AIM:To construct a network calcula-tor based on machine learning(ML)models to pre-dict the risk of delayed awakening from anaesthesia in breast cancer(BC)patients.METHODS:A total of 435 BC patients surgically treated at our hospital from January 2023 to June 2024 were selected.The Boruta algorithm was used to screen for important characteristic variables for the risk of delayed awak-ening from anaesthesia.All patients were randomly assigned to a training set(n=261)and a test set(n=174)based on a 3:2 ratio and nine ML models were constructed and trained.Nine ML models were evaluated on the basis of receiver operating charac-teristic(ROC)curves for a random sample of 10 sub-jects and the clinical utility of the models was as-sessed using decision curve analysis.Combined with SHapley Additive exPlanations(SHAP)bar graphs,summary graphs and force diagrams additional in-terpretation and visualization of the ML model.Con-struction of a network calculator for predicting the risk of delayed awakening from anesthesia in BC pa-tients using the R package.RESULTS:Of the 435 BC patients,25.1%experienced delayed awakening from anesthesia.Boruta algorithm screened seven feature variables.The ROC curve shows that the XG-Boost model has the highest area under the curve(AUC)for 10 random samples among the 9 ML mod-els,and the decision curve shows that the XGBoost model has a significant clinical net benefit.The SHAP bar graph shows the importance of ASA classi-fication,surgery time,anesthesia time,intraopera-tive blood loss,propofol,preoperative anemia,and intraoperative hypothermia,and the SHAP summa-ry graph reflects the distribution of the ranges of in-fluence of the seven important characteristic vari-ables,which are"separated at the ends."The SHAP force diagram visualization XGBoost model predict-ed the risk of delayed awakening from anesthesia for individual patients with a predictive value of 0.998 for patients with delayed awakening from an-esthesia and 0.008 91 for patients without delayed awakening from anesthesia.A web-based calculator(https://xz-nomogram.shinyapps.io/DE_web/)based on an interpretable XGBoost model effective-ly predicts the risk of delayed awakening from anes-thesia in BC patients.CONCLUSION:ASA classifica-tion,surgery time,propofol,intraoperative blood loss,anaesthesia time,preoperative anaemia and intraoperative hypothermia are important charac-teristic variables for the risk of delayed awakening from anaesthesia in BC patients.The network calcu-lator based on the interpretable XGBoost model can accurately and quickly quantify the risk of de-layed awakening from anaesthesia,which can help clinicians to effectively adjust the treatment strate-gy and better improve the prognosis of patients.

AIM:To construct a network calcula-tor based on machine learning(ML)models to pre-dict the risk of delayed awakening from anaesthesia in breast cancer(BC)patients.METHODS:A total of 435 BC patients surgically treated at our hospital from January 2023 to June 2024 were selected.The Boruta algorithm was used to screen for important characteristic variables for the risk of delayed awak-ening from anaesthesia.All patients were randomly assigned to a training set(n=261)and a test set(n=174)based on a 3:2 ratio and nine ML models were constructed and trained.Nine ML models were evaluated on the basis of receiver operating charac-teristic(ROC)curves for a random sample of 10 sub-jects and the clinical utility of the models was as-sessed using decision curve analysis.Combined with SHapley Additive exPlanations(SHAP)bar graphs,summary graphs and force diagrams additional in-terpretation and visualization of the ML model.Con-struction of a network calculator for predicting the risk of delayed awakening from anesthesia in BC pa-tients using the R package.RESULTS:Of the 435 BC patients,25.1%experienced delayed awakening from anesthesia.Boruta algorithm screened seven feature variables.The ROC curve shows that the XG-Boost model has the highest area under the curve(AUC)for 10 random samples among the 9 ML mod-els,and the decision curve shows that the XGBoost model has a significant clinical net benefit.The SHAP bar graph shows the importance of ASA classi-fication,surgery time,anesthesia time,intraopera-tive blood loss,propofol,preoperative anemia,and intraoperative hypothermia,and the SHAP summa-ry graph reflects the distribution of the ranges of in-fluence of the seven important characteristic vari-ables,which are"separated at the ends."The SHAP force diagram visualization XGBoost model predict-ed the risk of delayed awakening from anesthesia for individual patients with a predictive value of 0.998 for patients with delayed awakening from an-esthesia and 0.008 91 for patients without delayed awakening from anesthesia.A web-based calculator(https://xz-nomogram.shinyapps.io/DE_web/)based on an interpretable XGBoost model effective-ly predicts the risk of delayed awakening from anes-thesia in BC patients.CONCLUSION:ASA classifica-tion,surgery time,propofol,intraoperative blood loss,anaesthesia time,preoperative anaemia and intraoperative hypothermia are important charac-teristic variables for the risk of delayed awakening from anaesthesia in BC patients.The network calcu-lator based on the interpretable XGBoost model can accurately and quickly quantify the risk of de-layed awakening from anaesthesia,which can help clinicians to effectively adjust the treatment strate-gy and better improve the prognosis of patients.

Despite therapy with potent antiviral agents, chronic hepatitis B (CHB) patients remain at high risk of hepatocellular carcinoma (HCC). While metabolites have been rediscovered as active drivers of biological processes including carcinogenesis, the specific metabolites modulating HCC risk in CHB patients are largely unknown. Here, we demonstrate that baseline plasma from CHB patients who later developed HCC during follow-up exhibits growth-promoting properties in a case-control design nested within a large-scale, prospective cohort. Metabolomics analysis reveals a reduction in long-chain acylcarnitines (LCACs) in the baseline plasma of patients with HCC development. LCACs preferentially inhibit the proliferation of HCC cells in vitro at a physiological concentration and prevent the occurrence of HCC in vivo without hepatorenal toxicity. Uptake and metabolism of circulating LCACs increase the intracellular level of acetyl coenzyme A, which upregulates histone H3 Lys14 acetylation at the promoter region of KLF6 gene and thereby activates KLF6/p21 pathway. Indeed, blocking LCAC metabolism attenuates the difference in KLF6/p21 expression induced by baseline plasma of HCC/non-HCC patients. The deficiency of circulating LCACs represents a driver of HCC in CHB patients with viral control. These insights provide a promising direction for developing therapeutic strategies to reduce HCC risk further in the antiviral era.

Objective:To investigate the effect of dexmedetomidine(DEX)on the polarization of alveolar macrophages in-duced by lipopolysaccharide(LPS)and to explore the related mechanisms.Methods:Rat alveolar macrophages NR8383 were cul-tured in vitro.Experiment one was divided into control group,model group(1 μg/ml LPS),DEX low,medium and high dose groups(1,5,10 mg/kg DEX+10 mg/kg LPS).Experiment two was divided into DEX high dose group(10 mg/kg)and DEX high dose+Colive-lin(JAK2/STAT3 signaling pathway activator)group(10 mg/kg DEX+0.5 μmol/L Colivelin).The morphological changes of rat alveo-lar macrophages NR8383 were observed by inverted microscope;RT-PCR method was used to detect the expression levels of iNOS and Arg1 mRNA in NR8383 cells,and flow cytometry was used to detect the expression levels of CD86 and CD163 proteins in NR8383 cells;Western blot was used to detect the expression levels of surface marker proteins TNF-α,iNOS,SOCS,Arg1,TGF-β and JAK2/STAT3 signaling pathway related proteins in NR8383 cells.Results:Compared with control group,there were a lot of cell debris in the intercellular space of NR8383 in the model group,the proportions of iNOS mRNA,CD86 positive cells,and the expression levels of TNF-α,p-JAK2/JAK2,p-STAT3/STAT3 were significantly increased,the proportions of Arg1 mRNA,CD163 positive cells,and the expression levels of SOCS and TGF-β were significantly reduced(P<0.05);compared with the model group,the NR8383 intercellular cell debris in the DEX low,medium,and high dose groups were decreased,the proportions of iNOS mRNA,CD86 positive cells,and the expression levels of TNF-α,p-JAK2/JAK2,p-STAT3/STAT3 were significantly reduced,the proportions of Arg1 mRNA,CD163 positive cells,and the expression levels of SOCS and TGF-β were significantly increased(P<0.05).The reactivation of the JAK2/STAT3 signal pathway by Colivelin could weaken the role of DEX in LPS induced NR8383 cell polarization.Conclusion:DEX can inhibit the M1 polarization of NR8383 cells induced by LPS,which may be achieved by inhibiting the JAK2/STAT3 signaling pathway.

Objective:To evaluate the diagnostic value of hematological parameters for PCa with prostate-specific antigen(PSA)of 4-10 μg/L and construct a risk-stratification model with these parameters.Methods:We retrospectively analyzed the da-ta on the males undergoing the initial prostatic biopsy in the Affiliated Hospital of Xuzhou Medical University with PSA of 4-10 μg/L from March 2010 to April 2021.According to the results of biopsy,we classified the patients into a PCa and a non-PCa group,and compared the hematological parameters between the two groups.We performed univariate and multivariate logistic regression analyses,identified the independent risk factors for PCa,constructed a risk-stratification model for the prediction of PCa and evaluated its effi-ciency.Results:A total of 415 cases were included in this study,107(25.8％)in the PCa and 308(74.2％)in the non-PCa group.Compared with the non-PCa males,the PCa patients showed a significantly older age,higher ratios of neutrophil to lymphocyte and platelet to lymphocyte,systemic immune-inflammation index(SII),red blood cell distribution width and cystatin C(CysC)level(all P＜0.05),but lower red blood cell count and hemoglobin and free/total PSA(f/tPSA)levels(all P＜0.05).Multivariate logis-tic regression analysis indicated that age,f/tPSA,SII and CysC were independent risk factors for the prediction of PCa(all P＜0.05).Five prediction models were constructed based on the above risk factors,and the area under the ROC curve(AUC)of the four-parameter(age+f/tPSA+SII+CysC)model was 0.745(95％CI:0.694-0.796),significantly higher than those of the other mod-els(P＜0.05).A risk-stratification model(low-,intermediate-,and high-risk)was also constructed based on the total nomogram scores,which showed a comparable performance to that of the Prostate Imaging Reporting and Data System(PI-RADS)for the predic-tion of PCa(AUC:0.727[95％CI:0.650-0.804]vs 0.734[95％CI:0.658-0.811]).However,the prediction rate by the risk-stratification model was evidently higher in the low-risk males than in those with low PI-RADS scores(1-2)(39.4％vs 22.2％).Conclusion:SII and CysC are independent risk factors for the prediction of PCa in patients with gray-zone PSA levels.The risk-stratification model based on age,SII,CysC and f/tPSA is comparable to PI-RADS in the diagnostic efficiency of PCa,with an even higher prediction rate in low-risk patients than in those with low PI-RADS scores,and contributive to precision screening and reduction of excessive biopsies in the diagnosis of PCa with gray-zone PSA.

ObjectiveTo observe the apoptosis induced by paeoniflorin （PF） in non-small cell lung cancer （NSCLC） cells and explore its mechanism. MethodCell counting kit-8 （CCK-8） was used to detect the inhibition rates of H1299， H292 and A549 cells with different concentrations of PF （2.5， 5， 10， 20， 25 µmol·L^-1）， and to screen suitable concentrations of PF and experimental cells. The inhibitory effect of PF on lung cancer cells was detected by clone formation assay. The effect of PF on cell apoptosis was detected by flow cytometry with annexin V-FITC/propidium iodide （PI） double staining. With the right concentration of drugs， levels of apoptosis-associated protein B-cell lymphoma-2 （Bcl-2）， Bcl-2-associated X protein （Bax）， cleaved Caspase-3 and Caspase-3 were detected by Western blot. At the same time， the molecular expressions of hypoxia inducible factor -1α （HIF-1α） and Hippo signaling pathway were determined. ResultCompared with the blank group， PF significantly inhibited the growth of H1299， H292 and A549 cells of human lung cancer （P<0.01）. PF significantly induced apoptosis in A549 cells （P<0.01）， decreased the Bcl-2/Bax ratio （P<0.01）， and significantly increased the cleaved Caspase-3 expression （P<0.01）. Compared with those in the blank group， the expression levels of HIF-1α， transcriptional coactivator with PDZ-binding motif （TAZ）， large tumor suppressor 1 （LATS1）， Mps one binding 1 （MOB1） and Yes-associated protein （YAP） in A549 cells of the PF treatment group were significantly decreased （P<0.01）， while the expressions of p-LATS1， p-MOB1 and p-YAP were significantly increased （P<0.01）. At the same time， there was no significant effect on the expression levels of phosphorylated mammalian Ste20-like kinase 1 （p-MST1） and MST1， which did not reach a statistical difference. ConclusionAll data demonstrated that PF showed an anti-tumor effect by improving hypoxic conditions and inhibiting the abnormally activated Hippo signaling pathway， thereby inducing and promoting apoptosis in non-small cell lung cancer.

Objective To investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation(VA-ECMO).Methods We retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January,2015 and January,2022 using a convenience sampling method.The patients were divided into a derivation cohort(201 cases)and a validation cohort(101 cases).Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients,based on which a risk prediction model was established in the form of a nomogram.The receiver operator characteristic(ROC)curve,calibration curve and clinical decision curve were used to evaluate the discrimination ability,calibration and clinical validity of this model.Results The in-hospital mortality risk prediction model was established based the risk factors including hypertension(OR=3.694,95%CI:1.582-8.621),continuous renal replacement therapy(OR=9.661,95%CI:4.103-22.745),elevated Na2+ level(OR=1.048,95%CI:1.003-1.095)and increased hemoglobin level(OR=0.987,95%CI:0.977-0.998).In the derivation cohort,the area under the ROC curve(AUC)of this model was 0.829(95%CI:0.770-0.889),greater than those of the 4 single factors(all AUC<0.800),APACHE Ⅱ Score(AUC=0.777,95%CI:0.714-0.840)and the SOFA Score(AUC=0.721,95%CI:0.647-0.796).The results of internal validation showed that the AUC of the model was 0.774(95%CI:0.679-0.869),and the goodness of fit test showed a good fitting of this model(χ2=4.629,P>0.05).Conclusion The risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation,calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system,and thus can be used for assessing disease severity and prognostic risk level in critically ill patients.

Objective To investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation(VA-ECMO).Methods We retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January,2015 and January,2022 using a convenience sampling method.The patients were divided into a derivation cohort(201 cases)and a validation cohort(101 cases).Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients,based on which a risk prediction model was established in the form of a nomogram.The receiver operator characteristic(ROC)curve,calibration curve and clinical decision curve were used to evaluate the discrimination ability,calibration and clinical validity of this model.Results The in-hospital mortality risk prediction model was established based the risk factors including hypertension(OR=3.694,95%CI:1.582-8.621),continuous renal replacement therapy(OR=9.661,95%CI:4.103-22.745),elevated Na2+ level(OR=1.048,95%CI:1.003-1.095)and increased hemoglobin level(OR=0.987,95%CI:0.977-0.998).In the derivation cohort,the area under the ROC curve(AUC)of this model was 0.829(95%CI:0.770-0.889),greater than those of the 4 single factors(all AUC<0.800),APACHE Ⅱ Score(AUC=0.777,95%CI:0.714-0.840)and the SOFA Score(AUC=0.721,95%CI:0.647-0.796).The results of internal validation showed that the AUC of the model was 0.774(95%CI:0.679-0.869),and the goodness of fit test showed a good fitting of this model(χ2=4.629,P>0.05).Conclusion The risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation,calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system,and thus can be used for assessing disease severity and prognostic risk level in critically ill patients.

OBJECTIVE To explore the improvement effect and mechanism of petroleum ether extract of Saposhnikovia divaricata on rheumatoid arthritis （RA） rats by regulating neutrophil extracellular traps （NETs）. METHODS Establishment of rat RA model using bovine type Ⅱ collagen and Freund’s complete adjuvant. The model rats were randomly divided into model group and low-dose， middle-dose and high-dose groups （55， 110， 220 mg/kg） of petroleum ether extract of S. divaricata； the normal group without modeling was also established， with 10 rats in each group. Each group was given corresponding drugs or constant volume of 2% Tween-80 solution intragastrically， once a day， for consecutive 28 days. The toe swelling degree in rats was observed， and the arthritis index （AI） was scored. The serum levels of interleukin-1β （IL-1β）， IL-10， IL-17， tumor necrosis factor-α （TNF-α）， 25-hydroxyvitamin D3 [25（OH）D3]， myeloperoxidase （MPO）， neutrophil elastase （NE） and NETs in rats were detected. The histopathological changes in ankle joint were observed. The expression of citrullinated histone H3 （CitH3） in ankle joint as well as the expressions of cytochrome P450 24A1 （CYP24A1）， cytochrome P450 27B1 （CYP27B1），vitamin D receptor （VDR） and peptidyl arginine deiminase 4 疾病。E-mail：zhongyao626@126.com （PAD4） in synovium were all determined. RESULTS Compared with the model group， the toe swelling degree and AI score in the middle-dose and high-dose groups of petroleum ether extract of S. divaricata decreased significantly from day 14 to day 28 after administration （P＜0.05 or P＜0.01）. The serum levels of IL-1β， IL-17， TNF-α， MPO， NE and NETs decreased significantly， while the levels of IL-10 and 25（OH）D3 increased significantly （P＜0.05 or P＜0.01）. The widened ankle joint space and the improved structure were found； the expression of CitH3 in ankle joint， and the expressions of CYP24A1 and PAD4 in synovium were down-regulated significantly， while the expressions of CYP27B1 and VDR were up-regulated significantly in synovium （P＜0.05 or P＜0.01）. CONCLUSIONS The petroleum ether extract of S. divaricata may inhibit the production of NETs and improve the symptoms of RA by regulating the vitamin D system.

OBJECTIVE: To screen the prognostic biomarkers of metabolic genes in patients with multiple myeloma (MM), and construct a prognostic model of metabolic genes. METHODS: The histological database related to MM patients was searched. Data from MM patients and healthy controls with complete clinical information were selected for analysis.The second generation sequencing data and clinical information of bone marrow tissue of MM patients and healthy controls were collected from human protein atlas (HPA) and multiple myeloma research foundation (MMRF) databases. The gene set of metabolism-related pathways was extracted from Molecular Signatures Database (MSigDB) by Perl language. The biomarkers related to MM metabolism were screened by difference analysis, univariate Cox risk regression analysis and LASSO regression analysis, and the risk prognostic model and Nomogram were constructed. Risk curve and survival curve were used to verify the grouping effect of the model. Gene set enrichment analysis (GSEA) was used to study the difference of biological pathway enrichment between high risk group and low risk group. Multivariate Cox risk regression analysis was used to verify the independent prognostic ability of risk score. RESULTS: A total of 8 mRNAs which were significantly related to the survival and prognosis of MM patients were obtained (P<0.01). As molecular markers, MM patients could be divided into high-risk group and low-risk group. Survival curve and risk curve showed that the overall survival time of patients in the low-risk group was significantly better than that in the high risk group (P<0.001). GSEA results showed that signal pathways related to basic metabolism, cell differentiation and cell cycle were significantly enriched in the high-risk group, while ribosome and N polysaccharide biosynthesis signaling pathway were more enriched in the low-risk group. Multivariate Cox regression analysis showed that the risk score composed of the eight metabolism-related genes could be used as an independent risk factor for the prognosis of MM patients, and receiver operating characteristic curve (ROC) showed that the molecular signatures of metabolism-related genes had the best predictive effect. CONCLUSION Metabolism-related pathways play an important role in the pathogenesis and prognosis of patients with MM. The clinical significance of the risk assessment model for patients with MM constructed based on eight metabolism-related core genes needs to be confirmed by further clinical studies.
Humans ; Cell Cycle ; Multiple Myeloma/genetics* ; Prognosis ; Risk Factors