2,000 Families: identifying the research potential of an origins-of-migration study

Despite recent advances, critical areas in the analysis of European migration remain underdeveloped. We have only a limited understanding of the consequences of migration for migrants and their descendants, relative to staying behind; and our insights of intergenerational transmission is limited to two generations of those living in the destination countries. These limitations stem from a paucity of studies that incorporate comparison with non-migrants – and return migrants – in countries of origin and which trace processes of intergenerational transmission over multiple generations. This paper outlines the theoretical and methodological discussions in the field, design and data of the 2,000 Families study. The study comprises almost 50,000 members of migrant and non-migrant Turkish families across three family generations, living in Turkey and eight European countries. We provide indicative findings from the study, framed within a theoretical perspective of “dissimilation” from origins, and reflect on its potential for future migration research

Positional Obstructive Sleep Apnea: A Mild and Male Predominant Phenotype

[No Abstract Available

Anticoagulants in COVID-19: Is there a role for a D-dimer-driven dosing ?

[No Abstract Available

Experience of Reduced Dose Alteplase in Patients with Pulmonary Embolism Intermediate-High-Risk: A Single Centre Study

International Conference of the American-Thoracic-Society -- MAY 13-18, 2022 -- San Francisco, CA[No Abstract Available]Amer Thorac So

Pleuroparenchymal fibroelastosis in idiopathic pulmonary fibrosis: Survival analysis using visual and computer-based computed tomography assessment

Background Idiopathic pulmonary fibrosis (IPF) and pleuroparenchymal fibroelastosis (PPFE) are known to have poor outcomes but detailed examinations of prognostic significance of an association between these morphologic processes are lacking. Methods Retrospective observational study of independent derivation and validation cohorts of IPF populations. Upper-lobe PPFE extent was scored visually (vPPFE) as categories of absent, moderate, marked. Computerised upper-zone PPFE extent (cPPFE) was examined continuously and using a threshold of 2·5% pleural surface area. vPPFE and cPPFE were evaluated against 1-year FVC decline (estimated using mixed-effects models) and mortality. Multivariable models were adjusted for age, gender, smoking history, antifibrotic treatment and diffusion capacity for carbon monoxide. • View related content for this article Findings PPFE prevalence was 49% (derivation cohort, n = 142) and 72% (validation cohort, n = 145). vPPFE marginally contributed 3–14% to variance in interstitial lung disease (ILD) severity across both cohorts. In multivariable models, marked vPPFE was independently associated with 1-year FVC decline (derivation: regression coefficient 18·3, 95 CI 8·47–28·2%; validation: 7·51, 1·85–13·2%) and mortality (derivation: hazard ratio [HR] 7·70, 95% CI 3·50–16·9; validation: HR 3·01, 1·33–6·81). Similarly, continuous and dichotomised cPPFE were associated with 1-year FVC decline and mortality (cPPFE ≥ 2·5% derivation: HR 5·26, 3·00–9·22; validation: HR 2·06, 1·28–3·31). Individuals with cPPFE ≥ 2·5% or marked vPPFE had the lowest median survival, the cPPFE threshold demonstrated greater discrimination of poor outcomes at two and three years than marked vPPFE. Interpretation PPFE quantification supports distinction of IPF patients with a worse outcome independent of established ILD severity measures. This has the potential to improve prognostic management and elucidate separate pathways of disease progression

Prognostic Imaging Biomarker Discovery in Survival Analysis for Idiopathic Pulmonary Fibrosis

Imaging biomarkers derived from medical images play an important role in diagnosis, prognosis, and therapy response assessment. Developing prognostic imaging biomarkers which can achieve reliable survival prediction is essential for prognostication across various diseases and imaging modalities. In this work, we propose a method for discovering patch-level imaging patterns which we then use to predict mortality risk and identify prognostic biomarkers. Specifically, a contrastive learning model is first trained on patches to learn patch representations, followed by a clustering method to group similar underlying imaging patterns. The entire medical image can be thus represented by a long sequence of patch representations and their cluster assignments. Then a memory-efficient clustering Vision Transformer is proposed to aggregate all the patches to predict mortality risk of patients and identify high-risk patterns. To demonstrate the effectiveness and generalizability of our model, we test the survival prediction performance of our method on two sets of patients with idiopathic pulmonary fibrosis (IPF), a chronic, progressive, and life-threatening interstitial pneumonia of unknown etiology. Moreover, by comparing the high-risk imaging patterns extracted by our model with existing imaging patterns utilised in clinical practice, we can identify a novel biomarker that may help clinicians improve risk stratification of IPF patients