Background: Lung clearance of inhaled
Background : The number of immunocompromised hosts has been increasing steadily and a new pulmonary infiltrate in these patients is a potentially lethal condition which needs rapid diagnosis and treatment. In this study we sought to examine the clinical manifestations, radiologic findings, and therapeutic outcomes of pulmonary mycoses presenting as a new pulmonary infiltrate in immunocompromised hosts. Method : All cases presenting as a new pulmonary infiltrate in immunocompromised hosts and confirmed to be pulmonary mycoses by pathologic examination or by positive culture from a sterile site between October of 1996 and April of 1998 were included in the study and their chart and radiologic findings were retrospectively reviewed. Results : In all, 14 cases of pulmonary mycoses from 13 patients(male : female ratio = 8 : 5, median age 47 yr) were found. Twelve cases were diagnosed as aspergillosis while two were diagnosed as mucormycosis. Major risk factors for fungal infections were chemotherapy for hematologic malignancy(10 cases) and organ transplant recipients(4 cases). Three cases were receiving empirical amphotericin B at the time of appearance of new lung infiltrates. Cases in the hematologic malignancy group had more prominent symptoms : fever(9/10), cough(6/10), sputum(5/10), dyspnea(4/10), chest pain(5/10). Patients in the organ transplant group had minimal symptoms(p<0.05). On simple chest films, all of the cases presented as single or multiple nodules(6/14) or consolidations(8/14). High resolution computed tomograph showed peri-lesional ground glass opacities(14/14), pleural effusions(5/14), and cavitary changes(7/14). Definitive diagnostic methods were as follows : 10 cases underwent minithoracotomy, 2 underwent video-assisted thoracoscopic surgery, 1 underwent percutaneous needle aspiration and 1 case was diagnosed by culture of abscess fluid. All cases received treatment with amphotericin B with 1 case each being treated with liposomal amphotericin B and itraconazole due to renal toxicity. Lung lesion improved in 12 of 14 patient but 4 patients died before completing therapy. Conclusion : When a new lung infiltrate develops presenting either as a nodule or consolidation in a neutropenic patient with hematologic malignancy or in a transplant recipient, you should always consider pulmonary mycoses as one of the differential diagnosis. By performing aggressive work up and early treatment, we may improve prognosis of these patients.
Background : Over one-third of solitary pulmonary nodules are malignant, but most malignant SPNs are in the early stages at diagnosis and can be cured by surgical removal. Therefore, early diagnosis of malignant SPN is essential for the lifesaving of the patient. The incidence of pulmonary tuberculosis in Korea is somewhat higher than those of other countries and a large number of SPNs are found to be tuberculoma. Most primary physicians tend to regard newly detected solitary pulmonary nodule as tuberculoma with only noninvasive imaging such as CT and they prefer clinical observation if the findings suggest benignancy without further invasive procedures. Many kinds of noninvasive procedures for confirmatory diagnosis have been introduced to differentiate malignant SPNs from benign ones, but none of them has been satisfactory. FOG-PET is a unique tool for imaging and quantifying the status of glucose metabolism. On the basis that glucose metabolism is increased in the malignant transfomled cells compared with normal cells, FDG-PET is considered to be the satisfactory noninvasive procedure which can differentiate malignant SPNs from benign SPNs. So we performed FOG-PET in patients with solitary pulmonary nodule and evaluated the diagnostic accuracy in the diagnosis of malignant SPNs. Method : 34 patients with a solitary pulmonary nodule less than 6 cm of irs diameter who visited Samsung Medical Center from Semptember, 1994 to Semptember, 1995 were evaluated prospectively. Simple chest roentgenography, chest computer tomography, FOG-PET scan were performed for all patients. The results of FOG-PET were evaluated comparing with the results of final diagnosis confirmed by sputum study, PCNA, fiberoptic bronchoscopy, or thoracotomy. Results : (I) There was no significant difference in nodule size between malignant (3.1 1.5cm) and benign nodule(2.81.0cm)(p>0.05). (2) Peal SUV(standardized uptake value) of malignant nodules (6.93.7) was significantly higher than peak SUV of benign nodules(2.71.7) and time-activity curves showed continuous increase in malignant nodules. (3) Three false negative cases were found among eighteen malignant nodule by the FDG-PET imaging study and all three cases were nonmucinous bronchioloalveolar carcinoma less than 2 em diameter. (4) FOG-PET imaging resulted in 83% sensitivity, 100% specificity, 100% positive predictive value and 84% negative predictive value. Conclusion: FOG-PET imaging is a new noninvasive diagnostic method of solitary pulmonary nodule thai has a high accuracy of differential diagnosis between malignant and benign nodule. FDG-PET imaging could be used for the differential diagnosis of SPN which is not properly diagnosed with conventional methods before thoracotomy. Considering the high accuracy of FDG-PET imaging, this procedure may play an important role in making the dicision to perform thoracotomy in diffcult cases.
In the generation of Web 2.0, as many users start to make lots of web contents called user created contents by themselves, the World Wide Web is overflowing by countless information. Therefore, it becomes the key to find out meaningful information among lots of resources. Nowadays, the information retrieval is the most important thing throughout the whole field and several types of search services are developed and widely used in various fields to retrieve information that user really wants. Especially, the legal information search is one of the indispensable services in order to provide people with their convenience through searching the law necessary to their present situation as a channel getting knowledge about it. The Office of Legislation in Korea provides the Korean Law Information portal service to search the law information such as legislation, administrative rule, and judicial precedent from 2009, so people can conveniently find information related to the law. However, this service has limitation because the recent technology for search engine basically returns documents depending on whether the query is included in it or not as a search result. Therefore, it is really difficult to retrieve information related the law for general users who are not familiar with legal terms in the search engine using simple matching of keywords in spite of those kinds of efforts of the Office of Legislation in Korea, because there is a huge divergence between everyday words and legal terms which are especially from Chinese words. Generally, people try to access the law information using everyday words, so they have a difficulty to get the result that they exactly want. In this paper, we propose a term mapping methodology between everyday words and legal terms for general users who don't have sufficient background about legal terms, and we develop a search service that can provide the search results of law information from everyday words. This will be able to search the law information accurately without the knowledge of legal terminology. In other words, our research goal is to make a law information search system that general users are able to retrieval the law information with everyday words. First, this paper takes advantage of tags of internet blogs using the concept for collective intelligence to find out the term mapping relationship between everyday words and legal terms. In order to achieve our goal, we collect tags related to an everyday word from web blog posts. Generally, people add a non-hierarchical keyword or term like a synonym, especially called tag, in order to describe, classify, and manage their posts when they make any post in the internet blog. Second, the collected tags are clustered through the cluster analysis method, K-means. Then, we find a mapping relationship between an everyday word and a legal term using our estimation measure to select the fittest one that can match with an everyday word. Selected legal terms are given the definite relationship, and the relations between everyday words and legal terms are described using SKOS that is an ontology to describe the knowledge related to thesauri, classification schemes, taxonomies, and subject-heading. Thus, based on proposed mapping and searching methodologies, our legal information search system finds out a legal term mapped with user query and retrieves law information using a matched legal term, if users try to retrieve law information using an everyday word. Therefore, from our research, users can get exact results even if they do not have the knowledge related to legal terms. As a result of our research, we expect that general users who don't have professional legal background can conveniently and efficiently retrieve the legal information using everyday words.
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used