No abstract available.
Colostomy* ; Humans

The purpose of this research was to observe the management system of 18 free meal service centers for the low-income homebound elderly in Chungcheongbuk-Do In order researchers interviewed staff members of the free meal service centers. Based on the results of interviews with staff members of the meal service organization. only two centers used a standard recipe. and most of the meal preparation was controlled under the experience of volunteers. Only two meal service centers employed dietitians due to the lak of budgets. The cost of a meal per day ranged from 556 won to 2,750 won and the number of attendants at meal service ranged from 35 to 350 persons. The budget for most of the meal service centers is not enough to provide meals for the elderly who want to participated in free meal service programs. Home delivery meal service was not considered due to the lake of manpower and areal dispersion in rural ares. Most meal service organizations did not cooperate with other community service organizations. Several improvement strategies are recommended for the effective running of the free meal service centers. First, a nutrition specialist should be included in the staff members of the service organization to provide nutritious meal service to the elderly Second, a joint control system might be introduced into the present system to reduced the cost and the recover the limitations of areal dispersion. Third, service, coordination should be considered to overcome the problem of lake resources.
Aged ; Budgets ; Chungcheongbuk-do ; Humans ; Joints ; Lakes ; Meals* ; Nutritionists ; Running ; Social Welfare ; Specialization ; Volunteers

BACKGROUND/OBJECTIVES: Complementary and alternative medicines can be used to alleviate climacteric symptoms that significantly affect the quality of life of postmenopausal women. Isoflavones are the most common plant-based therapies for postmenopausal changes, but the results of previous studies have been controversial.MATERIALS/METHODS: To investigate whether isoflavones would affect menopausal symptoms as well as ovarian hormones, we performed a systematic review and metaanalysis. The PubMed and EMBASE databases were used to perform the systematic search.Included studies were limited to randomized controlled trials (RCTs) assessing the impact of isoflavone supplementation on menopausal symptoms. RESULTS: Eleven studies were included for the final quantitative assessment. Isoflavone intervention was varied between 49.3 and 135 mg of isoflavones per day for 12 wk–2 yrs.The meta-analysis showed that supplementation of isoflavones significantly increased the estradiol levels (standardized mean difference [SMD] = 0.615, P = 0.035) and Kupperman index (SMD = 3.121, P = 0.003) but had no significant effect on hot flashes, folliclestimulating hormone, and luteinizing hormone. However, both estradiol and the Kupperman index showed significant heterogeneity among studies (I2 = 94.7%, P < 0.001 and I2 = 98.1%, P < 0.001, respectively). CONCLUSIONS Although the results showed a significant SMD in estradiol and the Kupperman index, the results should be interpreted with caution due to the high heterogeneity. Further validation with a larger RCT will be necessary. Overall, isoflavone supplementation has distinct effects on the climacteric symptoms and hormonal changes in postmenopausal women.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

An association study with Korean schizophrenic patients(N=75) and normal controls(N=87) was performed to find the relationship between D9S158 polymorphism and schizophrenia using polymerase chain reaction. Eight different alleles of a dinucleotide polymorphism on D9S158 locus were observed in both group. When we compared the frequencies of alleles between schizophrenics and normal controls, there was no significant difference between two groups. To increase homogeneity of schizophrenic group, we divided schizophrenic group by clinical phenotypes such as family history, negative and positive symptoms(PANSS), soft neurologic signs(NES-K) and DSM-lV diagnostic subtypes. Then we compared the frequencies of alleles among subgroups of clinical phenotypes, and there were no significant differences between subgroups(p>.05). Although our findings fail to provide an evidence of association between schizophrenia and D9S158 locus, further investigation of other loci that are linked to NMDA receptor gene may be needed in genetically homogeneous subgroups of schizophrenia.
Alleles ; Chromosomes, Human, Pair 9 ; Humans ; N-Methylaspartate* ; Phenotype ; Polymerase Chain Reaction ; Schizophrenia

OBJECTIVES: Diabetes and alcohol dependence are considered as independent risk factors for cognitive impairment. This research was to investigate whether cognitive functions in diabetic alcohol dependent patients were more impaired than non-diabetic alcohol dependent patients. METHODS: A cross-sectional study was conducted in alcohol dependence patients (n=138). Patients with alcohol dependence diagnosed by Diagnostic and Statistical Manual of Mental Disorder, 4th edition, Text Revision underwent a 75 g oral glucose tolerance test, to classify to diabetics group and non-diabetics group. In addition to demographic and clinical characteristics, cognitive functions assessed using the Korean-Mini Mental Status Examination (K-MMSE), word list memory test, and word fluency test, word list recall test from Korean version of the consortium to establish a registry for Alzheimer's disease, and block design test, digit span test, and digit symbol test from Korean-Wechsler Adult Intellogence Scale were compared between the two groups. RESULTS: There was no significant difference in demographic and other clinical characteristics between the non-diabetic and diabetic alcoholic patients. Compared to non-diabetic alcoholic patients, diabetic alcoholic patients were more impaired on language of K-MMSE (p=0.028) and digit symbol test (p=0.044). CONCLUSION: These findings suggest the more severe impairment of selective cognitive functions in diabetic alcoholic patients than non-diabetic alcoholic patients. Future replication of these findings in a large population is necessary.
Adult ; Alcoholics ; Alcoholism ; Alzheimer Disease ; Cognition ; Comorbidity ; Cross-Sectional Studies ; Diabetes Mellitus ; Glucose Tolerance Test ; Humans ; Memory ; Mental Disorders ; Risk Factors