Objective:To investigate the impact of para-tumoral micro-metastasis(PTMM) and other clinicopathological characteristics on the prognosis of patients with intrahepatic cholangiocarcinoma (ICC).Methods:This is a retrospective cohort study. Clinical data from 137 ICC patients who underwent radical resection at the Hepatobiliary Surgery Center, Department of General Surgery, Huashan Hospital, Fudan University, between January 2017 and December 2022 were analyzed retrospectively. The cohort included 91 males and 46 females, with age ( M(IQR)) of 63 (13) years (range: 32 to 82 years). Kaplan-Meier curves were used to estimate median survival times, while Log-rank tests assessed differences in overall survival (OS) and recurrence-free survival (RFS). Univariate and multivariate Cox regression models were employed to identify factors associated with OS and RFS. Results:The median OS for all 137 ICC patients was 34 months, with 1-, 2-, and 3-year OS rates of 90.7%, 69.4%, and 39.5%, respectively. The results of univariate and multivariate Cox analysis showed that Child-Pugh grade, CA19-9, carcinoembryonic antigen, and PTMM were independent prognostic factors for OS in ICC patients after radical resection (all P<0.05), Child-Pugh grade, maximum tumor diameter, whether lymph node metastasis, and PTMM were independent prognostic factors for RFS in radical resection in ICC patients (all P<0.05). PTMM-positive patients had a median OS of 21 months and median RFS of 12 months, whereas PTMM-negative patients exhibited a median OS exceeding 60 months and median RFS of 36 months. Log-rank tests demonstrated statistically significant differences in OS and RFS between PTMM-positive and PTMM-negative patients ( P<0.01 and P=0.001, respectively). Conclusion:Preliminary findings suggest that PTMM holds significant prognostic value in evaluating outcomes for ICC patients undergoing curative resection.

Artificial intelligence (AI) technology is revolutionizing the precision diagnosis and treatment system in digestive system malignancies. In the preoperative stage, deep learning-driven multimodal data can significantly enhance the accuracy of early lesion detection and organ function assessment, while optimizing the prediction of the efficacy neoadjuvant therapy response with radiomics features. During surgery, the automatic surgical phase alorithm and intelligent pathology testing can effectively coordinate the surgical process; the combination of AI with augmented reality navigation system (AR) and mixed reality technology (MR) can improve the accuracy of intraoperative navigation; enhances intraoperative navigation accuracy; and the autonomous surgical robots can improve their ability to perform key tasks during surgery using sophisticated motion control algorithms. In postoperative management, federated learning promotes secure cross-institutional data sharing and alleviates the problem of data silos; and the development of interpretable models can also provide clearer decision-making basis for complication prediction and prognosis management. However, the current clinical translation process still faces three core challenges: (1) Quality control problems: the dual constraints of insufficient model generalization ability and lack of unified evaluation standards; (2) Methodological challenges: the logical opacity of decision-making and the cognitive barriers of the algorithm's causal reasoning process still affect its credibility; (3) Ethical and legal vacuum: the attribution of medical responsibility is unclear, and the legal system has not yet fully adapted to the development of AI applications. Based on this, the future should focus on: (1) Developing transparent AI systems to achieve a paradigm shift from "black box prediction" to "white box deductions"; (2) Establishing systematic AI evaluation standards and ethical and legal regulatory frameworks; (3) Promoting the construction of specialized biorepositories database and causal graph library for digestive oncology. The aim is to help AI upgrade from an auxiliary tool to an intelligent decision-making partner.

Artificial intelligence (AI) technology is revolutionizing the precision diagnosis and treatment system in digestive system malignancies. In the preoperative stage, deep learning-driven multimodal data can significantly enhance the accuracy of early lesion detection and organ function assessment, while optimizing the prediction of the efficacy neoadjuvant therapy response with radiomics features. During surgery, the automatic surgical phase alorithm and intelligent pathology testing can effectively coordinate the surgical process; the combination of AI with augmented reality navigation system (AR) and mixed reality technology (MR) can improve the accuracy of intraoperative navigation; enhances intraoperative navigation accuracy; and the autonomous surgical robots can improve their ability to perform key tasks during surgery using sophisticated motion control algorithms. In postoperative management, federated learning promotes secure cross-institutional data sharing and alleviates the problem of data silos; and the development of interpretable models can also provide clearer decision-making basis for complication prediction and prognosis management. However, the current clinical translation process still faces three core challenges: (1) Quality control problems: the dual constraints of insufficient model generalization ability and lack of unified evaluation standards; (2) Methodological challenges: the logical opacity of decision-making and the cognitive barriers of the algorithm's causal reasoning process still affect its credibility; (3) Ethical and legal vacuum: the attribution of medical responsibility is unclear, and the legal system has not yet fully adapted to the development of AI applications. Based on this, the future should focus on: (1) Developing transparent AI systems to achieve a paradigm shift from "black box prediction" to "white box deductions"; (2) Establishing systematic AI evaluation standards and ethical and legal regulatory frameworks; (3) Promoting the construction of specialized biorepositories database and causal graph library for digestive oncology. The aim is to help AI upgrade from an auxiliary tool to an intelligent decision-making partner.

Objective:To investigate the impact of para-tumoral micro-metastasis(PTMM) and other clinicopathological characteristics on the prognosis of patients with intrahepatic cholangiocarcinoma (ICC).Methods:This is a retrospective cohort study. Clinical data from 137 ICC patients who underwent radical resection at the Hepatobiliary Surgery Center, Department of General Surgery, Huashan Hospital, Fudan University, between January 2017 and December 2022 were analyzed retrospectively. The cohort included 91 males and 46 females, with age ( M(IQR)) of 63 (13) years (range: 32 to 82 years). Kaplan-Meier curves were used to estimate median survival times, while Log-rank tests assessed differences in overall survival (OS) and recurrence-free survival (RFS). Univariate and multivariate Cox regression models were employed to identify factors associated with OS and RFS. Results:The median OS for all 137 ICC patients was 34 months, with 1-, 2-, and 3-year OS rates of 90.7%, 69.4%, and 39.5%, respectively. The results of univariate and multivariate Cox analysis showed that Child-Pugh grade, CA19-9, carcinoembryonic antigen, and PTMM were independent prognostic factors for OS in ICC patients after radical resection (all P<0.05), Child-Pugh grade, maximum tumor diameter, whether lymph node metastasis, and PTMM were independent prognostic factors for RFS in radical resection in ICC patients (all P<0.05). PTMM-positive patients had a median OS of 21 months and median RFS of 12 months, whereas PTMM-negative patients exhibited a median OS exceeding 60 months and median RFS of 36 months. Log-rank tests demonstrated statistically significant differences in OS and RFS between PTMM-positive and PTMM-negative patients ( P<0.01 and P=0.001, respectively). Conclusion:Preliminary findings suggest that PTMM holds significant prognostic value in evaluating outcomes for ICC patients undergoing curative resection.

Objective To study the effects of intramedullary pressure on the fluid flow behavior in bones.Methods Multi-scale models of macro bone tissue and macro-meso osteon groups were established using the COMSOL Multiphysics software.Considering the interrelationship of different pore scales,such as the bone marrow cavity,Haversia canal,and bone lacunar-canaliculus,the pore pressure and flow rate of hollow bone tissues and bone tissues with intramedullary pressure were compared,and the effects of the amplitude and frequency of intramedullary pressure on the pressure and flow velocity of the liquid in the bone were analyzed.Results When intramedullary pressure was considered,the pore pressure in bone tissues with intramedullary pressure was 6.4 kPa higher than that in hollow bone tissues.The flow pressure increased significantly with an increase in the intramedullary pressure amplitude,but the flow velocity remained unchanged.The frequency of intramedullary pressure had little effect on pore pressure and flow velocity.Conclusions The multi-scale pore model established in this study can accurately analyze bone fluid flow behavior.These results are of great significance for an in-depth understanding of force conduction in the bone.

The concept of precision medicine and precision oncology has been widely accepted.However,the application of this concept still faces many challenges.Exosome investigation is a hot spot in the field of liquid biopsy.Exosomes are extracellular vesicles composed of a lipid bilayer and contain proteins and nucleic acids which regulate cell-cell communication.Exosomes can be isolated and enriched in various body fluids.It has the advantages of micro-invasive,stable and biological active,and it fits well with the practice of precision medicine.In this review,authors discussed potential application and challenge of exosomes in early diagnosis and metastasis monitoring of gastric cancer.