Factors associated with subsequent diabetes-related self-care activities: The role of social support and optimism

Aim This study aimed to explore how social support (external factor), optimism (internal factor) and their interaction associated with diabetes-related self-care activities (DRSCA) over 3 months among people with type 2 diabetes mellitus (T2DM). Design Both questionnaire-based and telephone-based survey were used. The data were collected face to face, the first time by questionnaire and the second time by telephone. Methods One hundred and fifty-five patients completed valid survey questionnaires (response rate was about 70% in the first and 62% in the second round). The association of social support and optimism with subsequent DRSCA was examined after adjusting for demographics and disease information. Results Based on results, optimism was significantly associated with subsequent DRSCA. In the dimensions of social support, objective social support and support use were significantly associated with subsequent DRSCA. The results showed that the mediation of optimism between the dimensions of social support and DRSCA was not significant after controlling for covariates. The results also indicated that social support and optimism played directly an important role in improving diabetes-related self-care activities

The association of diabetes-related self-care activities with perceived stress, anxiety, and fatigue: a cross-sectional study

Purpose: Many people with type 2 diabetes (T2DM) do not sustain sufficient diabetes-related self-care activities (DRSCA) in their daily lives. To provide additional information about the positive influence of DRSCA, this study was conducted to examine whether DRSCA were associated with reduced perceived stress, anxiety, and fatigue among people with T2DM and to explore the level of DRSCA, perceived stress, anxiety, and fatigue and their association with background information.Patients and methods: This study was a cross-sectional survey including 251 participants aged 18 years and older recruited from two hospitals in the eastern part of China. The study utilized self-report questionnaires that consisted of background information, DRSCA, perceived stress, anxiety, and fatigue. Hierarchical multiple regression analysis was conducted to explore the association of DRSCA with perceived stress, anxiety, and fatigue while adjusting for background information.Results: The results indicated that the level of self-care activities, stress, and fatigue was around middle level. The prevalence of anxiety was 19%. A high level of DRSCA was likely to reduce perceived stress but was not linked to anxiety and fatigue. Women were more susceptible to stress and anxiety, and people who had diabetes for >5 years were more likely to have anxiety. The background information included diabetes duration, standardized diabetes education, and high social support, all of which are factors that may influence DRSCA.Conclusion: The findings suggest that improving the level of DRSCA might effectively reduce perceived stress. The potential benefits of DRSCA can provide both motivational and evaluative data for self-care programs. In addition, the findings show that DRSCA were not linked to anxiety and fatigue, which implies that their positive influence on anxiety and fatigue may be offset by the load of frequent DRSCA. It is suggested that helping patients to make tailored plans to integrate DRSCA into their daily lives is needed. Meanwhile, in the background information, it is suggested that standardized diabetes education and high social support can benefit DRSCA; in improving psychological health, more attention should be paid to women and patients with diabetes duration >5 years

Use of Bayesian networks to dissect the complexity of genetic disease: application to the Genetic Analysis Workshop 17 simulated data

Complex diseases are often the downstream event of a number of risk factors, including both environmental and genetic variables. To better understand the mechanism of disease onset, it is of great interest to systematically investigate the crosstalk among various risk factors. Bayesian networks provide an intuitive graphical interface that captures not only the association but also the conditional independence and dependence structures among the variables, resulting in sparser relationships between risk factors and the disease phenotype than traditional correlation-based methods. In this paper, we apply a Bayesian network to dissect the complex regulatory relationships among disease traits and various risk factors for the Genetic Analysis Workshop 17 simulated data. We use the Bayesian network as a tool for the risk prediction of disease outcome

Recent changes of water discharge and sediment load in the Yellow River basin, China

The Yellow River basin contributes approximately 6% of the sediment load from all river systems globally, and the annual runoff directly supports 12% of the Chinese population. As a result, describing and understanding recent variations of water discharge and sediment load under global change scenarios are of considerable importance. The present study considers the annual hydrologic series of the water discharge and sediment load of the Yellow River basin obtained from 15 gauging stations (10 mainstream, 5 tributaries). The Mann-Kendall test method was adopted to detect both gradual and abrupt change of hydrological series since the 1950s. With the exception of the area draining to the Upper Tangnaihai station, results indicate that both water discharge and sediment load have decreased significantly (p<0.05). The declining trend is greater with distance downstream, and drainage area has a significant positive effect on the rate of decline. It is suggested that the abrupt change of the water discharge from the late 1980s to the early 1990s arose from human extraction, and that the abrupt change in sediment load was linked to disturbance from reservoir construction.Geography, PhysicalGeosciences, MultidisciplinarySCI(E)43ARTICLE4541-5613

Probe set algorithms: is there a rational best bet?

Affymetrix microarrays have become a standard experimental platform for studies of mRNA expression profiling. Their success is due, in part, to the multiple oligonucleotide features (probes) against each transcript (probe set). This multiple testing allows for more robust background assessments and gene expression measures, and has permitted the development of many computational methods to translate image data into a single normalized "signal" for mRNA transcript abundance. There are now many probe set algorithms that have been developed, with a gradual movement away from chip-by-chip methods (MAS5), to project-based model-fitting methods (dCHIP, RMA, others). Data interpretation is often profoundly changed by choice of algorithm, with disoriented biologists questioning what the "accurate" interpretation of their experiment is. Here, we summarize the debate concerning probe set algorithms. We provide examples of how changes in mismatch weight, normalizations, and construction of expression ratios each dramatically change data interpretation. All interpretations can be considered as computationally appropriate, but with varying biological credibility. We also illustrate the performance of two new hybrid algorithms (PLIER, GC-RMA) relative to more traditional algorithms (dCHIP, MAS5, Probe Profiler PCA, RMA) using an interactive power analysis tool. PLIER appears superior to other algorithms in avoiding false positives with poorly performing probe sets. Based on our interpretation of the literature, and examples presented here, we suggest that the variability in performance of probe set algorithms is more dependent upon assumptions regarding "background", than on calculations of "signal". We argue that "background" is an enormously complex variable that can only be vaguely quantified, and thus the "best" probe set algorithm will vary from project to project

An Effective Amperometric Biosensor Based on Gold Nanoelectrode Arrays

A sensitive amperometric biosensor based on gold nanoelectrode array (NEA) was investigated. The gold nanoelectrode array was fabricated by template-assisted electrodeposition on general electrodes, which shows an ordered well-defined 3D structure of nanowires. The sensitivity of the gold NEA to hydrogen peroxide is 37 times higher than that of the conventional electrode. The linear range of the platinum NEA toward H2O2is from 1 × 10−6to 1 × 10−2 M, covering four orders of magnitudes with detection limit of 1 × 10−7 M and a single noise ratio (S/N) of four. The enzyme electrode exhibits an excellent response performance to glucose with linear range from 1 × 10−5to 1 × 10−2 M and a fast response time within 8 s. The Michaelis–Menten constantkm and the maximum current densityimaxof the enzyme electrode were 4.97 mM and 84.60 μA cm−2, respectively. This special nanoelectrode may find potential application in other biosensors based on amperometric signals

Prognostic and predictive value of clinical and biochemical factors in breast cancer patients with bone metastases receiving "metronomic" zoledronic acid

<p>Abstract</p> <p>Background</p> <p>To assess prognostic and predictive effects of clinical and biochemical factors in our published randomized study of a weekly low dose (metronomic arm) versus a conventional dosage of zoledronic acid (conventional arm) in breast cancer patients with bone metastases.</p> <p>Methods</p> <p>Treatment outcome of 60 patients with bone metastases were used to assess impacts of following potential prognostic factors, estrogen receptor status, lymph node status, 2 year-disease free interval (DFI), numbers of chemotherapy regimens administered, interventions, and serum levels of VEGF, N-telopeptide of type I collagen (NTx), CEA, and CA 15-3.</p> <p>Results</p> <p>In univariate analyses, patients pretreated with 2 or fewer chemotherapy regimens, ER-positive tumors, 3 or fewer lymph nodes, DFI of more than 2 years, serum VEGF of less than 500 pg/mL after 3 months of intervention, serum CEA and CA 15-3 of less than ULN, and baseline serum NTx of less than 18 nM BCE had significantly longer progression free survival (PFS). The multivariate analysis showed that ER positivity (hazard ratio [HR], 0.295; 95% confidence interval [CI], 0.141-0.618; P = 0.001), serum VEGF of less than 500 pg/mL after 3 months of intervention (HR, 2.220; 95% CI, 1.136-4.338; P = 0.020), baseline serum NTx of less than 18 nM BCE (HR, 2.842; 95% CI, 1.458-5.539; P = 0.001), and 2 or fewer chemotherapy regimens received (HR, 7.803; 95% CI, 2.884-21.112; P = 0.000) were associated with a better PFS. When evaluating the predictive effect of the biochemical factors, an interaction between NTx and zoledronic acid intervention was shown (P = 0.005). The HR of weekly low dose versus a conventional dosage of zoledronic acid was estimated to be 2.309 (99% CI, 1.067-5.012) in patients with baseline serum NTx of more than 18 nM BCE, indicating a superiority of weekly low dose of zoledronic acid.</p> <p>Conclusions</p> <p>ER, serum VEGF level after intervention, and numbers of chemotherapy regimens administered are prognostic but not predictive factors in breast cancer patients with bone metastases. Patients with baseline serum NTx of more than 18 nM BCE might benefit more from weekly low-dose of zoledronic acid.</p> <p>Trial registration</p> <p>ClinicalTrials.gov unique identifier: ClinicalTrials.gov: <a href="http://www.clinicaltrials.gov/ct2/show/NCT00524849">NCT00524849</a></p