BACKGROUND:Carboxylesterase 1 and inflammatory factors play a crucial role in regulating lipid metabolism and glucose homeostasis.However,the effects of different exercise intensity interventions on carboxylesterase 1 and inflammatory factors in skeletal muscle of type 2 diabetic rats remain to be revealed. OBJECTIVE:To investigate the effects of different exercise intensity interventions on carboxylesterase 1 and inflammatory factors in skeletal muscle of type 2 diabetic rats. METHODS:Thirty-two 8-week-old male Sprague-Dawley rats were randomly divided into normal control group(n=12)and modeling group(n=20)after 1 week of adaptive feeding.Rat models of type 2 diabetes mellitus were prepared by high-fat diet and single injection of streptozotocin.After successful modeling,the rats were randomly divided into diabetic control group(n=6),moderate-intensity exercise group(n=6)and high-intensity intermittent exercise group(n=6).The latter two groups were subjected to treadmill training at corresponding intensities,once a day,50 minutes each,and 5 days per week.Exercise intervention in each group was carried out for 6 weeks.After the intervention,ELISA was used to detect blood glucose and blood lipids of rats.The morphological changes of skeletal muscle were observed by hematoxylin-eosin staining.The mRNA expression levels of carboxylesterase 1 and inflammatory cytokines were detected by real-time quantitative PCR.The protein expression levels of carboxylesterase 1 and inflammatory cytokines were detected by western blot and immunofluorescence. RESULTS AND CONCLUSION:Compared with the normal control group,fasting blood glucose,triglyceride,low-density lipoprotein cholesterol,insulin resistance index in the diabetic control group were significantly increased(P<0.01),insulin activity was decreased(P<0.05),and the mRNA and protein levels of carboxylesterase 1,never in mitosis gene A related kinase 7(NEK7)and interleukin 18 in skeletal muscle tissue were upregulated(P<0.05).Compared with the diabetic control group,fasting blood glucose,triglyceride,low-density lipoprotein cholesterol,and insulin resistance index in the moderate-intensity exercise group and high-intensity intermittent exercise group were down-regulated(P<0.05),and insulin activity was increased(P<0.05).Moreover,compared with the diabetic control group,the mRNA level of NEK7 and the protein levels of carboxylesterase 1,NEK7 and interleukin 18 in skeletal muscle were decreased in the moderate-intensity exercise group(P<0.05),while the mRNA levels of carboxylesterase 1,NEK7,NOD-like receptor heat protein domain associated protein 3 and interleukin 18 and the protein levels of carboxylesterase 1 and interleukin 18 in skeletal muscle were downregulated in the high-intensity intermittent exercise group(P<0.05).Hematoxylin-eosin staining showed that compared with the diabetic control group,the cavities of myofibers in the moderate-intensity exercise group became smaller,the number of internal cavities was reduced,and the cellular structure tended to be more intact;the myocytes of rats in the high-intensity intermittent exercise group were loosely arranged,with irregular tissue shape and increased cavities in myofibers.To conclude,both moderate-intensity exercise and high-intensity intermittent exercise can reduce blood glucose,lipid,insulin resistance and carboxylesterase 1 levels in type 2 diabetic rats.Moderate-intensity exercise can significantly reduce the expression level of NEK7 protein in skeletal muscle,while high-intensity intermittent exercise can significantly reduce the expression level of interleukin 18 protein in skeletal muscle.In addition,the level of carboxylesterase 1 is closely related to the levels of NEK7 and interleukin 18.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

ObjectiveTo investigate the role and mechanism of podoplanin （PDPN） in hepatic stellate cell （HSC） activation and liver fibrosis. MethodsLiver biopsy samples were collected from 75 patients with chronic hepatitis B who attended Department of Infectious Diseases， Putuo Hospital Affiliated to Shanghai University of Traditional Chinese Medicine， for the first time from September 2019 to June 2022， and RT-PCR and immunohistochemistry were used to measure the expression of PDPN in liver tissue of patients in different stages of liver fibrosis. A total of 12 male C57/BL6 mice were randomly divided into control group and model group. The mice in the model group were given intraperitoneal injection of 10% CCl₄， and those in the control group were injected with an equal volume of olive oil， for 6 weeks. HE staining and Sirius Red staining were used to observe liver histopathological changes； primary mouse liver cells were separated to measure the mRNA expression of PDPN in various types of cells； primary mouse HSCs were treated with PDPN protein， followed by treatment with the NF-‍κB inhibitor BAY11-708， to measure the expression of inflammatory factors in HSCs induced by PDPN. The independent-samples t test was used for comparison of normally distributed continuous data between two groups； a one-way analysis of variance was used for comparison between multiple groups， and the least significant difference t-test was used for further comparison between two groups. The Spearman correlation analysis was used to investigate data correlation. ResultsAs for the liver biopsy samples， there was a relatively low mRNA expression level of PDPN in normal liver， and there was a significant increase in the mRNA expression level of PDPN in liver tissue of stage S3 or S4 fibrosis （all P<0.001）. Immunohistochemical staining showed that PDPN was mainly expressed in the fibrous septum and the hepatic sinusoid， and the PDPN-positive area in S4 liver tissue was significantly higher than that in S0 liver tissue （t=8.892， P=0.001）. In normal mice， PDPN was mainly expressed in the hepatic sinusoid， and there was a significant increase in the expression of PDPN in CCl₄ model mice （t=0.95， P<0.001）， mainly in the fibrous septum. RT-PCR showed a significant increase in the mRNA expression of PDPN in the CCl₄ model mice （t=11.25， P=0.002）. Compared with hepatocytes， HSCs， Kupffer cells， and bile duct endothelial cells， hepatic sinusoidal endothelial cells showed a significantly high expression level of PDPN （F=20.56， P<0.001）. Compared with the control group， the primary mouse HSCs treated by PDPN protein for 15 minutes showed significant increases in the mRNA expression levels of the inflammation-related factors TNFα， CCL3， CXCL1， and CXCR1 （all P<0.05）， and there were significant reductions in the levels of these indicators after treatment with BAY11-7082 （all P<0.05）. ConclusionThere is an increase in the expression of PDPN mainly in hepatic sinusoidal endothelial cells during liver fibrosis， and PDPN regulates HSC activation and promotes the progression of liver fibrosis via the NF-κB signaling pathway.

[摘 要] 目的：探讨miR-3612靶向信号素（SEMA）4C对肝细胞癌细胞增殖与侵袭能力的影响。方法: 收集2020年5月至2021年9月间在皖南医学院第一附属医院弋矶山医院手术切除的肝细胞肝癌的40对癌组织和相应癌旁组织，常规培养肝细胞癌Hep3B和Huh7细胞，将其分为对照组、miR-3612 mimics-NC组、miR-3612 mimics组、miR-3612 inhibitor-NC组、miR-3612 inhibitor组、si-NC组、si-SEMA4C组、mimics-NC+pcDNA-NC组、miR-3612 mimics+pcDNA-NC组和miR-3612 mimics+pcDNA-SEMA4C组，用转染试剂将相应的核酸和质粒转染各组细胞。qPCR法检测miR-3612和SEMA4C mRNA在肝细胞癌组织和Hep3B和Huh7细胞中的表达，双荧光素酶报告基因实验和免疫共沉淀（RIP）实验验证miR-3612与SEMA4C的结合及调控关系，qPCR法和WB法检测转染后各组Hep3B和Huh7细胞中miR-3612、SEMA4C mRNA和蛋白的表达，CCK-8法、细胞划痕实验和Transwell小室实验分别检测各组Hep3B和Huh7细胞的增殖、迁移和侵袭能力。结果: miR-3612在肝细胞癌组织和Hep3B和Huh7细胞中呈低表达（P<0.001），而SEMA4C则呈高表达（P<0.001），过表达miR-3612可抑制Hep3B和Huh7细胞的增殖、迁移、侵袭和vimentin、SEMA4C蛋白的表达，促进E-cadherin蛋白的表达（P<0.05或P<0.01或P<0.001），敲低miR-3612则促进Hep3B和Huh7细胞的增殖、迁移、侵袭和SEMA4C蛋白的表达（P<0.05或P<0.01或P<0.001）。双荧光素酶报告基因实验和RIP实验证实miR-3612与SEMA4C可直接结合（P<0.001），miR-3612与SEMA4C的表达呈负相关也间接证明了这一点（P<0.001）。敲减SEMA4C能明显抑制Hep3B、Huh7细胞的增殖、侵袭和迁移能力（P<0.05或P<0.01或P<0.001），过表达SEMA4C可逆转过表达miR-3612对Hep3B和Huh7细胞增殖、迁移、侵袭和EMT的抑制作用（P<0.05或P<0.01或P<0.001）。结论: miR-3612通过调控SEMA4C表达影响Hep3B和Huh7细胞的恶性生物学行为，miR-3612有望成为临床肝细胞癌治疗的潜在靶点。

OBJECTIVE To obtain stronger cytotoxic activity of panaxadiol derivatives. METHODS The 3-amino panaxadiol was prepared by the bioelectronic isosteric principle, and then 18 derivatives of cinnamic acid, NO donor and other types of panaxadiol derivatives were synthesized, among them, 12 compounds had not been reported in the literature, and their structures had been confirmed by 1H-NMR, 13C-NMR and mass spectrometry. These compounds were evaluated for their cytotoxic activity by MTS assay against human leukemia cell line HL-60, liver cancer cell line SMMC-7721, lung cancer cell line A-549, breast cancer cell line MCF-7, and colon cancer cell line SW480. RESULTS These results showed that compounds 6c, 7 as well as 7j exhibited potent inhibitory activities against all five tumor cells, especially the IC50 values of compound 7 against HL-60 and SMMC-7721cells were 3.41 and 4.51 μmol·L−1, respectively. It was significantly superior to panaxadiol in cytotoxicity. CONCLUSION These results show that 7 and 7j can be used as promising lead compounds for further research.

Most patients with differentiated thyroid cancer have a good prognosis after radioiodine-131 therapy,but a small number of patients are insensitive to radioiodine-131 therapy and even continue to develop disease.At present,some targeted drugs can improve progression-free survival in patients with radioactive iodine-refractory differentiated thyroid cancer(RAIR-DTC),such as sorafenib and levatinib,have been approved for the treatment of RAIR-DTC.However,due to the presence of primary and acquired drug resistance,drug efficacy in these patients is unsatisfactory.This review introduces the acquired drug resistance mechanism of sorafenib in the regulation of mitogen-activated protein kinase(MAPK)and phosphatidylinositol-3-kinase(PI3K)pathways and proposes related treatment strategies,in order to provide a reference for similar drug resistance mechanism of sorafenib and effective treatment of RAIR-DTC.