• Biomedical Science Letters
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• v.29 no.4
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• pp.220-230
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• 2023

Identification of the pathogens causing infection is important in terms of patient's health management and infection control. Synovial fluids could be used as clinical samples to detect causative pathogens of prosthetic joint infections (PJIs) using molecular diagnostic assays, therefore, normalization and validation of clinical samples are necessary. Microbial culture is considered the gold standard for all infections, including PJIs. Recently, molecular diagnostic methods have been developed to overcome the limitation of microbial culture. Therefore, guideline for validating clinical samples to provide reliable results of molecular diagnostic assays for infectious diseases is required in clinical field. The present study aimed to develop an accurate validating method of synovial fluid clinical samples using GAPDH gene as an internal control to perform the quantitative PCR TaqMan probe assay to detect pathogens causing PJIs.

• BMB Reports
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• v.38 no.5
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• pp.555-562
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• 2005

Single nucleotide polymorphisms (SNPs) are becoming the most common type of markers used in genetic analysis. In the present report a SNP has been chosen to test the applicability of Real Time PCR to discriminate and quantify SNPs alleles on DNA pools. Amplification Refractory Mutation System (ARMS) and Mismatch Amplification Mutation Assay (MAMA) has been applied. Each assay has been pre-validated testing specificity and performances (linearity, PCR efficiency, interference limit, limit of detection, limit of quantification, precision and accuracy). Both the approaches achieve a precise and accurate estimation of the allele frequencies on pooled DNA samples in the range from 5% to 95% and don't require standard curves or calibrators. The lowest measurement that could be significantly distinguished from the background noise has been determined around the 1% for both the approaches, allowing to extend the range of quantifications from 1% to 99%. Furthermore applicability of Real Time PCR assays for general diagnostic purposes is discussed.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.139-152
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• 2012

The result of the analysis of a population pharmacokinetic model can directly influence the decision of the dose level applied to the targeted patients. Therefore the validation procedure of the final model is very important in this area. This paper reviews the validation methods of population pharmacokinetic models from a statistical viewpoint. In addition, the whole procedure of the analysis of population pharmacokinetics, from the base model to the final model (that includes various validation procedures for the final model) is tested with real clinical data.

• Proceedings of the PSK Conference
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• 2001.10a
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• pp.281.1-281.1
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• 2001

• Korean Journal of Radiology
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• v.22 no.3
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• pp.415-424
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• 2021

Objective: To determine whether noncontrast computed tomography (NCCT) models based on multivariable, radiomics features, and machine learning (ML) algorithms could further improve the discrimination of early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH). Materials and Methods: We retrospectively reviewed 261 patients with sICH who underwent initial NCCT within 6 hours of ictus and follow-up CT within 24 hours after initial NCCT, between April 2011 and March 2019. The clinical characteristics, imaging signs and radiomics features extracted from the initial NCCT images were used to construct models to discriminate early HE. A clinical-radiologic model was constructed using a multivariate logistic regression (LR) analysis. Radiomics models, a radiomics-radiologic model, and a combined model were constructed in the training cohort (n = 182) and independently verified in the validation cohort (n = 79). Receiver operating characteristic analysis and the area under the curve (AUC) were used to evaluate the discriminative power. Results: The AUC of the clinical-radiologic model for discriminating early HE was 0.766. The AUCs of the radiomics model for discriminating early HE built using the LR algorithm in the training and validation cohorts were 0.926 and 0.850, respectively. The AUCs of the radiomics-radiologic model in the training and validation cohorts were 0.946 and 0.867, respectively. The AUCs of the combined model in the training and validation cohorts were 0.960 and 0.867, respectively. Conclusion: NCCT models based on multivariable, radiomics features and ML algorithm could improve the discrimination of early HE. The combined model was the best recommended model to identify sICH patients at risk of early HE.

• ETRI Journal
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• v.32 no.3
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• pp.440-446
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• 2010

Array-based gas sensors now offer the potential of a robust analytical approach to odor measurement for medical use. We are developing a fast reliable method for detection of microbial infection by monitoring the headspace from the infected wound. In this paper, we present initial results obtained from wound-state monitoring for burn patients using an electronic nose incorporating an automated solid-phase microextraction (SPME) desorption system to enable the system to be used for clinical validation. SPME preconcentration is used for sampling of the headspace air and the response of the sensor module to variable concentrations of volatiles emitted from SPME fiber is evaluated. Gas chromatography-mass spectrometry studies prove that living bacteria, the typical infectious agents in clinical practice, can be distinguished from each other by means of a limited set of key volatile products. Principal component analysis results give the first indication that infected patients may be distinguished from uninfected patients. Microbial laboratory analysis using clinical samples verifies the performance of the system.

• Journal of Korean Academy of Nursing
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• v.35 no.6
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• pp.1063-1071
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• 2005

Purpose: This study was done to develop and test validity and reliability of on instrument for predicting nursing intention for SARS patient care. Method: The psychometric properties of a SARS patient care attrition prediction tool, based on the Theory of Planned Behavior, were examined in this study. The Three-phase design involved a) salient beliefs generated from clinical nurses (n=43) b) content validation by expert panel evaluations(n=5) c) face validation by plot testing (n=10) d) and instrument validation in a cross sectional survey (n=299). Psychometric analysis of survey data provided empirical evidence of the construct validity and reliability of the instrument. Result: Principal component analysis verified the hypothesized 6-factor solution, explaining $68.2\%$ of variance, and Alpha coefficients of .7538 to .9389 indicated a high internal consistency of the instrument. Conclusion: The instrument can be used by nurse administrators and researcher to assess clinical nurses' salient beliefs about caring for SARS patients, guide tailored intervention strategies to effective caring, and evaluate the effectiveness of interventions.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.643-648
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• 2012

There are lots of analysis to determine the relation between dependent variable and explanatory variables. Often the regression analysis is used to do this, and we can analyze the how much the explanatory variable can be related with dependent variable and how much the regression model can explain the data. But the validation check of regression model is usually determined by coefficient of determination. We should check the validation of regression coefficient with different methods. This paper introduces the method for validation check the regression coefficient using the jackknife regression and bootstrap regression in clinical data.

• Korean Journal of Radiology
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• v.21 no.12
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• pp.1345-1354
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• 2020

Objective: To evaluate radiomics analysis in studies on mild cognitive impairment (MCI) and Alzheimer's disease (AD) using a radiomics quality score (RQS) system to establish a roadmap for further improvement in clinical use. Materials and Methods: PubMed MEDLINE and EMBASE were searched using the terms 'cognitive impairment' or 'Alzheimer' or 'dementia' and 'radiomic' or 'texture' or 'radiogenomic' for articles published until March 2020. From 258 articles, 26 relevant original research articles were selected. Two neuroradiologists assessed the quality of the methodology according to the RQS. Adherence rates for the following six key domains were evaluated: image protocol and reproducibility, feature reduction and validation, biologic/clinical utility, performance index, high level of evidence, and open science. Results: The hippocampus was the most frequently analyzed (46.2%) anatomical structure. Of the 26 studies, 16 (61.5%) used an open source database (14 from Alzheimer's Disease Neuroimaging Initiative and 2 from Open Access Series of Imaging Studies). The mean RQS was 3.6 out of 36 (9.9%), and the basic adherence rate was 27.6%. Only one study (3.8%) performed external validation. The adherence rate was relatively high for reporting the imaging protocol (96.2%), multiple segmentation (76.9%), discrimination statistics (69.2%), and open science and data (65.4%) but low for conducting test-retest analysis (7.7%) and biologic correlation (3.8%). None of the studies stated potential clinical utility, conducted a phantom study, performed cut-off analysis or calibration statistics, was a prospective study, or conducted cost-effectiveness analysis, resulting in a low level of evidence. Conclusion: The quality of radiomics reporting in MCI and AD studies is suboptimal. Validation is necessary using external dataset, and improvements need to be made to feature reproducibility, feature selection, clinical utility, model performance index, and pursuits of a higher level of evidence.

• Korean Medical Education Review
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• v.23 no.1
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• pp.37-45
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• 2021

In light of the need for a tool to evaluate the clinical practice education environment as perceived by medical and nursing students, this study is was conducted to develop and validate the Korean version of the Undergraduate Clinical Education Environment Measure (K-UCEEM) as a measurement tool for managing the clinical practice education climate and quality of education. For validation, the UCEEM consisting of 25 items developed by Pia Strand in 2013 was adapted according to standard translation procedures. The K-UCEEM questionnaire was administered to 73 medical students and 135 nursing students who participated in clinical practice at one medical institution. Exploratory factor analysis and confirmatory factor analysis were conducted to confirm the validity of the instrument's structure. In order to determine referential validity, the relationships among stresses in clinical practice were examined, and differences in factor scores were compared by gender and college. It was confirmed that the scale of 24 items and five factors showed a moderate model fitness index. The reliability of the factors ranged from 0.786 to 0.867. In addition, all five factors were found to have negative correlations with the clinical practice stress sub-factor, and there were statistically significant differences by gender and college. Through this study, the validity and reliability of the K-UCEEM were verified. In the future, it is expected that further verification of the scale, as well as evaluation and improvement of the clinical practice education environment based on this scale, will occur.