Objective To explore the effect of different needle track management on Yttrium-90 microsphere selective internal radiation therapy (⁹⁰Y-SIRT) on liver cancer after liver biopsy. Methods A retrospective analysis was conducted on the clinical data of 21 patients with liver cancer who underwent Technetium-99m-macroaggregated albumin (^99mTc-MAA) evaluation and ⁹⁰Y-SIRT after liver biopsy from June 2023 to December 2024. The methods of needle track management, hepatic arteriovenous shunting, and lung shunt fraction (LSF) were recorded. The occurrence of hepatic arteriovenous fistula (HAVF), as well as the corresponding countermeasures were analyzed. Results Among the 21 liver cancer patients, 7 cases (medical glue group) underwent embolization of the needle tract with medical glue (N-butyl 2-cyanoacrylate [NBCA]) immediately after biopsy, and no significant HAVF was observed during the ^99mTc-MAA tests; 14 cases (non-medical glue group) were treated with delayed needle extraction or gelatin sponge particle blocking after biopsy, among which 7 cases developed significant HAVF, and the fistulas were treated with NBCA. The LSF of the medical glue group was significantly lower than that of the non-medical glue group ([7.06±2.33] % vs [12.43±7.73] %, P＝0.037). All 21 patients successfully completed ⁹⁰Y-SIRT. Conclusions Liver biopsy may affect ⁹⁰Y-SIRT by causing iatrogenic HAVF. Immediate NBCA-embolization of the needle tract after biopsy or timely NBCA-embolization of fistulas during ^99mTc-MAA tests may reduce the impact.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Intrahepatic cholangiocarcinoma is a malignant tumor with an extremely poor prognosis， and its pathogenesis is complex and remains unclear. In recent years， more and more studies have focused on the role of bile-gut axis in the development and progression of intrahepatic cholangiocarcinoma. Bile-gut axis refers to the complex interaction between bile and gut microbiota， including bile salt metabolism， dynamic changes of microbiota， inflammatory response， and immune system regulation. This article elaborates on the potential mechanisms of bile-gut axis in intrahepatic cholangiocarcinoma， especially gut microbiota dysbiosis， abnormal bile salt metabolism， chronic inflammatory response， and immune system interaction， this article aims to provide new perspectives and possible therapeutic targets for future research and promote the early diagnosis and effective treatment of intrahepatic cholangiocarcinoma.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

ObjectiveTo explore the application value of probe-based confocal laser endomicroscopy (pCLE) in rapidly detecting and evaluating the morphological characteristics of digestive tract tissues in mice. MethodsTwelve male SPF Kunming mice aged 6 weeks were randomly divided into two groups. Six mice were subjected to gastric gavage with 52% Red Star Erguotou to establish the model, and six were given saline by gastric gavage as a control. After 28 days of modeling, 3 mice were randomly selected from each group. After deep anesthesia induced by inhalation of 3% isoflurane, the mice were sacrificed by cervical dislocation. The stomach, duodenum, jejunum, and rectum tissues were excised and immersed in 1% fluorescein sodium solution for staining. The microstructure of the mucosal surface of each tissue was observed using pCLE. The remaining mice in the model group and the control group were deeply anesthetized by inhaling 3% isoflurane, then cardiac perfusion was performed successively with saline and 4% paraformaldehyde. The stomach, duodenum, jejunum, and rectum tissues were excised for dehydration, section and hematoxylin-eosin (HE) staining, and the morphological changes of the tissues were observed under a microscope. ResultsUnder pCLE imaging, fluorescence staining on the surface of the gastrointestinal mucosa was uniform in the control group; the morphology of gastric pits, intestinal villi, and intestinal crypts was intact, arranged compactly, and had distinct boundaries. In the model group, the gastrointestinal mucosa exhibited mucosal swelling and deformation, with uneven fluorescence staining and fluorescein leakage. Furthermore, some tissues showed defects or cell shedding, and the boundaries between adjacent characteristic structures (e.g., gastric pits, intestinal crypts) were blurred. HE staining showed that the gastrointestinal tissue structure of the control group mice was normal and well-organized, with no structural defects. Moreover, submucosal glands were uniform in size, with no hyperplasia observed, and no obvious inflammatory cell infiltration. In the model group, some gastrointestinal mucosal structures were defective and sparsely arranged; submucosal glands showed atrophy, accompanied by obvious inflammatory cell infiltration. The histological characteristics detected by pCLE were consistent with those of HE staining. ConclusionpCLE can be used to obtain rapid, real-time, large-scale, and high-resolution microscopic imaging of the gastrointestinal mucosa, realistically and comprehensively displaying its physiological and microstructural characteristics. It shows promising prospects and practical utility in the histological evaluation of digestive system injuries in small animals.

Purpose/Significance To summarize the application progress of artificial intelligence (AI) in the field of digestive endoscopy, to analyze the key challenges and look forward to future trends, so as to provide guidance for clinical practice and research. Method/Process Domestic and international literature databases such as PubMed, Web of Science, and CNKI are retrieved. The application research of AI in the auxiliary diagnosis, process optimization and treatment precision of digestive endoscopy is sorted out. Result/Conclusion Although AI has significant clinical value in the field of digestive endoscopy, its application still faces major challenges, and its prospects and limitations need to be further discussed.

Aim To investigate the dynamic changes of neuronal cells at different time points following acute cerebral ischemia-reperfusion injury by establishing a model of brain ischemia-reperfusion injury.Methods Thirty male Sprague-Dawley(SD)rats were ran-domly divided into six groups:sham group and cere-bral ischemia-reperfusion injury(IR)groups at differ-ent time points.Focal cerebral ischemia-reperfusion injury model was established using the middle cerebral artery occlusion(MCAO)technique.The Longa sco-ring method was used to assess neurobehavioral scores in rats.After successful model preparation,routine paraffin sections were made,and TUNEL staining and immunohistochemistry staining with NeuN antibody were performed to observe cell apoptosis and neuronal cell survival,respectively.Immunohistochemistry stai-ning was also performed to investigate the changes in glial fibrillary acidic protein(GFAP)as a marker for astrocytes,ionized calcium-binding adapter molecule 1(IBA-1)as a marker for microglia,and CD31 as a marker for endothelial cells at different time points.Results No significant changes were observed in neu-ronal cells of the sham group at different time points.In the cerebral ischemia-reperfusion injury groups,cell apoptosis was activated at IR3h and increased in quan-tity with morphological damage as time progressed.Ne-uN+neurons showed signs of ischemic injury after IR3h,with abnormal cell morphology.From 12 h,Ne-uN+neurons decreased in a time-dependent manner and reached their peak severity at 24 h.GFAP+astro-cytes decreased significantly after IR3h,while poorly labeled GFAP+astrocytes increased at IR 6 h and al-most disappeared in the infarcted area at 24 h and 48 h.The number of IBA-1+microglia-positive cells de-creased at IR3h,and their volume increased at IR6h.Microglial cell death was observed in the infarcted area at IR12h.CD31+endothelial cells around the infarc-ted cortex and striatum increased significantly after IR3h and persisted until 48 h.Conclusions After cerebral ischemia-reperfusion injury,the number of ap-optotic cells increases with the prolongation of time,and NeuN+neurons exhibit the most severe damage at 24 h.GFAP+astrocytes and microglial cells gradually die over time.The number of CD31+endothelial cells increases significantly around the infarcted cortex and striatum after 3 h of reperfusion and persists until 48 h.