Endoscopic spine surgery (ESS) is growing in popularity worldwide. An expanding body of literature demonstrates rapid functional recovery with reduced morbidity compared to open techniques. Both full endoscopic spine surgery, or uniportal endoscopy, and unilateral biportal endoscopy (UBE) can be employed in conjunction with various navigation and enabling technologies for assistance with localization of anatomic orientation and assessment of the intraoperative target spinal pathology. This review article describes various navigation technologies in ESS, including 2-dimensional (2D) fluoroscopic imaging, 2D fluoroscopic navigation, 3-dimensional C-arm navigation, augmented reality, and spinal robotics. Employment of enabling navigation and emerging technology with the registration of patient-specific anatomy enables clear delineation of anatomic landmarks and facilitation of a successful procedure. Additionally, avoidance of common pitfalls during use of navigation systems in ESS is discussed in this review.

Endoscopic spine surgery (ESS) is growing in popularity worldwide. An expanding body of literature demonstrates rapid functional recovery with reduced morbidity compared to open techniques. Both full endoscopic spine surgery, or uniportal endoscopy, and unilateral biportal endoscopy (UBE) can be employed in conjunction with various navigation and enabling technologies for assistance with localization of anatomic orientation and assessment of the intraoperative target spinal pathology. This review article describes various navigation technologies in ESS, including 2-dimensional (2D) fluoroscopic imaging, 2D fluoroscopic navigation, 3-dimensional C-arm navigation, augmented reality, and spinal robotics. Employment of enabling navigation and emerging technology with the registration of patient-specific anatomy enables clear delineation of anatomic landmarks and facilitation of a successful procedure. Additionally, avoidance of common pitfalls during use of navigation systems in ESS is discussed in this review.

Endoscopic spine surgery (ESS) is growing in popularity worldwide. An expanding body of literature demonstrates rapid functional recovery with reduced morbidity compared to open techniques. Both full endoscopic spine surgery, or uniportal endoscopy, and unilateral biportal endoscopy (UBE) can be employed in conjunction with various navigation and enabling technologies for assistance with localization of anatomic orientation and assessment of the intraoperative target spinal pathology. This review article describes various navigation technologies in ESS, including 2-dimensional (2D) fluoroscopic imaging, 2D fluoroscopic navigation, 3-dimensional C-arm navigation, augmented reality, and spinal robotics. Employment of enabling navigation and emerging technology with the registration of patient-specific anatomy enables clear delineation of anatomic landmarks and facilitation of a successful procedure. Additionally, avoidance of common pitfalls during use of navigation systems in ESS is discussed in this review.

Endoscopic spine surgery (ESS) is growing in popularity worldwide. An expanding body of literature demonstrates rapid functional recovery with reduced morbidity compared to open techniques. Both full endoscopic spine surgery, or uniportal endoscopy, and unilateral biportal endoscopy (UBE) can be employed in conjunction with various navigation and enabling technologies for assistance with localization of anatomic orientation and assessment of the intraoperative target spinal pathology. This review article describes various navigation technologies in ESS, including 2-dimensional (2D) fluoroscopic imaging, 2D fluoroscopic navigation, 3-dimensional C-arm navigation, augmented reality, and spinal robotics. Employment of enabling navigation and emerging technology with the registration of patient-specific anatomy enables clear delineation of anatomic landmarks and facilitation of a successful procedure. Additionally, avoidance of common pitfalls during use of navigation systems in ESS is discussed in this review.

Endoscopic spine surgery (ESS) is growing in popularity worldwide. An expanding body of literature demonstrates rapid functional recovery with reduced morbidity compared to open techniques. Both full endoscopic spine surgery, or uniportal endoscopy, and unilateral biportal endoscopy (UBE) can be employed in conjunction with various navigation and enabling technologies for assistance with localization of anatomic orientation and assessment of the intraoperative target spinal pathology. This review article describes various navigation technologies in ESS, including 2-dimensional (2D) fluoroscopic imaging, 2D fluoroscopic navigation, 3-dimensional C-arm navigation, augmented reality, and spinal robotics. Employment of enabling navigation and emerging technology with the registration of patient-specific anatomy enables clear delineation of anatomic landmarks and facilitation of a successful procedure. Additionally, avoidance of common pitfalls during use of navigation systems in ESS is discussed in this review.

The global burden of colorectal cancer (CRC) is expected to continuously increase. Through research performed in the past decades, the effects of various environmental factors on CRC development have been well identified. Diet, the gut microbiota and their metabolites are key environmental factors that profoundly affect CRC development. Major microbial metabolites with a relevance for CRC prevention and pathogenesis include dietary fiber-derived short-chain fatty acids, bile acid derivatives, indole metabolites, polyamines, trimethylamine-N-oxide, formate, and hydrogen sulfide. These metabolites regulate various cell types in the intestine, leading to an altered intestinal barrier, immunity, chronic inflammation, and tumorigenesis. The physical, chemical, and metabolic properties of these metabolites along with their distinct functions to trigger host receptors appear to largely determine their effects in regulating CRC development. In this review, we will discuss the current advances in our understanding of the major CRC-regulating microbial metabolites, focusing on their production and interactive effects on immune responses and tumorigenesis in the colon.

Objective: The International Cooperative Ataxia Rating Scale (ICARS) is a semiquantitative clinical scale for ataxia that is widely used in numerous countries. The purpose of this study was to investigate the validity and reliability of the Korean-translated version of the ICARS. Methods: Eighty-eight patients who presented with cerebellar ataxia were enrolled. We investigated the construct validity using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). We also investigated the internal consistency using Cronbach’s α and intrarater and interrater reliability using intraclass correlation coefficients. Results: The Korean-translated ICARS showed satisfactory construct validity using EFA and CFA. It also revealed good interrater and intrarater reliability and showed acceptable internal consistency. However, subscale 4 for assessing oculomotor disorder showed moderate internal consistency. Conclusion This is the first report to investigate the validity and reliability of the Korean-translated ICARS. Our results showed excellent construct and convergent validity. The reliability is also acceptable.

Nigrospora (Xylariales, Apiosporaceae) consists of species of terrestrial plant endophytes and pathogens. Nigrospora has also been reported in marine environments such as mangroves, sea fans, and macroalgae. However, limited research has been conducted on Nigrospora associated with macroalgae. Here, we isolated Nigrospora species from three types of algae (brown, green, and red algae) from Korean islands (Chuja, Jeju, and Ulleung) based on phylogenetic analyses of multigenetic markers: the internal transcribed spacers (ITS), betatubulin (BenA), and translation elongation factor 1 (TEF1-α). A total of 17 Nigrospora strains were isolated from macroalgae and identified as nine distinct species. The majority of Nigrospora species (seven) were found on brown algae, followed by red algae (three), and then green algae (two). To our understanding, this study represents the first account of N.cooperae, N. covidalis, N. guilinensis, N. lacticolonia, N. osmanthi, N. pyriformis, and N. rubi occurring in marine environments. Additionally, this study provides the first report of the occurrence of N. cooperae, N. covidalis, N. guilinensis, N. lacticolonia, and N. osmanthi in South Korea. This study will provide valuable insights for future research exploring the func tions of fungi in macroalgal communities.

BACKGROUND AND OBJECTIVES: We aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD), compared to Cox hazard regression. METHODS: Two CVD prediction models were developed from National Health Insurance Service-Health Screening Cohort (NHIS-HEALS): a Cox regression model and a DL model. Performance of each model was assessed in the internal and 2 external validation cohorts in Koreans (National Health Insurance Service-National Sample Cohort; NHIS-NSC) and in Europeans (Rotterdam Study). A total of 412,030 adults in the NHIS-HEALS; 178,875 adults in the NHIS-NSC; and the 4,296 adults in Rotterdam Study were included. RESULTS: Mean ages was 52 years (46% women) and there were 25,777 events (6.3%) in NHIS-HEALS during the follow-up. In internal validation, the DL approach demonstrated a C-statistic of 0.896 (95% confidence interval, 0.886–0.907) in men and 0.921 (0.908–0.934) in women and improved reclassification compared with Cox regression (net reclassification index [NRI], 24.8% in men, 29.0% in women). In external validation with NHIS-NSC, DL demonstrated a C-statistic of 0.868 (0.860–0.876) in men and 0.889 (0.876–0.898) in women, and improved reclassification compared with Cox regression (NRI, 24.9% in men, 26.2% in women). In external validation applied to the Rotterdam Study, DL demonstrated a C-statistic of 0.860 (0.824–0.897) in men and 0.867 (0.830–0.903) in women, and improved reclassification compared with Cox regression (NRI, 36.9% in men, 31.8% in women). CONCLUSIONS A DL algorithm exhibited greater discriminative accuracy than Cox model approaches.TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02931500

BACKGROUND AND OBJECTIVES: We aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD), compared to Cox hazard regression.METHODS: Two CVD prediction models were developed from National Health Insurance Service-Health Screening Cohort (NHIS-HEALS): a Cox regression model and a DL model. Performance of each model was assessed in the internal and 2 external validation cohorts in Koreans (National Health Insurance Service-National Sample Cohort; NHIS-NSC) and in Europeans (Rotterdam Study). A total of 412,030 adults in the NHIS-HEALS; 178,875 adults in the NHIS-NSC; and the 4,296 adults in Rotterdam Study were included.RESULTS: Mean ages was 52 years (46% women) and there were 25,777 events (6.3%) in NHIS-HEALS during the follow-up. In internal validation, the DL approach demonstrated a C-statistic of 0.896 (95% confidence interval, 0.886–0.907) in men and 0.921 (0.908–0.934) in women and improved reclassification compared with Cox regression (net reclassification index [NRI], 24.8% in men, 29.0% in women). In external validation with NHIS-NSC, DL demonstrated a C-statistic of 0.868 (0.860–0.876) in men and 0.889 (0.876–0.898) in women, and improved reclassification compared with Cox regression (NRI, 24.9% in men, 26.2% in women). In external validation applied to the Rotterdam Study, DL demonstrated a C-statistic of 0.860 (0.824–0.897) in men and 0.867 (0.830–0.903) in women, and improved reclassification compared with Cox regression (NRI, 36.9% in men, 31.8% in women).CONCLUSIONS: A DL algorithm exhibited greater discriminative accuracy than Cox model approaches.TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02931500
Adult ; Artificial Intelligence ; Cardiovascular Diseases ; Cohort Studies ; Female ; Follow-Up Studies ; Humans ; Insurance, Health ; Learning ; Male ; Mass Screening ; National Health Programs