scholarly journals Systemic Pulmonary Events Associated with Myelodysplastic Syndromes: A Retrospective Multicentre Study

2021 ◽  
Vol 10 (6) ◽  
pp. 1162
Author(s):  
Quentin Scanvion ◽  
Laurent Pascal ◽  
Thierno Sy ◽  
Lidwine Stervinou-Wémeau ◽  
Anne-Laure Lejeune ◽  
...  

Although pulmonary events are considered to be frequently associated with malignant haemopathies, they have been sparsely studied in the specific context of myelodysplastic syndromes (MDS). We aimed to describe their different types, their relative proportions and their relative effects on overall survival (OS). We conducted a multicentre retrospective cohort study. Patients with MDS (diagnosed according to the 2016 WHO classification) and pulmonary events were included. The inclusion period was 1 January 2007 to 31 December 2017 and patients were monitored until August 2019. Fifty-five hospitalized patients were included in the analysis. They had 113 separate pulmonary events. Thirteen patients (23.6%) had a systemic autoimmune disease associated with MDS. Median age at diagnosis of MDS was 77 years. Median time to onset of pulmonary events was 13 months. Pulmonary events comprised: 70 infectious diseases (62%); 27 interstitial lung diseases (23.9%), including 13 non-specific interstitial pneumonias and seven secondary organizing pneumonias or respiratory bronchiolitis–interstitial lung diseases; 10 pleural effusions (8.8%), including four cases of chronic organizing pleuritis with exudative effusion; and six pulmonary hypertensions (5.3%). The median OS of the cohort was 29 months after MDS diagnosis but OS was only 10 months after a pulmonary event. The OS was similar to that of the general myelodysplastic population. However, the occurrence of a pulmonary event appeared to be either an accelerating factor of death or an indicator for the worsening of the underlying MDS in our study. More than a third of pulmonary events were non-infectious and could be systemic manifestations of MDS.

Author(s):  
Masahiro Yamashita

The lymphatic system has several physiological roles, including fluid homeostasis and the activation of adaptive immunity by fluid drainage and cell transport. Lymphangiogenesis occurs in adult tissues during various pathologic conditions. In addition, lymphangiogenesis is closely linked to capillary angiogenesis, and the balanced interrelationship between capillary angiogenesis and lymphangiogenesis is essential for maintaining homeostasis in tissues. Recently, an increasing body of information regarding the biology of lymphatic endothelial cells has allowed us to immunohistochemically characterize lymphangiogenesis in several lung diseases. Particular interest has been given to the interstitial lung diseases. Idiopathic interstitial pneumonias (IIPs) are characterized by heterogeneity in pathologic changes and lesions, as typified by idiopathic pulmonary fibrosis/usual interstitial pneumonia. In IIPs, lymphangiogenesis is likely to have different types of localized functions within each disorder, corresponding to the heterogeneity of lesions in terms of inflammation and fibrosis. These functions include inhibitory absorption of interstitial fluid and small molecules and maturation of fibrosis by excessive interstitial fluid drainage, caused by an unbalanced relationship between capillary angiogenesis and lymphangiogenesis and trafficking of antigen-presenting cells and induction of fibrogenesis via CCL21 and CCR7 signals. Better understanding for regional functions of lymphangiogenesis might provide new treatment strategies tailored to lesion heterogeneity in these complicated diseases.


2021 ◽  
pp. 2004507
Author(s):  
Moisés Selman ◽  
Annie Pardo

Interstitial lung diseases (ILD) comprise a large and heterogeneous group of disorders of known and unknown etiology characterised by diffuse damage of the lung parenchyma. In the past years, it has become evident that patients with different types of ILD are at risk of developing progressive pulmonary fibrosis known as pulmonary fibrosing ILD (PF-ILD). This is a phenotype behaving similar to idiopathic pulmonary fibrosis, the archetypical example of progressive fibrosis. PF-ILD is not a distinct clinical entity but describes a group of ILD with a similar clinical behavior. This phenotype may occur in diseases displaying distinct etiologies and different biopathology during their initiation and development. Importantly, these entities may have the potential for improvement or stabilisation prior to entering in the progressive fibrosing phase. The crucial questions are (1) why a subset of patients develops a progressive and irreversible fibrotic phenotype even with appropriate treatment, and (2) what the pathogenic mechanisms driving progression possibly are. We here provide a framework highlighting putative mechanisms underlying progression, including genetic susceptibility, aging, epigenetics, the structural fibrotic distortion, the aberrant composition and stiffness of the extracellular matrix, and the emergence of distinct profibrotic cell subsets. Understanding the cellular and molecular mechanisms behind PF-ILD will provide the basis for identifying risk factors and appropriate therapeutical strategies.


2016 ◽  
Vol 34 (15_suppl) ◽  
pp. e18551-e18551
Author(s):  
Rama Nanah ◽  
Darci Zblewski ◽  
Mrinal M. Patnaik ◽  
Kebede Begna ◽  
Rhett P. Ketterling ◽  
...  

2018 ◽  
Vol 7 (2.7) ◽  
pp. 114
Author(s):  
S Ummay Atiya ◽  
N V.K Ramesh

Automated tissues characterization helps to diagnosis the various diseases including Interstitial lung diseases (ILD). The various features and the several classifiers are used in categorize the different layers depend on the pattern presented in the image. The different types of diseases may occur in the lungs and some of the diseases happen to leave the scars. These scars can be found in the High Resolution Computed Tomography (HRCT) and have different pattern. The different diseases cause the different pattern in the images and these is classified using the efficient classifier that helps to diagnosis the diseases. In this paper, review for the many researches regarding to the classification of the different pattern from the Computed Tomography (CT) images is presented. The evaluation of the efficiency of the methods in terms of classifier and database used for the research is made. The Deep Convolution Neural Network (CNN) provides the promising classifier efficiency compared to the other researches for different pattern. In general, there are five types of pattern is classified: Healthy, ground glass, honeycomb, Fibrosis, and emphysema.


Author(s):  
N Buda ◽  
M Piskunowicz ◽  
M Porzezińska ◽  
W Kosiak ◽  
Z Zdrojewski

2018 ◽  
Vol 1 (1) ◽  
pp. 25-29
Author(s):  
Mirgolib RAКHIMOV ◽  
◽  
Nematilla ARALOV ◽  
Shukhrat Ziyadullaev

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