Background: The 16S rRNA-targeted next-generation sequencing (NGS) has been widely used as the primary tool for microbiome analysis. However, whether the sequenced microbial diversity absolutely represents the original sample composition remains unclear. This study aimed to evaluate whether 16S rRNA gene-targeted NGS accurately captures bacterial community composition. Methods: Mock communities were constructed using equal amounts of DNA from 18 bacterial strains in three formats: genomic DNA, recombinant plasmids, and polymerase chain reaction (PCR) templates. The V3V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq. Results: Data regression analysis revealed that the recombinant plasmid produced more accurate and precise correlation curve than that by the gDNA and PCR products, with a slope closest to 1 (1.0082) and the highest R² value (0.9975). Despite the same input amount of bacterial DNA, the NGS read distribution varied across all three mock communities. Using multiple regression analysis, we found that the guanine-cytosine (GC) content of the V3V4 region, 16S rRNA gene, size of gDNA, and copy number of 16S rRNA were significantly associated with the NGS output of each bacterial species. Conclusion This study demonstrated that recombinant plasmids are the preferred option for quality control and that NGS output is biased owing to certain bacterial characteristics, such as %GC content, gDNA size, and 16S rRNA gene copy number. Further research is required to develop a system that compensates for NGS process biases using mock communities.

Background: The 16S rRNA-targeted next-generation sequencing (NGS) has been widely used as the primary tool for microbiome analysis. However, whether the sequenced microbial diversity absolutely represents the original sample composition remains unclear. This study aimed to evaluate whether 16S rRNA gene-targeted NGS accurately captures bacterial community composition. Methods: Mock communities were constructed using equal amounts of DNA from 18 bacterial strains in three formats: genomic DNA, recombinant plasmids, and polymerase chain reaction (PCR) templates. The V3V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq. Results: Data regression analysis revealed that the recombinant plasmid produced more accurate and precise correlation curve than that by the gDNA and PCR products, with a slope closest to 1 (1.0082) and the highest R² value (0.9975). Despite the same input amount of bacterial DNA, the NGS read distribution varied across all three mock communities. Using multiple regression analysis, we found that the guanine-cytosine (GC) content of the V3V4 region, 16S rRNA gene, size of gDNA, and copy number of 16S rRNA were significantly associated with the NGS output of each bacterial species. Conclusion This study demonstrated that recombinant plasmids are the preferred option for quality control and that NGS output is biased owing to certain bacterial characteristics, such as %GC content, gDNA size, and 16S rRNA gene copy number. Further research is required to develop a system that compensates for NGS process biases using mock communities.

Background: The 16S rRNA-targeted next-generation sequencing (NGS) has been widely used as the primary tool for microbiome analysis. However, whether the sequenced microbial diversity absolutely represents the original sample composition remains unclear. This study aimed to evaluate whether 16S rRNA gene-targeted NGS accurately captures bacterial community composition. Methods: Mock communities were constructed using equal amounts of DNA from 18 bacterial strains in three formats: genomic DNA, recombinant plasmids, and polymerase chain reaction (PCR) templates. The V3V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq. Results: Data regression analysis revealed that the recombinant plasmid produced more accurate and precise correlation curve than that by the gDNA and PCR products, with a slope closest to 1 (1.0082) and the highest R² value (0.9975). Despite the same input amount of bacterial DNA, the NGS read distribution varied across all three mock communities. Using multiple regression analysis, we found that the guanine-cytosine (GC) content of the V3V4 region, 16S rRNA gene, size of gDNA, and copy number of 16S rRNA were significantly associated with the NGS output of each bacterial species. Conclusion This study demonstrated that recombinant plasmids are the preferred option for quality control and that NGS output is biased owing to certain bacterial characteristics, such as %GC content, gDNA size, and 16S rRNA gene copy number. Further research is required to develop a system that compensates for NGS process biases using mock communities.

Background: The 16S rRNA-targeted next-generation sequencing (NGS) has been widely used as the primary tool for microbiome analysis. However, whether the sequenced microbial diversity absolutely represents the original sample composition remains unclear. This study aimed to evaluate whether 16S rRNA gene-targeted NGS accurately captures bacterial community composition. Methods: Mock communities were constructed using equal amounts of DNA from 18 bacterial strains in three formats: genomic DNA, recombinant plasmids, and polymerase chain reaction (PCR) templates. The V3V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq. Results: Data regression analysis revealed that the recombinant plasmid produced more accurate and precise correlation curve than that by the gDNA and PCR products, with a slope closest to 1 (1.0082) and the highest R² value (0.9975). Despite the same input amount of bacterial DNA, the NGS read distribution varied across all three mock communities. Using multiple regression analysis, we found that the guanine-cytosine (GC) content of the V3V4 region, 16S rRNA gene, size of gDNA, and copy number of 16S rRNA were significantly associated with the NGS output of each bacterial species. Conclusion This study demonstrated that recombinant plasmids are the preferred option for quality control and that NGS output is biased owing to certain bacterial characteristics, such as %GC content, gDNA size, and 16S rRNA gene copy number. Further research is required to develop a system that compensates for NGS process biases using mock communities.

Background: The 16S rRNA-targeted next-generation sequencing (NGS) has been widely used as the primary tool for microbiome analysis. However, whether the sequenced microbial diversity absolutely represents the original sample composition remains unclear. This study aimed to evaluate whether 16S rRNA gene-targeted NGS accurately captures bacterial community composition. Methods: Mock communities were constructed using equal amounts of DNA from 18 bacterial strains in three formats: genomic DNA, recombinant plasmids, and polymerase chain reaction (PCR) templates. The V3V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq. Results: Data regression analysis revealed that the recombinant plasmid produced more accurate and precise correlation curve than that by the gDNA and PCR products, with a slope closest to 1 (1.0082) and the highest R² value (0.9975). Despite the same input amount of bacterial DNA, the NGS read distribution varied across all three mock communities. Using multiple regression analysis, we found that the guanine-cytosine (GC) content of the V3V4 region, 16S rRNA gene, size of gDNA, and copy number of 16S rRNA were significantly associated with the NGS output of each bacterial species. Conclusion This study demonstrated that recombinant plasmids are the preferred option for quality control and that NGS output is biased owing to certain bacterial characteristics, such as %GC content, gDNA size, and 16S rRNA gene copy number. Further research is required to develop a system that compensates for NGS process biases using mock communities.

Purpose: Recently, we developed allele-discriminating priming system (ADPS) technology. This method increases the sensitivity of conventional quantitative polymerase chain reaction up to 100 folds, with limit of detection, 0.01%, with reinforced specificity. This prospective study aimed to develop and validate the accuracy of ADPS epidermal growth factor receptor (EGFR) Mutation Test Kit using clinical specimens. Materials and Methods: In total 189 formalin-fixed paraffin-embedded tumor tissues resected from patients with non–small cell lung cancer were used to perform a comparative evaluation of the ADPS EGFR Mutation Test Kit versus the cobas EGFR Mutation Test v2, which is the current gold standard. When the two methods had inconsistent results, next-generation sequencing–based CancerSCAN was utilized as a referee. Results: The overall agreement of the two methods was 97.4% (93.9%-99.1%); the positive percent agreement, 95.0% (88.7%-98.4%); and the negative percent agreement, 100.0% (95.9%-100.0%). EGFR mutations were detected at a frequency of 50.3% using the ADPS EGFR Mutation Test Kit and 52.9% using the cobas EGFR Mutation Test v2. There were 10 discrepant mutation calls between the two methods. CancerSCAN reproduced eight ADPS results. In two cases, mutant allele fraction was ultra-low at 0.02% and 0.06%, which are significantly below the limit of detection of the cobas assay and CancerSCAN. Based on the EGFR genotyping by ADPS, the treatment options could be switched in five patients. Conclusion The highly sensitive and specific ADPS EGFR Mutation Test Kit would be useful in detecting the patients who have lung cancer with EGFR mutation, and can benefit from the EGFR targeted therapy.

Objective: This study was to evaluate the effectiveness of an individually tailored program based on self-measurement of blood glucose on health behavior and HbA1c in diabetes and pre-diabetes patients. Methods: The program consisted of seven sessions for 12 weeks which were carried out every two weeks. Almost all sessions were progressed on untact method except for the first and last session. The 71 subjects were assessed for their knowledge of diabetes, health behavior, the experience of self-measurement of blood glucose, body mass Index (BMI) and hemoglobin A1c (HbA1c) at before and after the program. They were also evaluated on their degree of utilization of blood glucose measurements after the program. Results: Each mean score on their knowledge of diabetes, health behavior and the experience of self-measurement of blood glucose was significantly increased from 14.77, 25.50, and 2.70 to 15.41, 28.40, and 4.81, respectively. Each mean score on both BMI and HbA1c (n=53) was significantly decreased from 24.47kg/m2 and 7.27% to 24.01kg/m2 and 6.67%, respectively. The post-HbA1c had a significant negative correlation(r=-0.415) with the degree of utilization of blood glucose measurements. The degree of utilization of blood glucose measurements had a significant positive correlation(r=0.581) with post-health behavior. Conclusions The program shows effectiveness in improving HbA1c in Type 2 diabetes and pre-diabetes patients. The post-HbA1c might be related to the degree of utilization of blood glucose measurements which might be related to the health behavior.

Evidence of the ethical appropriateness and clinical benefits of shared decision-making (SDM) are accumulating. This study aimed to not only identify physicians’ perspectives on SDM, and practices related to end-of-life care in particular, but also to gauge the effect of SDM education on physicians in Korea. Methods: A 14-item questionnaire survey using a modified Delphi process was delivered to nephrologists and internal medicine trainees at 17 university hospitals. Results: A total of 309 physicians completed the survey. Although respondents reported that 69.9% of their practical decisions were made using SDM, 59.9% reported that it is not being applied appropriately. Only 12.3% of respondents had received education on SDM as part of their training. The main obstacles to appropriate SDM were identified as lack of time (46.0%), educational materials and tools (29.4%), and education on SDM (24.3%). Although only a few respondents had received training on SDM, the proportion of those who thought they were using SDM appropriately in actual practice was high; the proportion of those who chose lack of time and education as factors that hindered the proper application of SDM was low. Conclusion: The majority of respondents believed that SDM was not being implemented properly in Korea, despite its use in actual practice. To improve the effectiveness of SDM in the Korean medical system, appropriate training programs and supplemental policies that guarantee sufficient application time are required.

Purpose: The aim of this study is to evaluate the effect of body mass index (BMI) on peak serum growth hormone (GH) level after GH stimulation test in children with short stature. Methods: Data were obtained from retrospective medical record reviews of those who visited the pediatric endocrine clinic at St. Vincent’s Hospital of Catholic University for short stature from January 2010 to June 2019. A total of 115 children (66 boys and 49 girls) whose height was less than the third percentile according to age and sex underwent GH stimulation testing. Results: Of the 115 subjects, 47 were diagnosed with GH deficiency (GHD) and 68 were diagnosed with idiopathic short stature (ISS). In patients with GHD, weight standard deviation score (SDS) (P<0.001) and BMI SDS (P≤0.001) were higher, and free thyroxine (T4) level (P=0.012) was lower than those in the ISS group. In total subjects, peak serum GH level after GH stimulation test showed negative correlations with weight SDS (r=-0.465, P<0.001), BMI SDS (r=-0.398, P<0.001), and thyroid stimulating hormone (r=-0.248, P=0.008) and a positive correlation with free T4 (r=0.326, P<0.001). In multiple regression analysis, BMI SDS (P=0.003) was negatively associated with peak serum GH level in GH stimulation testing after adjusting for age, sex, pubertal status, and type of pharmacological stimulus. Conclusion The BMI SDS influences peak serum GH level after GH stimulation testing. We should consider BMI factors when interpreting the results of GH stimulation testing.

Purpose: The aim of this study is to evaluate the effect of body mass index (BMI) on peak serum growth hormone (GH) level after GH stimulation test in children with short stature. Methods: Data were obtained from retrospective medical record reviews of those who visited the pediatric endocrine clinic at St. Vincent’s Hospital of Catholic University for short stature from January 2010 to June 2019. A total of 115 children (66 boys and 49 girls) whose height was less than the third percentile according to age and sex underwent GH stimulation testing. Results: Of the 115 subjects, 47 were diagnosed with GH deficiency (GHD) and 68 were diagnosed with idiopathic short stature (ISS). In patients with GHD, weight standard deviation score (SDS) (P<0.001) and BMI SDS (P≤0.001) were higher, and free thyroxine (T4) level (P=0.012) was lower than those in the ISS group. In total subjects, peak serum GH level after GH stimulation test showed negative correlations with weight SDS (r=-0.465, P<0.001), BMI SDS (r=-0.398, P<0.001), and thyroid stimulating hormone (r=-0.248, P=0.008) and a positive correlation with free T4 (r=0.326, P<0.001). In multiple regression analysis, BMI SDS (P=0.003) was negatively associated with peak serum GH level in GH stimulation testing after adjusting for age, sex, pubertal status, and type of pharmacological stimulus. Conclusion The BMI SDS influences peak serum GH level after GH stimulation testing. We should consider BMI factors when interpreting the results of GH stimulation testing.