SBP-SD), these patients were divided into four groups: Q1(SBP-Mean<130 mmHg, SBP-SD<11.06 mmHg); Q2(SBP-Mean<130 mmHg, SBP-SD≥11.06 mmHg); Q3(SBP-Mean≥130 mmHg, SBP-SD<11.06 mmHg); Q4(SBP-Mean≥130 mmHg, SBP-SD≥11.06 mmHg). In the same way, according to whether PP-Mean was higher or lower than 80 mmHg(average PP-Mean)and PP-SD was higher or lower than 6.48 mmHg(average PP-SD), the patients were divided into Q1-Q4 groups.Results:After adjusting age, sex, and diabetes duration, Cox regression analysis showed that SBP-Mean, SBP-SD, PP-Mean, and PP-SD were the risk factors of DN. After the stratification according to SBP-Mean and SBP-SD, the patients in Q4 group( HR=1.976, P<0.001)had the highest risk while those in Q1 group displayed the lowest risk for DN. Additionally, the patients in Q3 group( HR=1.614, P<0.001)imposed a higher risk than that in Q2 group( HR=1.408, P<0.001). By stratificating the patients based on PP-Mean and PP-SD, the patients in Q4 group revealed the highest risk of DN( HR=1.370, P<0.001)while those in Q1 group had the lowest risk. In addition, the patients in Q3 group( HR=1.266, P<0.001)had a higher risk of DN compared with those in Q2 group( HR=1.212, P<0.001). Conclusion:SBP and PP variabilities are the predictors of DN in patients with type 2 diabetes.

Objective:To investigate the relationship between HbA 1C variability and diabetic retinopathy(DR) in patients with type 2 diabetes and to explore the influencing factors of HbA 1C variability. Methods:Type 2 diabetic patients who received dilated funduscopic examination annually, were stratified into two groups based on the presence or absence of DR, with a median follow-up period of 4 years(2-5 years). Intrapersonal means and SDs of all recorded HbA 1C measurements were calculated. A 1C-SD represented the measure of HbA 1C variability. In addition, medical history and clinical data of all subjects were collected and analyzed. Subjects were divided into four quartiles based on their A 1C-Mean and A 1C-SD data: Q1(A 1C-Mean<7%, A 1C-SD<0.76%); Q2(A 1C-Mean<7%, A 1C-SD≥0.76%); Q3(A 1C-Mean≥7%, A 1C-SD<0.76%); Q4(A 1C-Mean≥7%, A 1C-SD≥0.76%). Results:Multivariate linear regression showed that exercise, insulin( P<0.01), and smoking( P=0.004) are the influencing factors of HbA 1C variability. Adjusted for age, sex, and diabetes duration, Cox regression analysis revealed that HbA 1C variability was an independent risk factor for DR. Meanwhile, patients in Q4 group had the highest DR prevalence(HR=1.676, P<0.01) while Q1 group had the lowest. In addition, patients in Q2 group(HR=1.437, P=0.005) had a higher risk of DR than those in Q3 group(HR=1.361, P<0.01). Conclusions:HbA 1C variability is an independent predictor of DR in patients with type 2 diabetes. It may play a greater role in DR development than mean HbA 1C does when the mean value of HbA 1C variability index is above 0.75%.

Objective:To evaluate the effect of urinary albumin creatinine ratio (UACR) on diabetic retinopathy (DR) in patients with type 2 diabetes. Receiver operating characteristic (ROC) curve was applied to find the cut-off value of UACR for diagnosing DR.Methods:A prospective cohort study of 2 490 patients with type 2 diabetes was conducted with a mean follow-up of 7 years ranging from 3 to 10 years. Dilated fundus examination was performed once a year, and patient history and clinical data were collected and analyzed. Patients were divided into three groups according to the UACR: Q1, normal urinary albumin group (UACR<30 mg/g), Q2, microalbuminuria group (30 mg/g≤UACR≤299 mg/g), and Q3, macroalbuminuria group (UACR>300 mg/g), respectively. Cox regression analysis was used to explore the influence of UACR and other factors on DR, and ROC curve was drawn to evaluate the value of UACR in diagnosis of DR.Results:Cox regression analysis showed that UACR was the risk factor of DR( HR=1.108, 95% CI 1.023-1.241, P<0.001). It showed that the patients in Q3 group had the highest risk of proliferative DR ( HR=3.128, 95% CI 2.025-4.831, P<0.001), the patients in Q2 group followed( HR=1.918, 95% CI 1.355-2.714, P<0.001), and the patients in Q1 group were the lowest. ROC curve analysis showed that area under UACR curve was 0.746(95% CI 0.681-0.812, P<0.001), and the cut-off value, sensitivity, and specificity for the diagnosis of PDR were 54.12mg/g, 0.769, and 0.653, respectively. Conclusion:The UACR can predict the progression of PDR in type 2 diabetes patients, therefore it may be used as a preliminary predictor for the progression of DR.