Time-based measurement of personal mite allergen bioaerosol exposure over 24 hour periods

© 2016 Tovey et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Allergic diseases such as asthma and rhinitis are common in many countries. Globally the most common allergen associated with symptoms is produced by house dust mites. Although the bed has often been cited as the main site of exposure to mite allergens, surprisingly this has not yet been directly established by measurement due to a lack of suitable methods. Here we report on the development of novel methods to determine the pattern of personal exposure to mite allergen bioaerosols over 24-hour periods and applied this in a small field study using 10 normal adults. Air was sampled using a miniature time-based air-sampler of in-house design located close to the breathing zone of the participants, colocated with a miniature time-lapse camera. Airborne particles, drawn into the sampler at 2L/min via a narrow slot, were impacted onto the peripheral surface of a disk mounted on the hour-hand of either a 12 or 24 hour clock motor. The impaction surface was either an electret cloth, or an adhesive film; both novel for these purposes. Following a review of the time-lapse images, disks were post-hoc cut into subsamples corresponding to eight predetermined categories of indoor or outdoor location, extracted and analysed for mite allergen Der p 1 by an amplified ELISA. Allergen was detected in 57.2% of the total of 353 subsamples collected during 20 days of sampling. Exposure patterns varied over time. Higher concentrations of airborne mite allergen were typically measured in samples collected from domestic locations in the day and evening. Indoor domestic Der p 1 exposures accounted for 59.5% of total exposure, whereas total in-bed-asleep exposure, which varied 80 fold between individuals, accounted overall for 9.85% of total exposure, suggesting beds are not often the main site of exposure. This study establishes the feasibility of novel methods for determining the time-geography of personal exposure to many bioaerosols and identifies new areas for future technical development and clinical applications

Asthma Phenotypes in Childhood

INTRODUCTION: Asthma is no longer thought of as a single disease, but rather a collection of varying symptoms expressing different disease patterns. One of the ongoing challenges is understanding the underlying pathophysiological mechanisms that may be responsible for the varying responses to treatment. Areas Covered: This review provides an overview of our current understanding of the asthma phenotype concept in childhood and describes key findings from both conventional and data-driven methods. Expert Commentary: With the vast amounts of data generated from cohorts, there is hope that we can elucidate distinct pathophysiological mechanisms, or endotypes. In return, this would lead to better patient stratification and disease management, thereby providing true personalised medicine

Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as asthma endotypes. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies

Absence of back to school peaks in human rhinovirus detections and respiratory symptoms in a cohort of children with asthma

© 2016 Wiley Periodicals, Inc. Much of what is known about the seasonality of human rhinovirus (hRV) infections has been learned from the study of acute asthma exacerbations presenting to emergency care, including those among children at the start of the school term. Much less is known about the patterns of hRVs in the community. In this study, viruses and day-to-day symptoms of asthma and colds were monitored twice weekly in 67 children with asthma aged 5-12 years, over a 15 month period in Sydney, Australia. Overall hRV was detected in 314/1232 (25.5%) of nasal wash samples and 142/1231 (11.5%) of exhaled breath samples; of these, 231 and 24 respectively were genotyped. HRVs were detected with similar prevalence rate throughout the year, including no peak in hRV prevalence following return to school. No peaks were seen in asthma and cold symptoms using twice-weekly diary records. However, over the same period in the community, there were peaks in asthma emergency visits both at a large local hospital and in state-wide hospitalizations, following both return to school (February) and in late autumn (May) in children of the same age. This study suggests that hRV infections are common throughout the year among children, and differences in virus prevalence alone may not account for peaks in asthma symptoms

Loss of STOP Protein Impairs Peripheral Olfactory Neurogenesis

International audienceIn conclusion, STOP protein seems to be involved in the establishment of synapses in the olfactory glomerulus. Our results indicate that the olfactory system of STOP null mice is a well-suited experimental model (1) for the study of the mechanism of action of STOP protein in synaptic function/plasticity and (2) for pathophysiological studies of the mechanisms of altered neuronal connections in schizophrenia

Performance of the LACE index to predict 30-day hospital readmissions in patients with chronic obstructive pulmonary disease

Maryam A Hakim,1,2 Frances L Garden,2,3 Matthew D Jennings,4 Claudia C Dobler1,2,3,5 1Department of Respiratory Medicine, Liverpool Hospital, 2South Western Sydney Clinical School, University of New South Wales, 3Ingham Institute for Applied Medical Research, 4Department of Physiotherapy, Liverpool Hospital, Sydney, NSW, Australia; 5Evidence-Based Practice Center, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA Background and objective: Patients hospitalized for acute exacerbation of chronic obstructive pulmonary disease (COPD) have a high 30-day hospital readmission rate, which has a large impact on the health care system and patients’ quality of life. The use of a prediction model to quantify a patient’s risk of readmission may assist in directing interventions to patients who will benefit most. The objective of this study was to calculate the rate of 30-day readmissions and evaluate the accuracy of the LACE index (length of stay, acuity of admission, co-morbidities, and emergency department visits within the last 6 months) for 30-day readmissions in a general hospital population of COPD patients.Methods: All patients admitted with a principal diagnosis of COPD to Liverpool Hospital, a tertiary hospital in Sydney, Australia, between 2006 and 2016 were included in the study. A LACE index score was calculated for each patient and assessed using receiver operator characteristic curves.Results: During the study period, 2,662 patients had 5,979 hospitalizations for COPD. Four percent of patients died in hospital and 25% were readmitted within 30 days; 56% of all 30-day readmissions were again due to COPD. The most common reasons for readmission, following COPD, were heart failure, pneumonia, and chest pain. The LACE index had moderate discriminative ability to predict 30-day readmission (C-statistic =0.63).Conclusion: The 30-day hospital readmission rate was 25% following hospitalization for COPD in an Australian tertiary hospital and as such comparable to international published rates. The LACE index only had moderate discriminative ability to predict 30-day readmission in patients hospitalized for COPD. Keywords: predictor model, risk prediction, unwarranted variatio

Performance of the LACE index to predict 30-day hospital readmissions in patients with chronic obstructive pulmonary disease

Background and objective: Patients hospitalized for acute exacerbation of chronic obstructive pulmonary disease (COPD) have a high 30-day hospital readmission rate, which has a large impact on the health care system and patients’ quality of life. The use of a prediction model to quantify a patient’s risk of readmission may assist in directing interventions to patients who will benefit most. The objective of this study was to calculate the rate of 30-day readmissions and evaluate the accuracy of the LACE index (length of stay, acuity of admission, co-morbidities, and emergency department visits within the last 6 months) for 30-day readmissions in a general hospital population of COPD patients. Methods: All patients admitted with a principal diagnosis of COPD to Liverpool Hospital, a tertiary hospital in Sydney, Australia, between 2006 and 2016 were included in the study. A LACE index score was calculated for each patient and assessed using receiver operator characteristic curves. Results: During the study period, 2,662 patients had 5,979 hospitalizations for COPD. Four percent of patients died in hospital and 25% were readmitted within 30 days; 56% of all 30-day readmissions were again due to COPD. The most common reasons for readmission, following COPD, were heart failure, pneumonia, and chest pain. The LACE index had moderate discriminative ability to predict 30-day readmission (C-statistic =0.63). Conclusion: The 30-day hospital readmission rate was 25% following hospitalization for COPD in an Australian tertiary hospital and as such comparable to international published rates. The LACE index only had moderate discriminative ability to predict 30-day readmission in patients hospitalized for COPD