In recent years, the worldwide incidence rate of peripheral arterial and aortic diseases has increased year by year, significantly increasing the cardiovascular mortality and incidence rate of the whole population. In the past, peripheral arterial and aortic diseases were often more prone to missed diagnosis and delayed treatment compared to coronary artery disease. The 2024 ESC guidelines for the management of peripheral arterial and aortic diseases for the first time combines peripheral arterial and aortic diseases, integrating and updating the 2017 guidelines for peripheral arterial disease and the 2014 guidelines for aortic disease. The aim is to provide standardized recommendations for the management of systemic arterial diseases, ensuring that patients can receive coherent and comprehensive diagnosis and treatment, thereby improving prognosis. This article interprets the main content of the guideline in order to provide reference and assistance for the clinical diagnosis and treatment of peripheral arterial and aortic diseases in China at the current stage.

ObjectiveTo investigate the characteristics of mitochondrial translational initiation factor 2 （MTIF2） gene methylation and its association with the development and progression of hepatocellular carcinoma （HCC）. MethodsMethSurv and EWAS Data Hub were used to perform the standardized analysis and the cluster analysis of MTIF2 methylation samples， including survival curve analysis， methylation signature analysis， the association of tumor signaling pathways， and a comparative analysis based on pan-cancer database. The independent-samples t test was used for comparison 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 Cox proportional hazards model was used to perform the univariate and multivariate survival analyses of methylation level at the CpG site. The Kaplan-Meier method was used to investigate the survival differences between the patients with low methylation level and those with high methylation level， and the Log-likelihood ratio method was used for survival difference analysis. ResultsGlobal clustering of MTIF2 methylation showed that there was no significant difference in MTIF2 gene methylation level between different races， ethnicities， BMI levels， and ages. The Kaplan-Meier survival curve analysis showed that the patients with N-Shore hypermethylation of the MTIF2 gene had a significantly better prognosis than those with hypomethylation （hazard ratio ［HR］=0.492， P<0.001）， while there was no significant difference in survival rate between the patients with different CpG island and S-Shore methylation levels （P>0.05）. The methylation profile of the MTIF2 gene based on different ages， sexes， BMI levels， races， ethnicities， and clinical stages showed that the N-Shore and CpG island methylation levels of the MTIF2 gene decreased with the increase in age， and the Caucasian population had significantly lower N-Shore methylation levels of the MTIF2 gene than the Asian population （P<0.05）； the patients with clinical stage Ⅳ had significantly lower N-Shore and CpG island methylation levels of the MTIF2 gene than those with stage Ⅰ/Ⅱ （P<0.05）. Clinical validation showed that the patients with stage Ⅲ/Ⅳ HCC had a significantly lower methylation level of the MTIF2 gene than those with stage Ⅰ/Ⅱ HCC and the normal population （P<0.05）. ConclusionN-Shore hypomethylation of the MTIF2 gene is a risk factor for the development and progression of HCC.

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 DNA repair regulation in the process of hepatocellular carcinoma （HCC） recurrence. MethodsHCC tissue samples were collected from the patients with recurrence within two years or the patients with a good prognosis after 5 years， and the Tandem Mass Tag-labeled quantification proteomic study was used to analyze the differentially expressed proteins enriched in the four pathways of DNA replication， mismatch repair， base excision repair， and nucleotide excision repair， and the regulatory pathways and targets that play a key role in the process of HCC recurrence were analyzed to predict the possible regulatory mechanisms. The independent samples t-test was used for comparison of 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. ResultsFor the eukaryotic replication complex pathway， there were significant reductions in the protein expression levels of MCM2 （P=0.018）， MCM3 （P=0.047）， MCM4 （P=0.014）， MCM5 （P=0.008）， MCM6 （P=0.006）， MCM7 （P=0.007）， PCNA （P=0.019）， RFC4 （P=0.002）， RFC5 （P<0.001）， and LIG1 （P=0.042）； for the nucleotide excision repair pathway， there were significant reductions in the protein expression levels of PCNA （P=0.019）， RFC4 （P=0.002）， RFC5 （P<0.001）， and LIG1 （P=0.042）； for the base excision repair pathway， there were significant reductions in the protein expression levels of PCNA （P=0.019） and LIG1 （P=0.042） in the HCC recurrence group； for the mismatch repair pathway， there were significant reductions in the protein expression levels of MSH2 （P=0.026）， MSH6 （P=0.006）， RFC4 （P=0.002）， RFC5 （P<0.001）， PCNA （P=0.019）， and LIG1 （P=0.042） in recurrent HCC tissue. The differentially expressed proteins were involved in the important components of MCM complex， DNA polymerase complex， ligase LIG1， long patch base shear repair complex （long patch BER）， and DNA mismatch repair protein complex. The clinical sample validation analysis of important differentially expressed proteins regulated by DNA repair showed that except for MCM6 with a trend of reduction， the recurrence group also had significant reductions in the relative protein expression levels of MCM5 （P=0.008）， MCM7 （P=0.007）， RCF4 （P=0.002）， RCF5 （P<0.001）， and MSH6 （P=0.006）. ConclusionThere are significant reductions or deletions of multiple complex protein components in the process of DNA repair during HCC recurrence.

Objective To explore the effect of Wenyang Huazhuo Tongluo recipe on pulmonary microvascular injury in mice with scleroderma based on mitophagy.Methods Fifty mice were randomly divided into blank control group(0.9％NaCl,by gavage),control group(0.9％NaCl,by gavage),model group,Wenyang Huazhuo Tongluo recipe group(47mg·kg-1·d-1 Wenyang Huazhuo Tongluo recipe by gavage),positive control group(10 mg·kg-1·d-1 KC7F2 dissolved in phosphate buffer solution intraperitoneal injection),continuous administration for 4 weeks.The expression levels of in vitro membrane translocation enzyme 20(TOMM20),hypoxia inducible factor-1α(HIF-1α),B cell lymphoma-2/adenovirus E1B-19 kDa interacting protein 3(BNIP3),PTEN inducible muscle enzyme protein 1(PINK1)and E3 ubiquitin ligase(Parkin)in lung tissue were detected by immunohistochemistry(IHC).Western blot(WB)was used to detect the expression levels of mitophagy-related proteins(TOMM20,LC3B)and HIF-1α/BNIP3/PINK1/Parkin pathway proteins in pulmonary microvascular endothelial cells.Results The relative content of HIF-1α in microvascular endothelial cells of lung tissue in the control group,model group,Wenyang Huazhuo Tongluo recipe group and positive control group were 0.17±0.02,0.98±0.01,0.66±0.03 and 0.48±0.01;the relative content of BNIP3 were 0.40±0.02,0.74±0.01,0.56±0.01 and 0.60±0.02;the relative content of PINK1 were 0.26±0.04,0.88±0.01,0.65±0.02 and 0.67±0.02;the relative contents of Parkin were 0.33±0.02,0.89±0.01,0.65±0.02 and 0.77±0.02;the relative contents of TOMM20 were 1.10±0.02,0.58±0.01,1.02±0.01 and 0.98±0.03;the relative contents of LC3B-Ⅰ/LC3B-Ⅱ were 0.24±0.01,0.80±0.01,0.53±0.02 and 0.70±0.02,respectively.The content of HIF-1α,BNIP3,PINK1,Parkin and LC3B-Ⅰ/LC3B-Ⅱ in model group was higher than those in control group.Wenyang Huazhuo Tongluo recipe can effectively reduce its content.The content of TOMM20 in the model group was lower than that in control group,and Wenyang Huazhuo Tongluo recipe can effectively increase its content.Conclusion Wenyang Huazhuo Tongluo recipe may inhibit mitophagy and improve SSc pulmonary microvascular injury by increasing TOMM20 and inhibiting the protein expression of LC3B and HIF-1α/BNIP3/PINK1/Parkin signaling pathway.

ObjectiveGQDs has become a superstar among zero-dimensional carbon-based materials. As one of the most abundant and important biological elements, its unique optical properties, high dispersion and biocompatibility have attracted extensive attention from scientists. This paper aims to investigate the effect of GQDs on cell viability, apoptosis and inflammatory factor expression in RAW264.7 macrophages and evaluate cell imaging capability of GQDs in vitro, which could provide theoretical basis for the safe application of GQDs in biomedical field. MethodsGraphene oxide was prepared by modified Hummer’s method. H₂O₂ and W₁₈O₄₉ interacted with each other under hydrothermal conditions to produce hydroxyl radicals, which can cut graphene oxide into GQDs using a top-down approach. The microstructure of GQDs was analyzed in detail by X-ray powder diffraction, X-ray photoelectron spectroscopy, transmission electron microscopy, atomic force microscopy, scanning electron microscopy and Fourier infrared transform. The biocompatibility of GQDs on macrophage was evaluated by CCK-8 and dead/alive staining. Flow cytometry results showed the apoptosis of RAW264.7 macrophages induced by GQDs. mRNA expression of inflammatory factors was evaluated byRT-qPCR. Cell imaging was exhibited by laser scanning confocal. ResultsHydroxyl radicals are produced by H₂O₂ and W₁₈O₄₉ under hydrothermal conditions, which contribute to cut graphene oxide into 3-5 nm GQDs in one step. The quantum yield of this method is 43%. Fluorescence lifetime of these blue GQDs is 1.67 ns. The Zigzag-type site and defect state of the triplet carbene radical lead to the excitation wavelength dependence of GQDs, and the optimal excitation and emission wavelengths are 330 nm and 400 nm, respectively. The boundary effect and amphiphilicity of quantum dots make GQDs possess abundant functional groups, vacancy defects and high dispersion, which results in GQDs exhibits good water solubility. RAW264.7 macrophages are incubated with different concentration in DEME medium for 24 h, 48 h and 72 h to evaluate cell. The survival rate of RAW264.7 cells is significantly dependent on the concentration and time of GQDs. CCK-8 and dead/alive staining show that GQDs have high biocompatibility. The effect of 200 mg/L GQDs on apoptosis of RAW264.7 cells is revealed by the scatter plot of bivariate flow cytometry. Under the stimulation of LPS+INF‑γ, the expression of TNF-α was increased in RAW264.7 cells, which co-acted with other cytokines to participate in the immune response of RAW264.7 cells in vitro, and mediated the production of IL-1β inflammatory factor in RAW264.7 cells, thereby inducing apoptosis of RAW264.7 cells. The results of RT-qPCR showed that GQDs can inhibit the growth of RAW264.7 cells in vitro, and stimulate them to increase TNF-α expression in RAW264.7 cells, which make cell membrane rupture and produce IL-1β inflammatory factors to induce cell apoptosis. The high biocompatibility of GQDs is attributed to the rich oxygen-containing functional groups (―COOH, ―OH, and CO) on the surface of GQDs, which makes its surface negatively charged and easy to be swallowed into the cell interior when interacting with the cell membrane with low affinity. Transmission electron microscopy (TEM) observed that the GQDs were swallowed into the cells. Furthermore, laser confocal results displayed that blue GQDs has certain ability of cell imaging in vitro. ConclusionThe water solubility, low toxicity, fluorescence properties and the induction effect of inflammatory factors of GQDs provide broad prospects for their application in the field of immunotherapy and cell imaging in the future.