ObjectiveTo investigate the molecular mechanism by which the Bushen Kaixuan Tongluo prescription exerts a renal protective effect in mice with diabetic kidney disease （DKD） by regulating the cyclic adenosine monophosphate （cAMP） signaling pathway. MethodsThirty specific pathogen-free （SPF） male db/db mice were adaptively fed for three weeks. Mice with a random tail vein blood glucose level ≥ 11.1 mmol·L^-1 and urinary albumin-creatinine ratio （ACR） ≥ 30 mg·g^-1 were considered successfully modeled. The successfully modeled mice were randomly divided into five groups with six mice in each group： the model group， the low-， medium-， and high-dose Bushen Kaixuan Tongluo prescription groups （administered at doses of 7， 14， 28 g·kg^-1·d^-1 respectively）， and the positive drug irbesartan group （administered at a dose of 20 mg·kg^-1·d^-1）. Additionally， six db/m mice were selected as the blank group. Mice in each group were given intragastric administration of the Bushen Kaixuan Tongluo prescription at the corresponding concentrations， irbesartan， or an equal volume of pure water， and the intervention lasted for 12 weeks. During the experiment， the general conditions， body weight changes， and renal function indicators of the mice were dynamically monitored. After the intervention， a blood glucose meter was used to measure the fasting blood glucose （FBG） of the mice. An automatic biochemical analyzer was employed to detect the levels of serum creatinine （SCr）， blood urea nitrogen （BUN）， urinary microalbumin （uALB）， ACR， aspartate aminotransferase （AST）， alanine aminotransferase （ALT）， total cholesterol （TC）， triglycerides （TG）， leptin （LEP）， glycosylated serum protein （GSP）， and insulin （INS） in the mice. Renal tissues were collected for hematoxylin-eosin （HE） staining， periodic acid-Schiff （PAS） staining， and Masson's trichrome staining to observe the histopathological changes. Immunohistochemistry （IHC） was used to detect the expressions of protein kinase A （PKA） and cAMP response element-binding protein （CREB） in the mice. Western blot analysis was performed to determine the expression levels of PKA， phosphorylated protein kinase A （p-PKA）， CREB， phosphorylated cAMP response element-binding protein （p-CREB）， and B-cell lymphoma-2 （Bcl-2） proteins in the renal tissues of the mice. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect the mRNA expression levels of PKA， CREB， and Bcl-2 in the renal tissues of the mice. ResultsCompared with the blank group， the mice in the model group showed listlessness， decreased activity， and a significant increase in body weight （P<0.01）. Biochemical indicators revealed that the levels of BUN， uALB， ACR， AST， ALT， TC， TG， FBG， LEP， GSP， and INS were significantly increased （P<0.01）， while SCr showed an increasing trend with no statistically significant difference. Compared with the model group， the mice in the Bushen Kaixuan Tongluo prescription intervention groups had improved general conditions and a decreasing trend in body weight. Biochemical indicators showed that the levels of BUN， uALB， ACR， TC， GSP， and INS were significantly decreased （P<0.05）， while SCr， AST， ALT， TG， and LEP showed a decreasing trend with no statistically significant difference. Renal histopathological analysis showed that the model group exhibited typical DKD pathological features such as thickening of the glomerular basement membrane， expansion of the mesangial matrix， and deposition of collagen fibers in the renal tubulointerstitium， and all treatment groups could alleviate the above pathological damages. The IHC results showed that compared with the blank group， the expression levels of p-PKA and p-CREB in the renal tissues of the model group were significantly decreased （P<0.01）. Compared with the model group， the expression level of p-PKA in the medium-dose Bushen Kaixuan Tongluo prescription group was significantly increased （P<0.01）， while the expression level of p-CREB showed an increasing trend with no statistically significant difference. Western blot results showed that compared with the blank group， the expression levels of p-PKA/PKA， p-CREB/CREB， and Bcl-2 in the model group were significantly decreased （P<0.05）. Compared with the model group， the expression levels of these proteins in the medium-dose Bushen Kaixuan Tongluo prescription group were significantly increased （P<0.01）. Real-time PCR results showed that compared with the blank group， the mRNA expressions of PKA， CREB， and Bcl-2 in the model group were significantly down-regulated （P<0.05）. Compared with the model group， the mRNA expressions of these genes in the medium-dose Bushen Kaixuan Tongluo prescription group were significantly up-regulated （P<0.05）. ConclusionThe Bushen Kaixuan Tongluo prescription can improve the liver and kidney functions of db/db mice， correct lipid metabolism disorders and glucose metabolism imbalance. Its renal protective effect is associated with up-regulating the cAMP signaling pathway to improve renal fibrosis and reduce the level of oxidative stress， thereby protecting renal function.

ObjectiveProtein-protein interactions (PPIs) are fundamental to the execution of biological functions within living cells. However, traditional biochemical methods, such as co-immunoprecipitation (Co-IP), often fail to capture transient, weak, or membrane-associated interactions due to the stringent detergent requirements for cell lysis. Proximity labeling (PL) has emerged in recent years as a transformative technology for mapping the proteomes of specific subcellular compartments and identifying dynamic interactomes in situ. Golgi protein 73 (GP73, also known as GOLPH2), a resident type II Golgi transmembrane protein, is a well-recognized clinical biomarker for liver diseases, including hepatocellular carcinoma (HCC). Despite its clinical significance, the comprehensive physiological and pathological functions of GP73 remain partially understood. This study aims to establish an APEX2-mediated proximity labeling system specifically targeting GP73 to map its interactome in a living cellular environment, thereby providing new insights into its molecular roles and regulatory mechanisms. MethodsTo achieve spatial specificity, we first constructed a stable cell line expressing a fusion protein consisting of GP73 and the engineered soybean peroxidase APEX2. The localization of the GP73-APEX2 fusion protein was validated to ensure it correctly targeted the Golgi apparatus. The proximity labeling reaction was initiated by incubating the cells with biotin-phenol (BP) for 30 min, followed by a brief (1 min) treatment with1 mmol/L hydrogen peroxide (H₂O₂). This catalytic reaction converts BP into highly reactive, short-lived biotin-phenoxyl radicals that covalently attach to endogenous proteins within a small labeling radius of the GP73-APEX2 enzyme. Subsequently, the cells were quenched, and biotinylated proteins were enriched using high-affinity streptavidin-coated magnetic beads. The captured “neighbor” proteins were subjected to on-bead digestion and analyzed via liquid chromatography-tandem mass spectrometry (LC-MS/MS) for high-throughput identification. Rigorous bioinformatics analysis, including Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction network mapping, was performed to interpret the biological significance of the identified candidates. ResultsOur results demonstrate the successful establishment of a robust and sensitive APEX2-based proximity labeling system for GP73. We identified a total of 95 high-confidence interacting proteins that were significantly enriched in the GP73 proximity proteome compared to control groups. Bioinformatics analysis revealed that these interactors were predominantly associated with biological processes such as vesicular transport, protein localization, and, most notably, molecular functions related to “ribosome binding” and “translation regulation”. This suggested an unexpected role for the Golgi-resident GP73 in the cellular translation machinery. To validate these findings, we performed targeted biochemical assays which confirmed a direct interaction between GP73 and the subunits of the eukaryotic translation initiation factor 3 (eIF3) complex, specifically EIF3G and EIF3I. Furthermore, functional validation using the surface sensing of translation (SUnSET) assay—a non-radioactive method to monitor protein synthesis—revealed that the overexpression of GP73 significantly promoted global protein translation levels in the cell, whereas its depletion or inhibition resulted in reduced translation efficiency. ConclusionThis study successfully utilized APEX2-mediated proximity labeling to provide the first systematic map of GP73 interactome in living cells. Our findings uncover a novel, unconventional function of GP73 as a regulator of cellular protein translation, likely mediated through its interaction with the eIF3 complex. This discovery significantly broadens our understanding of the biological roles of GP73 beyond its traditional function in the Golgi apparatus and suggests that it may act as a bridge between Golgi-related trafficking and the protein synthesis machinery. Furthermore, the technical framework established in this study provides a valuable template for investigating other complex organelle-associated protein networks and resolving transient macromolecular interactions in various physiological and pathological contexts.

The number of patients with reduced blood volume due to haemorrhage, fractures, severe infections, extensive burns and tumours is increasing, and traditional blood products are no longer able to meet the increasing clinical demand. Therefore, plasma substitutes have become particularly important in fluid resuscitation, especially artificial colloidal solutions, which have a sustained volume expansion time and a good volume expansion effect, and can significantly improve the circulatory status of patients. This article aims to review the classification of artificial colloidal plasma substitutes and their research progress in clinical practice, in order provide a more rigorous, professional and standardized reference for medicine.

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: Zinc finger protein 639 (ZNF639) is often found within the overlapping amplicon of PIK3CA, and previous studies suggest its involvement in the pathogenesis of esophageal and oral squamous cell carcinomas. However, its expression and significance in breast cancer remain uncharacterized. Methods: Immunohistochemical analysis of ZNF639 was performed using tissue microarrays.Functional studies, including colony formation, Transwell cell migration, and in vivo metastasis, were conducted on breast tumor cells with ZNF639 knockdown via small interfering RNA transfection. Results: Reduced ZNF639 immunoreactivity was observed in 82% of the breast cancer samples, independent of hormone receptor and human epidermal growth factor receptor 2 status. In multivariate Cox regression analyses, ZNF639 expression was associated with favorable survival outcomes, including recurrence-free survival (hazard ratio, 0.35; 95% confidence interval [CI], 0.14–0.89) and overall survival (hazard ratio, 0.41; 95% CI, 0.16– 1.05). ZNF639 knockdown increased clonogenicity, cell motility, and lung metastasis in NOD/ SCID mice. Following the ZNF639 knockdown, the expression of Snail1, vimentin, and C-C chemokine ligand 20 (CCL20) was upregulated, and the changes in cell phenotype mediated by ZNF639 were reversed by the subsequent knockdown of CCL20. Conclusion Low ZNF639 expression is a novel prognostic factor for recurrence-free survival in patients with breast cancer.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

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.