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: Although the combination of androgen deprivation therapy with docetaxel or abiraterone acetate and prednisone has become a standard treatment for metastatic hormone-sensitive prostate cancer (mHSPC) and has shown improved overall survival, a subset of patients still progress to castration-resistant disease. However, the underlying molecular features in these patients remain poorly understood. Materials and Methods: We performed single-cell RNA sequencing (scRNA-seq) on 12 tissue samples, including 2 mHSPC samples, 7 primary prostate cancer (PCa) samples, and 3 matched normal samples from 7 patients. Raw sequencing data was processed by Cell Ranger software (10X Genomics) and aligned to the human reference genome (GRCh38). A comprehensive analysis of samples from patients with mHSPC was also conducted and validated using a cohort of 52 patients with mHSPC. Results: Our results identified distinct subpopulations within luminal and mononuclear phagocyte (MNP) clusters characterized by proliferative activation associated with unfavorable clinical outcomes. Furthermore, we observed that the MNP cluster exhibited significant proliferation activity. To understand the underlying mechanisms associated with aggressiveness, we conducted cell-cell interaction, copy number variation and pseudotime analysis. Utilizing 87 network genes from scRNA-seq, we classified 52 mHSPC patients into 2 molecular subtypes and demonstrated their correlation with distinct transcriptomic profiles and survival outcomes. Importantly, a 14-gene signature derived from 3 distinct gene sets exhibited a strong association with patient survival and drug response. The prognostic value of our findings was further validated in large-scale cohorts comprising localized PCa, metastatic PCa, mHSPC, and metastatic castration-resistant PCa patients. Conclusion This study provides valuable insights into the identification of high-risk patients, novel biomarkers, and potential therapeutic targets for individuals with mHSPC. Furthermore, the results in this study can serve as a basis for future investigations aimed at refining prognostic strategies and developing targeted therapies for patients with mHSPC.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

OBJECTIVES: This study aimed to examine the association between the food-based index of dietary inflammatory potential (FBDI) and the risk of mild cognitive impairment (MCI) in Korean older adults. METHODS: The subjects were 798 Korean adults aged 60 years and older. The FBDI was calculated based on the intake of 7 anti-inflammatory and 3 inflammatory food groups. Cognitive function was assessed using the Korean version of the Mini-Mental State Examination. A general linear model and multiple logistic regression were applied to assess the association between FBDI and the risk of MCI. RESULTS: As the FBDI increased, the intake of white rice, cookies/candies, and sweetened drinks tended to increase, but the intake of niacin, β-carotene, calcium, and potassium tended to decrease (p for trend<0.05). The highest FBDI group had a higher MCI risk (odds ratio [OR], 1.60; 95% confidence interval [CI], 1.01 to 2.52) than the lowest FBDI group, adjusted for gender, age, and education level; and this trend was significant in a fully adjusted model (p for trend=0.039). No significant associations were found in men after adjusting for confounding factors. Among women, MCI risk increased as the FBDI increased (p for trend=0.007); and the highest FBDI group had a higher MCI risk (OR, 2.22; 95% CI, 1.04 to 4.74) than the lowest FBDI group in a fully adjusted model. CONCLUSIONS These results suggest that the appropriate intake of anti-inflammatory foods and nutrients may be associated with a reduced risk of MCI among older adults.

OBJECTIVES: This study aimed to examine the association between the food-based index of dietary inflammatory potential (FBDI) and the risk of mild cognitive impairment (MCI) in Korean older adults. METHODS: The subjects were 798 Korean adults aged 60 years and older. The FBDI was calculated based on the intake of 7 anti-inflammatory and 3 inflammatory food groups. Cognitive function was assessed using the Korean version of the Mini-Mental State Examination. A general linear model and multiple logistic regression were applied to assess the association between FBDI and the risk of MCI. RESULTS: As the FBDI increased, the intake of white rice, cookies/candies, and sweetened drinks tended to increase, but the intake of niacin, β-carotene, calcium, and potassium tended to decrease (p for trend<0.05). The highest FBDI group had a higher MCI risk (odds ratio [OR], 1.60; 95% confidence interval [CI], 1.01 to 2.52) than the lowest FBDI group, adjusted for gender, age, and education level; and this trend was significant in a fully adjusted model (p for trend=0.039). No significant associations were found in men after adjusting for confounding factors. Among women, MCI risk increased as the FBDI increased (p for trend=0.007); and the highest FBDI group had a higher MCI risk (OR, 2.22; 95% CI, 1.04 to 4.74) than the lowest FBDI group in a fully adjusted model. CONCLUSIONS These results suggest that the appropriate intake of anti-inflammatory foods and nutrients may be associated with a reduced risk of MCI among older adults.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.