Epidemiologic merit of obese-years, the combination of degree and duration of obesity

This study aims to test the effect of combining the degree and the duration of obesity into a single variable-obese-years-and to examine whether obese-years is a better predictor of the risk of diabetes than simply body mass index (BMI) or duration of obesity. Of the original cohort of the Framingham Heart Study, 5,036 participants were followed up every 2 years for up to 48 years (from 1948). The variable, obese-years, was defined by multiplying for each participant the number of BMI units above 30 kg/m(2) by the number of years lived at that BMI. Associations with diabetes were analyzed by using time-dependent Cox proportional hazards regression models adjusted for potential confounders. The incidence of type-2 diabetes increased as the number of obese-years increased, with adjusted hazard ratios of 1.07 (95% confidence interval: 1.06, 1.09) per additional 10 obese-years. The dose-response relation between diabetes incidence and obese-years varied by sex and smoking status. The Akaike Information Criterion was lowest in the model containing obese-years compared with models containing either the degree or duration of obesity alone. A construct of obese-years is strongly associated with risk of diabetes and could be a better indicator of the health risks associated with increasing body weight than BMI or duration of obesity alone.

Combination of decisions from a multiple neural network classifier system

In this paper, a study of the effectiveness of a multiple classifier system (MCS) in a medical diagnostic task is described. A hybrid network, based on the integration of a fuzzy ARTMAP and the probabilistic neural network, is employed as the basis of the MCS. Outputs from multiple networks are combined using some decision combination method to reach a final prediction. By using a real medical database, a set of experiments has been conducted to evaluate the performance of the MSC with different network configurations. The experimental results reveal the potential of the MCS as a useful decision support tool in the medical field.

Capecitabine and oral cyclophosphamide : a novel oral treatment combination for advanced cancer

Background: This dose escalation study assessed feasibility of a totally oral chemotherapy regimen using cyclophosphamide and capecitabine. The rationale for this combination was based on the observation that preclinical models of cyclophosphamide up-regulated tumor thymidine phosphorylase and increased the activation of capecitabine. Methods: Eligible patients with advanced cancer were treated with oral cyclophosphamide and capecitabine on a 28-day cycle. If no dose limiting toxicities (DLT) were encountered during the first two treatment cycles, the next patient group was assigned to the next highest dose level until the maximum tolerable dose (MTD) was determined. Results: Twenty-seven patients entered treatment. The majority of non-DLT were grades 1 and 2. DLT experienced in the first 8-week observation period were grade 3 diarrhea (one patient, level III) and grade 3 emesis (two patients, level V). MTD was observed at level 5, 1331 mg/m2/day capecitabine days 1–28 with 125 mg/m2/day cyclophosphamide days 1–14 of the 28-day cycle. The recommended phase II dose is therefore 1331 mg/m2/day capecitabine with 100 mg/m2/day cyclophosphamide. The best response evaluation showed four partial responses (breast, colon, ovary and pancreas). Conclusion: Cyclophosphamide and capecitabine can be combined at their full oral single agent dose with promising tolerability and activity.

Improving load forecasting accuracy through combination of best forecasts

Neural network (NN) models have been widely used in the literature for short-term load forecasting. Their popularity is mainly due to their excellent learning and approximation capability. However, their forecasting performance significantly depends on several factors including initializing parameters, training algorithm, and NN structure. To minimize negative effects of these factors, this paper proposes a practically simple, yet effective and an efficient method to combine forecasts generated by NN models. The proposed method includes three main phases: (i) training NNs with different structures, (ii) selecting best NN models based on their forecasting performance for a validation set, and (iii) combination of forecasts for selected best NNs. Forecast combination is performed through calculating the mean of forecasts generated by best NN models. The performance of the proposed method is examined using real world data set. Comparative studies demonstrate that the accuracy of combined forecasts is significantly superior to those obtained from individual NN models.

Estimating nephron number in the developing kidney using the physical disector/fractionator combination

Over the last decade the development of new molecular biology tools, advanced microscopy, live imaging and systems biology approaches have revolutionized our conception of how embryonic development proceeds. One fundamental aspect of development biology is the concept of morphogenesis: understanding how a group of multipotent cells organize and differentiate into a complex organ. In Kidney Development: Methods and Protocols, expert researchers in the field detail different approaches to tackle kidney development. These approaches include culture and live imaging aspects of kidney development, analyzing the 3-dimensional aspects of branching morphogenesis as well as nephrogenesis, manipulation of the gene/protein expression during kidney development as well as in the adult kidney, and how to assess kidney malformation and disease. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and key tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Kidney Development: Methods and Protocols seeks to aid scientists in the further study of the process of morphogenesis which is fundamental important not only for studying developmental biology but also for regenerative medicine.

Do leatherback turtles Dermochelys coriacea forage during the breeding season? A combination of data-logging devices provide new insights

Animals which undertake migrations from foraging grounds to suitable breeding areas must adopt strategies in these new conditions in order to minimise the rate at which body condition deteriorates (which will occur due to oogenesis or provisioning for young). For some animals this involves continuing foraging, whereas for others the optimal strategy is to fast during the breeding season. The leatherback turtle undertakes long-distance migrations from temperate zones to tropical breeding areas, and in some of these areas it has been shown to exhibit diving behaviour indicative of foraging. We used conventional time–depth recorders and a single novel mouth-opening sensor to investigate the foraging behaviour of leatherback turtles in the southern Caribbean. Diving behaviour suggested attempted foraging on vertically migrating prey with significantly more diving to a more consistent depth occurring during the night. No obvious prey manipulation was detected by the mouth sensor, but rhythmic mouth opening did occur during specific phases of dives, suggesting that the turtle was relying on gustatory cues to sense its immediate environment. Patterns of diving in conjunction with these mouth-opening activities imply that leatherbacks are attempting to forage during the breeding season and that gustatory cues are important to leatherbacks.

Detection of fingertips based on the combination of color information and circle detection

Fingertips of human hand play an important role in hand-based interaction with computers. Therefore, identification of fingertips' positions on hand image is vital for developing a human computer interaction system. All most all of the research works for fingertips detection, initially isolate hand image from the background image. Most of these techniques develop color based segmentation methods because human skin color possess an exceptional characterises that can be used to isolate hand from the rest of the image quite easily. Sometimes color image segmentation becomes difficult due to illumination and background variations. To make it simple and reliable, this paper proposes a robust method for detecting fingertips of a hand image based on the combination of color segmentation and circle detection. Due to the characteristics of circularity of fingertips regions of hand boundary, any existing circle detection algorithms can be applied to detect circles at fingertips region. It is difficult to detect fingertips solely based on the circle detection method. For this reason, initially the proposed method detects all the circular regions on the image applying Circle Hough Transformation (CHT) then the fingertips are selected based on the color characteristics of the fingertips regions. Experimental results show that the proposed approach is promising.

Synergistic antibacterial effects of theaflavin in combination with ampicillin against hospital isolates of Stenotrophomonas Maltophilia

Stenotrophomonas maltophilia is an important opportunistic nosocomial pathogen that shows intrinsic resistance to many antibiotics. This often limits treatment options and can cause lengthy hospital stays. Combination treatments are often used to combat resistance and using natural compounds such as polyphenols could give increased treatment options and even the reuse of antibiotics to which high levels of resistance have been observed. A checkerboard assay was used to determine if any synergy exists between ampicillin and the polyphenol theaflavin against 9 clinical isolates and one control isolate (NCTC 13014) of S. maltophilia. It was discovered that significant synergy (P  0.05) does exist between theaflavin and ampicillin, reducing the mean MIC of ampicillin from 12.5-22.9 µg/mL, in liquid culture, to 3.125-6.25 µg/mL. The FIC index was calculated to be 0.22-0.35 confirming synergy. From these results, significant potential for medical applications can be seen and further investigation is recommended.

An adjunctive antidepressant nutraceutical combination in treating major depression: study protocol and clinical considerations

Current treatment for major depressive disorder (MDD), a prevalent and disabling mental illness, is inadequate, with two-thirds of people treated with first-line antidepressants not achieving remission. MDD is for many a chronic condition, often requiring multiple treatment attempts, thus development of additional interventions is urgently required. An emerging approach to improve non-response to antidepressants is the use of adjunctive nutraceuticals. The pathophysiology of MDD is considered to involve a range of abnormalities (monoamine impairment, neuro-endocrinological changes, reduced brain-derived neurotrophic factor, and cytokine alterations). By targeting an array of these key neurobiological pathways via specific nutraceuticals (S-adenosyl methionine; [SAMe], 5-HTP [active tryptophan], folinic acid [active folic acid], omega-3 fatty acids, and zinc), there is the potential to provide a more comprehensive therapeutic biological approach to treat depression. We are currently conducting a National Health and Medical Research Council funded study in Australia (APP1048222). The clinical trial is phase II/III, multi-site, 3-arm, 8-week, randomised, double-blind, placebo-controlled study using SAMe + folinic acid versus a combination nutraceutical (SAMe, 5-HTP, folinic acid, omega-3, and zinc) or matching placebo in 300 currently depressed participants with diagnosed MDD who are non-responsive to current antidepressants (ANZCTR, protocol number: 12613001300763). The results may provide evidence for a novel adjunctive neurobiological approach for treating depression.

Application of air quality combination forecasting to Bogota

The bulk of existing work on the statistical forecasting of air quality is based on either neural networks or linear regressions, which are both subject to important drawbacks. In particular, while neural networks are complicated and prone to in-sample overfitting, linear regressions are highly dependent on the specification of the regression function. The present paper shows how combining linear regression forecasts can be used to circumvent all of these problems. The usefulness of the proposed combination approach is verified using both Monte Carlo simulation and an extensive application to air quality in Bogota, one of the largest and most polluted cities in Latin America. © 2014 Elsevier Ltd.

Microencapsulation of coupled folate and chitosan nanoparticles for targeted delivery of combination drugs to colon

Folate-chitosan nanoparticles, co-loaded with 5-fluourouacil (5-FU) and leucovorin (LV) and prepared by ionic gelation technology were physically microencapsulated by enteric polymer using a solvent evaporation method. Average particle size of the microencapsulated particles was in the range of 15 to 35 µm. High drug encapsulation efficiency was obtained for both 5-FU and LV in the microencapsulated particles. Both drugs were in amorphous state in the microencapsulated particles. By enteric coating, excellent pH-dependent release profile was achieved and no drug release was observed in simulated gastric and intestinal fluids. However, when the pH value reached the soluble threshold of Eudragit S-100, a constant and slow drug release was observed. The results indicated that these microencapsulated particles are a promising vehicle for selectively targeting drugs to colon in the chemotherapy of colon cancer.