Effectiveness of initiating biologics in severe asthma patients with high steroid exposure

Peer reviewedPostprin

Biomarker-defined clusters by level of Type 2 inflammatory involvement in severe asthma

Peer reviewedPostprin

Real-life biomarker response to anti-IL5 and anti-IgE therapies in severe asthma patients

Funding: This study was conducted by the Observational and Pragmatic Research Institute (OPRI) Pte Ltd and was partially funded by Optimum Patient Care Global and AstraZeneca Ltd. No funding was received by OPRI for its contribution.Peer reviewedPostprin

Demographic and Clinical Characteristics of Patients with Severe Asthma in the Asian Pacific Region : data from the International Severe Asthma Registry (ISAR)

Peer reviewe

Characterization of patients in the International Severe Asthma Registry with high steroid exposure who did or did not initiate biologic therapy

Peer reviewedPostprin

Association between T2-related co-morbidities and effectiveness of biologics in severe asthma

Acknowledgments The authors thank Mr. Joash Tan (BSc, Hons), of the Observational and Pragmatic Research Institute (OPRI), and Ms Andrea Lim (BSc, Hons) of the Observational Pragmatic Research Institute (OPRI) for their editorial and formatting assistance that supported the development of this publication. Funding statement: This study was conducted by the Observational and Pragmatic Research Institute (OPRI) Pte Ltd and was partially funded by Optimum Patient Care Global and AstraZeneca Ltd. AstraZeneca UK LimitedPeer reviewe

Negotiating Health and Migration Aspirations: Lay Health Beliefs among Chinese Rural-to-Urban Migrant Workers in Shanghai and Beijing

Adopting both demographic and ethnographic approaches, this thesis examines the processes through which health inequalities are reinforced and reproduced among rural migrants in contemporary urban China. It places a particular focus on what appears to be a common struggle shared by rural migrant workers, that of meeting their migration aspirations and expectations while afflicted by health, illness and disease-related constraints. This thesis will first examine the demographic health characteristics of Chinese migrant workers’ before utilising ethnographic research approaches to examine their subjective constructions of health knowledge and lay health practices. By contrasting how migrant workers, specifically migrant parents, manage their family health problems in different individual and social settings, my thesis explores the micro-mechanisms of the reproduction of health inequalities as reflected in migrant workers’ understandings and interpretations of health-related behaviours, lay health beliefs and lay aetiologic accounts. Ultimately, this thesis illustrates the processes through which social inequalities have become embedded in health, which, in turn, shape people’s subjective understandings of achievement and health. Similar to other migrant workers over the world, the health challenges faced by Chinese rural-to-urban migrant workers are influenced by many other broad social inequalities and limitations they encounter in new spaces. As this thesis demonstrates, it is not simply enough to address the health challenges of migrant workers in a vacuum, focusing on illness or disease alone. A greater focus must be placed on understanding the aspirations of migrant workers and their changing perspectives throughout their migration journeys

Power quality composite disturbance deep feature extraction and classification based on SCG optimized SSAE-FFNN

With the development of the smart grid, power quality issues have been widespread in the power grid and it threaten the safety and stability of the power grid. The monitoring data of power quality disturbances (PQDs) increase rapidly, and it is of great significance to achieve deep feature extraction and intelligent classification of PQDs in large-scale systems for power system pollution detection and management. To this end, stacked sparse auto encoder (SSAE) and feedforward neural network (FFNN) based method for composite PQDs classification is proposed in this paper. Firstly, a PQDs simulation model is constructed based on IEEE standard. Then, a PQDs classification model based on SSAE-FFNN is established, and the scaled conjugate gradient (SCG) algorithm is used to optimize the model, in order to accelerate gradient descent and improve training efficiency. Next, to reduce the reconstruction loss of the stacked network and extract deep low-dimensional features, the layer-wise training and fine-tuning strategy of SSAE are constructed. Finally, the examples are used to verify the classification effect, robustness, generalization and applicable scenario scale of the proposed method. The results show that the method can effectively identify composite PQDs and it has a high accuracy even for both error-containing disturbances and 21 sets of measured disturbance data of a local municipal grid

Abnormal Appearance Detection of Substation Based on Image Comparison

Based on image comparison, a novel algorithm for abnormal appearance detection of substation is proposed. Previous spatial states of an object are compared to its current representation in a digital image. Firstly, saliency maps are acquired using a fast implementation method of salient region detection. Based on saliency maps, image registration was completed by ORB (Oriented Fast and Rotated Brief). Then, sliding widow algorithm is applied to transform the whole image comparison problem into sub-image comparison problem. Textural feature and shape feature vectors (TSFVs) representing contents of images are generated by feature level fusion. Finally, decisions are automatically made as to whether or not change at the outline has occurred by the Euclidean distance of TEFVs. Experimental results show that the proposed method has good performance in abnormal appearance detection of substation

Abnormal Appearance Detection of Substation Based on Image Comparison

Based on image comparison, a novel algorithm for abnormal appearance detection of substation is proposed. Previous spatial states of an object are compared to its current representation in a digital image. Firstly, saliency maps are acquired using a fast implementation method of salient region detection. Based on saliency maps, image registration was completed by ORB (Oriented Fast and Rotated Brief). Then, sliding widow algorithm is applied to transform the whole image comparison problem into sub-image comparison problem. Textural feature and shape feature vectors (TSFVs) representing contents of images are generated by feature level fusion. Finally, decisions are automatically made as to whether or not change at the outline has occurred by the Euclidean distance of TEFVs. Experimental results show that the proposed method has good performance in abnormal appearance detection of substation