Focal Stenosis in Right Upper Lobe Bronchus in a Recurrently Wheezing Child Sequentially Studied by Multidetector-row Spiral Computed Tomography and Scintigraphy

Lower respiratory tract infections associated with wheezing are not uncommon in infants and young children. Among the wheezing-associated disorders, allergic etiologies are more commonly encountered than anatomic anomalies. We present a 3-year-old girl with a sudden attack of asthmatic symptoms including dyspnea, cyanosis and diffuse wheezing. Based on a history of choking, and atelectasis in the right upper lobe detected by chest films, flexible tracheobronchoscopy was arranged and incidentally detected a stenotic orifice in the right upper lobe bronchus. Multidetector-row spiral computed tomography and pulmonary scintigraphy subsequently also disclosed the focal stenosis. She suffered from recurrent wheezing, pneumonia and lung atelectasis during 1 year of follow-up. We emphasize the diagnosis, clinical course and management of focal stenosis in the right upper lobe bronchus

Using BPMN to Model a Patient Safety Promulgation Service Based on a Clinical Process

In this paper, we used business process modeling standards to enhance the development of a patient-safety promulgation process with a web service. A web service that is related to a work process is typically in an organization that has been designed, built and stored in a web service repository. The purpose of using a web service is that it can be shared with multiple functional units within an organization via an Intranet. This paper further develops this approach and discusses the use of a visual modeling language to describe a clinical workflow clearly. The final outcome is business process re-engineering and is presented as a business process modeling notation. The workflow management we designed integrates a process for each functional unit in the organization to customize the workflow requirements that suit their needs. 748 cases had been notified among 13 months, the system play its role well even when notify proceeding had been changed.&nbsp

Immune reconstitution inflammatory syndrome of Kaposi’s sarcoma in an HIV-infected patient

We present a case of Kaposi’s sarcoma-related immune reconstitution inflammatory syndrome in an HIV-infected patient who developed fever, worsening pulmonary infiltrates with respiratory distress, and progression of skin tumors at the popliteal region and thigh that resulted in limitation on movement of the right knee joint at 3.5 months following a significant increase of CD4 count after combination antiretroviral therapy

Predicting the Severity and Prognosis of Trismus after Intensity-Modulated Radiation Therapy for Oral Cancer Patients by Magnetic Resonance Imaging

To develop magnetic resonance imaging (MRI) indicators to predict trismus outcome for post-operative oral cavity cancer patients who received adjuvant intensity-modulated radiation therapy (IMRT), 22 patients with oral cancer treated with IMRT were studied over a two-year period. Signal abnormality scores (SA scores) were computed from Likert-type ratings of the abnormalities of nine masticator structures and compared with the Mann-Whitney U-test and Kruskal–Wallis one-way ANOVA test between groups. Seventeen patients (77.3%) experienced different degrees of trismus during the two-year follow-up period. The SA score correlated with the trismus grade (r = 0.52, p<0.005). Patients having progressive trismus had higher mean doses of radiation to multiple structures, including the masticator and lateral pterygoid muscles, and the parotid gland (p<0.05). In addition, this group also had higher SA-masticator muscle dose product at 6 months and SA scores at 12 months (p<0.05). At the optimum cut-off points of 0.38 for the propensity score, the sensitivity was 100% and the specificity was 93% for predicting the prognosis of the trismus patients. The SA score, as determined using MRI, can reflect the radiation injury and correlate to trismus severity. Together with the radiation dose, it could serve as a useful biomarker to predict the outcome and guide the management of trismus following radiation therapy

Terpenoids from the Octocorals Menella sp. (Plexauridae) and Lobophytum crassum (Alcyonacea)

A new germacrane-type sesquiterpenoid, menelloide E (1), and a new cembrane-type diterpenoid, lobocrassin F (2), were isolated from the octocorals Menella sp. and Lobophytum crassum, respectively. The structures of terpenoids 1 and 2 were determined by spectroscopic and chemical methods and compound 2 was found to display a significant inhibitory effect on the release of elastase by human neutrophils

Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online)

BACKGROUND: The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level. RESULTS: Here, we build an integrative platform, the Encyclopedia of Hepatocellular Carcinoma genes Online, dubbed EHCO , to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs. CONCLUSION: This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment

Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan

PurposeTo compare the diagnostic performance of radiomic analysis with machine learning (ML) model with a convolutional neural network (CNN) in differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs).MethodsA retrospective study was performed in patients with PMTs and undergoing surgical resection or biopsy in National Cheng Kung University Hospital, Tainan, Taiwan, E-Da Hospital, Kaohsiung, Taiwan, and Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan between January 2010 and December 2019. Clinical data including age, sex, myasthenia gravis (MG) symptoms and pathologic diagnosis were collected. The datasets were divided into UECT (unenhanced computed tomography) and CECT (enhanced computed tomography) for analysis and modelling. Radiomics model and 3D CNN model were used to differentiate TETs from non-TET PMTs (including cyst, malignant germ cell tumor, lymphoma and teratoma). The macro F1-score and receiver operating characteristic (ROC) analysis were performed to evaluate the prediction models.ResultIn the UECT dataset, there were 297 patients with TETs and 79 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 83.95%, ROC-AUC = 0.9117) had better performance than the 3D CNN model (macro F1-score = 75.54%, ROC-AUC = 0.9015). In the CECT dataset, there were 296 patients with TETs and 77 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 85.65%, ROC-AUC = 0.9464) had better performance than the 3D CNN model (macro F1-score = 81.01%, ROC-AUC = 0.9275).ConclusionOur study revealed that the individualized prediction model integrating clinical information and radiomic features using machine learning demonstrated better predictive performance in the differentiation of TETs from other PMTs at chest CT scan than 3D CNN model