This study investigated the molecular mechanism of phenotypic switching of vascular smooth muscle cells (VSMCs), which play a crucial role in vascular remodeling using 9H-Carbazol-3-yl 4-aminobenzoate (CAB). CAB significantly attenuated platelet-derived growth factor (PDGF)-induced VSMC proliferation and migration. CAB suppressed PDGF-induced STAT3 activation by directly binding to the SH2 domain of STAT3. Downregulation of STAT3 phosphorylation by CAB attenuated CIAPIN1/JAK2/STAT3 axis through a decrease in CIAPIN1 transcription. Furthermore, abrogated CIAPIN1 decreased KLF4-mediated VSMC dedifferentiation and increased CDKN1B-induced cell cycle arrest and MMP9 suppression. CAB inhibited intimal hyperplasia in injury-induced neointima animal models by inhibition of the CIAPIN1/JAK2/STAT3 axis. However, CIAPIN1 overexpression attenuated CAB-mediated suppression of VSMC proliferation, migration, phenotypic switching, and intimal hyperplasia. Our study clarified the molecular mechanism underlying STAT3 inhibition of VSMC phenotypic switching and vascular remodeling and identified novel active CAB. These findings demonstrated that STAT3 can be a major regulator to control CIAPIN1/JAK2/STAT3 axis that may be a therapeutic target for treating vascular proliferative diseases.

Background: s/Aims: While large-for-size syndrome is uncommon in liver transplantation (LT), it can result in fatal outcome. To prevent such fatality, we manufactured 3D-printed intra-abdominal cavity replicas to provide intuitive understanding of the sizes of the graft and the patient’s abdomen in patients with small body size between July 2020 and February 2022. Methods: Clinical outcomes were compared between patients using our 3D model during LT, and patients who underwent LT without 3D model by using 1 : 5 ratio propensity score-matched analysis. Results: After matching, a total of 20 patients using 3D-printed abdominal cavity model and 100 patients of the control group were included in this study. There were no significant differences in 30-day postoperative complication (50.0% vs. 64.0%, p = 0.356) and the incidence of large-for-size syndrome (0% vs. 7%, p = 0.599). Overall survival of the 3D-printed group was similar to that of the control group (p = 0.665), but graft survival was significantly superior in the 3D-printed group, compared to the control group (p = 0.034). Conclusions Since it showed better graft survival, as well as low cost and short production time, our 3D-printing protocol can be a feasible option for patients with small abdominal cavity to prevent large-for-size syndrome after LT.

Background: s/Aims: While large-for-size syndrome is uncommon in liver transplantation (LT), it can result in fatal outcome. To prevent such fatality, we manufactured 3D-printed intra-abdominal cavity replicas to provide intuitive understanding of the sizes of the graft and the patient’s abdomen in patients with small body size between July 2020 and February 2022. Methods: Clinical outcomes were compared between patients using our 3D model during LT, and patients who underwent LT without 3D model by using 1 : 5 ratio propensity score-matched analysis. Results: After matching, a total of 20 patients using 3D-printed abdominal cavity model and 100 patients of the control group were included in this study. There were no significant differences in 30-day postoperative complication (50.0% vs. 64.0%, p = 0.356) and the incidence of large-for-size syndrome (0% vs. 7%, p = 0.599). Overall survival of the 3D-printed group was similar to that of the control group (p = 0.665), but graft survival was significantly superior in the 3D-printed group, compared to the control group (p = 0.034). Conclusions Since it showed better graft survival, as well as low cost and short production time, our 3D-printing protocol can be a feasible option for patients with small abdominal cavity to prevent large-for-size syndrome after LT.

Background: s/Aims: While large-for-size syndrome is uncommon in liver transplantation (LT), it can result in fatal outcome. To prevent such fatality, we manufactured 3D-printed intra-abdominal cavity replicas to provide intuitive understanding of the sizes of the graft and the patient’s abdomen in patients with small body size between July 2020 and February 2022. Methods: Clinical outcomes were compared between patients using our 3D model during LT, and patients who underwent LT without 3D model by using 1 : 5 ratio propensity score-matched analysis. Results: After matching, a total of 20 patients using 3D-printed abdominal cavity model and 100 patients of the control group were included in this study. There were no significant differences in 30-day postoperative complication (50.0% vs. 64.0%, p = 0.356) and the incidence of large-for-size syndrome (0% vs. 7%, p = 0.599). Overall survival of the 3D-printed group was similar to that of the control group (p = 0.665), but graft survival was significantly superior in the 3D-printed group, compared to the control group (p = 0.034). Conclusions Since it showed better graft survival, as well as low cost and short production time, our 3D-printing protocol can be a feasible option for patients with small abdominal cavity to prevent large-for-size syndrome after LT.

Purpose: This study aimed to determine whether micro-flow imaging (MFI) offers diagnostic performance comparable to that of contrast-enhanced ultrasonography (CEUS) in detecting segmental congestion among patients undergoing living donor liver transplantation (LDLT). Methods: Data from 63 patients who underwent LDLT between May and December 2022 were retrospectively analyzed. MFI and CEUS data collected on the first postoperative day were quantified. Segmental congestion was assessed based on imaging findings and laboratory data, including liver enzymes and total bilirubin levels. The reference standard was a postoperative contrast-enhanced computed tomography scan performed within 2 weeks of surgery. Additionally, a subgroup analysis examined patients who underwent reconstruction of the middle hepatic vein territory. Results: The sensitivity and specificity of MFI were 73.9% and 67.5%, respectively. In comparison, CEUS demonstrated a sensitivity of 78.3% and a specificity of 75.0%. These findings suggest comparable diagnostic performance, with no significant differences in sensitivity (P=0.655) or specificity (P=0.257) between the two modalities. Additionally, early postoperative laboratory values did not show significant differences between patients with and without congestion. The subgroup analysis also indicated similar diagnostic performance between MFI and CEUS. Conclusion MFI without contrast enhancement yielded results comparable to those of CEUS in detecting segmental congestion after LDLT. Therefore, MFI may be considered a viable alternative to CEUS.

Purpose: This study aimed to determine whether micro-flow imaging (MFI) offers diagnostic performance comparable to that of contrast-enhanced ultrasonography (CEUS) in detecting segmental congestion among patients undergoing living donor liver transplantation (LDLT). Methods: Data from 63 patients who underwent LDLT between May and December 2022 were retrospectively analyzed. MFI and CEUS data collected on the first postoperative day were quantified. Segmental congestion was assessed based on imaging findings and laboratory data, including liver enzymes and total bilirubin levels. The reference standard was a postoperative contrast-enhanced computed tomography scan performed within 2 weeks of surgery. Additionally, a subgroup analysis examined patients who underwent reconstruction of the middle hepatic vein territory. Results: The sensitivity and specificity of MFI were 73.9% and 67.5%, respectively. In comparison, CEUS demonstrated a sensitivity of 78.3% and a specificity of 75.0%. These findings suggest comparable diagnostic performance, with no significant differences in sensitivity (P=0.655) or specificity (P=0.257) between the two modalities. Additionally, early postoperative laboratory values did not show significant differences between patients with and without congestion. The subgroup analysis also indicated similar diagnostic performance between MFI and CEUS. Conclusion MFI without contrast enhancement yielded results comparable to those of CEUS in detecting segmental congestion after LDLT. Therefore, MFI may be considered a viable alternative to CEUS.

Purpose: The purpose of this study is to build a prediction model for estimating graft weight about different graft volumetry methods combined with other variables. Methods: Donors who underwent living-donor right hepatectomy from March 2021 to March 2023 were included. Estimated graft volume measured by conventional method and 3-dimensional (3D) software were collected as well as the actual graft weight. Linear regression was used to build a prediction model. Donor groups were divided according to the 3D volumetry of <700 cm3 , 700–899 cm3 , and ≥900 cm3 to compare the performance of different models. Results: A total of 119 donors were included. Conventional volumetry showed R2 of 0.656 (P < 0.001) while 3D software showed R2 of 0.776 (P < 0.001). The R2 of the multivariable model was 0.842 (P < 0.001) including for 3D volume (β = 0.623, P < 0.001), body mass index (β = 7.648, P < 0.001), and amount of weight loss (β = –7.252, P < 0.001). The median errors between different models and actual graft weight did not differ in donor groups (<700 and 700–899 cm3 ), while the median error of univariable linear model using 3D software (122.5; interquartile range [IQR], 61.5–179.8) was significantly higher than multivariable-adjusted linear model (41.5; IQR, 24.8–69.8; P = 0.003) in donors with estimated graft weight ≥900 cm3 . Conclusion The univariable 3D volumetry model showed an acceptable outcome for donors with an estimated graft volume <900 cm3 . For donors with an estimated graft volume ≥900 cm3 , the multivariable-adjusted linear model showed higher accuracy.

Systemic inflammatory response (SIR) is a crucial determinant of disease progression and survival in patients with colorectal cancer. This study investigated the prognostic relevance of changes in the platelet count on survival and the predictive value of changes in the platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) on the pathological tumor response to preoperative chemoradiotherapy (CRT) in patients with microsatellite instability-high (MSI-H) rectal cancer. From 2011 to 2022, data of 46 consecutive patients with MSI-H rectal cancer who were treated with preoperative CRT followed by curative surgery at Kyungpook National University Chilgok Hospital (Daegu, South Korea) were retrospectively analyzed. A 235 cut-off value was used to define whether PLR was high or low. Any change in the PLR or NLR was calculated on the basis of subtracting the pre-CRT PLR or NLR from the post-CRT values. Both pre-CRT and post-CRT values of the NLR and PLR were not significantly associated with clinical outcomes. Simple logistic regression analysis showed that a change in the PLR following CRT was not significantly associated with survival outcomes; however, patients who maintained a high change in the PLR following CRT showed significantly better pathologic T-stage. No statistically significant association was noted between changes in the platelet count and clinical outcomes of patients. The results suggested that changes in the PLR following CRT are associated with pathologic T-stage of the group. However, the SIR markers showed no prognostic values on the survival outcomes of the patients with MSI-H/mismatch repair-deficient (dMMR) locally advanced rectal cancer (LARC).

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70–0.83 for GPT-4, 0.51–0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51–0.59 for GPT-4, 0.47–0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. Conclusions GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.

Background: Brugada syndrome (BrS) and arrhythmogenic cardiomyopathy (ACM) are inherited cardiac diseases that may predispose to ventricular arrhythmia. Although overlapping features between BrS and ACM have been demonstrated previously, it remains to be determined whether genetic testing for ACM-related genes is needed in BrS probands.MethodBased on a single-center, retrospective registry of BrS, we aimed to verify genetic profiles of BrS using a next-generation sequencing panel, and further analyzed genetic testing of ACM-related variants in Brugada phenotypes. Results: Among a total of 119 Brugada phenotypes, 114 patients (95.8%) were male and the mean age of onset was 43.6 years. Genetic variants were identified in 25 of the 42 patients who underwent genetic testing. Fifteen patients had BrS-related genotypes, including SCN5A in 6 patients, and non-SCN5A genes in 9 patients (SCN10A, HCN4, SCN3B, and KCNE3). Nineteen patients underwent additional genetic testing with cardiomyopathy panel, which revealed ACM-related genotypes (2 PKP2, 1 DSG2, 1 TMEM43, 1 JUP, and 1 DSP) present in 6 patients (31.5%). None of the patients had structural or electrocardiographic features that fulfilled the diagnostic criteria for ACM. Conclusions In clinical setting, ACM-related genes were identified in a significant proportion of Brugada phenotypes, supporting the argument that genetic testing of ACM overlapping is needed. Follow-up imaging studies should be considered to monitor if the disease progresses to ACM.