• Archives of Pharmacal Research
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• v.25 no.3
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• pp.387-391
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• 2002

The oral absorption and disposition of itraconazole were studied in rats, rabbits and dogs. Serum levels of itraconazole and its active metabolite, hydroxyitraconazole, were determined by a validated HPLC method. The absorption of itraconazole was relatively rapid in rats and dogs but was slower in rabbits. The terminal elimination half-life ($T_{1/2,{\lambda}z}$), time to the peak concentration ($T_{max}$), dose and weight normalized area under the curve (AUC) and the peak concentration ($C_{max}$) of itraconazole found in the dog were comparable to those reported in humans. As in humans, the metabolite to parent drug AUC ratios in rats and dogs were greater than unity but was less in rabbits. The dog appears to be an appropriate animal model while the rat, not the rabbit, may be used as an alternative animal model in predicting the oral absorption of itraconazole in humans.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5593-5596
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• 2014

Background: The objective of this study was to determine a diagnostic classification scheme using a decision tree based model. Materials and Methods: The study was conducted as a retrospective case-control study in Imam Khomeini hospital in Tehran during 2001 to 2009. Data, including demographic and clinical-pathological characteristics, were uniformly collected from 624 females, 312 of them were referred with positive diagnosis of breast cancer (cases) and 312 healthy women (controls). The decision tree was implemented to develop a diagnostic classification scheme using CART 6.0 Software. The AUC (area under curve), was measured as the overall performance of diagnostic classification of the decision tree. Results: Five variables as main risk factors of breast cancer and six subgroups as high risk were identified. The results indicated that increasing age, low age at menarche, single and divorced statues, irregular menarche pattern and family history of breast cancer are the important diagnostic factors in Iranian breast cancer patients. The sensitivity and specificity of the analysis were 66% and 86.9% respectively. The high AUC (0.82) also showed an excellent classification and diagnostic performance of the model. Conclusions: Decision tree based model appears to be suitable for identifying risk factors and high or low risk subgroups. It can also assists clinicians in making a decision, since it can identify underlying prognostic relationships and understanding the model is very explicit.

• Genomics & Informatics
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• v.11 no.1
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• pp.46-51
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• 2013

Normal-karyotype acute myeloid leukemia (NK-AML) is a highly malignant and cytogenetically heterogeneous hematologic cancer. We searched for somatic mutations from 10 pairs of tumor and normal cells by using a highly efficient and reliable analysis workflow for whole-exome sequencing data and performed association tests between the NK-AML and somatic mutations. We identified 21 nonsynonymous single nucleotide variants (SNVs) located in a coding region of 18 genes. Among them, the SNVs of three leukemia-related genes (MUC4, CNTNAP2, and GNAS) reported in previous studies were replicated in this study. We conducted stepwise genetic risk score (GRS) models composed of the NK-AML susceptible variants and evaluated the prediction accuracy of each GRS model by computing the area under the receiver operating characteristic curve (AUC). The GRS model that was composed of five SNVs (rs75156964, rs56213454, rs6604516, rs10888338, and rs2443878) showed 100% prediction accuracy, and the combined effect of the three reported genes was validated in the current study (AUC, 0.98; 95% confidence interval, 0.92 to 1.00). Further study with large sample sizes is warranted to validate the combined effect of these somatic point mutations, and the discovery of novel markers may provide an opportunity to develop novel diagnostic and therapeutic targets for NK-AML.

• Journal of Nutrition and Health
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• v.43 no.3
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• pp.215-223
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• 2010

Sasa borealis leaf has been known to have anti-diabetic properties. In this study, we tried to evaluate the effects of Sasa borealis leaf extract (SBE) on the inhibition of $\alpha$-glucosidase activity and postprandial glycemic response following ingestion of four carbohydrate-rich foods; cooked rice, ramen (instant noodle), noodle, and bread. Fourteen healthy female adults consumed 50 g of glucose (control) or one of the four foods containing 50 g of available carbohydrate with or without 2,000 mg of SBE. The activity of $\alpha$-glucosidase was inhibited dose-dependently by SBE. With SBE, blood glucose concentration at 15 min and the positive area under the curve (AUC) of postprandial glycemic response at 15 min and 30 min after consuming each of the four foods were reduced significantly. As the result, total positive AUC during 120 min was decreased in case of taking cooked rice or bread. Glycemic index and glycemic load of the four foods were declined from 13% to 23% with SBE. The results of this study suggest that SBE may be effective for postprandial glucose control by inhibiting $\alpha$-glucosidase activity.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.98-106
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• 2022

A condition of the retina known as hypertensive retinopathy (HR) is connected to high blood pressure. The severity and persistence of hypertension are directly correlated with the incidence of HR. To avoid blindness, it is essential to recognize and assess HR as soon as possible. Few computer-aided systems are currently available that can diagnose HR issues. On the other hand, those systems focused on gathering characteristics from a variety of retinopathy-related HR lesions and categorizing them using conventional machine-learning algorithms. Consequently, for limited applications, significant and complicated image processing methods are necessary. As seen in recent similar systems, the preciseness of classification is likewise lacking. To address these issues, a new CAD HR-diagnosis system employing the advanced Deep Dense CNN Learning (DD-CNN) technology is being developed to early identify HR. The HR-diagnosis system utilized a convolutional neural network that was previously trained as a feature extractor. The statistical investigation of more than 1400 retinography images is undertaken to assess the accuracy of the implemented system using several performance metrics such as specificity (SP), sensitivity (SE), area under the receiver operating curve (AUC), and accuracy (ACC). On average, we achieved a SE of 97%, ACC of 98%, SP of 99%, and AUC of 0.98. These results indicate that the proposed DD-CNN classifier is used to diagnose hypertensive retinopathy.

• Genomics & Informatics
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• v.14 no.4
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• pp.138-148
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• 2016

The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the "large p and small n" problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

• Korean Journal of Clinical Pharmacy
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• v.13 no.2
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• pp.67-71
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• 2003

The purpose of this study was to investigate the effect of aspirin (5, 10, 20 mg/kg) on the pharmacokinetics of metformin $(15\;mg/kg)$ in rabbits. The plasma concentration of metformin was decreased significantly (p<0.05) by co-administration of aspirin (10, 20 mg/kg) compared with control. Area under the plasma concentration-time curve (AUC) of metformin was decreased significantly (p<0.05) by co-administration of aspirin (10, 20mg/kg) compared with control. Relative bioavailability $(R.B\%)$ of metformin by co-administration was 79.3 (5 mg/kg), 57.5 (10 mg/kg) and 62.5 (20 mg/kg). Peak plasma concentration of metformin was significantly (p<0.05) decreased by co-administration of aspirin (10, 20 mg/kg) compared with control. The elimination rate constant $(K_{el})$ of metformin was increased by co-administration of aspirin (10, 20 mg/kg) compared with control. The terminal half-lifes $(t_{1/2})$ and mean resident time (MRT) of metformin by co-administration of aspirin (10, 20 mg/kg) were decreased significantly (p<0.05) compared with control. It is considered that the significantly decreased plasma concentration and AUC of metrormin is due to increase of elimination in urine acidified by co-administration of aspirin. The results suggest that the dosage of metformin should be adjusted when metformin is co-administered with aspirin in the clinical situation.

• Korean Journal of Adult Nursing
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• v.26 no.6
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• pp.668-680
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• 2014

Purpose: The aim of this study was to investigate the accuracy of infrared temperature measurements compared to axillary temperature in order to detect fever in patients. Methods: Studies published between 1946 and 2012 from periodicals indexed in Ovid Medline, Embase, CINAHL, Cochrane, KoreaMed, NDSL, KERIS and other databases were selected using the following key words: "infrared thermometer". QUADAS-II was utilized to assess the internal validity of the diagnostic studies. Selected studies were analyzed through a meta-analysis using MetaDisc 1.4. Results: Twenty-one diagnostic studies with high methodological quality were included representing 3,623 subjects in total. Results of the meta-analysis showed that the pooled sensitivity, specificity, and area under the curve (AUC) of infrared tympanic thermometers were 0.73 (95% CI 0.70~0.75), 0.92 (95% CI 0.91~0.92), and 0.90, respectively. For axillary temperature readings, the pooled sensitivity was 0.67 (95% CI 0.62~0.73), the pooled specificity was 0.87 (95% CI 0.85~0.90), and the AUC was 0.80. Conclusion: Infrared tympanic temperature can predict axillary temperature in normothermic and in febrile patients with an acceptable level of diagnostic accuracy. However, further research is necessary to substantiate this finding in patients with hyperthermia.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.11
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• pp.4467-4474
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• 2014

Background: We aimed to comprehensively review the evidence for using sputum DNA to detect non-small cell lung cancer (NSCLC). Materials and Methods: We searched PubMed, Science Direct, Web of Science, Chinese Biological Medicine (CBM), Chinese National Knowledge Infrastructure (CNKI), Wanfang, Vip Databases and Google Scholar from 2003 to 2013. The meta-analysis was carried out using a random-effect model with sensitivity, specificity, diagnostic odd ratios (DOR), summary receiver operating characteristic curves (ROC curves), area under the curve (AUC), and 95% confidence intervals (CI) as effect measurements. Results: There were twenty-two studies meeting the inclusion criteria for the meta-analysis. Combined sensitivity and specificity were 0.62 (95%CI: 0.59-0.65) and 0.73 (95%CI: 0.70-0.75), respectively. The DOR was 10.3 (95%CI: 5.88-18.1) and the AUC was 0.78. Conclusions: The overall accuracy of the test was currently not strong enough for the detection of NSCLC for clinical application. Dscovery and evaluation of additional biomarkers with improved sensitivity and specificity from studies rated high quality deserve further attention.

• Journal of Sasang Constitutional Medicine
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• v.32 no.2
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• pp.10-21
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• 2020

Objectives Genome-wide association studies(GWAS) is a useful method to identify genetic associations for various phenotypes. The purpose of this study was to develop predictive models for Sasang constitution types using genetic factors. Methods The genotypes of the 1,999 subjects was performed using Axiom Precision Medicine Research Array (PMRA) by Life Technologies. All participants were prescribed Sasang Constitution-specific herbal remedies for the treatment, and showed improvement of original symptoms as confirmed by Korean medicine doctor. The genotypes were imputed by using the IMPUTE program. Association analysis was conducted using a logistic regression model to discover Single Nucleotide Polymorphism (SNP), adjusting for age, sex, and BMI. Results & Conclusions We developed models to predict Korean medicine constitution types using identified genectic factors and sex, age, BMI using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN). Each maximum Area Under the Curve (AUC) of Teaeum, Soeum, Soyang is 0.894, 0.868, 0.767, respectively. Each AUC of the models increased by 6~17% more than that of models except for genetic factors. By developing the predictive models, we confirmed usefulness of genetic factors related with types. It demonstrates a mechanism for more accurate prediction through genetic factors related with type.