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.

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.

This study aims to investigate the molecular mechanism by which naringin alleviates cerebral ischemia/reperfusion(CI/R) injury through DRP1/LRRK2/MCU signaling axis. A total of 60 SD rats were randomly divided into the sham group, the model group, the sodium Danshensu group, and low-, medium-, and high-dose(50, 100, and 200 mg·kg~(-1)) naringin groups, with 10 rats in each group. Except for the sham group, a transient middle cerebral artery occlusion/reperfusion(tMCAO/R) model was established in SD rats using the suture method. Longa 5-point scale was used to assess neurological deficits. 2,3,5-Triphenyl tetrazolium chloride(TTC) staining was used to detect the volume percentage of cerebral infarction in rats. Hematoxylin-eosin(HE) staining and Nissl staining were employed to assess neuronal structural alterations and the number of Nissl bodies in cortex, respectively. Western blot was used to determine the protein expression levels of B-cell lymphoma-2 gene(Bcl-2), Bcl-2-associated X protein(Bax), cleaved cysteine-aspartate protease-3(cleaved caspase-3), mitochondrial calcium uniporter(MCU), microtubule-associated protein 1 light chain 3(LC3), and P62. Mitochondrial structure and autophagy in cortical neurons were observed by transmission electron microscopy. Immunofluorescence assay was used to quantify the fluorescence intensities of MCU and mitochondrial calcium ion, as well as the co-localization of dynamin-related protein 1(DRP1) with leucine-rich repeat kinase 2(LRRK2) and translocase of outer mitochondrial membrane 20(TOMM20) with LC3 in cortical mitochondria. The results showed that compared with the model group, naringin significantly decreased the volume percentage of cerebral infarction and neurological deficit score in tMCAO/R rats, alleviated the structural damage and Nissl body loss of cortical neurons in tMCAO/R rats, inhibited autophagosomes in cortical neurons, and increased the average diameter of cortical mitochondria. The Western blot results showed that compared to the sham group, the model group exhibited increased levels of cleaved caspase-3, Bax, MCU, and the LC3Ⅱ/LC3Ⅰ ratio in the cortex and reduced protein levels of Bcl-2 and P62. However, naringin down-regulated the protein expression of cleaved caspase-3, Bax, MCU and the ratio of LC3Ⅱ/LC3Ⅰ ratio and up-regulated the expression of Bcl-2 and P62 proteins in cortical area. In addition, immunofluorescence analysis showed that compared with the model group, naringin and positive drug treatments significantly decreased the fluorescence intensities of MCU and mitochondrial calcium ion. Meanwhile, the co-localization of DRP1 with LRRK2 and TOMM20 with LC3 in cortical mitochondria was also decreased significantly after the intervention. These findings suggest that naringin can alleviate cortical neuronal damage in tMCAO/R rats by inhibiting DRP1/LRRK2/MCU-mediated mitochondrial fragmentation and the resultant excessive mitophagy.
Animals ; Rats, Sprague-Dawley ; Reperfusion Injury/genetics* ; Flavanones/administration & dosage* ; Rats ; Dynamins/genetics* ; Male ; Brain Ischemia/genetics* ; Protein Serine-Threonine Kinases/genetics* ; Signal Transduction/drug effects* ; Humans ; Drugs, Chinese Herbal/administration & dosage*

To investigate the effects and mechanisms of the water extract from Sorbus tianschanica(STE) on asthmatic airway inflammation, the mice were randomly divided into six groups, including a control group, a model group, a positive drug dexamethasone group(2 mg·kg~(-1)), a low-dose STE group(1 g·kg~(-1)), a medium-dose STE group(2 g·kg~(-1)), and a high-dose STE group(4 g·kg~(-1)). Except for the control group, all groups were subjected to ovalbumin induction to establish an asthma mouse model. The anti-inflammatory effects of STE were evaluated by examining pathological changes in lung tissue and measuring the levels of interleukin(IL)-4 and IL-5 in bronchoalveolar lavage fluid(BALF). Transcriptomic and proteomic methods were further employed to analyze differentially expressed genes and proteins, as well as their associated signaling pathways in lung tissue. Subsequently, the expression changes of key genes were verified by reverse transcription-quantitative polymerase chain reaction(RT-qPCR), and immunohistochemistry and Western blot methods were used to explore the regulatory mechanisms of STE in the pathogenesis of asthma in mice. Molecular docking was performed by using AutoDock Vina software to evaluate the binding affinity of the main active components in STE with the target proteins, including phosphatidylinositol-3-kinase catalytic subunit α(PIK3CA), Toll-like receptor 4(TLR4), protein kinase B1(Akt1), and matrix metallopeptidase 9(MMP9). The results showed significant inflammatory cell infiltration and fibrous tissue proliferation in the lung tissue of mice in the model group. However, these pathological changes were markedly reduced following STE intervention. Compared with those of the control group, the expression levels of IL-4 and IL-5 in the BALF of the model group were significantly increased but notably decreased following STE intervention. Transcriptomic and proteomic analyses identified key genes and proteins associated with allergic asthma, including tumor necrosis factor(TNF), IL-6, TLR4, PIK3CA, and MMP9. RT-qPCR validation revealed that high-dose STE intervention significantly downregulated the expressions of PIK3CA, IL-6, Akt1, MMP9, IL-13, nuclear factor-kappa B(NF-κB), TNF, CXC motif chemokine ligand 1(CXCL1), and TLR4 mRNAs and significantly upregulated the expression of signal transducer and activator of transcription 1(STAT1) mRNA. Western blot and immunohistochemical analyses confirmed that STE significantly downregulated the expressions of MMP9, TLR4, PIK3CA, and phosphorylated protein kinase B(p-Akt) in lung tissue of asthmatic mice. Moreover, molecular docking demonstrated that kaempferol-3,7-diglucoside, isoquercitrin, quercetin-3-gentiobioside, and hyperoside in STE exhibited stable binding affinities with PIK3CA, TLR4, Akt1, and MMP9, suggesting that the active components may exert anti-inflammatory effects by targeting and modulating asthma-related signaling pathways. In summary, STE exerts anti-asthmatic effects by inhibiting the expressions of PIK3CA, MMP9, p-Akt, and TLR4 and regulating the TLR4/PI3K/Akt/MMP9 signaling pathway.
Animals ; Asthma/metabolism* ; Toll-Like Receptor 4/metabolism* ; Signal Transduction/drug effects* ; Mice ; Phosphatidylinositol 3-Kinases/metabolism* ; Proto-Oncogene Proteins c-akt/metabolism* ; Matrix Metalloproteinase 9/metabolism* ; Mice, Inbred BALB C ; Drugs, Chinese Herbal/administration & dosage* ; Female ; Humans ; Lung/immunology* ; Male

OBJECTIVES: To explore the bioactive components in Jiawei Xiaoyao Pills (JWXYP) and their mechanisms for alleviating depression-like behaviors. METHODS: The active compounds, key targets, and pathways of JWXYP were identified using TCMSP and TCMIP databases. Thirty-six SD rats were randomized equally into 6 groups including a control group and 5 chronic unpredictable mild stress (CUMS)-induced depression groups. After modeling, the 5 model groups were treated with daily gavage of normal saline, 1.8 mg/kg fluoxetine hydrochloride (positive control drug), or JWXYP at 1.44, 2.88, and 4.32 g/kg. The depression-like behaviors of the rats were evaluated using behavioral tests, and pathological changes in the liver and hippocampus were examined with HE staining. The biochemical indicators in the serum and brain tissues were detected using ELISA. Serum metabolomics analysis was performed to identify the differential metabolites using OPLS-DA, and gut microbiota changes were analyzed using 16S rDNA sequencing. RESULTS: Network pharmacology revealed that menthone and paeonol in JWXYP were capable of penetrating the blood-brain barrier to regulate inflammatory pathways and protect the nervous system. In the rat models subjected to CUMS, treatment with JWXYP significantly improved body weight loss, sucrose preference and open field activities, reduced liver inflammation, alleviated structural changes in the hippocampal neurons, decreased serum levels of TNF‑α, IL-1β, IL-6 and LBP, and increased 5-HT and VIP concentrations in the serum and brain tissue, and these effects were the most pronounced in the high-dose group. Metabolomics analysis showed changes in such metabolites as indole-3-acetamide and acetyl-L-carnitine in JWXYP-treated rats, involving the pathways for bile acid biosynthesis and amino acid metabolism. 16S rDNA analysis demonstrated increased gut microbiota diversity and increased abundance of Lactobacillus species in JWXYP-treated rats. CONCLUSIONS JWXYP alleviates depression-like symptoms in rats by regulating the neurotransmitters, inhibiting inflammation and oxidation, and modulating gut microbiota.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Gastrointestinal Microbiome/drug effects* ; Rats, Sprague-Dawley ; Depression/drug therapy* ; Neurotransmitter Agents/metabolism* ; Rats ; Inflammation ; Male ; Hippocampus ; Behavior, Animal/drug effects*

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.