BACKGROUND: This study used MDS-HC 2.0 (Minimum Data Set-Home Care) to analyze the health and the state of function of the traveling health objects. This study was intended to make use of it with the basic materials for providing them with traveling health service suited for the requirement on the health of the traveling health objects. METHODS: The subjects of this study were 1160 people (over 65 years) living under management control of local Health Center from September 7th to October 3th, 2006. This study was analyzed with inter RAI program & SPSS/WIN 10.0, chi-square -test, t-test, and ANOVA. RESULTS: The result showed that 8.97 CAPs per an elderly person was identified and the subjects over 60% had an injury problem from a fall, health prevention service, vision, IADL, pain, and cognition. The number of CAPs in general was high in higher age and the less educated, and those without a job. But, in subjects that had a life partner and a spouse, the number of CAPs was low. In CAPs by the distinction of sex, CAPs which was much more in man than women in statistics were the improvement of health, the abuse of alcohol and drinking wine, bedsore, and the weak supply system. CAPs which women had much more were the function of the heart and the lungs, pain, the performance of the doctor's advice, health prevention service, and incontinence of urine and insertion of catheter. According to the results comparing CAPs by the level of the ADL, the number of CAPs was shown that the group of ADL 2 was higher than group ADL 1. The matter in which the traveling health service had to be applied in all both ADL1 and ADL2 was injury from a fall, health prevention service, and vision. Conculsion: MDS-HC is applicable to decide the care needs for health and social service supplies. The results can be further applicable for careplan, and referral criteria in continuum of care service over long-term care spectrums.
Activities of Daily Living ; Aged ; Catheters ; Cognition ; Continuity of Patient Care ; Drinking ; Equipment and Supplies ; Female ; Health Services ; Heart ; Humans ; Long-Term Care ; Lung ; Pressure Ulcer ; Referral and Consultation ; Social Work ; Spouses ; Vision, Ocular ; Wine

The five-year survival rate of patients after curative resection of hepatocellular carcinoma (HCC) has been reported to be 30 to 50 %, however the actual survival rate may be different. We analyzed the actual 5-year survival rate and prognostic factors after curative resection of HCC. Retrospective analysis was performed on 63 HCC patients who underwent curative resection from 1998 to 1999. A total of 63 cases were reviewed, consisting of 53 men and 10 women, with a median age of 49 years. These cases included all four pathologic T stages (pT stage) and had the following representation: stage 1 (1 case), stage 2 (17 cases), stage 3 (38 cases), and stage 4 (7 cases). In our study, the actual 5-year survival rate was 57.0% and the median survival time was 60 months. In addition, the patients in our study had an actual 5-year disease-free survival rate of 50.2% and a median disease-free survival time of 46 months. Thirty-one patients had recurrences, with a majority occurring within one year (65%). These patients with early recurrences had a poor actual 5-year survival rate of 5%. A univariate analysis showed that the prognostic factors influencing survival rate were the presence of satellite nodules, increased pT stage, HCC recurrence, and the time to recurrence (within one year). Interestingly, microvascular invasion made a difference in survival rate but was not statistically significant (p = 0.08). Furthermore, factors influencing the disease free survival rate include the presence of satellite nodules, microvascular invasion, and pT stage. Multivariate analysis identified pT stage as the only statistically related factor in determining the disease-free survival rate. The most important prognostic factor of HCC is recurrence. Moreover, the major risk factor for recurrence is an advanced pT stage. Therefore, performing prospective studies of postoperative adjuvant therapy is necessary to prevent recurrences after hepatic resection. Furthermore, active preventative treatment and early diagnosis of recurrences should be of the highest priority in the care of high-risk patient groups that have an advanced pT stage.
Survival Rate ; Retrospective Studies ; Middle Aged ; Male ; Liver Neoplasms/*mortality/pathology/*surgery ; Liver/pathology/surgery ; Humans ; *Hepatectomy ; Female ; Carcinoma, Hepatocellular/*mortality/pathology/*surgery ; Aged ; Adult

The prognosis of primary hepatocellular carcinoma is very poor, and a curative resection is capable only in few selected patients. Most patients die from tumor recurrence. The rate of intrahepatic recurrence of hepatocellular carcinoma is much higher than extrahepatic recurrence or metastasis. The most common site of extrahepatic metastasis is lung, and followed by lymph nodes, bone and adrenal gland. Common sites of node metastasis are celiac lymph nodes and hepatoduodenal lymph nodes, however paraaortic lymph node metastasis is very rare. Here, we report an experience of solitary paraaortic lymph node metastasis from hepatocellular carcinoma which was misdiagnosed as primary retroperitoneal tumor by preoperative imaging studies.
Adrenal Glands ; Carcinoma, Hepatocellular* ; Humans ; Lung ; Lymph Nodes* ; Neoplasm Metastasis* ; Prognosis ; Recurrence ; Retroperitoneal Neoplasms

Among 80 types of yeast isolated from wild flowers in Daejeon, Korea, two species that have not yet been identified by phylogenetic analysis of the internal transcribed spacer-2 (ITS2) genes and 26S rDNA sequences were identified as Candida sp. 44-C-1 and Cryptococcus sp. 9-D-1. Neither of the newly identified species formed ascospores, while Candida sp. 44-C-1 formed pseudomycelium and Cryptococcus sp. 9-D-1 did not.
Candida ; Cryptococcus ; DNA, Ribosomal ; Flowers ; Korea ; Yeasts

Background: Programmed death ligand 1 (PD-L1) expression cannot currently be predicted through radiological findings. This study aimed to develop a prediction model capable of differentiating between positive and negative PD-L1 expression through a radiomics-based investigation of computed tomography (CT) images in patients with urothelial carcinoma. Methods: Sixty-four patients with urothelial carcinoma who underwent immunohistochemical testing for PD-L1 were retrospectively reviewed. The number of patients in the positive and negative PD-L1 groups (PD-L1 expression >5%) was 14 and 50, respectively. CT images obtained 90 seconds after contrast medium administration were selected for radiomic extraction. For all tumors, 1,691 radiomic features were extracted from CT using a manually segmented three-dimensional volume of interest. Univariate and multivariate logistic regression analyses were performed to identify radiomic features that were significant predictors of PD-L1 expression. For the radiomics-based model, a receiver operating characteristic (ROC) analysis was performed. Results: Among 64 patients, 14 were included in the PD-L1 positive group. Logistic regression analysis found that the following radiomic features significantly predicted PD-L1 expression: wavelet-low-pass, low-pass, and high-pass filters (LLH)_gray-level size-zone matrix (GLSZM)_SmallAreaEmphasis, wavelet-LLH_firstorder_Energy, log-sigma-0-5-mm-3D_GLSZM_SmallAreaHighGrayLevelEmphasis, original_shape_Maximum2DDiameterColumn, wavelet-low-pass, low-pass, and low-pass filters (LLL)_gray-level run-length matrix (GLRLM)_ShortRunEmphasis, and exponential_firstorder_Kurtosis. The radiomics signature was –4.0934+21.6224 (wavelet-LLH_GLSZM_SmallAreaEmphasis)+0.0044 (wavelet-LLH_firstorder_Energy)–4.7389 (log-sigma-0-5-mm-3D_GLSZM_SmallAreaHighGrayLevelEmphasis)+0.0573 (original_shape_Maximum2DDiameterColumn)–29.5892 (wavelet-LLL_GLRLM_ShortRunEmphasis)–0.4324 (exponential_firstorder_Kurtosis). The area under the ROC curve model representing the radiomics signature for differentiating cases that were deemed PD-L1 positive based on immunohistochemistry was 0.96. Conclusions This preliminary radiomics model derived from contrast-enhanced CT predicted PD-L1 positivity in patients with urothelial cancer.

Background: Programmed death ligand 1 (PD-L1) expression cannot currently be predicted through radiological findings. This study aimed to develop a prediction model capable of differentiating between positive and negative PD-L1 expression through a radiomics-based investigation of computed tomography (CT) images in patients with urothelial carcinoma. Methods: Sixty-four patients with urothelial carcinoma who underwent immunohistochemical testing for PD-L1 were retrospectively reviewed. The number of patients in the positive and negative PD-L1 groups (PD-L1 expression >5%) was 14 and 50, respectively. CT images obtained 90 seconds after contrast medium administration were selected for radiomic extraction. For all tumors, 1,691 radiomic features were extracted from CT using a manually segmented three-dimensional volume of interest. Univariate and multivariate logistic regression analyses were performed to identify radiomic features that were significant predictors of PD-L1 expression. For the radiomics-based model, a receiver operating characteristic (ROC) analysis was performed. Results: Among 64 patients, 14 were included in the PD-L1 positive group. Logistic regression analysis found that the following radiomic features significantly predicted PD-L1 expression: wavelet-low-pass, low-pass, and high-pass filters (LLH)_gray-level size-zone matrix (GLSZM)_SmallAreaEmphasis, wavelet-LLH_firstorder_Energy, log-sigma-0-5-mm-3D_GLSZM_SmallAreaHighGrayLevelEmphasis, original_shape_Maximum2DDiameterColumn, wavelet-low-pass, low-pass, and low-pass filters (LLL)_gray-level run-length matrix (GLRLM)_ShortRunEmphasis, and exponential_firstorder_Kurtosis. The radiomics signature was –4.0934+21.6224 (wavelet-LLH_GLSZM_SmallAreaEmphasis)+0.0044 (wavelet-LLH_firstorder_Energy)–4.7389 (log-sigma-0-5-mm-3D_GLSZM_SmallAreaHighGrayLevelEmphasis)+0.0573 (original_shape_Maximum2DDiameterColumn)–29.5892 (wavelet-LLL_GLRLM_ShortRunEmphasis)–0.4324 (exponential_firstorder_Kurtosis). The area under the ROC curve model representing the radiomics signature for differentiating cases that were deemed PD-L1 positive based on immunohistochemistry was 0.96. Conclusions This preliminary radiomics model derived from contrast-enhanced CT predicted PD-L1 positivity in patients with urothelial cancer.

Background: Programmed death ligand 1 (PD-L1) expression cannot currently be predicted through radiological findings. This study aimed to develop a prediction model capable of differentiating between positive and negative PD-L1 expression through a radiomics-based investigation of computed tomography (CT) images in patients with urothelial carcinoma. Methods: Sixty-four patients with urothelial carcinoma who underwent immunohistochemical testing for PD-L1 were retrospectively reviewed. The number of patients in the positive and negative PD-L1 groups (PD-L1 expression >5%) was 14 and 50, respectively. CT images obtained 90 seconds after contrast medium administration were selected for radiomic extraction. For all tumors, 1,691 radiomic features were extracted from CT using a manually segmented three-dimensional volume of interest. Univariate and multivariate logistic regression analyses were performed to identify radiomic features that were significant predictors of PD-L1 expression. For the radiomics-based model, a receiver operating characteristic (ROC) analysis was performed. Results: Among 64 patients, 14 were included in the PD-L1 positive group. Logistic regression analysis found that the following radiomic features significantly predicted PD-L1 expression: wavelet-low-pass, low-pass, and high-pass filters (LLH)_gray-level size-zone matrix (GLSZM)_SmallAreaEmphasis, wavelet-LLH_firstorder_Energy, log-sigma-0-5-mm-3D_GLSZM_SmallAreaHighGrayLevelEmphasis, original_shape_Maximum2DDiameterColumn, wavelet-low-pass, low-pass, and low-pass filters (LLL)_gray-level run-length matrix (GLRLM)_ShortRunEmphasis, and exponential_firstorder_Kurtosis. The radiomics signature was –4.0934+21.6224 (wavelet-LLH_GLSZM_SmallAreaEmphasis)+0.0044 (wavelet-LLH_firstorder_Energy)–4.7389 (log-sigma-0-5-mm-3D_GLSZM_SmallAreaHighGrayLevelEmphasis)+0.0573 (original_shape_Maximum2DDiameterColumn)–29.5892 (wavelet-LLL_GLRLM_ShortRunEmphasis)–0.4324 (exponential_firstorder_Kurtosis). The area under the ROC curve model representing the radiomics signature for differentiating cases that were deemed PD-L1 positive based on immunohistochemistry was 0.96. Conclusions This preliminary radiomics model derived from contrast-enhanced CT predicted PD-L1 positivity in patients with urothelial cancer.

Background: Programmed death ligand 1 (PD-L1) expression cannot currently be predicted through radiological findings. This study aimed to develop a prediction model capable of differentiating between positive and negative PD-L1 expression through a radiomics-based investigation of computed tomography (CT) images in patients with urothelial carcinoma. Methods: Sixty-four patients with urothelial carcinoma who underwent immunohistochemical testing for PD-L1 were retrospectively reviewed. The number of patients in the positive and negative PD-L1 groups (PD-L1 expression >5%) was 14 and 50, respectively. CT images obtained 90 seconds after contrast medium administration were selected for radiomic extraction. For all tumors, 1,691 radiomic features were extracted from CT using a manually segmented three-dimensional volume of interest. Univariate and multivariate logistic regression analyses were performed to identify radiomic features that were significant predictors of PD-L1 expression. For the radiomics-based model, a receiver operating characteristic (ROC) analysis was performed. Results: Among 64 patients, 14 were included in the PD-L1 positive group. Logistic regression analysis found that the following radiomic features significantly predicted PD-L1 expression: wavelet-low-pass, low-pass, and high-pass filters (LLH)_gray-level size-zone matrix (GLSZM)_SmallAreaEmphasis, wavelet-LLH_firstorder_Energy, log-sigma-0-5-mm-3D_GLSZM_SmallAreaHighGrayLevelEmphasis, original_shape_Maximum2DDiameterColumn, wavelet-low-pass, low-pass, and low-pass filters (LLL)_gray-level run-length matrix (GLRLM)_ShortRunEmphasis, and exponential_firstorder_Kurtosis. The radiomics signature was –4.0934+21.6224 (wavelet-LLH_GLSZM_SmallAreaEmphasis)+0.0044 (wavelet-LLH_firstorder_Energy)–4.7389 (log-sigma-0-5-mm-3D_GLSZM_SmallAreaHighGrayLevelEmphasis)+0.0573 (original_shape_Maximum2DDiameterColumn)–29.5892 (wavelet-LLL_GLRLM_ShortRunEmphasis)–0.4324 (exponential_firstorder_Kurtosis). The area under the ROC curve model representing the radiomics signature for differentiating cases that were deemed PD-L1 positive based on immunohistochemistry was 0.96. Conclusions This preliminary radiomics model derived from contrast-enhanced CT predicted PD-L1 positivity in patients with urothelial cancer.

OBJECTIVETo evaluate the expression of three different RASSF1 transcripts and its clinical significance in lung carcinomas.

METHODSThe mRNA expression of RASSF1A, RASSF1B and RASSF1C was detected by RT-PCR in 51 human lung cancer tissues and 51 matched normal tissues.

RESULTS1. The mRNA expression of three RASSF1 transcripts was detectable in all non-cancer tissues. However, high rate of expression loss of RASSF1A and RASSF1B existed in lung cancer tissues, which was 53.2% (2851) and 37.3% (19/51), respectively. RASSF1C was expressed in all of the tumor tissues. 2. Loss or abnormal down-regulation of RASSF1A was positively related with lymph node metastasis and TNM stage (P < 0.05) and 3. RASSF1B and RASSF1C mRNA expression was not correlated with TNM stage, histological type, differentiation grade or smoking index.

CONCLUSIONThere is a significant expression difference among the three RASSF1 transcripts in lung carcinoma. RASSF1A, closely associated with lymph metastasis and TNM stage of lung carcinoma, should be a new tumor suppressor gene.

Chromosome Deletion ; Chromosomes, Human, Pair 3 ; Genes, Tumor Suppressor ; Humans ; Lung Neoplasms ; genetics ; pathology ; Lymphatic Metastasis ; Neoplasm Staging ; RNA, Messenger ; analysis ; Tumor Suppressor Proteins ; genetics

PURPOSE: To evaluate the incidence and clinical features of age-related macular degeneration (AMD) in Korea. METHODS: Web-based (www.armd-nova.or.kr) registration was conducted for AMD patients aged 50 or more who were newly diagnosed by retinal specialists in Korea from August 20, 2005 to August 20, 2006. Patient data including ophthalmologic examination, fundus photography, fluorescein angiogram and/or indocyanin green angiogram (ICG), past medical history, behavioral habit, combined systemic diseases were up-loaded. RESULTS: Among finally enrolled 1,141 newly diagnosed AMD patients, 690 patients (60.5%) were male and 451 patients (39.5%) were female. The average age of AMD patients was 69.7+/-8.0. Early AMD was observed in 190 patients and 951 patients had late AMD. Classic choroidal neovascular membrane (CNVM) was observed in 18.6% of exudative AMD patients and 63.4 % had occult CNVM. Subfoveal CNVM was observed in 80.4% of the patients with CNVM. Among the 580 exudative AMD eyes that performed indocyanin green angiography (ICG), 184 eyes (31.7%) had polypoidal choroidal vasculopathy (PCV) and 36 eyes (6.2%) showed retinal angiomatous proliferation (RAP). Age, male gender, smoking, diabetes and hypertension significantly increased the risk of the AMD among Koreans. CONCLUSIONS: Because of the low rate of participation by retinal specialists, definite incidence of AMD was not obtainable. However, the estimated 1-year AMD incidence in the Pusan area of Korea is at least 0.4%. In contrast to Western people, 31.7% of exudative AMD cases were revealed to be PCV and 6.2% were revealed to be RAP. This discrepancy between ethnic groups should be considered in the diagnosis and treatment modality selection of Korean AMD patients.
Aged ; Angiography ; Choroid ; Ethnic Groups ; Eye ; Female ; Fluorescein ; Humans ; Hypertension ; Incidence ; Korea ; Macular Degeneration ; Male ; Membranes ; Photography ; Retinaldehyde ; Smoke ; Smoking ; Specialization