Purpose: Neuroendocrine carcinomas (NECs) of the stomach are extremely rare, but fatal. However, our understanding of the genetic alterations in gastric NECs is limited. We aimed to evaluate genomic and clinicopathological characteristics of gastric NECs and mixed adenoneuroendocrine carcinomas (MANECs). Materials and Methods: Fourteen gastric NECs, three gastric MANECs, and 1,381 gastric adenocarcinomas were retrieved from the departmental next-generation sequencing database between 2017 and 2022. Clinicopathological parameters and next-generation sequencing test results were retrospectively collected and reviewed. Results: Gastric NECs and MANECs frequently harbored alterations of TP53, RB1, SMARCA4, RICTOR, APC, TOP1, SLX4, EGFR, BRCA2, and TERT. In contrast, gastric adenocarcinomas exhibited alterations of TP53, CDH1, LRP1B, ARID1A, ERBB2, GNAS, CCNE1, NOTCH, and MYC. Mutations of AKT3, RB1, and SLX4; amplification of BRCA2 and RICTOR; and deletion of ADAMTS18, DDX11, KLRC3, KRAS, MAX, NFKBIA, NUDT7, and RB1 were significantly more frequent in gastric NECs and MANECs than in gastric adenocarcinomas. The presence of LRP1B mutation was significantly associated with longer overall survival (OS), whereas RB1 mutation and advanced TNM stage were associated with shorter OS. Conclusion We identified frequently mutated genes and potential predictors of survival in patients with gastric NECs and MANECs.

Purpose: Neuroendocrine carcinomas (NECs) of the stomach are extremely rare, but fatal. However, our understanding of the genetic alterations in gastric NECs is limited. We aimed to evaluate genomic and clinicopathological characteristics of gastric NECs and mixed adenoneuroendocrine carcinomas (MANECs). Materials and Methods: Fourteen gastric NECs, three gastric MANECs, and 1,381 gastric adenocarcinomas were retrieved from the departmental next-generation sequencing database between 2017 and 2022. Clinicopathological parameters and next-generation sequencing test results were retrospectively collected and reviewed. Results: Gastric NECs and MANECs frequently harbored alterations of TP53, RB1, SMARCA4, RICTOR, APC, TOP1, SLX4, EGFR, BRCA2, and TERT. In contrast, gastric adenocarcinomas exhibited alterations of TP53, CDH1, LRP1B, ARID1A, ERBB2, GNAS, CCNE1, NOTCH, and MYC. Mutations of AKT3, RB1, and SLX4; amplification of BRCA2 and RICTOR; and deletion of ADAMTS18, DDX11, KLRC3, KRAS, MAX, NFKBIA, NUDT7, and RB1 were significantly more frequent in gastric NECs and MANECs than in gastric adenocarcinomas. The presence of LRP1B mutation was significantly associated with longer overall survival (OS), whereas RB1 mutation and advanced TNM stage were associated with shorter OS. Conclusion We identified frequently mutated genes and potential predictors of survival in patients with gastric NECs and MANECs.

Purpose: Neuroendocrine carcinomas (NECs) of the stomach are extremely rare, but fatal. However, our understanding of the genetic alterations in gastric NECs is limited. We aimed to evaluate genomic and clinicopathological characteristics of gastric NECs and mixed adenoneuroendocrine carcinomas (MANECs). Materials and Methods: Fourteen gastric NECs, three gastric MANECs, and 1,381 gastric adenocarcinomas were retrieved from the departmental next-generation sequencing database between 2017 and 2022. Clinicopathological parameters and next-generation sequencing test results were retrospectively collected and reviewed. Results: Gastric NECs and MANECs frequently harbored alterations of TP53, RB1, SMARCA4, RICTOR, APC, TOP1, SLX4, EGFR, BRCA2, and TERT. In contrast, gastric adenocarcinomas exhibited alterations of TP53, CDH1, LRP1B, ARID1A, ERBB2, GNAS, CCNE1, NOTCH, and MYC. Mutations of AKT3, RB1, and SLX4; amplification of BRCA2 and RICTOR; and deletion of ADAMTS18, DDX11, KLRC3, KRAS, MAX, NFKBIA, NUDT7, and RB1 were significantly more frequent in gastric NECs and MANECs than in gastric adenocarcinomas. The presence of LRP1B mutation was significantly associated with longer overall survival (OS), whereas RB1 mutation and advanced TNM stage were associated with shorter OS. Conclusion We identified frequently mutated genes and potential predictors of survival in patients with gastric NECs and MANECs.

Infiltration and activation of peripheral immune cells are critical in the progression of multiple sclerosis and its experimental animal model, experimental autoimmune encephalomyelitis (EAE). This study investigates the role of high mobility group box 1 (HMGB1) in oligodendrocyte precursor cells (OPCs) in modulating pathogenic T cells infiltrating the central nervous system through the blood-brain barrier (BBB) by using OPC-specific HMGB1 knockout (KO) mice. We found that HMGB1 released from OPCs promotes BBB disruption, subsequently allowing increased immune cell infiltration. The migration of CD4+ T cells isolated from EAE-induced mice was enhanced when co-cultured with OPCs compared to oligodendrocytes (OLs). OPC-specific HMGB1 KO mice exhibited lower BBB permeability and reduced immune cell infiltration into the CNS, leading to less damage to the myelin sheath and mitigated EAE progression. CD4+ T cell migration was also reduced when co-cultured with HMGB1 knock-out OPCs. Our findings reveal that HMGB1 secretion from OPCs is crucial for regulating immune cell infiltration and provides insights into the immunomodulatory function of OPCs in autoimmune diseases.
Animals ; Encephalomyelitis, Autoimmune, Experimental/metabolism* ; HMGB1 Protein/deficiency* ; Mice, Knockout ; Oligodendrocyte Precursor Cells/immunology* ; Mice, Inbred C57BL ; CD4-Positive T-Lymphocytes/immunology* ; Cell Movement ; Blood-Brain Barrier/immunology* ; Mice ; Myelin Sheath/pathology* ; Disease Models, Animal ; Coculture Techniques ; Oligodendroglia/metabolism* ; Female ; Cells, Cultured

Background/Aims: The proposed eosinophilic esophagitis (EoE) endoscopic reference score serves to diagnose and evaluate treatment responses in EoE.Nevertheless, the validated reference score thresholds for diagnosis and treatment response in Asian patients are yet to be established.This study aims to establish these thresholds for the first time among Asian patients with EoE. Methods: Patients presenting with ≥ 15 eosinophils/high power field and esophageal dysfunction symptoms between August 2007 andNovember 2021 were included. Age- and sex-matched non-EoE controls were also enrolled. Baseline characteristics, endoscopic reference score features, and scores were compared between patients and controls. Among patients, endoscopic reference score features and scores, along with peak eosinophil counts, were evaluated both before and after treatment. The optimal threshold was determined based on sensitivity, specificity, and the Youden index. Results: Overall, 102 patients were enrolled (74.5% men; mean age, 46.9 years). The mean endoscopic reference score was 2.65 and 0.52 for patients and controls, respectively (P < 0.001). An endoscopic reference score ≥ 2 was identified as the optimal diagnostic threshold for EoE (sensitivity, 0.79; specificity, 0.86; Youden index, 0.66). Post-treatment data regarding endoscopic findings and histology wereavailable for 30 patients. Regarding histologic response, an endoscopic reference score of ≤ 3 demonstrated the optimal threshold(sensitivity, 0.95; specificity, 0.88; Youden index, 0.83). Conclusions The optimal diagnostic and treatment response thresholds were determined to be endoscopic reference scores of ≥ 2 and ≤ 3,respectively. Further studies involving a larger patient cohort are necessary to validate these findings.

Background/Aims: The proposed eosinophilic esophagitis (EoE) endoscopic reference score serves to diagnose and evaluate treatment responses in EoE.Nevertheless, the validated reference score thresholds for diagnosis and treatment response in Asian patients are yet to be established.This study aims to establish these thresholds for the first time among Asian patients with EoE. Methods: Patients presenting with ≥ 15 eosinophils/high power field and esophageal dysfunction symptoms between August 2007 andNovember 2021 were included. Age- and sex-matched non-EoE controls were also enrolled. Baseline characteristics, endoscopic reference score features, and scores were compared between patients and controls. Among patients, endoscopic reference score features and scores, along with peak eosinophil counts, were evaluated both before and after treatment. The optimal threshold was determined based on sensitivity, specificity, and the Youden index. Results: Overall, 102 patients were enrolled (74.5% men; mean age, 46.9 years). The mean endoscopic reference score was 2.65 and 0.52 for patients and controls, respectively (P < 0.001). An endoscopic reference score ≥ 2 was identified as the optimal diagnostic threshold for EoE (sensitivity, 0.79; specificity, 0.86; Youden index, 0.66). Post-treatment data regarding endoscopic findings and histology wereavailable for 30 patients. Regarding histologic response, an endoscopic reference score of ≤ 3 demonstrated the optimal threshold(sensitivity, 0.95; specificity, 0.88; Youden index, 0.83). Conclusions The optimal diagnostic and treatment response thresholds were determined to be endoscopic reference scores of ≥ 2 and ≤ 3,respectively. Further studies involving a larger patient cohort are necessary to validate these findings.

Background/Aims: The proposed eosinophilic esophagitis (EoE) endoscopic reference score serves to diagnose and evaluate treatment responses in EoE.Nevertheless, the validated reference score thresholds for diagnosis and treatment response in Asian patients are yet to be established.This study aims to establish these thresholds for the first time among Asian patients with EoE. Methods: Patients presenting with ≥ 15 eosinophils/high power field and esophageal dysfunction symptoms between August 2007 andNovember 2021 were included. Age- and sex-matched non-EoE controls were also enrolled. Baseline characteristics, endoscopic reference score features, and scores were compared between patients and controls. Among patients, endoscopic reference score features and scores, along with peak eosinophil counts, were evaluated both before and after treatment. The optimal threshold was determined based on sensitivity, specificity, and the Youden index. Results: Overall, 102 patients were enrolled (74.5% men; mean age, 46.9 years). The mean endoscopic reference score was 2.65 and 0.52 for patients and controls, respectively (P < 0.001). An endoscopic reference score ≥ 2 was identified as the optimal diagnostic threshold for EoE (sensitivity, 0.79; specificity, 0.86; Youden index, 0.66). Post-treatment data regarding endoscopic findings and histology wereavailable for 30 patients. Regarding histologic response, an endoscopic reference score of ≤ 3 demonstrated the optimal threshold(sensitivity, 0.95; specificity, 0.88; Youden index, 0.83). Conclusions The optimal diagnostic and treatment response thresholds were determined to be endoscopic reference scores of ≥ 2 and ≤ 3,respectively. Further studies involving a larger patient cohort are necessary to validate these findings.

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.

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.

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.