Stroke is the leading cause of death and disability among adults in China， and its common complications include digestive system abnormalities， cognitive impairment， depression， stroke-associated pneumonia， and hemiplegia. The combination of traditional Chinese and Western medicine has great potential in treating post-stroke complications. Xinglou Chengqitang （XLCQT） is a representative prescription of alleviating the disease in the upper part by treating the lower part. It has definite therapeutic effect and high safety. Clinically， XLCQT is often used to treat stroke and its complications. However， the quantity and quality of clinical trials of XLCQT in treating post-stroke complications need to be improved. Additionally， since the basic research is weak， the material basis and multi-target mechanism for the efficacy of this prescription are unknown. This article reviews XLCQT in terms of the pharmacodynamic basis， medicinal properties， safety evaluation， and progress in clinical research and mechanisms in treating post-stroke complications. This article summarizes 22 key active ingredients of XLCQT in treating acute stroke complicated with syndrome of phlegm heat and fu-organ excess. Among these key active ingredients， resveratrol， kaempferol， luteolin， chrysoeriol， apigenin， （+）-catechin， and adenosine have good pharmacokinetic properties and high bioavailability. The mechanisms of XLCQT in treating post-stroke complications are complex， including inflammatory response， brain-gut axis， hypothalamic-pituitary-adrenal （HPA） axis， intestinal flora， neurotrophic factors， autophagy， oxidative stress， and free radical damage. This review helps to deeply understand the pharmacodynamic basis and mechanisms of XLCQT in treating post-stroke complications and provides a theoretical basis for the clinical application of XLCQT against post-stroke complications and the development of drugs.

As the core carrier of the inheritance and innovation of traditional Chinese medicine （TCM）， the modern expanded application of classic formulae is an inevitable trend for TCM to adapt to the changes in disease spectrum and achieve academic development. However， several challenges remain， including the vague definition of syndrome-formula ontology between ancient and modern times， the insufficient adaptability of the evidence grading system， and the disconnection between theory and clinical practice， having severely restricted the precise application and standardized development of classic formulae. Based on the current status of the modern expanded application of classic formulae， and grounded in the core theory of formula-syndrome correspondence in TCM， this paper constructs a theoretical framework of "syndrome-formula ontology reconstruction". The framework systematically expounds its core connotations， theoretical foundations and practical logic， and further clarifies the reconstruction direction of the TCM-specific evidence grading system by taking correspondence between formula and syndrome as the core， constructing a multi-dimensional and integrated evaluation framework， and adhering to the orientation of clinical application. The ultimate goal is to form a theoretical paradigm characterized by "syndrome-formula ontology reconstruction-evidence grading reconstruction-precise clinical application"， thereby providing theoretical support for the digital inheritance， evidence-based development， and modern application of classical prescriptions.

BACKGROUND: Renal osteodystrophy (ROD) is a skeletal pathology associated with chronic kidney disease-mineral and bone disorder (CKD-MBD) that is characterized by aberrant bone mineralization and remodeling. ROD increases the risk of fracture and mortality in CKD patients. The underlying mechanisms of ROD remain elusive, partially due to the absence of an appropriate animal model. To address this gap, we established a stable mouse model of ROD using an optimized adenine-enriched diet and conducted exploratory analyses through ribonucleic acid sequencing (RNA-seq). METHODS: Eight-week-old male C57BL/6J mice were randomly allocated into three groups: control group ( n = 5), adenine and high-phosphate (HP) diet group ( n = 20), and the optimized adenine-containing diet group ( n = 20) for 12 weeks. We assessed the skeletal characteristics of model mice through blood biochemistry, microcomputed tomography (micro-CT), and bone histomorphometry. RNA-seq was utilized to profile gene expression changes of ROD. We elucidated the functions of differentially expressed genes (DEGs) using gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and gene set enrichment analysis (GSEA). DEGs were validated via quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS: By the fifth week, adenine followed by an HP diet induced rapid weight loss and high mortality rates in the mouse group, precluding further model development. Mice with optimized adenine diet-induced ROD displayed significant abnormalities in serum creatinine and blood urea nitrogen levels, accompanied by pronounced hyperparathyroidism and hyperphosphatemia. The femur bone mineral density (BMD) of the model mice was lower than that of control mice, with substantial bone loss and cortical porosity. ROD mice exhibited substantial bone turnover with an increase in osteoblast and osteoclast markers. Transcriptomic profiling revealed 1907 genes with upregulated expression and 723 genes with downregulated expression in the femurs of ROD mice relative to those of control mice. Pathway analyses indicated significant enrichment of upregulated genes in the sphingolipid metabolism pathway. The significant upregulation of alkaline ceramidase 1 ( Acer1 ), alkaline ceramidase 2 ( Acer2 ), prosaposin-like 1 ( Psapl1 ), adenosine A1 receptor ( Adora1 ), and sphingosine-1-phosphate receptor 5 ( S1pr5 ) were successfully validated in mouse femurs by qRT-PCR. CONCLUSIONS Optimized adenine diet mouse model may be a valuable proxy for studying ROD. RNA-seq analysis revealed that the sphingolipid metabolism pathway is likely a key player in ROD pathogenesis, thereby providing new avenues for therapeutic intervention.
Animals ; Mice ; Chronic Kidney Disease-Mineral and Bone Disorder/genetics* ; Male ; Disease Models, Animal ; Mice, Inbred C57BL ; Sphingolipids/metabolism* ; Transcriptome/genetics* ; Signal Transduction/genetics* ; X-Ray Microtomography ; Adenine

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Objective:To investigate the protective effect and its mechanism of proscillaridin A (PSD-A) on podocyte injury in lupus nephritis (LN).Methods:Molecular docking and surface plasmon resonance techniques were used to analyze the binding status of PSD-A to signal transducer and activator of transcription 1 (STAT1). The immortalized human podocyte injury model in the lupus group was induced by the serum of systemic lupus erythematosus patients, and the control and PSD-A intervention (2 nmol/L, 4 nmol/L) groups were also set up. Six female 12-week-old C57BL/6 mice were designated as the control group, and 12 female 12-week-old MRL/lpr lupus mice were randomly divided into lupus group and PSD-A intervention group by random number table method. The PSD-A intervention group was intraperitoneally injected with 5 mg/kg PSD-A, once per week for 6 consecutive weeks. While the control group and the lupus group were intraperitoneally injected with the same volume of the solvent without PSD-A. Western blotting and real-time quantitative PCR were employed to detect the relative protein and mRNA expression levels of podocin, STAT1, and interferon-induced protein with tetratricopeptide repeat 1 (IFIT1) in podocytes of each group. Enzyme-linked immunosorbent assay was used to detect the levels of serum anti-double strand DNA antibody and interferon-α in mice. Coomassie brilliant blue was used to detect the urinary protein level. HE, PAS, Masson and PASM staining and transmission electron microscopy were used to observe the pathological changes of renal tissues. Immunohistochemistry was used to examine the protein expression of podocin, STAT1 and IFIT1 in renal tissues.Results:Molecular docking and surface plasmon resonance techniques proved that PSD-A could bind to STAT1 protein and they exhibited a robust binding affinity. The podocyte experiments showed that, compared with the lupus group, the relative expression levels of podocin protein and mRNA in the PSD-A intervention group were upregulated, while the relative expression levels of STAT1 and IFIT1 protein and mRNA were downregulated (all P<0.05). The animal experiments showed that, compared with the lupus group, the serum levels of anti-double strand DNA antibody, interferon-α, and urinary protein in PSD-A intervention group were decreased, the pathological damage of renal tissues was alleviated, and the injury of renal podocytes was reduced. Immunohistochemical staining showed that the relative protein expression levels of STAT1 and IFIT1 of renal tissues in the PSD-A intervention group were lower than those in the lupus group (all P<0.05). Conclusion:PSD-A can play a protective role in podocyte injury in LN, and its mechanism may be related to the inhibition of the STAT1 signaling pathway.

Central venous lesions are challenging in the maintenance of hemodialysis vascular access, with endovascular therapy as the preferred treatment. Coated stent grafts, with superior primary patency rates and the ability to mitigate the risk of vascular rupture and bleeding, have become one of the clinical options. However, they pose a risk of occluding important tributary veins. This report describes a case of right innominate vein occlusion treated with a small-caliber coated stent graft, resulting in postoperative symptoms of intracranial venous hypertension. This case highlights the need to pay attention to neurological symptoms caused by central venous lesions and conduct a more meticulous assessment of contralateral venous return before placing coated stent grafts, to avoid irreversible neurological symptoms.

OBJECTIVE To investigate the neuroprotective effects of Ditan Decoction(DTD)on ischemic stroke.METHODS A mouse middle cerebral artery occlusion(MCAO)model was used to induce cerebral ischemia and assess the role of DTD in post-stroke NVU injury.DTD was gavaged once a day for 3 days after MCAO.Transwell neutrophil chemotaxis assay was used to explore the role of DTD in the neutrophil chemotaxis.RESULTS In the MCAO model,DTD treatment significantly reduced infarct volume(P<0.01)and attenuated blood-brain barrier disruption,as evidenced by decreased IgG leakage and preserved laminin expression(P<0.05).Furthermore,DTD suppressed neutrophil infiltration into ischemic brain tissue,as demonstrated by reduced neutrophil elastase(P<0.01)and myeloperoxidase(P<0.05)levels.Mechanistically,DTD inhibited neutrophil chemotaxis in a dose-dependent manner and downregulated phosphodiesterase 4B(PDE4B),a key regulator of neutrophil migration(P<0.05).Molecular docking analysis i-dentified four active DTD components-apigenin,vitexin,chlorogenic acid,and orientin-with strong binding affinities to PDE4B(bind-ing energies<-5 kcal·mol-1),suggesting their potential role in mediating DTD's therapeutic effects.CONCLUSION These find-ings highlight DTD as a promising intervention for ischemic stroke,targeting NVU preservation and PDE4B-dependent neutrophil mod-ulation.

Mesangial cell proliferation is an early pathological indicator of diabetic nephropathy (DN). Growing evidence highlights the pivotal role of paired-related homeobox 1 (Prrx1), a key regulator of cellular proliferation and tissue differentiation, in various disease pathogenesis. Notably, Prrx1 is highly expressed in mesangial cells under DN conditions. Both in vitro and in vivo studies have demonstrated that Prrx1 overexpression promotes mesangial cell proliferation and contributes to renal fibrosis in db/m mice. Conversely, Prrx1 knockdown markedly suppresses hyperglycemia-induced mesangial cell proliferation and mitigates renal fibrosis in db/db mice. Mechanistically, Prrx1 directly interacts with the Yes-associated protein 1 (YAP) promoter, leading to the upregulation of YAP expression. This upregulation promotes mesangial cell proliferation and exacerbates renal fibrosis. These findings emphasize the crucial role of Prrx1 upregulation in high glucose-induced mesangial cell proliferation, ultimately leading to renal fibrosis in DN. Therefore, targeting Prrx1 to downregulate its expression presents a promising therapeutic strategy for treating renal fibrosis associated with DN.

Objective To comprehensively analyze the mechanism of Astragalus and Magnolia officinalis in treating prostate cancer based on the principles of network pharmacology.Methods Active molecular targets of Astragalus and Magnolia officinalis were predicted using the TCMSP and SwissTargetPrediction databases.Prostate cancer-related targets were screened via Genecards,DisGeNET,and OMIM databases.A"disease-active ingredient-target"network was constructed using Venny software,identifying 69 candidate key target genes.A protein-protein interaction(PPI)network was built using the STRING database,followed by GO and KEGG enrichment pathway analyses performed with R language.Constructing a subcutaneous tumor model in nude mice through in vivo experiments and intervening with active ingredients from Astragalus membranaceus and Magnolia offi-cinalis.Results Molecular docking analysis revealed that active components such as astragaloside IV(MOL000438)and honokiol(MOL000398)exhibited significant binding activity with the key target proteins of prostate cancer,including AKT1,ESR1,PPARG,PTGS2,and SRC.Notably,Honokiol demonstrated a binding energy of-8.7 kcal/mol with estrogen receptor α(ESR1,PDB:1a52),forming stable hydrogen bond interaction with the LEU-391 residue.The in vivo experiments further confirmed that the Astragalus-Magnolia active component group showed smaller subcutaneous xenograft tumor volumes in nude mice as compared to the model group(P<0.05).Immunohistochemical analysis revealed significant downregulation of PPARG and PTGS2 protein expression in tumor tissues(P<0.05).QPCR results indicated that the formula bidirectionally regulated gene expression:pro-apoptotic factor AKT1 was upregulated(P<0.05),while cancer-associated genes PTGS2,PPARG,SRC,and ESR1 were downregulated(P<0.05).Conclusion The combination of Astragalus and Magnolia may exert anti-prostate cancer effects through multi-target and multi-pathway synergistic mechanisms,demonstrating favorable binding activity and therapeutic potential.

Objective:To analyze the management benefits of information-based management mode for medical imaging equipment,and promote rational management for equipment and improve management benefits of equipment.Methods:According to the existing information management system and electronic medical record system at Tangdu hospital,Air Force Medical University,a management platform for medical imaging equipment under information-based management mode for medical imaging equipment was constructed to manage medical imaging equipment.A total of 9 imaging equipment included magnetic resonance(MR)equipment,computed tomography(CT)equipment and X-ray equipment that were in clinical use during 2022 and 2023 were selected,and the conventional management mode was adopted to manage equipment during January and December 2022,and the information-based management mode was adopted to manage them during January and December 2023.The comprehensive management benefits of the two modes for medical imaging equipment were compared.A self-designed questionnaire was used to survey satisfaction levels of 80 patients who underwent diagnosis and treatment by using these equipment.Results:The annual examination amount,annual revenue,annual operating cost,and annual net profit of MRI,CT,and X-ray equipment of adopting information-based management mode were all higher than those of adopting conventional management mode,and the investment payback period was shorter than that of adopting conventional management mode,and the cost-effectiveness evaluation was superior to conventional management mode.The average utilization rate of equipment,rate of maintenance and upkeep,and rate of controlling risk of adopting information-based management mode were respectively(95.36±6.02)%,(92.36±4.36)%and(94.36±4.15)%,which were significantly higher than those of adopting conventional management mode(t=14.317,11.62,12.508,P<0.05).Patients'satisfactions for MRI equipment,CT equipment and X-ray equipment that adopted information-based management mode were significantly higher than these that adopted conventional management mode,and the differences were significant(x2=5.741,6.260,5.331,P<0.05).Conclusion:The information-based management mode for medical imaging equipment can real-timely obtain the situations of usage and monitoring for medical imaging equipment,and enhance cost-effectiveness and management effectiveness at the maximum extent,and enhance users'satisfaction in using medical equipment.