64 research outputs found

    Characteristics of Nontuberculous Mycobacterial Infections at a Midwestern Tertiary Hospital: A Retrospective Study of 365 Patients.

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    Background: The prevalence of infections due to nontuberculous mycobacteria (NTM) is increasing worldwide, yet little is known about the epidemiology and pathophysiology of these ubiquitous environmental organisms. Pulmonary disease due to Mycobacterium avium complex is most prevalent, but many other NTM species can cause disease in virtually any organ system. As NTM becomes an increasingly common cause of morbidity and mortality, more information is needed about the epidemiology of NTM disease. Methods: We conducted a retrospective chart review of all patients with cultures that grew NTM at a Midwestern tertiary hospital from 1996 to 2017. Information on demographics, medical history, clinical findings, treatment, and outcome was obtained from medical records of all NTM isolates. American Thoracic Society/Infectious Diseases Society of America criteria were used to define pulmonary NTM infections. Results: We identified 1064 NTM isolates, 365 of which met criteria for NTM infection. Pulmonary cases predominated (185 of 365; 50.7%), followed by skin/soft tissue (56 of 365; 15.3%), disseminated (40 of 365; 11%), and lymphatic (28 of 365; 7.7%) disease. Conclusions: This large cohort provides information on the demographics, risk factors, and disease course of patients with pulmonary and extrapulmonary NTM infections. Most patients had medical comorbidities that resulted in anatomic, genetic, or immunologic risk factors for NTM infection. Further population-based studies and increased disease surveillance are warranted to further characterize NTM infection prevalence and trends

    Optimal planning target margin for prostate radiotherapy based on interfractional and intrafractional variability assessment during 1.5T MRI-guided radiotherapy

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    IntroductionWe analyzed daily pre-treatment- (PRE) and real-time motion monitoring- (MM) MRI scans of patients receiving definitive prostate radiotherapy (RT) with 1.5 T MRI guidance to assess interfractional and intrafractional variability of the prostate and suggest optimal planning target volume (PTV) margin.Materials and methodsRigid registration between PRE-MRI and planning CT images based on the pelvic bone and prostate anatomy were performed. Interfractional setup margin (SM) and interobserver variability (IO) were assessed by comparing the centroid values of prostate contours delineated on PRE-MRIs. MM-MRIs were used for internal margin (IM) assessment, and PTV margin was calculated using the van Herk formula.ResultsWe delineated 400 prostate contours on PRE-MRI images. SM was 0.57 ± 0.42, 2.45 ± 1.98, and 2.28 ± 2.08 mm in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions, respectively, after bone localization and 0.76 ± 0.57, 1.89 ± 1.60, and 2.02 ± 1.79 mm in the LR, AP, and SI directions, respectively, after prostate localization. IO was 1.06 ± 0.58, 2.32 ± 1.08, and 3.30 ± 1.85 mm in the LR, AP, and SI directions, respectively, after bone localization and 1.11 ± 0.55, 2.13 ± 1.07, and 3.53 ± 1.65 mm in the LR, AP, and SI directions, respectively, after prostate localization. Average IM was 2.12 ± 0.86, 2.24 ± 1.07, and 2.84 ± 0.88 mm in the LR, AP, and SI directions, respectively. Calculated PTV margin was 2.21, 5.16, and 5.40 mm in the LR, AP, and SI directions, respectively.ConclusionsMovements in the SI direction were the largest source of variability in definitive prostate RT, and interobserver variability was a non-negligible source of margin. The optimal PTV margin should also consider the internal margin

    Network-based Trajectory Analysis of Topic Interaction Map for Text Mining of COVID-19 Biomedical Literature

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    Since the emergence of the worldwide pandemic of COVID-19, relevant research has been published at a dazzling pace, which makes it hard to follow the research in this area without dedicated efforts. It is practically impossible to implement this task manually due to the high volume of the relevant literature. Text mining has been considered to be a powerful approach to address this challenge, especially the topic modeling, a well-known unsupervised method that aims to reveal latent topics from the literature. However, in spite of its potential utility, the results generated from this approach are often investigated manually. Hence, its application to the COVID-19 literature is not straightforward and expert knowledge is needed to make meaningful interpretations. In order to address these challenges, we propose a novel analytical framework for estimating topic interactions and effective visualization for topic interpretation. Here we assumed that topics constituting a paper can be positioned on an interaction map, which belongs to a high-dimensional Euclidean space. Based on this assumption, after summarizing topics with their topic-word distributions using the biterm topic model, we mapped these latent topics on networks to visualize relationships among the topics. Moreover, in the proposed approach, we developed a score that is helpful to select meaningful words that characterize the topic. We interpret the relationships among topics by tracking the change of relationships among topics using a trajectory plot generated with different levels of word richness. These results together provide deeply mined and intuitive representation of relationships among topics related to a specific research area. The application of this proposed framework to the PubMed literature shows that our approach facilitates understanding of the topics constituting the COVID-19 knowledge

    A Superinfected Pulmonary Valve Myxoma

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