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.

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.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent cause of chronic liver disease worldwide, and its intricate pathogenesis presents challenges in the development of new drugs. As a common way of post-translational modification, acetylation regulates protein stability, enzyme activity, and subcellular localization, occurring extensively in MASLD-associated processes such as lipid metabolism, inflammatory response, and oxidative stress. In this paper, we comprehensively review the mechanism of acetylation in MASLD, analyze the expression levels of acetylases in liver tissues of MASLD patients from the gene expression omnibus (GEO), discuss the changes in relevant enzyme expression and mechanisms in animal models, and further explore the feasibility of targeting acetylation for MASLD treatment, in the hope of offering a new perspective for advancing drug discovery in the field of MASLD.

Background Anxiety and depression are common perinatal mental health issues that often occur together and can have serious negative effects on both maternal and infant health. Objective To examine the relationships between lifestyle factors and comorbid anxiety and depression (CAD) among pregnant women in Shanghai. Methods The study estimated the prevalence of CAD during the first, second, and third trimesters of pregnancy using the Self-rating Anxiety Scale (SAS) and Center for Epidemiological Studies-Depression (CES-D) based on data from the China National Birth Cohort (CNBC) embryonic-derived diseases with assisted reproductive technology (ART) sub-cohort. Information on demographics, sleep status, nutritional intake, and exercise during each trimester was collected through self-made questionnaires, the Pittsburgh Sleep Quality Index (PSQI), and the Food Frequency Questionnaire (FFQ). Lifestyle factors (such as sleep status, nutritional intake, and exercise during each trimester) were analyzed using logistic regression and generalized linear mixed models (GLMM) to determine their impacts on the prevalence of CAD (yes or no) among pregnant women. Results A total of 2876 pregnant women were included in this study. The prevalence of CAD was 10.6% (305), 3.6% (103), and 5.5% (159) in the first, second, and third trimesters of pregnancy, respectively. The logistic regression analysis revealed that poor sleep quality throughout the entire pregnancy were statistically associated with an increased prevalence of CAD, and the odds ratios (OR) with 95% confidence intervals (CI) were 2.817 (1.845, 4.301), 2.840 (1.855, 4.347), and 9.316 (5.835, 14.876) for the first, second, and third trimesters, respectively, when compared to good sleep quality. Additionally, compared to an intake frequency of 7 times per week, the frequency of egg intake ≤3 times per week in the first trimester (OR=2.025, 95%CI: 1.197, 3.425) and the frequency of egg intake of 4–6 times per week (OR=1.896, 95%CI: 1.117, 3.216) or ≤3 times per week (OR=1.906, 95%CI: 1.082, 3.357) in the third trimester were associated with an increased risk of CAD (P<0.05). Moreover, when compared to a frequency of exercise >3 times per week, never or almost never exercising in the second trimester (OR=2.218, 95%CI: 1.220, 4.035) was associated with an increased risk of CAD (P<0.05). The GLMM analysis also demonstrated a significant association between poor sleep quality, lower exercise frequency, or lower intake frequency of vegetables, eggs, or milk and an increased risk of CAD (P<0.05). Conclusion The prevalence of CAD among pregnant women in Shanghai follows a U-shaped distribution, with the highest rate occurring in early pregnancy and the lowest rate in mid-pregnancy. Factors such as poor sleep quality, inadequate intake of vegetables, eggs, or milk, and lack of exercise during pregnancy may increase the risk of CAD. Implementing lifestyle interventions during pregnancy could potentially reduce the risk of mental health problems and improve the overall health of both mothers and babies.

Objective: To investigate the prevalence and associated factors of depressive symptoms among middle and high school students in Zhejiang Province, so as to provide scientific basis for the implementation of depressive intervention. Methods: Based on the health status and associated factors of middle and high school students in the project "Monitoring of Common Diseases and Health Influencing Factors of Students" during 2018 to 2021, a total of 73 309 students including middle school, ordinary high school and vocational high school surveyed in 11 cities of Zhejiang Province were selected by multi stage stratified cluster random sampling method. From 2018 to 2021, there were 6 008, 21 917, 23 712 and 21 672 students, respectively. The Chi square test and Logistic regression model were used to analyze the influencing factors of depressive symptoms in middle and high school students. Results: From 2018 to 2021, depressive symptoms detection rate of middle school students was 14.8%, with higher rate in girls (17.1%) than in boys (12.7%), higher rate in high school (17.1% in ordinary high school, 17.6% in vocational high school) than middle school (12.5%)( χ 2=278.77, 327.22, P <0.05). Univariate analysis showed that there were statistically significant differences in depressive symptoms detection rate among middle school students with different years (2018: 16.7%，2019: 17.9% , 2020: 13.1%, 2021: 13.0%), residence (yes: 16.3%, no:13.5%), body mass index classification (not overweight or obesity: 14.8%, overweight: 14.2%, Obesity: 15.7%), weekly exercise days (0-2 d: 17.1%, 3-5 d: 12.5%, 6-7 d: 13.1%) and bullying (yes: 35.5%, no: 10.7%) ( χ 2=293.40, 118.35, 7.83, 287.24, 4 978.84, P <0.05). Multivariate Logistic regression analysis showed that female students, ordinary high schools, vocational high schools, obesity, school bullying were positively correlated with depression ( OR =1.65, 1.70, 1.60, 1.12, 5.21), exercise 3 to 5 days per week, exercise 6 to 7 days per week were negatively correlated with depression ( OR=0.77, 0.81, P <0.01). Conclusions Depressive symptoms among middle and high school students in Zhejiang Province are prominent. Strengthening mental health education for students and providing attention and support from families, schools, and society are essential steps to reduce the occurrence of depressive symptoms among these students.

Objective To establish a population pharmacokinetic(PPK)model of duloxetine in Chinese subjects.Methods Based on the data of intensive sampling of duloxetine hydrochloride enteric coated tablets in 36 healthy subjects after single/multiple administrations,a PPK model of duloxetine was established using NONMEM software.The effects of gender,age,body weight,albumin,serum creatinine,glutamic pyruvic transaminase and dose on pharmacokinetic parameters were investigated by stepwise forward and backward methods.Model validation includes goodness of fit,visual prediction check and bootstrap.Results The PPK model of duloxetine was a one compartment model with first-order elimination and the absorption characteristics were described by the transit model,and the dose was a covariate of clearance.The inter-individual variability of clearance,volume of distribution,mean transit time and number of transit compartments were 54.71％,56.86％,27.30％and 87.71％,respectively.Conclusion The transit model more reasonably describes the absorption characteristics of duloxetine in Chinese subjects.