ObjectiveTo explore the role of cucurbitacin B （CuB） in inducing ferroptosis in 4T1 cells and its mechanism. MethodsThe effects of CuB（0.2， 0.4， 0.8 μmol·L^-1）on the proliferation ability of 4T1 cells in vitro were detected using the methyl thiazolyl tetrazolium （MTT） assay. The clonogenic ability of 4T1 cells was detected by the plate cloning assay， and the levels of lactate dehydrogenase （LDH） in 4T1 cells were detected by the use of a kit. The mitochondrial membrane potential and reactive oxygen species （ROS） levels in 4T1 cells were detected by flow cytometry， and the mitochondrial ultrastructure of 4T1 cells was observed by transmission electron microscopy. The western blot was used to detect the expression of ferroptosis-related protein p53 in 4T1 cells， solute carrier family 7 member 11 （SCL7A11）， glutathione peroxidase 4 （GPX4）， long-chain acyl-CoA synthetase 4 （ACSL4）， transferrin receptor protein 1 （TFR1）， and ferritin heavy chain 1 （FTH1）. ResultsCompared with that in the blank group， the survival rate of 4T1 cells in CuB groups was significantly decreased （P<0.05）， and the number of cell clones in CuB groups was significantly reduced （P<0.01）. In addition， compared with that in the blank group， the leakage of LDH in cells in CuB groups was significantly increased （P<0.01）， and the mitochondrial membrane potential of cells in CuB groups decreased significantly （P<0.01）. Cellular ROS levels were significantly elevated in CuB groups （P<0.01）. The mitochondria of cells in CuB groups were obviously wrinkled， and the mitochondrial cristae were reduced or even disappeared. Compared with that in the blank group， the protein expression of p53， ACSL4， and TFR1 were significantly up-regulated in CuB groups （P<0.05）， and that of SLC7A11， GPX4， and FTH1 were significantly down-regulated （P<0.05）. ConclusionCuB may inhibit SLC7A11 and GPX4 expression by up-regulating the expression of p53， which in turn regulates the p53/SLC7A11/GPX4 signaling pathway axis and accelerates the generation of lipid peroxidation substrate by up-regulating the expression of ACSL4. It up-regulates TFR1 expression to promote cellular uptake of Fe³⁺ and down-regulates the expression of FTH1 to reduce the ability of iron storage， resulting in an elevated free Fe²⁺ level. It catalyzes the Fenton reaction， generates excess ROS， imbalances the antioxidant system and iron metabolism， and then induces ferroptosis in 4T1 cells.

ObjectiveTo explore the role of cucurbitacin B （CuB） in inducing ferroptosis in 4T1 cells and its mechanism. MethodsThe effects of CuB（0.2， 0.4， 0.8 μmol·L^-1）on the proliferation ability of 4T1 cells in vitro were detected using the methyl thiazolyl tetrazolium （MTT） assay. The clonogenic ability of 4T1 cells was detected by the plate cloning assay， and the levels of lactate dehydrogenase （LDH） in 4T1 cells were detected by the use of a kit. The mitochondrial membrane potential and reactive oxygen species （ROS） levels in 4T1 cells were detected by flow cytometry， and the mitochondrial ultrastructure of 4T1 cells was observed by transmission electron microscopy. The western blot was used to detect the expression of ferroptosis-related protein p53 in 4T1 cells， solute carrier family 7 member 11 （SCL7A11）， glutathione peroxidase 4 （GPX4）， long-chain acyl-CoA synthetase 4 （ACSL4）， transferrin receptor protein 1 （TFR1）， and ferritin heavy chain 1 （FTH1）. ResultsCompared with that in the blank group， the survival rate of 4T1 cells in CuB groups was significantly decreased （P<0.05）， and the number of cell clones in CuB groups was significantly reduced （P<0.01）. In addition， compared with that in the blank group， the leakage of LDH in cells in CuB groups was significantly increased （P<0.01）， and the mitochondrial membrane potential of cells in CuB groups decreased significantly （P<0.01）. Cellular ROS levels were significantly elevated in CuB groups （P<0.01）. The mitochondria of cells in CuB groups were obviously wrinkled， and the mitochondrial cristae were reduced or even disappeared. Compared with that in the blank group， the protein expression of p53， ACSL4， and TFR1 were significantly up-regulated in CuB groups （P<0.05）， and that of SLC7A11， GPX4， and FTH1 were significantly down-regulated （P<0.05）. ConclusionCuB may inhibit SLC7A11 and GPX4 expression by up-regulating the expression of p53， which in turn regulates the p53/SLC7A11/GPX4 signaling pathway axis and accelerates the generation of lipid peroxidation substrate by up-regulating the expression of ACSL4. It up-regulates TFR1 expression to promote cellular uptake of Fe³⁺ and down-regulates the expression of FTH1 to reduce the ability of iron storage， resulting in an elevated free Fe²⁺ level. It catalyzes the Fenton reaction， generates excess ROS， imbalances the antioxidant system and iron metabolism， and then induces ferroptosis in 4T1 cells.

Objective To investigate the effect and mechanism of miglitol on regulating the energy metabolism of brown adipocytes by activating UCP1 and preventing cold injury in mice after cold exposure. Methods Primary brown adipocytes were induced into mature adipocytes, the effect of miglitol on the viability of brown adipocytes was investigated by MTT method, the lipid droplet consumption level of cells after drug administration was investigated by Oil Red O staining technology, and the level of UCP1, a key protein of thermogenesis in brown adipocytes, was detected by Western blotting. The activity of anti-frostbite was investigated in cold exposure at 4 ℃ and −20 ℃. KM mice, which were randomly divided into control group, cold exposure group, miglitol group and all-trans retinoic acid group, and after 7 days of repeated administration, the body surface temperature of mice was detected by infrared thermal imaging system, the anal temperature change was detected by anal thermometer, and the expression levels of UCP1 and PGC1-α in adipose tissue were detected by immunoblotting. Results Compared with the control group, the lipid droplet consumption and UCP1 expression levels in brown adipocytes in the miglitol group were significantly increased. The levels of body surface temperature and rectal temperature increased significantly after cold exposure, and the levels of UCP1 and PGC1α in the brown adipose tissue of mice increased significantly, which indicated that the miglitol could activate the critical proteins UCP1 and PGC1α of the thermogenesis pathway, increase the thermogenesis of mice after cold exposure, and thus improve the effect of cold injury for toe swelling. Conclusion Miglitol could play a role in improving cold injury and body temperature in mice by increasing the level of UCP1 and PGC1α, which are key targets of the thermogenesis pathway to promote the thermogenesis of brown fat.

Objective To establish an HPLC method to determine the concentration of inositol nicotinate（IN） in rat skin, and study the pharmacokinetic characteristics of IN after transdermal administration of heparin sodium inositol nicotinate cream in rats. Methods HPLC method was used to establish a simple and rapid analytical method for the determination of IN concentration in the skin of rats at different time points after administration. The established method was used to study the pharmacokinetics of IN after transdermal administration of heparin sodium inositol nicotinate cream in rats, and the pharmacokinetic parameters were fitted with DAS software. Results The linearity of the analytical method was good in the concentration range of 0.25-20 μg/ml, the quantitative limit was 0.25 μg/ml, and the average recovery rate was 96.18%. The pharmacokinetic parameters of IN after transdermal administration of heparin sodium inositol nicotinate cream in rats were as follows: t_1/2 was （4.555±2.054） h, T_max was （6±0）h, C_max was （16.929±2.153）mg/L, AUC_0−t was （150.665±16.568） mg·h /L ,AUC_0−∞ was （161.074±23.917） mg·h /L, MRT_（0−t） was （9.044±0.618）h, MRT_{（0−∞）} was （10.444±1.91） h, CLz/F was （0.19±0.03） L/（h·kg）, and Vz/F was （1.19±0.437） L/（h·kg）. Conclusion IN could quickly penetrate the skin and accumulate in the skin for a long time, which was beneficial to the pharmacological action of drugs on the lesion site for a long time. The method is simple, rapid, specific and reproducible, which could be successfully applied to the pharmacokinetic study of IN after transdermal administration in rats.

ObjectiveTo observe the clinical effect of modified Huanglian Wendantang on cardiovascular risk factors in patients with metabolic syndrome under the guidance of treating disease before its onset. MethodsA total of 82 patients with metabolic syndrome treated in the First Affiliated Hospital of Heilongjiang University of Chinese Medicine from July 2023 to July 2024 were selected and allocated into an observation group （41 cases） and a control group （41 cases） by the random number table method. The control group received routine treatment， and the observation group was treated with modified Huanglian Wendantang on the basis of routine treatment. Both groups were treated for 8 weeks. The therapeutic effects on TCM symptoms after treatment in the two groups were evaluated. The levels of obesity degree indicators， blood pressure indicators， glucose and lipid metabolism indicators， inflammatory factors， and vascular endothelial function indicators before and after treatment in the two groups were measured， and the treatment safety was evaluated. ResultsAfter treatment， the total response rate of TCM symptoms in the observation group was 97.56% （40/41）， which was higher than that （87.80%， 36/41） in the control group （χ²=5.205， P<0.05）. After treatment， both groups showed declines （P<0.05） in systolic blood pressure （SBD）， diastolic blood pressure （DBP）， triglyceride （TG）， total cholesterol （TC）， low density lipoprotein cholesterol （LDL-C）， fasting blood glucose， 2-hour postprandial blood glucose （2 h PG）， glycosylated hemoglobin （HbA1c）， fasting insulin （FINS）， Homeostatic Model Assessment for Insulin Resistance （HOMA-IR）， body mass index （BMI）， waist circumference （WC）， waist-to-hip ratio （WHR）， leptin （LEP）， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， endothelin-1 （ET-1）， and inducible nitric oxide synthase （iNOS）. Moreover， the declines in the observation group were more obvious than those in the control group （P<0.05， P<0.01）. After treatment， both groups showed elevated levels of high density lipoprotein cholesterol （HDL-C）， adiponectin （ADP）， nitric oxide （NO）， and endothelial nitric oxide synthase （eNOS） （P<0.05）， and the above indexes in the observation group were higher than those in the control group （P<0.01）. ConclusionUnder the guidance of the thought of treating disease before its onset， modified Huanglian Wendantang was used to treat patients with metabolic syndrome. The decoction improved the clinical efficacy by ameliorating IR to improve insulin sensitivity， reducing inflammation， and protecting the vascular endothelial function. It inhibits cardiovascular risk factors without inducing adverse reactions， being worthy of clinical application and promotion.

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.

Objective: To develop a modified calculation formula for renal tumor volume and tumor contact surface area (CSA) based on the modeling results of 3D Slicer software, and to create a webpage of the calculation formula for use. Methods: The general information and tumor anatomical data of 98 patients who underwent partial nephrectomy during Jan.2021 and Jul.2023 in the Second Affiliated Hospital of Xi'an Jiaotong University were retrospectively analyzed.The imaging data were input into 3D Slicer software in the form of Dicom files for tumor and ipsilateral kidney modeling to obtain tumor anatomical data.The relationship between tumor anatomical parameters and tumor volume and CSA was analyzed using multifactorial linear regression.The initial modified formulas (V2, C2) and the optimized modified formulas (V3, C3) for tumor volume over CSA were established, respectively, after insignificant variables were eliminated.The mean square error (MSE) and Akaike information criterion (AIC) of the modified and traditional formulas (V1, C1) were compared, and the formula with the smallest MSE and AIC was selected as the optimal tumor volume and CSA calculation formula.The median tumor volume and CSA obtained from 3D modeling were used as the cutoff values.The optimal formula and conventional formula were applied to calculate tumor volume and CSA for all patients, and risk stratification was performed for all patients based on these cutoff values, and the perioperative indicators of patients in the upper and lower groups were compared.Finally, an online calculation tool was developed based on HTML. Results: Based on multifactorial linear regression analysis, we obtained the modified tumor volume calculation formula: V=0.382abc+2.488a+2.372b－4.146c+1.948（V2）, V=0.469abc－4.586c+13.816（V3）; the modified tumor CSA calculation formula CSA=2.469a －2.262L －19.23a+6.206b+1.212c+18.017L+1.616h－3.97h －2.185h/h －0.388（C2）, CSA=2.376a －2.144L －20.157a+5.024b+1.128c+17.578L+2.525h－2.634（C3）.Both of the modified volume formula (MSE=151.298 vs. 127.807 vs. 104.106) and modified CSA formula (MSE=309.878 vs.23.556 vs.30.388) had smaller errors compared to the conventional formula.The modified volume calculation formula showed that bleeding was more and thermal ischemia time was longer in patients with larger tumor volumes than in patients with smaller tumor volumes (P<0.05); and the modified CSA calculation formula showed that bleeding was more, surgery and thermal ischemia time were longer in patients with high CSA than in patients with low CSA (P＜0.05).Finally, V3 and C3 are selected as the best calculation formula, and a web page (https://lizihao-bot.github.io/RCC-Calculate/) was established for easy use. Conclusion: This study combined data from a medical information technology platform with numerical modeling methods to provide a faster and more accurate method to calculate the renal tumor volume and CSA.Meanwhile, a webpage version of the tool was developed to enhance its practicability.

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.

BACKGROUND: The main cause of restenosis after percutaneous transluminal angioplasty (PTA) is the excessive proliferation and migration of vascular smooth muscle cells (VSMCs). Lin28a has been reported to play critical regulatory roles in this process. However, whether CCAAT/enhancer-binding proteins β (C/EBPβ) binds to the Lin28a promoter and drives the progression of restenosis has not been clarified. Therefore, in the present study, we aim to clarify the role of C/EBPβ-Lin28a axis in restenosis. METHODS: Restenosis and atherosclerosis rat models of type 2 diabetes ( n   =  20, for each group) were established by subjecting to PTA. Subsequently, the difference in DNA methylation status and expression of C/EBPβ between the two groups were assessed. EdU, Transwell, and rescue assays were performed to assess the effect of C/EBPβ on the proliferation and migration of VSMCs. DNA methylation status was further assessed using Methyltarget sequencing. The interaction between Lin28a and ten-eleven translocation 1 (TET1) was analysed using co-immunoprecipitation (Co-IP) assay. Student's t -test and one-way analysis of variance were used for statistical analysis. RESULTS: C/EBPβ expression was upregulated and accompanied by hypomethylation of its promoter in restenosis when compared with atherosclerosis. In vitroC/EBPβ overexpression facilitated the proliferation and migration of VSMCs and was associated with increased Lin28a expression. Conversely, C/EBPβ knockdown resulted in the opposite effects. Chromatin immunoprecipitation assays further demonstrated that C/EBPβ could directly bind to Lin28a promoter. Increased C/EBPβ expression and enhanced proliferation and migration of VSMCs were observed after decitabine treatment. Further, mechanical stretch promoted C/EBPβ and Lin28a expression accompanied by C/EBPβ hypomethylation. Additionally, Lin28a overexpression reduced C/EBPβ methylation via recruiting TET1 and enhanced C/EBPβ-mediated proliferation and migration of VSMCs. The opposite was noted in Lin28a knockdown cells. CONCLUSION Our findings suggest that the C/EBPβ-Lin28a axis is a driver of restenosis progression, and presents a promising therapeutic target for restenosis.
Animals ; Cell Proliferation/genetics* ; Cell Movement/genetics* ; Muscle, Smooth, Vascular/metabolism* ; Rats ; DNA Methylation/physiology* ; CCAAT-Enhancer-Binding Protein-beta/genetics* ; Male ; Myocytes, Smooth Muscle/cytology* ; Rats, Sprague-Dawley ; RNA-Binding Proteins/genetics* ; Cells, Cultured ; Coronary Restenosis/metabolism*