Pancreatic cancer has emerged as one of the most challenging malignancies worldwide,with its high resistance to chemotherapy being the primary cause of treatment failure.Therefore,enhancing the chemosensitivity of pancreatic cancer has become a major focus of current research.In this study,we in-vestigated how Bufotaline,a bufadienolide extracted from the traditional Chinese medicine toad venom,exhibits its antitumor activity.Specifically,we explored the potential of Bufotaline to enhance the chemo-sensitivity of pancreatic cancer cells to Adriamycin and elucidated its underlying molecular mechanisms.Using CCK-8 and colony formation assays,we demonstrated that Bufotaline enhances the inhibitory effect of Adriamycin on the survival of pancreatic cancer cell lines Patu-8988T,Aspc-1,and Patu-8988S.No-tably,Bufotaline treatment reduced the IC50 of Adriamycin in drug-resistant pancreatic cancer cells to lev-els comparable to those in non-resistant cells.Results from Western blot,immunofluorescence,comet as-say,and TUNEL assays revealed that Bufotaline promotes Adriamycin-induced DNA damage in pancreatic cancer cells.RNA-seq analysis of Patu-8988T cells treated with Adriamycin alone or in combination with Bufotaline showed significant changes in gene expression,and qRT-PCR analysis further confirmed that Bu-fotaline downregulates the expression of DNA damage repair proteins NBS1 and RAD50.Moreover,Western blot analysis revealed that Bufotaline reduces the levels of DNA damage response repair proteins,and Im-munofluorescence experiments indicated that Bufotaline inhibits the activation of the ATM/CHK2 signaling pathway.Finally,in a subcutaneous xenograft mouse model,the combination of Adriamycin and Bufotaline treatment significantly suppressed pancreatic cancer cell growth.In conclusion,Bufotaline enhances Adria-mycin-induced chemosensitivity in pancreatic cancer cells;the combination of Adriamycin and Bufotaline downregulates the expression of DNA damage response repair proteins NBS1 and RAD50,and inhibits the ATM/CHK2-mediated DDR signaling pathway,thereby delaying DNA damage repair.

Diabetic encephalopathy(DE)is a complication of the central nervous system induced by diabetes,characterized by insidious onset and complex pathological mechanisms.Recent research has revealed that the pathological mechanisms of DE are closely associated with the imbalance of intercellular communica-tion across multiple organs.Exosomes,serving as vital media-tors of intercellular communication,are involved in the patholog-ical progression of DE and hold promise as diagnostic biomarkers and therapeutic targets for DE.Traditional Chinese medicine(TCM)can improve communication between brain cells and be-tween peripheral and brain tissues by regulating exosomes and their contents,thereby preventing and treating DE.Meanwhile,exosomes,as targeted delivery carriers of active molecules,can carry TCM monomers more easily across the blood-brain barrier to prevent and treat DE.In light of this,this review summarizes the regulatory role of exosomes in the pathological progression of DE and explores the enormous potential of TCM in the preven-tion and treatment of DE by regulating exosomes and using exo-somes as traditional Chinese medicine carriers,aiming to pro-vide new evidence for the prevention and treatment of DE with TCM.

Pancreatic cancer has emerged as one of the most challenging malignancies worldwide,with its high resistance to chemotherapy being the primary cause of treatment failure.Therefore,enhancing the chemosensitivity of pancreatic cancer has become a major focus of current research.In this study,we in-vestigated how Bufotaline,a bufadienolide extracted from the traditional Chinese medicine toad venom,exhibits its antitumor activity.Specifically,we explored the potential of Bufotaline to enhance the chemo-sensitivity of pancreatic cancer cells to Adriamycin and elucidated its underlying molecular mechanisms.Using CCK-8 and colony formation assays,we demonstrated that Bufotaline enhances the inhibitory effect of Adriamycin on the survival of pancreatic cancer cell lines Patu-8988T,Aspc-1,and Patu-8988S.No-tably,Bufotaline treatment reduced the IC50 of Adriamycin in drug-resistant pancreatic cancer cells to lev-els comparable to those in non-resistant cells.Results from Western blot,immunofluorescence,comet as-say,and TUNEL assays revealed that Bufotaline promotes Adriamycin-induced DNA damage in pancreatic cancer cells.RNA-seq analysis of Patu-8988T cells treated with Adriamycin alone or in combination with Bufotaline showed significant changes in gene expression,and qRT-PCR analysis further confirmed that Bu-fotaline downregulates the expression of DNA damage repair proteins NBS1 and RAD50.Moreover,Western blot analysis revealed that Bufotaline reduces the levels of DNA damage response repair proteins,and Im-munofluorescence experiments indicated that Bufotaline inhibits the activation of the ATM/CHK2 signaling pathway.Finally,in a subcutaneous xenograft mouse model,the combination of Adriamycin and Bufotaline treatment significantly suppressed pancreatic cancer cell growth.In conclusion,Bufotaline enhances Adria-mycin-induced chemosensitivity in pancreatic cancer cells;the combination of Adriamycin and Bufotaline downregulates the expression of DNA damage response repair proteins NBS1 and RAD50,and inhibits the ATM/CHK2-mediated DDR signaling pathway,thereby delaying DNA damage repair.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.