Aging is a risk factor for development of chronic kidney disease and diabetes mellitus with commonly shared features of chronic inflammation and increased oxidative stress. Here, we investigated the effect of pan-Nox-inhibitor, APX-115, on renal function in aging diabetic mice. Methods: Diabetes was induced by intraperitoneal injection of streptozotocin at 50 mg/kg/day for 5 days in 52-week-old C57BL/6J mice. APX-115 was administered by oral gavage at a dose of 60 mg/kg/day for 12 weeks in nondiabetic and diabetic aging mice. Results: APX-115 significantly improved insulin resistance in diabetic aging mice. Urinary level of 8-isoprostane was significantly increased in diabetic aging mice than nondiabetic aging mice, and APX-115 treatment reduced 8-isoprostane level. Urinary albumin and nephrin excretion were significantly higher in diabetic aging mice than nondiabetic aging mice. Although APX-115 did not significantly decrease albuminuria, APX-115 markedly improved mesangial expansion, macrophage infiltration, and expression of fibrosis molecules such as transforming growth factor beta 1 and plasminogen activator inhibitor 1. Interestingly, the expression of all Nox isoforms including Nox1, Nox2, and Nox4 was significantly increased in diabetic aging kidneys, and APX-115 treatment decreased Nox1, Nox2, and Nox4 protein expression in the kidney. Furthermore, Klotho expression was significantly decreased in diabetic aging kidneys, and APX-115 restored Klotho level. Conclusion: Our results provide evidence that pan-Nox inhibition may improve systemic insulin resistance and decrease oxidative stress, inflammation, and fibrosis in aging diabetic status and may have potential protective effects on aging diabetic kidney.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Aging is a risk factor for development of chronic kidney disease and diabetes mellitus with commonly shared features of chronic inflammation and increased oxidative stress. Here, we investigated the effect of pan-Nox-inhibitor, APX-115, on renal function in aging diabetic mice. Methods: Diabetes was induced by intraperitoneal injection of streptozotocin at 50 mg/kg/day for 5 days in 52-week-old C57BL/6J mice. APX-115 was administered by oral gavage at a dose of 60 mg/kg/day for 12 weeks in nondiabetic and diabetic aging mice. Results: APX-115 significantly improved insulin resistance in diabetic aging mice. Urinary level of 8-isoprostane was significantly increased in diabetic aging mice than nondiabetic aging mice, and APX-115 treatment reduced 8-isoprostane level. Urinary albumin and nephrin excretion were significantly higher in diabetic aging mice than nondiabetic aging mice. Although APX-115 did not significantly decrease albuminuria, APX-115 markedly improved mesangial expansion, macrophage infiltration, and expression of fibrosis molecules such as transforming growth factor beta 1 and plasminogen activator inhibitor 1. Interestingly, the expression of all Nox isoforms including Nox1, Nox2, and Nox4 was significantly increased in diabetic aging kidneys, and APX-115 treatment decreased Nox1, Nox2, and Nox4 protein expression in the kidney. Furthermore, Klotho expression was significantly decreased in diabetic aging kidneys, and APX-115 restored Klotho level. Conclusion: Our results provide evidence that pan-Nox inhibition may improve systemic insulin resistance and decrease oxidative stress, inflammation, and fibrosis in aging diabetic status and may have potential protective effects on aging diabetic kidney.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Aging is a risk factor for development of chronic kidney disease and diabetes mellitus with commonly shared features of chronic inflammation and increased oxidative stress. Here, we investigated the effect of pan-Nox-inhibitor, APX-115, on renal function in aging diabetic mice. Methods: Diabetes was induced by intraperitoneal injection of streptozotocin at 50 mg/kg/day for 5 days in 52-week-old C57BL/6J mice. APX-115 was administered by oral gavage at a dose of 60 mg/kg/day for 12 weeks in nondiabetic and diabetic aging mice. Results: APX-115 significantly improved insulin resistance in diabetic aging mice. Urinary level of 8-isoprostane was significantly increased in diabetic aging mice than nondiabetic aging mice, and APX-115 treatment reduced 8-isoprostane level. Urinary albumin and nephrin excretion were significantly higher in diabetic aging mice than nondiabetic aging mice. Although APX-115 did not significantly decrease albuminuria, APX-115 markedly improved mesangial expansion, macrophage infiltration, and expression of fibrosis molecules such as transforming growth factor beta 1 and plasminogen activator inhibitor 1. Interestingly, the expression of all Nox isoforms including Nox1, Nox2, and Nox4 was significantly increased in diabetic aging kidneys, and APX-115 treatment decreased Nox1, Nox2, and Nox4 protein expression in the kidney. Furthermore, Klotho expression was significantly decreased in diabetic aging kidneys, and APX-115 restored Klotho level. Conclusion: Our results provide evidence that pan-Nox inhibition may improve systemic insulin resistance and decrease oxidative stress, inflammation, and fibrosis in aging diabetic status and may have potential protective effects on aging diabetic kidney.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Aging is a risk factor for development of chronic kidney disease and diabetes mellitus with commonly shared features of chronic inflammation and increased oxidative stress. Here, we investigated the effect of pan-Nox-inhibitor, APX-115, on renal function in aging diabetic mice. Methods: Diabetes was induced by intraperitoneal injection of streptozotocin at 50 mg/kg/day for 5 days in 52-week-old C57BL/6J mice. APX-115 was administered by oral gavage at a dose of 60 mg/kg/day for 12 weeks in nondiabetic and diabetic aging mice. Results: APX-115 significantly improved insulin resistance in diabetic aging mice. Urinary level of 8-isoprostane was significantly increased in diabetic aging mice than nondiabetic aging mice, and APX-115 treatment reduced 8-isoprostane level. Urinary albumin and nephrin excretion were significantly higher in diabetic aging mice than nondiabetic aging mice. Although APX-115 did not significantly decrease albuminuria, APX-115 markedly improved mesangial expansion, macrophage infiltration, and expression of fibrosis molecules such as transforming growth factor beta 1 and plasminogen activator inhibitor 1. Interestingly, the expression of all Nox isoforms including Nox1, Nox2, and Nox4 was significantly increased in diabetic aging kidneys, and APX-115 treatment decreased Nox1, Nox2, and Nox4 protein expression in the kidney. Furthermore, Klotho expression was significantly decreased in diabetic aging kidneys, and APX-115 restored Klotho level. Conclusion: Our results provide evidence that pan-Nox inhibition may improve systemic insulin resistance and decrease oxidative stress, inflammation, and fibrosis in aging diabetic status and may have potential protective effects on aging diabetic kidney.

Colorectal cancer is the third most common cancer in Korea and the third leading cause of death from cancer. Treatment outcomes for colon cancer are steadily improving due to national health screening programs with advances in diagnostic methods, surgical techniques, and therapeutic agents.. The Korea Colon Cancer Multidisciplinary (KCCM) Committee intends to provide professionals who treat colon cancer with the most up-to-date, evidence-based practice guidelines to improve outcomes and help them make decisions that reflect their patients’ values and preferences. These guidelines have been established by consensus reached by the KCCM Guideline Committee based on a systematic literature review and evidence synthesis and by considering the national health insurance system in real clinical practice settings. Each recommendation is presented with a recommendation strength and level of evidence based on the consensus of the committee.

Background/Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression. Methods: Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD. Results: After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort. Conclusions We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.

Background: This study aimed to generate a Z score calculation model for coronary artery diameter of normal children and adolescents to be adopted as the standard calculation method with consensus in clinical practice. Methods: This study was a retrospective, multicenter study that collected data from multiple institutions across South Korea. Data were analyzed to determine the model that best fit the relationship between the diameter of coronary arteries and independent demographic parameters. Linear, power, logarithmic, exponential, and square root polynomial models were tested for best fit. Results: Data of 2,030 subjects were collected from 16 institutions. Separate calculation models for each sex were developed because the impact of demographic variables on the diameter of coronary arteries differs according to sex. The final model was the polynomial formula with an exponential relationship between the diameter of coronary arteries and body surface area using the DuBois formula. Conclusion A new coronary artery diameter Z score model was developed and is anticipated to be applicable in clinical practice. The new model will help establish a consensus-based Z score model.