Objective To investigate the potential value of a deep learning(DL)model based on non-contrast CT images in predicting contrast medium extravasation in contrast-enhanced CT scans of tumor patients.Methods A total of 298 tumor patients were retrospectively selected,including 90 patients with extravasation and 208 without extravasation,and divided into training set(207 patients),validation set(46 patients),and external test set(45 patients)in a ratio of 7︰1.5︰1.5.U-Net was employed to segment the right common carotid artery/internal jugular vein and right subclavian artery/vein in non-contrast CT images,and ResNet50 was utilized to extract imaging features to construct the DL model,which was subsequently integrated with independent clinical predictors to establish the combined model.The segmentation performance of the DL model was evaluated using Dice similarity coefficient(DSC)and Intersection over Union(IoU),while the area under the curve(AUC),accuracy,sensitivity,and specificity of the model were calculated.Results The DL model demonstrated superior vascular segmentation(DSC 0.81-0.95,IoU 0.79-0.90).The combined model achieved optimal predictive performance,with AUC of 0.961[95%confidence interval(CI)0.924-0.983],0.949(95%CI 0.840-0.992),and 0.891(95%CI 0.762-0.964)in the training,validation,and external test sets,respectively.Its accuracy,sensitivity,and specificity were consistently higher than those of the standalone clinical model.Conclusion The DL model based on non-contrast CT images shows significant potential value in predicting contrast medium extravasation risk in tumor patients,providing an objective and intelligent tool for clinical risk assessment.

Objective:To analyze the association of apolipoprotein E(ApoE),soluble semaphoring 4D(sSema4D),and cyclophilin A(CypA)levels with unfavorable outcome in patients with chronic heart failure(CHF).Methods:We retrospectively enrolled 108 patients with CHF admitted in the Fifth Affiliated Hospital of Zhengzhou Universi-ty between March 2019 and March 2022.According to the outcome during 6-month follow-up,patients were di-vided into favorable outcome group(n=71)and unfavorable outcome group(n=37).ApoE,sSema4D and CypA levels were compared between the two groups,and multivariate Logistic regression was employed to identify the risk factors of unfavorable outcome within 6-month follow-up in CHF patients.Results:Compared to patients in the favorable outcome group,those in the unfavorable outcome group had significant higher proportion of NYHA classⅣ(59.46％vs.25.35％),serum creatinine[(80.74±3.89)μmol/L vs.(71.36±3.63)μmol/L],ApoE[(69.87±4.25)mg/L vs.(47.36±3.17)mg/L],sSema4D[(916.62±7.32)ng/L vs.(426.42±6.25)ng/L]and CypA[(6.74±1.32)ng/L vs.(4.38±0.72)ng/L](P＜0.001 all).Multivariate Logistic regression indicated that NY-HA class Ⅳ(OR=2.782,95％CI 1.166～6.636),serum creatinine(OR=29.893,95％CI 6.782～131.768),ApoE(OR=12.046,95％CI 4.694～30.913),sSema4D(OR=8.390,95％CI 2.260～31.146)and CypA(OR=6.486,95％CI 1.780～23.635)were independent risk factors for unfavorable outcome within 6-month follow-up in CHF patients(P＜0.05 or＜0.01).Conclusion:The levels of ApoE,sSema4D and CypA were closely associated with short-term unfavorable outcome in patients with chronic heart failure.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

BACKGROUND:Early diagnosis of pulmonary fibrosis is the foundation for timely antifibrotic drug therapy.Therefore,exploring and discovering ideal biomarkers that can be effectively used for the early diagnosis of pulmonary fibrosis is crucial for the treatment of the disease.OBJECTIVE:To conduct an in-depth analysis of key autophagy-related genes involved in the process of pulmonary fibrosis by means of bioinformatics and machine learning techniques,in order to investigate whether autophagy-related core genes of pulmonary fibrosis can be used as reliable biomarkers in the assessment of the progression of pulmonary fibrosis.METHODS:Two datasets of pulmonary fibrosis,GSE24206 and GSE110147,were downloaded from the Gene Expression Omnibus(GEO)database(a public database developed and maintained by the U.S.National Center for Biotechnology Information to store and share bioinformatics data),and the gene expression matrices of these two datasets were normalized by using the"limma"package in R software.The autophagy-related genes were extracted from GeneCards database(a database created by the U.S.National Center for Biotechnology Information,which automatically integrates gene-centric data from about 200 Web sources,including genomic,transcriptomic,proteomic,genetic,clinical,and functional information).Differential gene analysis was performed on the pulmonary fibrosis dataset,and the common genes were extracted by cross-comparing the differential genes with the autophagy genes,so as to identify autophagy genes that may play a role in the process of pulmonary fibrosis.The intersecting genes were analyzed for functional enrichment and cellular immune infiltration by gene ontology and Kyoto Encyclopedia of Genes and Genomes.Core genes of pulmonary fibrosis associated with autophagy were screened by protein-protein interactions and machine learning,and core genes were subjected to the enrichment analysis.Diagnostic models were constructed from the identified core genes.Calibration curves were used to assess the predictive ability of the line graph model.An external dataset,GSE21369,was used to perform a receiver operating characteristic curve analysis to validate the expression profiles of pulmonary fibrosis genes associated with autophagy,as well as to predict Chinese herbs associated with the genes IL6 and COL1A2 via the Coremine database.Finally,human embryonic lung fibroblasts were cultured and modelled by transforming growth factor-β1 treatment,and the relative expression of genes in the model cells was verified using qRT-PCR.RESULTS AND CONCLUSION:(1)A total of 51 pulmonary fibrosis differential genes and 25 genes intersecting with autophagy genes were obtained.Gene ontology analysis showed that the 25 intersecting genes were related to extracellular matrix tissue,collagen metabolism,collagen pro-fibroblasts,and growth factor binding,etc.The results of Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that they were mainly related to the Phosphatidylinositol 3-kinase/protein kinase B signaling pathway and the signaling pathway of the extracellular matrix-receptor interactions.(2)Immunoinfiltration analysis revealed that the expression of activated memory CD4+T cells,M0 macrophages,and resting dendritic cells was significantly elevated in the pulmonary fibrosis group(P＜0.05),showing a strong correlation.(3)Two autophagy signature genes involved in the progression of pulmonary fibrosis were identified:COL1A2 and IL6.The column-line diagram model showed that the two core genes predicted the onset of pulmonary fibrosis more accurately,and the receiver operating characteristic curve analysis showed that the two characteristic genes had diagnostic significance.COL1A2 and IL6 were related to the cell-cycle pathway,mitogen-activated protein kinase signaling pathway,Janus kinase-signal transduction and activator of transcription signaling pathway and cytokine-cytokine receptor interactions.A total of 20 Chinese herbs were predicted to be related to COL1A2 and IL6 genes,and their efficacies were mainly to clear away heat and detoxify toxins and to invigorate blood and move qi.COL1A2 and IL6 were verified to be highly expressed in pulmonary fibrosis.To conclude,COL1A2 and IL6 may be potential diagnostic biomarkers for pulmonary fibrosis,but its specificity to pulmonary fibrosis needs to be further investigated.

Objective:To analyze the association of apolipoprotein E(ApoE),soluble semaphoring 4D(sSema4D),and cyclophilin A(CypA)levels with unfavorable outcome in patients with chronic heart failure(CHF).Methods:We retrospectively enrolled 108 patients with CHF admitted in the Fifth Affiliated Hospital of Zhengzhou Universi-ty between March 2019 and March 2022.According to the outcome during 6-month follow-up,patients were di-vided into favorable outcome group(n=71)and unfavorable outcome group(n=37).ApoE,sSema4D and CypA levels were compared between the two groups,and multivariate Logistic regression was employed to identify the risk factors of unfavorable outcome within 6-month follow-up in CHF patients.Results:Compared to patients in the favorable outcome group,those in the unfavorable outcome group had significant higher proportion of NYHA classⅣ(59.46％vs.25.35％),serum creatinine[(80.74±3.89)μmol/L vs.(71.36±3.63)μmol/L],ApoE[(69.87±4.25)mg/L vs.(47.36±3.17)mg/L],sSema4D[(916.62±7.32)ng/L vs.(426.42±6.25)ng/L]and CypA[(6.74±1.32)ng/L vs.(4.38±0.72)ng/L](P＜0.001 all).Multivariate Logistic regression indicated that NY-HA class Ⅳ(OR=2.782,95％CI 1.166～6.636),serum creatinine(OR=29.893,95％CI 6.782～131.768),ApoE(OR=12.046,95％CI 4.694～30.913),sSema4D(OR=8.390,95％CI 2.260～31.146)and CypA(OR=6.486,95％CI 1.780～23.635)were independent risk factors for unfavorable outcome within 6-month follow-up in CHF patients(P＜0.05 or＜0.01).Conclusion:The levels of ApoE,sSema4D and CypA were closely associated with short-term unfavorable outcome in patients with chronic heart failure.

By reconstructing the integrated Chinese and western medicine diagnostic and treatment system， the "Two Reconstructions" theoretical framework establishes a standardized pathway of "classification-staging-syndrome differentiation"， which improves the accuracy of disease identification and strengthens the capacity for full-course intervention； in addition， by reconstructing the modern materia medica system， it innovatively integrates the traditional properties and efficacy of Chinese herbal medicinals with modern pharmacological mechanisms， forming a "state-target co-regulation" precise medication model， and builds a dose-effect theoretical system for prescriptions and medicinals， thereby enhancing both the targeting accuracy and dosage precision of therapeutic interventions. The "Two Reconstructions" theorecitcal framework is a key strategy for enhancing clinical efficacy. It can precisely identify "states" and "targets" for directed intervention， shift the focus of prevention and treatment earlier to enable full-cycle management， establish standardized paradigms for reproducible and evaluable efficacy， and expand the scope of clinical practice to address conditions without typical syndromes and critical illnesses. As a systematic pathway for innovation in TCM， this theoretical framework provides valuable insights and references for promoting the high-quality development of integrative Chinese and western medicine.

Objective To compare the application effect of platelet rich fibrin(PRF)prepared at different centrifugal speeds in tooth extraction wounds using network meta-analysis statistical method.Methods PubMed,Co-chrane Library,Embase,Web of Science,CNKI,Wanfang,VIP and China Biology Medicine disc(CBM)databases were searched to collect the randomized controlled trials on the effect of PRF in tooth extraction from January 1,2024.Note-Express software was used to select literatures,RevMan5.3 was used to evaluate the quality of the included literatures,and Stata14.0 software was used to perform mesh meta-analysis on outcome indicators such as bone mineral density,bone height loss,visual analog scale of pain(VAS),dry groove,and bleeding after tooth extraction.Results A total of 31 randomized controlled trials were included,including 2 824 extraction sites,involving 7 rotational speeds for PRF preparation(3 000,2 800,2 700,2 300,1 500,1 400,1 300 r/min).The results of cu-mulative sequencing showed that PRF prepared by 2 700 r/min centrifugation had the best effect in improving bone mineral density.In terms of improving the buccal bone height,the PRF prepared by 2 700 r/min centrifu-gation had the best effect.In terms of pain improvement,PRF prepared by 1 500 r/min centrifugation had the best effect;PRF prepared by 2 500 r/min centrifugation had the best effect in reducing the incidence of dry trough.PRF prepared by 2 500 r/min centrifugation was the most effective in reducing bleeding.Conclusion Based on the available evidence,PRF prepared with 2 700 r/min has the best effect on improving bone density and bone height after tooth extraction.In terms of pain improvement,1 500 r/min was the most effective;2 500 r/min was the most effective in improving the incidence of dry trough and bleeding.

Objective To explore the efficacy of DSA-guided intrathecal drug delivery system combined with Zi Wu Liu Zhu Acupoint Therapy for management of cancer pain and provide reference for its standardized clinical application.Methods and Results Recommendations were formulated based on literature review and expert group discussion,and consensus was reached following expert consultation.The consensus recommendations are comprehensive,covering the entire treatment procedures from preoperative assessment and preparation,surgical operation process,postoperative management and traditional Chinese medicine treatment to individualized treatment planning.The study results showed that the treatment plans combining traditional Chinese with Western medicine effectively alleviated cancer pain,reduced the use of opioid drugs,and significantly improved the quality of life and enhanced immune function of the patients.Postoperative follow-up suggested good treatment tolerance among the patients without serious complications.Conclusion The formulated consensus is comprehensive and can provide reference for clinicians to use DSA-guided intrathecal drug delivery system combined with Zi Wu Liu Zhu Acupoint Therapy.The combined treatment has a high clinical value with a good safety profile for management of cancer pain.

Recent studies have indicated that the expression of ubiquitin-specific protease 51 (USP51), a novel deubiquitinating enzyme (DUB) that mediates protein degradation as part of the ubiquitin‒proteasome system (UPS), is associated with tumor progression and therapeutic resistance in multiple malignancies. However, the underlying mechanisms and signaling networks involved in USP51-mediated regulation of malignant phenotypes remain largely unknown. The present study provides evidence of USP51's functions as the prominent DUB in chemoresistant triple-negative breast cancer (TNBC) cells. At the molecular level, ectopic expression of USP51 stabilized the 78 kDa Glucose-Regulated Protein (GRP78) protein through deubiquitination, thereby increasing its expression and localization on the cell surface. Furthermore, the upregulation of cell surface GRP78 increased the activity of ATP binding cassette subfamily B member 1 (ABCB1), the main efflux pump of doxorubicin (DOX), ultimately decreasing its accumulation in TNBC cells and promoting the development of drug resistance both in vitro and in vivo. Clinically, we found significant correlations among USP51, GRP78, and ABCB1 expression in TNBC patients with chemoresistance. Elevated USP51, GRP78, and ABCB1 levels were also strongly associated with a poor patient prognosis. Importantly, we revealed an alternative intervention for specific pharmacological targeting of USP51 for TNBC cell chemosensitization. In conclusion, these findings collectively indicate that the USP51/GRP78/ABCB1 network is a key contributor to the malignant progression and chemotherapeutic resistance of TNBC cells, underscoring the pivotal role of USP51 as a novel therapeutic target for cancer management.

In recent decades, the prevalence of hyperuricemia and gout has increased dramatically due to lifestyle changes. The drugs currently recommended for hyperuricemia are associated with adverse reactions that limit their clinical use. In this study, we report that berberine (BBR) is an effective drug candidate for the treatment of hyperuricemia, with its mechanism potentially involving the modulation of gut microbiota and its metabolite, succinic acid. BBR has demonstrated good therapeutic effects in both acute and chronic animal models of hyperuricemia. In a clinical trial, oral administration of BBR for 6 months reduced blood uric acid levels in 22 participants by modulating the gut microbiota, which led to an increase in the abundance of Bacteroides and a decrease in Clostridium sensu stricto_1. Furthermore, Bacteroides fragilis was transplanted into ICR mice, and the results showed that Bacteroides fragilis exerted a therapeutic effect on uric acid similar to that of BBR. Notably, succinic acid, a metabolite of Bacteroides, significantly reduced uric acid levels. Subsequent cell and animal experiments revealed that the intestinal metabolite, succinic acid, regulated the upstream uric acid synthesis pathway in the liver by inhibiting adenosine monophosphate deaminase 2 (AMPD2), an enzyme responsible for converting adenosine monophosphate (AMP) to inosine monophosphate (IMP). This inhibition resulted in a decrease in IMP levels and an increase in phosphate levels. The reduction in IMP led to a decreased downstream production of hypoxanthine, xanthine, and uric acid. BBR also demonstrated excellent renoprotective effects, improving nephropathy associated with hyperuricemia. In summary, BBR has the potential to be an effective treatment for hyperuricemia through the gut-liver axis.