Tiotropium and olodaterol fixed-dose combination versus mono-components in COPD (GOLD 2-4)

Efficacy and safety of tiotropium+olodaterol fixed-dose combination (FDC) compared with the mono-components was evaluated in patients with moderate to very severe chronic obstructive pulmonary disease (COPD) in two replicate, randomised, double-blind, parallel-group, multicentre, phase III trials. Patients received tiotropium+olodaterol FDC 2.5/5 μg or 5/5 μg, tiotropium 2.5 μg or 5 μg, or olodaterol 5 μg delivered once-daily via Respimat inhaler over 52 weeks. Primary end points were forced expiratory volume in 1 s (FEV1) area under the curve from 0 to 3 h (AUC0-3) response, trough FEV1 response and St George's Respiratory Questionnaire (SGRQ) total score at 24 weeks. In total, 5162 patients (2624 in Study 1237.5 and 2538 in Study 1237.6) received treatment. Both FDCs significantly improved FEV1 AUC0-3 and trough FEV1 response versus the mono-components in both studies. Statistically significant improvements in SGRQ total score versus the mono-components were only seen for tiotropium+olodaterol FDC 5/5 μg. Incidence of adverse events was comparable between the FDCs and the mono-components. These studies demonstrated significant improvements in lung function and health-related quality of life with once-daily tiotropium+olodaterol FDC versus mono-components over 1 year in patients with moderate to very severe COPD.

Extended Tree Augmented Naive Classifier

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.

A tree augmented classifier based on Extreme Imprecise Dirichlet Model

We present TANC, a TAN classifier (tree-augmented naive) based on imprecise probabilities. TANC models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM). A first contribution of this paper is the experimental comparison between EDM and the global Imprecise Dirichlet Model using the naive credal classifier (NCC), with the aim of showing that EDM is a sensible approximation of the global IDM. TANC is able to deal with missing data in a conservative manner by considering all possible completions (without assuming them to be missing-at-random), but avoiding an exponential increase of the computational time. By experiments on real data sets, we show that TANC is more reliable than the Bayesian TAN and that it provides better performance compared to previous TANs based on imprecise probabilities. Yet, TANC is sometimes outperformed by NCC because the learned TAN structures are too complex; this calls for novel algorithms for learning the TAN structures, better suited for an imprecise probability classifier.

A Tree Augmented Classifier Based on Extreme Imprecise Dirichlet Model

In this paper we present TANC, i.e., a tree-augmented naive credal classifier based on imprecise probabilities; it models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM) (Cano et al., 2007) and deals conservatively with missing data in the training set, without assuming them to be missing-at-random. The EDM is an approximation of the global Imprecise Dirichlet Model (IDM), which considerably simplifies the computation of upper and lower probabilities; yet, having been only recently introduced, the quality of the provided approximation needs still to be verified. As first contribution, we extensively compare the output of the naive credal classifier (one of the few cases in which the global IDM can be exactly implemented) when learned with the EDM and the global IDM; the output of the classifier appears to be identical in the vast majority of cases, thus supporting the adoption of the EDM in real classification problems. Then, by experiments we show that TANC is more reliable than the precise TAN (learned with uniform prior), and also that it provides better performance compared to a previous (Zaffalon, 2003) TAN model based on imprecise probabilities. TANC treats missing data by considering all possible completions of the training set, but avoiding an exponential increase of the computational times; eventually, we present some preliminary results with missing data.

Automatically Annotating Structured Web Data Using a SVM-Based Multiclass Classifier

In this paper, we propose a new learning approach to Web data annotation, where a support vector machine-based multiclass classifier is trained to assign labels to data items. For data record extraction, a data section re-segmentation algorithm based on visual and content features is introduced to improve the performance of Web data record extraction. We have implemented the proposed approach and tested it with a large set of Web query result pages in different domains. Our experimental results show that our proposed approach is highly effective and efficient.

Machine learning for transient discovery in Pan-STARRS1 difference imaging

Efficient identification and follow-up of astronomical transients is hindered by the need for humans to manually select promising candidates from data streams that contain many false positives. These artefacts arise in the difference images that are produced by most major ground-based time-domain surveys with large format CCD cameras. This dependence on humans to reject bogus detections is unsustainable for next generation all-sky surveys and significant effort is now being invested to solve the problem computationally. In this paper, we explore a simple machine learning approach to real-bogus classification by constructing a training set from the image data of similar to 32 000 real astrophysical transients and bogus detections from the Pan-STARRS1 Medium Deep Survey. We derive our feature representation from the pixel intensity values of a 20 x 20 pixel stamp around the centre of the candidates. This differs from previous work in that it works directly on the pixels rather than catalogued domain knowledge for feature design or selection. Three machine learning algorithms are trained (artificial neural networks, support vector machines and random forests) and their performances are tested on a held-out subset of 25 per cent of the training data. We find the best results from the random forest classifier and demonstrate that by accepting a false positive rate of 1 per cent, the classifier initially suggests a missed detection rate of around 10 per cent. However, we also find that a combination of bright star variability, nuclear transients and uncertainty in human labelling means that our best estimate of the missed detection rate is approximately 6 per cent.

Design of a Dissolving Microneedle Platform for Transdermal Delivery of a Fixed-Dose Combination of Cardiovascular Drugs

Microneedles (MNs) are a minimally invasive drug delivery platform, designed to enhance transdermal drug delivery by breaching the stratum corneum. For the first time, this study describes the simultaneous delivery of a combination of three drugs using a dissolving polymeric MN system. In the present study, aspirin, lisinopril dihydrate, and atorvastatin calcium trihydrate were used as exemplar cardiovascular drugs and formulated into MN arrays using two biocompatible polymers, poly(vinylpyrrollidone) and poly(methylvinylether/maleic acid). Following fabrication, dissolution, mechanical testing, and determination of drug recovery from the MN arrays, in vitro drug delivery studies were undertaken, followed by HPLC analysis. All three drugs were successfully delivered in vitro across neonatal porcine skin, with similar permeation profiles achieved from both polymer formulations. An average of 126.3 ± 18.1 μg of atorvastatin calcium trihydrate was delivered, notably lower than the 687.9 ± 101.3 μg of lisinopril and 3924 ± 1011 μg of aspirin, because of the hydrophobic nature of the atorvastatin molecule and hence poor dissolution from the array. Polymer deposition into the skin may be an issue with repeat application of such a MN array, hence future work will consider more appropriate MN systems for continuous use, alongside tailoring delivery to less hydrophilic compounds.

Inappropriate prescribing of combination inhalers in Northern Ireland: retrospective cross-sectional cohort study of prescribing practice in primary care

BACKGROUND: Asthma management guidelines advocate a stepwise approach to asthma therapy, including the addition of a long-acting bronchodilator to inhaled steroid therapy at step 3. This is almost exclusively prescribed as inhaled combination therapy.

AIMS: To examine whether asthma prescribing practice for inhaled combination therapy (inhaled corticosteroid/long-acting β2-agonist (ICS/LABA)) in primary care in Northern Ireland is in line with national asthma management guidelines.

METHODS: Using data from the Northern Ireland Enhanced Prescribing Database, we examined initiation of ICS/LABA in subjects aged 5-35 years in 2010.

RESULTS: A total of 2,640 subjects (67%) had no inhaled corticosteroid monotherapy (ICS) in the study year or six months of the preceding year (lead-in period) and, extending this to a 12-month lead-in period, 52% had no prior ICS. 41% of first prescriptions for ICS/LABA were dispensed in January to March. Prior to ICS/LABA prescription, in the previous six months only 17% had a short-acting β2-agonist (SABA) dispensed, 5% received oral steroids, and 17% received an antibiotic.

CONCLUSIONS: ICS/LABA therapy was initiated in the majority of young subjects with asthma without prior inhaled steroid therapy. Most prescriptions were initiated in the January to March period. However, the prescribing of ICS/LABA did not appear to be driven by asthma symptoms (17% received SABA in the previous 6 months) or severe asthma exacerbation (only 5% received oral steroids). Significant reductions in ICS/LABA, with associated cost savings, would occur if the asthma prescribing guidelines were followed in primary care.