Physical activity counselling during pulmonary rehabilitation in patients with COPD : a randomised controlled trial

Background Pulmonary rehabilitation programs only modestly enhance daily physical activity levels in patients with chronic obstructive pulmonary disease (COPD). This randomised controlled trial investigates the additional effect of an individual activity counselling program during pulmonary rehabilitation on physical activity levels in patients with moderate to very severe COPD. Methods Eighty patients (66 +/- 7 years, 81% male, forced expiratory volume in 1 second 45 +/- 16% of predicted) referred for a six-month multidisciplinary pulmonary rehabilitation program were randomised. The intervention group was offered an additional eight-session activity counselling program. The primary outcomes were daily walking time and time spent in at least moderate intense activities. Results Baseline daily walking time was similar in the intervention and control group (median 33 [interquartile range 16-47] vs 29 [17-44]) whereas daily time spent in at least moderate intensity was somewhat higher in the intervention group (17[4-50] vs 12[2-26] min). No significant intervention*time interaction effects were observed in daily physical activity levels. In the whole group, daily walking time and time spent in at least moderate intense activities did not significantly change over time. Conclusions The present study identified no additional effect of eight individual activity counselling sessions during pulmonary rehabilitation to enhance physical activity levels in patients with COPD

Standardizing the analysis of physical activity in patients with COPD following a pulmonary rehabilitation program

BACKGROUND: There is a wide variability in measurement methodology of physical activity. This study investigated the effect of different analysis techniques on the statistical power of physical activity outcomes aft er pulmonary rehabilitation. METHODS: Physical activity was measured with an activity monitor armband in 57 patients with COPD (mean +/- SD age, 66 +/- 7 years; FEV 1, 46 +/- 17% predicted) before and aft er 3 months of pulmonary rehabilitation. The choice of the outcome (daily number of steps [STEPS], time spent in at least moderate physical activity [TMA], mean metabolic equivalents of task level [METS], and activity time [ACT]), impact of weekends, number of days of assessment, post-processing techniques, and influence of duration of daylight time (DT) on the sample size to achieve a power of 0.8 were investigated. RESULTS: The STEPS and ACT (1.6-2.3 metabolic equivalents of task) were the most sensitive outcomes. Excluding weekends decreased the sample size for STEPS (83 vs 56), TMA (160 vs 148), and METS (251 vs 207). Using 4 weekdays (STEPS and TMA) or 5 weekdays (METS) rendered the lowest sample size. Excluding days with, 8 h wearing time reduced the sample size for STEPS (56 vs 51). Differences in DT were an important confounder. CONCLUSIONS: Changes in physical activity following pulmonary rehabilitation are best measured for 4 weekdays, including only days with at least 8 h of wearing time (during waking hours) and considering the difference in DT as a covariate in the analysis

Deep learning for infrared thermal image based machine health monitoring

The condition of a machine can automatically be identified by creating and classifying features that summarize characteristics of measured signals. Currently, experts, in their respective fields, devise these features based on their knowledge. Hence, the performance and usefulness depends on the expert's knowledge of the underlying physics or statistics. Furthermore, if new and additional conditions should be detectable, experts have to implement new feature extraction methods. To mitigate the drawbacks of feature engineering, a method from the subfield of feature learning, i.e., deep learning (DL), more specifically convolutional neural networks (NNs), is researched in this paper. The objective of this paper is to investigate if and how DL can be applied to infrared thermal (IRT) video to automatically determine the condition of the machine. By applying this method on IRT data in two use cases, i.e., machinefault detection and oil-level prediction, we show that the proposed system is able to detect many conditions in rotating machinery very accurately (i.e., 95 and 91.67% accuracy for the respective use cases), without requiring any detailed knowledge about the underlying physics, and thus having the potential to significantly simplify condition monitoring using complex sensor data. Furthermore, we show that by using the trained NNs, important regions in the IRT images can be identified related to specific conditions, which can potentially lead to new physical insights

Data-driven imbalance and hard particle detection in rotating machinery using infrared thermal imaging

Currently, temperature-based condition monitoring cannot be used to accurately identify potential faults early in a rotating machines' lifetime since temperature changes are only detectable when the fault escalates. However, currently only point measurements, i.e. thermocouples, are used. In this article, infrared thermal imaging is used which - as opposed to simple thermocouples - provides spatial temperature information. This information proves crucial for the identification of several machine conditions and faults. In this paper the conditions considered are outer-raceway damage in bearings, hard-particle contamination in lubricant and several gradations of shaft imbalance. The fault detection is done using an image processing and machine learning solution which can accurately detect the majority of the faults and conditions in our data set. (C) 2017 Elsevier B.V. All rights reserved

Expression cloning and production of human heavy-chain-only antibodies from murine transgenic plasma cells

Several technologies have been developed to isolate human antibodies against different target antigens as a source of potential therapeutics, including hybridoma technology, phage and yeast display systems. For conventional antibodies, this involves either random pairing of VH and variable light (VL) domains in combinatorial display libraries or isolation of cognate pairs of VH and VL domains from human B cells or from transgenic mice carrying human immunoglobulin loci followed by single-cell sorting, single-cell RT-PCR, and bulk cloning of isolated natural VH-VL pairs. Heavy-chain-only antibodies (HCAbs) that naturally occur in camelids require only heavy immunoglobulin chain cloning. Here, we present an automatable novel, high-throughput technology for rapid direct cloning and production of fully human HCAbs from sorted population of transgenic mouse plasma cells carrying a human HCAb locus. Utility of the technique is demonstrated by isolation of diverse sets of sequence unique,soluble, high-affinity influenza A strain X-31 hemagglutinin-specific HCAbs

Daily physical activity in subjects with newly diagnosed COPD

Background Background Patients undergoing tumour necrosis factor (TNF)-alpha antagonist therapy are at increased risk of latent tuberculosis infection (LTBI) reactivation. The aim of this study was to determine the optimum available screening strategy for identifying patients for tuberculosis (TB) chemoprophylaxis. Methods Methods We conducted a prospective observational study of consecutive adults with chronic rheumatological disease referred for LTBI screening prior to commencement of TNF-alpha antagonist therapy. All patients included had calculation of TB risk according to age, ethnicity and year of UK entry, as described in the 2005 British Thoracic Society (BTS) guidelines and measurement of tuberculin skin test (TST) and T.Spot.TB. Results Results There were 187 patients included in the study, with 157 patients (84%) taking immunosuppressants. 137 patients would require further risk stratification according to the BTS algorithm, with 110 (80.3%) classified as being at low risk of having LTBI. There were 39 patients (35.5%) who were categorised as low risk but were either TST and/or T.Spot positive and would not have received chemoprophylaxis according to the BTS algorithm. Combination of all three methods (risk stratification and/or positive T.Spot and/or positive TST) identified 66 patients out of 137 who would potentially be offered chemoprophylaxis, which was greater than any single test or two-test combination. Conclusion Conclusion Performing both a TST and T.Spot in patients on immunosuppressants prior to commencement of TNF-alpha antagonist therapy gives an additional yield of potential LTBI compared with use of risk stratification tables alone. Our results suggest that use of all three screening modalities gives the highest yield of patients potentially requiring chemoprophylaxis