Zoekresultaten

Profiling of patients with COPD for adequate referral to exercise‑based care

A loss of physical functioning (i.e., a low physical capacity and/or a low physical activity) is a common feature in patients with chronic obstructive pulmonary disease (COPD). To date, the primary care physiotherapy and specialized pulmonary rehabilitation are clearly underused, and limited to patients with a moderate to very severe degree of airflow limitation (GOLD stage 2 or higher). However, improved referral rates are a necessity to lower the burden for patients with COPD and for society. Therefore, a multidisciplinary group of healthcare professionals and scientists proposes a new model for referral of patients with COPD to the right type of exercise-based care, irrespective of the degree of airflow limitation. Indeed, disease instability (recent hospitalization, yes/no), the burden of disease (no/low, mild/moderate or high), physical capacity (low or preserved) and physical activity (low or preserved) need to be used to allocate patients to one of the six distinct patient profiles. Patients with profile 1 or 2 will not be referred for physiotherapy; patients with profiles 3–5 will be referred for primary care physiotherapy; and patients with profile 6 will be referred for screening for specialized pulmonary rehabilitation. The proposed Dutch model has the intention to get the right patient with COPD allocated to the right type of exercise-based care and at the right moment.

Profiling masquerade festival attendees in Ghana

This article describes a study profiling 241 attendees to the 2014 masquerade festival in Winneba in Southern Ghana according to their support for and past membership of the competing masquerade groups. Three cohorts of attendees were used to highlight the differences that exist in terms of their demographics and trip/event profile, information sources, and how satisfied they were with various facilities and services provided by the event organizers. The study found that two cohorts belonged to or supported a masquerade group while the majority neither belonged to nor supported a masquerade group nor ever been masqueraders. The demographic profile of the majority subsample showed they were likely to be female, young, employed with above-average incomes and educational attainment. Their main source of information on the festival was friends, and they also tended to be intolerable of the long ticket queues and other appalling conditions of the event venue more than the other subgroups and this had implications for marketing and product development

Amino acid profiling from fingerprints

UPLC-MS is a commonly used technique to first separate complex samples and subsequently quantify molecules of interest. Herein we describe the use of UPLC-MS using an amide stationary phase to quantify non-derivatized amino acids extracted from fingerprints. As detector either a triple-quadrupole MS/MS or a TOF-MS detector was used. This method allows for a simple and fast sample preparation, which facilitates the analysis of large amounts of samples.

Flexible, fast and accurate sequence alignment profiling on GPGPU with PaSWAS

To obtain large-scale sequence alignments in a fast and flexible way is an important step in the analyses of next generation sequencing data. Applications based on the Smith-Waterman (SW) algorithm are often either not fast enough, limited to dedicated tasks or not sufficiently accurate due to statistical issues. Current SW implementations that run on graphics hardware do not report the alignment details necessary for further analysis.

Blood-Based Immune Profiling Combined with Machine Learning Discriminates Psoriatic Arthritis from Psoriasis Patients

Psoriasis (Pso) is a chronic inflammatory skin disease, and up to 30% of Pso patients develop psoriatic arthritis (PsA), which can lead to irreversible joint damage. Early detection of PsA in Pso patients is crucial for timely treatment but difficult for dermatologists to implement. We, therefore, aimed to find disease-specific immune profiles, discriminating Pso from PsA patients, possibly facilitating the correct identification of Pso patients in need of referral to a rheumatology clinic. The phenotypes of peripheral blood immune cells of consecutive Pso and PsA patients were analyzed, and disease-specific immune profiles were identified via a machine learning approach. This approach resulted in a random forest classification model capable of distinguishing PsA from Pso (mean AUC = 0.95). Key PsA-classifying cell subsets selected included increased proportions ofdifferentiated CD4+CD196+CD183-CD194+ and CD4+CD196-CD183-CD194+ T-cells and reduced proportions of CD196+ and CD197+ monocytes, memory CD4+ and CD8+ T-cell subsets and CD4+ regulatory T-cells. Within PsA, joint scores showed an association with memory CD8+CD45RACD197- effector T-cells and CD197+ monocytes. To conclude, through the integration of in-depth flow cytometry and machine learning, we identified an immune cell profile discriminating PsA from Pso. This immune profile may aid in timely diagnosing PsA in Pso.

Amino acid profiling from fingerprints, a novel methodology using UPLC-MS