Combination antidepressants : use by GPs and psychiatrists

Background : Current treatment of depression fails to achieve remission in 50% of patients. Combinations of two antidepressants are used by some Australian psychiatrists.

Objective : This article investigates the pros and cons of combination antidepressant therapy and provides suggestions for when to consider their use, which combinations to choose, and how to introduce combination antidepressant therapies.

Discussion : Combining two antidepressants is a controversial strategy, with supporters and critics arguing its efficacy and safety from opposing perspectives. The use of combination antidepressant therapies may facilitate remission from depression. However, there is limited evidence supporting these treatments, and safety concerns are often cited. There is some support for combination therapies in selected cases from international bodies. After considering risks and benefits on a case-by-case basis, careful use of selected combination antidepressant therapy may be one of a range of effective treatments for some individuals suffering from depression.

Combination pharmacotherapy in unipolar depression

It is estimated that between 60 and 80% of those with major depressive disorder do not achieve full symptomatic remission from first-line antidepressant monotherapy. Residual depressive symptoms substantially impair quality of life and add to the risk of recurrence. It is now clear that depression would benefit from more vigorous treatment, in order to ameliorate its disease burden. While there are established algorithms in situations of treatment resistance, the use of combination pharmacotherapy in unipolar depression is a relatively under-investigated area of treatment and may be an effective and tolerable strategy that maximizes the available resources. This paper reviews the current evidence for combination pharmacotherapy in unipolar depression and discusses its clinical applications.

Hybrid neural-evolutionary model for electricity price forecasting

Evolving artificial neural networks has attracted much attention among researchers recently, especially in the fields where plenty of data exist but explanatory theories and models are lacking or based upon too many simplifying assumptions. Financial time series forecasting is one of them. A hybrid model is used to forecast the hourly electricity price from the California Power Exchange. A collaborative approach is adopted to combine ANN and evolutionary algorithm. The main contributions of this thesis include: Investigated the effect of changing values of several important parameters on the performance of the model, and selected the best combination of these parameters; good forecasting results have been obtained with the implemented hybrid model when the best combination of parameters is used. The lowest MAPE through a single run is 5. 28134%. And the lowest averaged MAPE over 10 runs is 6.088%, over 30 runs is 6.786%; through the investigation of the parameter period, it is found that by including future values of the homogenous moments of the instant being forecasted into the input vector, forecasting accuracy is greatly enhanced. A comparison of results with other works reported in the literature shows that the proposed model gives superior performance on the same data set.

Multicentre phase I/II study of PI-88, a heparanase inhibitor in combination with docetaxel in patients with metastatic castrate-resistant prostate cancer

Background: Docetaxel (Taxotere) improve survival and prostate-specific antigen (PSA) response rates in patients with metastatic castrate-resistant prostate cancer (CRPC). We studied the combination of PI-88, an inhibitor of angiogenesis and heparanase activity, and docetaxel in chemotherapy-naive CRPC.

Patients and methods: We conducted a multicentre open-label phase I/II trial of PI-88 in combination with docetaxel. The primary end point was PSA response. Secondary end points included toxicity, radiologic response and overall survival. Doses of PI-88 were escalated to the maximum tolerated dose; whereas docetaxel was given at a fixed 75 mg/m2 dose every three weeks

Results: Twenty-one patients were enrolled in the dose-escalation component. A further 35 patients were randomly allocated to the study to evaluate the two schedules in phase II trial. The trial was stopped early by the Safety Data Review Board due to a higher-than-expected febrile neutropenia of 27%. In the pooled population, the PSA response (50% reduction) was 70%, median survival was 61 weeks (6–99 weeks) and 1-year survival was 71%.

Conclusions: The regimen of docetaxel and PI-88 is active in CRPC but associated with significant haematologic toxicity. Further evaluation of different scheduling and dosing of PI-88 and docetaxel may be warranted to optimise efficacy with a more manageable safety profile.

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.

Short term load forecasting using a hybrid neural network

Short Term Load Forecasting (STLF) is very important from the power systems grid operation point of view. STLF involves forecasting load demand in a short term time frame. The short term time frame may consist of half hourly prediction up to weekly prediction. Accurate forecasting would benefit the utility in terms of reliability and stability of the grid ensuring adequate supply is present to meet with the load demand. Apart from that it would also affect the financial performance of the utility company. An accurate forecast would result in better savings while maintaining the security of the grid. This paper outlines the STLF using a novel hybrid online learning neural network, known as the Gaussian Regression (GR). This new hybrid neural network is a combination of two existing online learning neural networks which are the Gaussian Adaptive Resonance Theory (GA) and the Generalized Regression Neural Network (GRNN). Both GA and GRNN implemented online learning, but each of them suffers from limitation. Originally GA is used for unsupervised clustering by compressing the training samples into several categories. A supervised version of GA is available, namely Gaussian ARTMAP (GAM). However, the GAM is still not capable on solving regression problem. On the other hand, GRNN is designed for solving real value estimation (regression) problem, but the learning process would involve of memorizing all training samples, hence high computational cost. The hybrid GR is considered an enhanced version of GRNN with compression ability while still maintains online learning properties. Simulation results show that GR has comparable prediction accuracy and has less prototype as compared to the original GRNN as well as the Support Vector Regression.

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.

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.

Evaluation and comparison of type reduction algorithms from a forecast accuracy perspective

A variety of type reduction (TR) algorithms have been proposed for interval type-2 fuzzy logic systems (IT2 FLSs). The focus of existing literature is mainly on computational requirements of TR algorithm. Often researchers give more rewards to computationally less expensive TR algorithms. This paper evaluates and compares five frequently used TR algorithms from a forecasting performance perspective. Algorithms are judged based on the generalization power of IT2 FLS models developed using them. Four synthetic and real world case studies with different levels of uncertainty are considered to examine effects of TR algorithms on forecasts accuracies. It is found that Coupland-Jonh TR algorithm leads to models with a better forecasting performance. However, there is no clear relationship between the width of the type reduced set and TR algorithm.

Neural network and interval type-2 fuzzy system for stock price forecasting

Stock price forecast has long been received special attention of investors and financial institutions. As stock prices are changeable over time and increasingly uncertain in modern financial markets, their forecasting becomes more important than ever before. A hybrid approach consisting of two components, a neural network and a fuzzy logic system, is proposed in this paper for stock price prediction. The first component of the hybrid, i.e. a feedforward neural network (FFNN), is used to select inputs that are highly relevant to the dependent variables. An interval type-2 fuzzy logic system (IT2 FLS) is employed as the second component of the hybrid forecasting method. The IT2 FLS’s parameters are initialized through deployment of the k-means clustering method and they are adjusted by the genetic algorithm. Experimental results demonstrate the efficiency of the FFNN input selection approach as it reduces the complexity and increase the accuracy of the forecasting models. In addition, IT2 FLS outperforms the widely used type-1 FLS and FFNN models in stock price forecasting. The combination of the FFNN and the IT2 FLS produces dominant forecasting accuracy compared to employing only the IT2 FLSs without the FFNN input selection.