Background: The World Health Organization has declared the end of the coronavirus disease 2019 (COVID-19) public health emergency. However, this did not indicate the end of COVID-19. Several months after the infection, numerous patients complain of respiratory or nonspecific symptoms; this condition is called long COVID. Even patients with mild COVID-19 can experience long COVID, thus the burden of long COVID remains considerable. Therefore, we conducted this study to comprehensively analyze the effects of long COVID using multi-faceted assessments. Materials and Methods: We conducted a prospective cohort study involving patients diagnosed with COVID-19 between February 2020 and September 2021 in six tertiary hospitals in Korea. Patients were followed up at 1, 3, 6, 12, 18, and 24 months after discharge. Long COVID was defined as the persistence of three or more COVID-19-related symptoms. The primary outcome of this study was the prevalence of long COVID after the period of COVID-19. Results: During the study period, 290 patients were enrolled. Among them, 54.5 and 34.6% experienced long COVID within 6 months and after more than 18 months, respectively. Several patients showed abnormal results when tested for post-traumatic stress disorder (17.4%) and anxiety (31.9%) after 18 months. In patients who underwent follow-up chest computed tomography 18 months after COVID-19, abnormal findings remained at 51.9%. Males (odds ratio [OR], 0.17; 95% confidence interval [CI], 0.05–0.53; P=0.004) and elderly (OR, 1.04; 95% CI, 1.00–1.09; P=0.04) showed a significant association with long COVID after 12–18 months in a multivariable logistic regression analysis. Conclusion Many patients still showed long COVID after 18 months post SARS-CoV-2 infection. When managing these patients, the assessment of multiple aspects is necessary.

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 World Health Organization has declared the end of the coronavirus disease 2019 (COVID-19) public health emergency. However, this did not indicate the end of COVID-19. Several months after the infection, numerous patients complain of respiratory or nonspecific symptoms; this condition is called long COVID. Even patients with mild COVID-19 can experience long COVID, thus the burden of long COVID remains considerable. Therefore, we conducted this study to comprehensively analyze the effects of long COVID using multi-faceted assessments. Materials and Methods: We conducted a prospective cohort study involving patients diagnosed with COVID-19 between February 2020 and September 2021 in six tertiary hospitals in Korea. Patients were followed up at 1, 3, 6, 12, 18, and 24 months after discharge. Long COVID was defined as the persistence of three or more COVID-19-related symptoms. The primary outcome of this study was the prevalence of long COVID after the period of COVID-19. Results: During the study period, 290 patients were enrolled. Among them, 54.5 and 34.6% experienced long COVID within 6 months and after more than 18 months, respectively. Several patients showed abnormal results when tested for post-traumatic stress disorder (17.4%) and anxiety (31.9%) after 18 months. In patients who underwent follow-up chest computed tomography 18 months after COVID-19, abnormal findings remained at 51.9%. Males (odds ratio [OR], 0.17; 95% confidence interval [CI], 0.05–0.53; P=0.004) and elderly (OR, 1.04; 95% CI, 1.00–1.09; P=0.04) showed a significant association with long COVID after 12–18 months in a multivariable logistic regression analysis. Conclusion Many patients still showed long COVID after 18 months post SARS-CoV-2 infection. When managing these patients, the assessment of multiple aspects is necessary.

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 World Health Organization has declared the end of the coronavirus disease 2019 (COVID-19) public health emergency. However, this did not indicate the end of COVID-19. Several months after the infection, numerous patients complain of respiratory or nonspecific symptoms; this condition is called long COVID. Even patients with mild COVID-19 can experience long COVID, thus the burden of long COVID remains considerable. Therefore, we conducted this study to comprehensively analyze the effects of long COVID using multi-faceted assessments. Materials and Methods: We conducted a prospective cohort study involving patients diagnosed with COVID-19 between February 2020 and September 2021 in six tertiary hospitals in Korea. Patients were followed up at 1, 3, 6, 12, 18, and 24 months after discharge. Long COVID was defined as the persistence of three or more COVID-19-related symptoms. The primary outcome of this study was the prevalence of long COVID after the period of COVID-19. Results: During the study period, 290 patients were enrolled. Among them, 54.5 and 34.6% experienced long COVID within 6 months and after more than 18 months, respectively. Several patients showed abnormal results when tested for post-traumatic stress disorder (17.4%) and anxiety (31.9%) after 18 months. In patients who underwent follow-up chest computed tomography 18 months after COVID-19, abnormal findings remained at 51.9%. Males (odds ratio [OR], 0.17; 95% confidence interval [CI], 0.05–0.53; P=0.004) and elderly (OR, 1.04; 95% CI, 1.00–1.09; P=0.04) showed a significant association with long COVID after 12–18 months in a multivariable logistic regression analysis. Conclusion Many patients still showed long COVID after 18 months post SARS-CoV-2 infection. When managing these patients, the assessment of multiple aspects is necessary.

Objective: Effective triage of febrile patients in the emergency department is crucial during times of overcrowding to prioritize care and allocate resources, especially during pandemics. However, available triage tools often require laboratory data and lack accuracy. We aimed to develop a simple and accurate triage tool for febrile patients by modifying the quick Sequential Organ Failure Assessment (qSOFA) score. Methods: We retrospectively analyzed data from 7,303 febrile patients and created modified versions of qSOFA using factors identified through multivariable analysis. The performance of these modified qSOFAs in predicting in-hospital mortality and intensive care unit (ICU) admission was compared using the area under the receiver operating characteristic curve (AUROC). Results: Through multivariable analysis, the identified factors were age (“A” factor), male sex (“M” factor), oxygen saturation measured by pulse oximetry (SpO2; “S” factor), and lactate level (“L” factor). The AUROCs of ASqSOFA (in-hospital mortality: 0.812 [95% confidence interval, 0.789–0.835]; ICU admission: 0.794 [95% confidence interval, 0.771–0.817]) were simple and not inferior to those of other more complex models (e.g., ASMqSOFA, ASLqSOFA, and ASMLqSOFA). ASqSOFA also displayed significantly higher AUROC than other triage scales, such as the Modified Early Warning Score and Korean Triage and Acuity Scale. The optimal cutoff score of ASqSOFA for the outcome was 2, and the score for redistribution to a lower level emergency department was 0. Conclusion We demonstrated that ASqSOFA can be employed as a simple and efficient triage tool for emergency febrile patients to aid in resource distribution during overcrowding. It also may be applicable in prehospital settings for febrile patient triage.

Rare endocrine diseases are complex conditions that require lifelong specialized care due to their chronic nature and associated long-term complications. In Korea, a lack of nationwide data on clinical practice and outcomes has limited progress in patient care. Therefore, the Multicenter Networks for Ideal Outcomes of Pediatric Rare Endocrine and Metabolic Disease (OUTSPREAD) study was initiated. This study involves 30 centers across Korea. The study aims to improve the long-term prognosis of Korean patients with rare endocrine diseases by collecting comprehensive clinical data, biospecimens, and patient-reported outcomes to identify complications and unmet needs in patient care. Patients with childhood-onset pituitary, adrenal, or gonadal disorders, such as craniopharyngioma, congenital adrenal hyperplasia (CAH), and Turner syndrome were prioritized. The planned enrollment is 1,300 patients during the first study phase (2022–2024). Clinical, biochemical, and imaging data from diagnosis, treatment, and follow-up during 1980–2023 were retrospectively reviewed. For patients who agreed to participate in the prospective cohort, clinical data and biospecimens will be prospectively collected to discover ideal biomarkers that predict the effectiveness of disease control measures and prognosis. Patient-reported outcomes, including quality of life and depression scales, will be evaluated to assess psychosocial outcomes. Additionally, a substudy on CAH patients will develop a steroid hormone profiling method using liquid chromatography-tandem mass spectrometry to improve diagnosis and monitoring of treatment outcomes. This study will address unmet clinical needs by discovering ideal biomarkers, introducing evidence-based treatment guidelines, and ultimately improving long-term outcomes in the areas of rare endocrine and metabolic diseases.