Glycoprotein non-metastatic melanoma protein B (GPNMB) is a transmembrane glycoprotein that plays an important role in various physiological and pathological processes. In recent years, its role in lung diseases has gradually attracted attention. Studies have found that GPNMB is abnormally expressed in lung diseases and is involved in regulating pathological processes such as inflammatory responses, fibrosis, and tumorigenesis. This article systematically reviews the research progress of GPNMB in common lung diseases such as chronic obstructive pulmonary disease (COPD), pulmonary fibrosis, and lung cancer, and explores its potential as a therapeutic target, providing new insights for the diagnosis and treatment of lung diseases in the future.

Lupus nephritis (LN), one of the most severe complications of systemic lupus erythematosus (SLE), has a complex pathogenesis involving various endogenous factors including autoimmune complex deposition, inflammatory cell infiltration, and cellular damage. Recent research has increasingly highlighted the prominent role of inflammasomes, particularly the nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome, in LN pathogenesis. Substantial evidence has confirmed its significant role in both the onset and progression of LN. Given that the NLRP3 inflammasome is a critical factor in triggering and exacerbating LN, its mechanism of action warrants in-depth exploration. Furthermore, research on intervention strategies targeting the NLRP3 inflammasome to ameliorate LN is of great significance. This article reviews the latest advances in the role of the NLRP3 inflammasome in LN pathogenesis and related intervention studies, which may offer new insights for the clinical diagnosis and treatment of LN.
Humans ; Lupus Nephritis/etiology* ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Inflammasomes/immunology* ; Animals

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.

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 Through visual analysis of literatures related to the treatment of chronic glomerulonephritis(CGN),the research hotspots and trends in this field were discussed.Methods Relevant literatures from CNKI,Wanfang Data Knowledge Service Platform,VIP,SinoMed,PubMed from 2010 to 2024 were retrieved,and analyzed using CiteSpace 6.2.R4 software.Results A total of 8887 articles in Chinese and 117 articles in English were included.The countries,institutions and authors who published most were China,the Affiliated Hospital for Liaoning University of Traditional Chinese Medicine and Wang Yiping.The research hotspots mainly focued on therapeutic drugs and clinical efficacy,and the research trend tended to be the treatment mechanism.Conclusion To further strengthen collaboration among different countries,institutions and authors,and to delve deeper into the mechanistic studies of CGN,will effectively promote the research progress in this field.

Objective:To explore the characteristics of the brain functional networks in children with spastic cerebral palsy (SCP) while at rest and to correlate them with motor functioning.Methods:Thirty-six children with SCP were enrolled as the SCP group, while thirty-four age-matched healthy children were recruited as the control group (the HC group). Functional near-infrared spectroscopy was used to detect changes in the concentration of oxygenated hemoglobin in the children′s cerebral cortex while at rest. The left prefrontal cortex (LPFC), right prefrontal cortex (RPFC), left motor cortex (LMC), and right motor cortex (RMC) were selected as regions of interest. Phase locking values (PLVs) were used to evaluate the strength of functional connectivity (FC) among these brain regions, and graph theory methods were applied to analyze the topological properties of the brain networks. Motor functioning was assessed using the gross motor function measure (GMFM).Results:The analyses of FC strength revealed that the SCP group had significantly weaker FC among all of the regions of interest while at rest compared to the HC group. Their PLVs for LPFC-RPFC, LPFC-RMC, RPFC-RMC and LMC-RMC connectivity were all significantly smaller. Graph theory analysis showed that the SCP group had significantly lower global efficiency (GE) and smaller clustering coefficients (CCs) and network density (D), while their characteristic path lengths were significantly longer. According to the correlation analysis, the PLVs for LMC-RMC connections in the SCP group were positively correlated with their scores on dimensions D and E of the GMFM ( r=0.496 and r=0.579 respectively). GE ( r=0.587 and r=0.642) and CC ( r=0.318 and r=0.759) showed similar significant positive correlations with GMFM dimensions D and E. Conclusions:At rest, the functional networks in the brains of children with SCP exhibit abnormalities closely associated with their motor dysfunction.

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.

Objective:To explore the application effects of the solution-focused approach combined with empathic nursing in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI) .Methods:A total of 96 AMI patients who underwent PCI in the Department of Cardiology at Cangzhou Central Hospital from March 2020 to March 2023 were selected using convenience sampling. They were randomly assigned to an experimental group ( n=48) and a control group ( n=48) using a random number table. The control group received routine nursing care, while the experimental group received a solution-focused approach combined with empathic nursing. Medication adherence, coping strategies, and patient satisfaction were compared between the two groups. Results:After the intervention, medication adherence and satisfaction scores in the experimental group were significantly higher than those in the control group, and the differences werestatistically significant ( P<0.05) . Additionally, the experimental group scored higher in confrontation coping, and lower in avoidance and resignation coping than the control group, and the differences were statistically significant ( P<0.01) . Conclusions:The combination of a solution-focused approach and empathic nursing can effectively improve medication adherence, coping strategies, and patient satisfaction in AMI patients after PCI.