1.Clinical study of three-dimensional CT reconstruction technology guiding preserving the left colonic artery during laparoscopic radical resection of rectal cancer
Jun-feng OUYANG ; Yan FU ; Wen-qiang SHEN ; Yu-fei HU
Journal of Regional Anatomy and Operative Surgery 2025;34(1):58-61
Objective To investigate the significance and clinical value of preoperative three-dimensional CT reconstruction of the whole abdomen guiding preserving the left colonic artery during laparoscopic radical resection of rectal cancer. Methods Patients with rectal cancer who underwent laparoscopic radical resection of rectal cancer in our hospital from January 2021 to January 2022 were selected,then 87 patients with left colonic artery were screened out through three-dimensional CT reconstruction of the whole abdomen before surgery,who were randomly divided into the observation group (received intraoperative low ligation of the inferior mesenteric artery,with preservation of the left colonic artery) with 40 cases and the control group (received intraoperative high ligation of the inferior mesenteric artery without preservation of the left colonic artery) with 47 cases. The perioperative indexes and postoperative pathological results of the two groups were compared. Results The intraoperative blood loss in the observation group was higher than that in the control group,and the operation time was longer than that in the control group,with statistically significant differences (P<0.05). The incidences of postoperative intestinal obstruction and anastomotic leakage in the observation group were lower than those in the control group,and the first postoperative discharge time was earlier than that in the control group,with statistically significant differences (P<0.05). There was no significant difference in the degree of tumor differentiation,tumor size,adenocarcinoma classification,number of lymph nodes dissected at the root of the inferior mesenteric artery,or the stage of intestinal wall invasion depth of tumor between the two groups (P>0.05). Conclusion The application of the three-dimensional CT reconstruction of the whole abdomen before surgery can understand the branch classification of the inferior mesenteric artery in advance,determine the location of the tumor and its adjacent relationship with the surrounding tissues,and determine the resection extent of intestinal segment. Intraoperative preservation of the left colonic artery can reduce the occurrence of postoperative complications in patients,which has positive clinical significance.
2.Discriminating Tumor Deposits From Metastatic Lymph Nodes in Rectal Cancer: A Pilot Study Utilizing Dynamic Contrast-Enhanced MRI
Xue-han WU ; Yu-tao QUE ; Xin-yue YANG ; Zi-qiang WEN ; Yu-ru MA ; Zhi-wen ZHANG ; Quan-meng LIU ; Wen-jie FAN ; Li DING ; Yue-jiao LANG ; Yun-zhu WU ; Jian-peng YUAN ; Shen-ping YU ; Yi-yan LIU ; Yan CHEN
Korean Journal of Radiology 2025;26(5):400-410
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.
3.Discriminating Tumor Deposits From Metastatic Lymph Nodes in Rectal Cancer: A Pilot Study Utilizing Dynamic Contrast-Enhanced MRI
Xue-han WU ; Yu-tao QUE ; Xin-yue YANG ; Zi-qiang WEN ; Yu-ru MA ; Zhi-wen ZHANG ; Quan-meng LIU ; Wen-jie FAN ; Li DING ; Yue-jiao LANG ; Yun-zhu WU ; Jian-peng YUAN ; Shen-ping YU ; Yi-yan LIU ; Yan CHEN
Korean Journal of Radiology 2025;26(5):400-410
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.
4.Discriminating Tumor Deposits From Metastatic Lymph Nodes in Rectal Cancer: A Pilot Study Utilizing Dynamic Contrast-Enhanced MRI
Xue-han WU ; Yu-tao QUE ; Xin-yue YANG ; Zi-qiang WEN ; Yu-ru MA ; Zhi-wen ZHANG ; Quan-meng LIU ; Wen-jie FAN ; Li DING ; Yue-jiao LANG ; Yun-zhu WU ; Jian-peng YUAN ; Shen-ping YU ; Yi-yan LIU ; Yan CHEN
Korean Journal of Radiology 2025;26(5):400-410
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.
5.Discriminating Tumor Deposits From Metastatic Lymph Nodes in Rectal Cancer: A Pilot Study Utilizing Dynamic Contrast-Enhanced MRI
Xue-han WU ; Yu-tao QUE ; Xin-yue YANG ; Zi-qiang WEN ; Yu-ru MA ; Zhi-wen ZHANG ; Quan-meng LIU ; Wen-jie FAN ; Li DING ; Yue-jiao LANG ; Yun-zhu WU ; Jian-peng YUAN ; Shen-ping YU ; Yi-yan LIU ; Yan CHEN
Korean Journal of Radiology 2025;26(5):400-410
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.
6.Discriminating Tumor Deposits From Metastatic Lymph Nodes in Rectal Cancer: A Pilot Study Utilizing Dynamic Contrast-Enhanced MRI
Xue-han WU ; Yu-tao QUE ; Xin-yue YANG ; Zi-qiang WEN ; Yu-ru MA ; Zhi-wen ZHANG ; Quan-meng LIU ; Wen-jie FAN ; Li DING ; Yue-jiao LANG ; Yun-zhu WU ; Jian-peng YUAN ; Shen-ping YU ; Yi-yan LIU ; Yan CHEN
Korean Journal of Radiology 2025;26(5):400-410
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.
7.Prevalence of frailty and importance of influencing factors in adults in Shaanxi Province
Zongkai LI ; Yan HUANG ; Ziping WANG ; Hui JING ; Yuxin TENG ; Yezhou LIU ; Yuan SHEN ; Qiang LI ; Baibing MI ; Jiaomei YANG ; Hong YAN ; Shaonong DANG
Chinese Journal of Epidemiology 2025;46(1):131-139
Objective:To understand the prevalence of frailty and the importance of its influencing factors in adult population in Shaanxi Province.Methods:The data were from Shaanxi baseline survey of natural population cohort study in northwest China during 2018-2019. The frailty index (FI) was constructed to evaluate the frailty status of the population, and XGboost model combined with Shapley method was used to analyze the importance of the sociodemographic and life behavior factors affecting the prevalence of frailty by gender and age.Results:A total of 25 079 subjects were included, in whom 964 (3.8%) had frailty, and there was no significant difference in the overall prevalence of frailty between women (3.9%) and men (3.8%) ( P=0.629), but there was a gender specific difference in the distribution of FI ( P<0.001), and the proportion of the pre-frailty in men was higher than that in women. The prevalence of frailty increased with age ( P<0.001), the prevalence of frailty were 1.3%, 2.5% and 7.8% in young, middle-aged and elderly women, respectively, and 1.9%, 2.7% and 5.5% in young, middle-aged and elderly men, respectively. Sociodemographic characteristics and lifestyle patterns were both influencing factors for the prevalence of frailty, but their importance varied with gender and age. The top five contributing factors were education level, staying up late, annual family income level, sedentary time and marital status in young women, and staying up late, smoking, annual family income level, sedentary time and drinking in young men. The top five contributing factors were education level, annual family income level, passive exposure to smoking, staying up late, and sedentary time in middle-aged women, and annual family income level, education level, sedentary time, staying up late and drinking in middle-aged men. The top five contributing factors were annual family income level, passive exposure to smoking, sedentary time, marital status, and smartphone use in elderly women, and education level, annual family income level, smoking, smartphone use and sedentary time in elderly men. Conclusions:There are gender specific differences in the distribution of FI in Shaanxi. The prevalence of frailty increased with age, but young and middle-aged people also have frailty risk. The prevalence of frailty in young men was mainly related to unhealthy life behaviors, such as staying up late, smoking, sedentary behavior and drinking, while the prevalence of frailty in middle-aged and elderly men and women were more affected by sociodemographic factors, such as education level, economic status and marital status.
8.Guideline for Adult Weight Management in China
Weiqing WANG ; Qin WAN ; Jianhua MA ; Guang WANG ; Yufan WANG ; Guixia WANG ; Yongquan SHI ; Tingjun YE ; Xiaoguang SHI ; Jian KUANG ; Bo FENG ; Xiuyan FENG ; Guang NING ; Yiming MU ; Hongyu KUANG ; Xiaoping XING ; Chunli PIAO ; Xingbo CHENG ; Zhifeng CHENG ; Yufang BI ; Yan BI ; Wenshan LYU ; Dalong ZHU ; Cuiyan ZHU ; Wei ZHU ; Fei HUA ; Fei XIANG ; Shuang YAN ; Zilin SUN ; Yadong SUN ; Liqin SUN ; Luying SUN ; Li YAN ; Yanbing LI ; Hong LI ; Shu LI ; Ling LI ; Yiming LI ; Chenzhong LI ; Hua YANG ; Jinkui YANG ; Ling YANG ; Ying YANG ; Tao YANG ; Xiao YANG ; Xinhua XIAO ; Dan WU ; Jinsong KUANG ; Lanjie HE ; Wei GU ; Jie SHEN ; Yongfeng SONG ; Qiao ZHANG ; Hong ZHANG ; Yuwei ZHANG ; Junqing ZHANG ; Xianfeng ZHANG ; Miao ZHANG ; Yifei ZHANG ; Yingli LU ; Hong CHEN ; Li CHEN ; Bing CHEN ; Shihong CHEN ; Guiyan CHEN ; Haibing CHEN ; Lei CHEN ; Yanyan CHEN ; Genben CHEN ; Yikun ZHOU ; Xianghai ZHOU ; Qiang ZHOU ; Jiaqiang ZHOU ; Hongting ZHENG ; Zhongyan SHAN ; Jiajun ZHAO ; Dong ZHAO ; Ji HU ; Jiang HU ; Xinguo HOU ; Bimin SHI ; Tianpei HONG ; Mingxia YUAN ; Weibo XIA ; Xuejiang GU ; Yong XU ; Shuguang PANG ; Tianshu GAO ; Zuhua GAO ; Xiaohui GUO ; Hongyi CAO ; Mingfeng CAO ; Xiaopei CAO ; Jing MA ; Bin LU ; Zhen LIANG ; Jun LIANG ; Min LONG ; Yongde PENG ; Jin LU ; Hongyun LU ; Yan LU ; Chunping ZENG ; Binhong WEN ; Xueyong LOU ; Qingbo GUAN ; Lin LIAO ; Xin LIAO ; Ping XIONG ; Yaoming XUE
Chinese Journal of Endocrinology and Metabolism 2025;41(11):891-907
Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.
9.Pseudolaric Acid B Alleviates Non-alcoholic Fatty Liver Disease by Targeting PPARα to Regulate Lipid Metabolism and Promote Mitochondrial Biogenesis.
Shu-Yan LIU ; Xiao-Wei ZHANG ; Gai GAO ; Chang-Xin LIU ; Hui CHEN ; Zhong-Xue FU ; Jiang-Yan XU ; Zhen-Zhen WANG ; Zhen-Qiang ZHANG ; Zhi-Shen XIE
Chinese journal of integrative medicine 2025;31(10):877-888
OBJECTIVE:
To investigate the therapeutic potential of pseudolaric acid B (PAB) on non-alcoholic fatty liver disease (NAFLD) and its underlying molecular mechanism in vitro and in vivo.
METHODS:
Eight-week-old male C57BL/6J mice (n=32) were fed either a normal chow diet (NCD) or a high-fat diet (HFD) for 8 weeks. The HFD mice were divided into 3 groups according to a simple random method, including HFD, PAB low-dose [10 mg/(kg·d), PAB-L], and PAB high-dose [20 mg/(kg·d), PAB-H] groups. After 8 weeks of treatment, glucose metabolism and insulin resistance were assessed by oral glucose tolerance test (OGTT) and insulin tolerance test (ITT). Biochemical assays were used to measure the serum and cellular levels of total cholesterol (TC), triglycerides (TG), aspartate aminotransferase (AST), alanine aminotransferase (ALT), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). White adipose tissue (WAT), brown adipose tissue (BAT) and liver tissue were subjected to hematoxylin and eosin (H&E) staining or Oil Red O staining to observe the alterations in adipose tissue and liver injury. PharmMapper and DisGeNet were used to predict the NAFLD-related PAB targets. Peroxisome proliferator-activated receptor alpha (PPARα) pathway involvement was suggested by Kyoto Encyclopedia of Genes and Genomes (KEGG) and search tool Retrieval of Interacting Genes (STRING) analyses. Luciferase reporter assay, cellular thermal shift assay (CETSA), and drug affinity responsive target stability assay (DARTS) were conducted to confirm direct binding of PAB with PPARα. Molecular dynamics simulations were applied to further validate target engagement. RT-qPCR and Western blot were performed to assess the downstream genes and proteins expression, and validated by PPARα inhibitor MK886.
RESULTS:
PAB significantly reduced serum TC, TG, LDL-C, AST, and ALT levels, and increased HDL-C level in HFD mice (P<0.01). Target prediction analysis indicated a significant correlation between PAB and PPARα pathway. PAB direct target binding with PPARα was confirmed through luciferase reporter assay, CETSA, and DARTS (P<0.05 or P<0.01). The target engagement between PAB and PPARα protein was further confirmed by molecular dynamics simulations and the top 3 amino acid residues, LEU321, MET355, and PHE273 showed the most significant changes in mutational energy. Subsequently, PAB upregulated the genes expressions involved in lipid metabolism and mitochondrial biogenesis downstream of PPARα (P<0.05 or P<0.01). Significantly, the PPARα inhibitor MK886 effectively reversed the lipid-lowering and PPARα activation properties of PAB (P<0.05 or P<0.01).
CONCLUSION
PAB mitigates lipid accumulation, ameliorates liver damage, and improves mitochondrial biogenesis by binding with PPARα, thus presenting a potential candidate for pharmaceutical development in the treatment of NAFLD.
Animals
;
PPAR alpha/metabolism*
;
Non-alcoholic Fatty Liver Disease/pathology*
;
Male
;
Mice, Inbred C57BL
;
Lipid Metabolism/drug effects*
;
Diterpenes/therapeutic use*
;
Organelle Biogenesis
;
Diet, High-Fat
;
Humans
;
Mice
;
Liver/metabolism*
;
Insulin Resistance
;
Mitochondria/metabolism*
;
Molecular Docking Simulation
10.IsoVISoR: Towards 3D Mesoscale Brain Mapping of Large Mammals at Isotropic Sub-micron Resolution.
Chao-Yu YANG ; Yan SHEN ; Xiaoyang QI ; Lufeng DING ; Yanyang XIAO ; Qingyuan ZHU ; Hao WANG ; Cheng XU ; Pak-Ming LAU ; Pengcheng ZHOU ; Fang XU ; Guo-Qiang BI
Neuroscience Bulletin 2025;41(2):344-348

Result Analysis
Print
Save
E-mail