ObjectiveTo investigate the association between noninvasive liver fibrosis markers and carotid plaque （CP） in patients with lean metabolic dysfunction-associated fatty liver disease （MAFLD）， and to provide a basis for screening high-risk populations. MethodsA total of 957 patients with lean MAFLD who underwent physical examination in Subei People’s Hospital from January 2021 to June 2023 was enrolled as the observation cohort， with the presence or absence of CP as the outcome， and fibrosis-4 （FIB-4） index and nonalcoholic fatty liver disease fibrosis score （NFS） were used to assess liver fibrosis degree. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical data between two groups. The multivariate logistic regression analysis， the restricted cubic spline analysis， the receiver operating characteristic curve， and the mediation effect analysis were used to investigate the association between liver fibrosis degree and CP. ResultsThe prevalence rate of CP was 36.6% in the lean MAFLD population. Compared with the non-CP group（n=607）， the CP group （n=350） had a significantly higher proportion of male patients， a significantly higher proportion of patients with smoking/diabetes/hypertension， and significantly higher levels of age， creatinine， blood urea nitrogen， triglycerides， fasting blood glucose， aspartate aminotransferase， aspartate aminotransferase/alanine aminotransferase ratio， NFS， and FIB-4 index， as well as significantly lower levels of platelet count and albumin （all P<0.05）. The multivariate logistic regression analysis showed that after adjustment for confounding factors， FIB-4 index （odds ratio［OR］=2.979， 95% confidence interval［CI］：2.141 — 4.219， P<0.001） and NFS （OR=1.747， 95%CI： 1.499 — 2.046， P<0.001） were positively correlated with CP. Both FIB-4 index and NFS had a good value in predicting CP. Hypertension had a significant indirect effect on the prevalence rate of CP through its impact on liver fibrosis markers， and its mediating effect accounted for 39.5% — 40.8% of the total effect （P<0.001）. ConclusionIn patients with lean MAFLD， NFS and FIB-4 index are significantly positively correlated with the prevalence rate of CP， and they can be used as potential epidemiological predictive indicators. Liver fibrosis markers may play a mediating role in the association between hypertension and CP. Interventions targeting hypertension and liver fibrosis markers may help to prevent and delay the progression of CP.

Background At present, the treatment of silicosis is still limited, and no method is available to cure the disease. miRNAs are involved in the process of fibrosis at the transcriptional level by directly degrading target gene mRNA or inhibiting its translation. However, how miR-130a-3p regulates silicosis fibrosis has not been fully elucidated yet. Objective To investigate whether miR-130a-3p promotes epithelial-mesenchymal transition (EMT) by inhibiting peroxisome proliferators-activated receptors gamma (PPAR-γ), thereby pro-moting the process of silicotic fibrosis. To identify effective new targets for the treatment of silicotic fibrosis. Methods (1) Animal experiments: C57BL/6J mice were intratracheally injected with a one-time dose of 10 mg silica suspension (dissolved in 100 μL saline) as positive lung exposure. A silicosis model group was established 28 d after the exposure. A control group was injected with the same amount of normal saline into the trachea. Hematoxylin-eosin staining and Sirius red staining were used to observe the pathological changes and collagen deposition in lung tissues respectively. Realtime fluorescence-based quantitative polymerase chain reaction (RT-qPCR) was used to assay the expression of miR-130a-3p and PPAR-γ mRNA in lung tissues. Western blotting was used to detect the protein expression of PPAR-γ, transforming growth factor (TGF)-β1, E-cadherin, α-smooth muscle actin (α-SMA), and Collagen Ⅰ in lung tissues. (2) Cells experiments: Mouse lung epithelial cells (MLE-12) were induced with 5 µg·L⁻¹ TGF-β1 for different time (0, 12, 24, 48 h). RT-qPCR was used to detect the expression of miR-130a-3p and PPAR-γ mRNA in cells. The binding relationship between miR-130a-3p and PPAR-γ mRNA was verified by dual luciferase reporter gene assay. MLE-12 cells were stimulated by 5 µg·L⁻¹ TGF-β1 after transfection of miR-130a-3p inhibitor, and Western blotting was used to measure the protein expression of PPAR-γ, E-cadherin, and α-SMA in the TGF-β1-induced cells. Results In the silicosis model group, the alveolar septum was widened and the pulmonary nodules were formed. The Sirius red staining collagen deposition in pulmonary nodules indicated that a silicosis fibrosis model was successfully established. The expressions of TGF-β1, α-SMA, and Collagen Ⅰ proteins were increased, and the expressions of E-cadherin and PPAR-γ proteins were decreased in lung tissues of the silicosis group, compared with the control group (P<0.05 or P<0.01). The expression of miR-130a-3p was increased and the expression of PPAR-γ mRNA was decreased in lung tissues of the silicosis model (P<0.01). The expression of miR-130a-3p was significantly increased, while the expression of PPAR-γ mRNA was decreased in the TGF-β1 induced MLE-12 cells (P<0.05 or P<0.01). The dual luciferase reporter assay showed a direct relationship between miR-130a-3p and PPAR-γ mRNA in MLE-12 cells. The transfection of miR-130a-3p inhibitor in the TGF-β1 induced MLE-12 cells inhibited the decrease of PPAR-γ and E-cadherin proteins, and the increase of α-SMA protein in the MLE-12 cells induced by TGF-β1 (P<0.05 or P<0.01). Conclusion miR-130a-3p promotes the development of silicosis fibrosis by targeting PPAR-γ to increase pulmonary EMT.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Liver fibrosis is a chronic liver injury resulting from factors like viral hepatitis, autoimmune hepatitis, non-alcoholic steatohepatitis, fatty liver disease, and cholestatic liver disease. Liver transplantation is currently the gold standard for treating severe liver diseases. However, it is limited by a shortage of donor organs and the necessity for lifelong immunosuppressive therapy. Mesenchymal stem cells (MSCs) can differentiate into various liver cells and enhance liver function when transplanted into patients due to their differentiation and proliferation capabilities. Therefore, it can be used as an alternative therapy for treating liver diseases, especially for liver cirrhosis, liver failure, and liver transplant complications. However, due to the potential tumorigenic effects of MSCs, researchers are exploring a new approach to treating liver fibrosis using extracellular vesicles (exosomes) secreted by stem cells. Many studies show that exosomes released by stem cells can promote liver injury repair through various pathways, contributing to the treatment of liver fibrosis. In this review, we focus on the molecular mechanisms by which stem cell exosomes affect liver fibrosis through different pathways and their potential therapeutic targets. Additionally, we discuss the advantages of exosome therapy over stem cell therapy and the possible future directions of exosome research, including the prospects for clinical applications and the challenges to be overcome.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Liver fibrosis is a chronic liver injury resulting from factors like viral hepatitis, autoimmune hepatitis, non-alcoholic steatohepatitis, fatty liver disease, and cholestatic liver disease. Liver transplantation is currently the gold standard for treating severe liver diseases. However, it is limited by a shortage of donor organs and the necessity for lifelong immunosuppressive therapy. Mesenchymal stem cells (MSCs) can differentiate into various liver cells and enhance liver function when transplanted into patients due to their differentiation and proliferation capabilities. Therefore, it can be used as an alternative therapy for treating liver diseases, especially for liver cirrhosis, liver failure, and liver transplant complications. However, due to the potential tumorigenic effects of MSCs, researchers are exploring a new approach to treating liver fibrosis using extracellular vesicles (exosomes) secreted by stem cells. Many studies show that exosomes released by stem cells can promote liver injury repair through various pathways, contributing to the treatment of liver fibrosis. In this review, we focus on the molecular mechanisms by which stem cell exosomes affect liver fibrosis through different pathways and their potential therapeutic targets. Additionally, we discuss the advantages of exosome therapy over stem cell therapy and the possible future directions of exosome research, including the prospects for clinical applications and the challenges to be overcome.

Liver fibrosis is a chronic liver injury resulting from factors like viral hepatitis, autoimmune hepatitis, non-alcoholic steatohepatitis, fatty liver disease, and cholestatic liver disease. Liver transplantation is currently the gold standard for treating severe liver diseases. However, it is limited by a shortage of donor organs and the necessity for lifelong immunosuppressive therapy. Mesenchymal stem cells (MSCs) can differentiate into various liver cells and enhance liver function when transplanted into patients due to their differentiation and proliferation capabilities. Therefore, it can be used as an alternative therapy for treating liver diseases, especially for liver cirrhosis, liver failure, and liver transplant complications. However, due to the potential tumorigenic effects of MSCs, researchers are exploring a new approach to treating liver fibrosis using extracellular vesicles (exosomes) secreted by stem cells. Many studies show that exosomes released by stem cells can promote liver injury repair through various pathways, contributing to the treatment of liver fibrosis. In this review, we focus on the molecular mechanisms by which stem cell exosomes affect liver fibrosis through different pathways and their potential therapeutic targets. Additionally, we discuss the advantages of exosome therapy over stem cell therapy and the possible future directions of exosome research, including the prospects for clinical applications and the challenges to be overcome.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.