1.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.
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.Establish a Graded Method to Avoid HLA Class I Antibodies Cor-responding Antigen and Combining HLAMatchmaker Application in Improving the CCI Value after Platelet Transfusion for Patients with IPTR
Su-Qing GAO ; Yun-Ping XU ; Chang-Ru LUO ; Da-Cheng LI ; Long PEN ; Tong LIU ; Qiong-Cai ZOU
Journal of Experimental Hematology 2024;32(1):242-249
Objective:To establish a graded method to avoid mean fluorescence intensity(MFI)threshold of HLA Class I antibodies corresponding antigen,and the HLAMatchmaker program has been used to select the minimum mismatch value of donor-patient epitopes.Evaluate the application value of combining both methods in selecting HLA compatible platelets(PTL)for patients with immune platelet transfusion failure(IPTR)in improving platelet the corrected count increment(CCI).Methods:A total 7 807 PLT cross-matching compatible were performed by the solid-phase red cell adherence(SPRCA)method for 51 IPTR patients.The Luminex single antigen flow cytometry was used to detect HLA Class I antibodies in patients,and detected the MFI value for different specificity antigens of HLA Class I antibodies,was graded into strong positive group(MFI>4 000,level 1),medium positive group(1 000<MFI 4 000,2),weak positive group(500<MFI≤1 000,3),and one negative control group(MFI≤500).The results of 7 807 SPRCA their negative/positive reaction wells were enrolled and statistically analyzed in different grades and the four groups,the statistical differences between the four groups were compared.Multiple applications for the select HLA Class I compatible donor events were made for patients in two cases,and HLAMatchmaker program was used to calculate the number of HLA Class I epitopes mismatches between the donors and patients.The donor with the minimum number of epitopes mismatches was selected,while avoiding the corresponding antigens of HLA Class I antibodies in levels 1 and 2,the provision of HLA compatible platelets for IPTR.After the transfusions,the CCI value of the platelet transfusion efficacy evaluation index was calculated,and the clinical evaluation of the transfusion effect was obtained through statistical analysis.Results:There were statistically significant differences in the positive results of SPRCA immunoassay among the strong positive group,medium positive group,and weak positive group of 51 IPTR patients with different specific of HLA-I class antibodies and corresponding antigens(all P<0.001).The positive results showed a range from high to low,with strong positive group>medium positive group>weak positive group.There were a statistical difference among between the strongly positive or moderately positive groups and the negative control group(P<0.001).There was no statistical difference between the weakly positive group and the negative control group(P>0.05).The strong positive group was set as the corresponding specific HLA Class I site corresponding antigen grade 1 avoidance threshold,the medium positive group as the grade 2 avoidance thresholds,and the weak positive group as the grade 3 avoidance threshold.In the case of donor platelet shortage,it is not necessary to avoid the weak positive group.Avoiding the strategy of donor antigens and HLAMatchmaker program scores≤7 corresponding to HLA Class I antibodies of levels 1 and 2,with CCI values>4.5 × 109/L within 24 hours,can obtain effective clinical platelet transfusion conclusions.Conclusion:When selecting HLA Class I compatible donors for IPTR patients,the grading avoids HLA Class I antibodies corresponding to donor antigens,and the donor selection strategy with the minimum scores of HLAMatchmaker program is comprehensively selected.The negative result confirmed by platelet cross-matching experiments has certain practical application value for improving platelet count in IPTR patients.
7.The Role of NK Cells in Allogeneic Hematopoietic Stem Cell Micro-Transplantation for Acute Myeloid leukemia
Ru-Yu LIU ; Chang-Lin YU ; Jian-Hui QIAO ; Bo CAI ; Qi-Yun SUN ; Yi WANG ; Tie-Qiang LIU ; Shan JIANG ; Tian-Yao ZHANG ; Hui-Sheng AI ; Mei GUO ; Kai-Xun HU
Journal of Experimental Hematology 2024;32(2):546-555
Objective:To explore the role of NK cells in allogeneic hematopoietic stem cell micro-transplantation(MST)in the treatment of patients with acute myeloid leukemia(AML).Methods:Data from 93 AML patients treated with MST at our center from 2013-2018 were retrospectively analyzed.The induction regimen was anthracycline and cytarabine combined with peripheral blood stem cells transplantation mobilization by granulocyte colony stimulating factor(GPBSC),followed by 2-4 courses of intensive treatment with medium to high doses of cytarabine combined with GPBSC after achieving complete remission(CR).The therapeutic effects of one and two courses of MST induction therapy on 42 patients who did not reach CR before transplantation were evaluated.Cox proportional hazards regression analysis was used to analyze the impact of donor NK cell dose and KIR genotype,including KIR ligand mismatch,2DS1,haplotype,and HLA-Cw ligands on survival prognosis of patients.Results:Forty-two patients received MST induction therapy,and the CR rate was 57.1%after 1 course and 73.7%after 2 courses.Multivariate analysis showed that,medium and high doses of NK cells was significantly associated with improved disease-free survival(DFS)of patients(HR=0.27,P=0.005;HR=0.21,P=0.001),and high doses of NK cells was significantly associated with improved overall survival(OS)of patients(HR=0.15,P=0.000).Donor 2DS1 positive significantly increases OS of patients(HR=0.25,P=0.011).For high-risk patients under 60 years old,patients of the donor-recipient KIR ligand mismatch group had longer DFS compared to the nonmismatch group(P=0.036);donor 2DS1 positive significantly prolonged OS of patients(P=0.009).Conclusion:NK cell dose,KIR ligand mismatch and 2DS1 influence the therapeutic effect of MST,improve the survival of AML patients.
8.Research on the equity of Chinese medicine human resource allocation and its driving paths in China:An analysis based on fsQCA method
Yong-Yi GUAN ; Jing ZHAO ; Yun-Han SU ; Ya-Ru LI ; Xin-Ran WANG ; Xin-Yu LIU
Chinese Journal of Health Policy 2024;17(10):46-51
Objective:To analyze the equity of Traditional Chinese medicine(TCM)human resource allocation across 31 provinces in China and explore its influencing pathways,aiming to provide scientific reference for optimizing the allocation of TCM human resources.Methods:The Health Resource Density Index(HRDI)was employed to measure the equity of TCM human resource allocation in China,and the fuzzy-set qualitative comparative analysis(fsQCA)was utilized to explore the configurational pathways influencing this equity.Results:Based on the data from 2021,the HRDI of TCM human resources in China exhibited significant regional disparities,manifesting as a distribution pattern of"high in the east and low in the west."Three pathways promoting high equity were identified:the internal-external balance-driven pathway(H1),the economy-demand co-driven pathway(H2),and the government-led driving pathway(H3).Meanwhile,three pathways leading to low equity were also recognized:the economy-demand constraint pathway(L1)and the internal-external constraint pathways(L2、L3).Conclusion:There are notable regional disparities in the equity of TCM human resource allocation in China,with multiple factors jointly influencing this equity,among which population density serves as a core factor.In subsequent efforts to enhance equity,it is advisable to consider optimizing the synergies among multiple factors and implementing precise policies for different regions to promote efficient allocation and balanced development of TCM human resources.
9.Emerging role of Jumonji domain-containing protein D3 in inflammatory diseases
Li XIANG ; Chen RU-YI ; Shi JIN-JIN ; Li CHANG-YUN ; Liu YAN-JUN ; Gao CHANG ; Gao MING-RONG ; Zhang SHUN ; Lu JIAN-FEI ; Cao JIA-FENG ; Yang GUAN-JUN ; Chen JIONG
Journal of Pharmaceutical Analysis 2024;14(9):1282-1300
Jumonji domain-containing protein D3(JMJD3)is a 2-oxoglutarate-dependent dioxygenase that specif-ically removes transcriptional repression marks di-and tri-methylated groups from lysine 27 on histone 3(H3K27me2/3).The erasure of these marks leads to the activation of some associated genes,thereby influencing various biological processes,such as development,differentiation,and immune response.However,comprehensive descriptions regarding the relationship between JMJD3 and inflammation are lacking.Here,we provide a comprehensive overview of JMJD3,including its structure,functions,and involvement in inflammatory pathways.In addition,we summarize the evidence supporting JMJD3's role in several inflammatory diseases,as well as the potential therapeutic applications of JMJD3 inhibitors.Additionally,we also discuss the challenges and opportunities associated with investigating the functions of JMJD3 and developing targeted inhibitors and propose feasible solutions to provide valuable insights into the functional exploration and discovery of potential drugs targeting JMJD3 for inflammatory diseases.
10. Analysis of cerebral gray matter structure in multiple sclerosis and neuromyelitis optica
Xiao-Li LIU ; Ai-Xue WU ; Ru-Hua LI ; An-Ting WU ; Cheng-Chun CHEN ; Lin XU ; Cai-Yun WEN ; Dai-Qian CHEN
Acta Anatomica Sinica 2024;55(1):17-24
Objective The volume and cortical thickness of gray matter in patients with multiple sclerosis (MS) and neuromyelitis optica (NMO) were compared and analyzed by voxel⁃based morphometry (VBM) and surface⁃based morphometry (SBM), and the differences in the structural changes of gray matter in the two diseases were discussed. Methods A total of 21 MS patients, 16 NMO patients and 19 healthy controls were scanned by routine MRI sequence. The data were processed and analyzed by VBM and SBM method based on the statistical parameter tool SPM12 of Matlab2014a platform and the small tool CAT12 under SPM12. Results Compared with the normal control group (NC), after Gaussian random field (GRF) correction, the gray matter volume in MS group was significantly reduced in left superior occipital, left cuneus, left calcarine, left precuneus, left postcentral, left central paracentral lobule, right cuneus, left middle frontal, left superior frontal and left superior medial frontal (P<0. 05). After family wise error (FWE) correction, the thickness of left paracentral, left superiorfrontal and left precuneus cortex in MS group was significantly reduced (P<0. 05). Compared with the NC group, after GRF correction, the gray matter volume in the left postcentral, left precentral, left inferior parietal, right precentral and right middle frontal in NMO group was significantly increased (P<0. 05). In NMO group, the volume of gray matter in left middle occipital, left superior occipital, left inferior temporal, right middle occipital, left superior frontal orbital, right middle cingulum, left anterior cingulum, right angular and left precuneus were significantly decreased (P<0. 05). Brain regions showed no significant differences in cortical thickness between NMO groups after FWE correction. Compared with the NMO group, after GRF correction, the gray matter volume in the right fusiform and right middle frontal in MS group was increased significantly(P<0. 05). In MS group, the gray matter volume of left thalamus, left pallidum, left precentral, left middle frontal, left middle temporal, right pallidum, left inferior parietal and right superior parietal were significantly decreased (P<0. 05). After FWE correction, the thickness of left inferiorparietal, left superiorparietal, left supramarginal, left paracentral, left superiorfrontal and left precuneus cortex in MS group decreased significantly (P<0. 05). Conclusion The atrophy of brain gray matter structure in MS patients mainly involves the left parietal region, while NMO patients are not sensitive to the change of brain gray matter structure. The significant difference in brain gray matter volume between MS patients and NMO patients is mainly located in the deep cerebral nucleus mass.

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