Objective To explore the application value of predictive model based on nutritional status combined with clinical data in infectious complications after radical gastrectomy for gastric canc-er.Methods The clinical data on 394 gastric cancer patients who underwent radical gastrectomy at Suzhou Ninth Hospital Affiliated to Soochow University from January 2017 to July 2024 were retrospec-tively collected.According to whether the patients developed infection after surgery,they were divided into infection group(n=73)and non-infection group(n=321)based on whether they developed postoperative infections or not.The least absolute shrinkage and selection operator(LASSO)method was used to obtain the optimal related features associated with infectious complications after radical gas-trectomy for gastric cancer.Multivariate Logistic regression analysis was conducted to screen for risk factors of infectious complications after radical gastrectomy.R software was used to randomly select 70％of data as training set(for establishing the nomogram model)and 30％of data as test set.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve,Bootstrap sampling validation,and decision curve analysis(DCA)were used to assess the value of the predictive model.Results of the 394 gastric cancer patients,73 developed postoperative infectious complications,in-cluding 53.42％with lung infections,17.81％with intra-abdominal infections,16.44％with uri-nary tract infections,and 12.33％with wound infections.Age,diabetes mellitus,preoperative nu-tritional risk,high preoperative PIV,combined organ resection,and prolonged operation time were identified as risk factors for infectious complications after radical gastrectomy(P＜0.05).In the training set,the AUC of the nomogram model was 0.898,and the AUC validated in the test set was 0.891.Bootstrap sampling validation with 1,000 iterations showed that the average errors between the predicted probabilities and actual values in the training and test sets were 0.019 and 0.024,re-spectively.DCA results indicated that the model had clinical utility.Conclusion Infectious com-plications after gastrectomy for gastric cancer are common,and the predictive model based on nutri-tional assessment combined with clinical data has high value in predicting the risk of infectious com-plications after gastrectomy.

Objective To investigate the expression of ferritinophagy-related gene ELAV-like RNA binding protein 1(ELAVL1)in multiple myeloma(MM)and elucidate its diagnostic and prognostic value for MM.Methods First,we analyzed ELAVL1 expression level in healthy controls and MM patients using data from the GEO and TCGA databases.Subsequently,bone marrow specimens were collected from 28 newly diagnosed MM patients and 20 healthy controls,and qRT-PCR was employed to validate ELAVL1 expression.The diagnostic and prognostic potential of ELAVL1 was assessed using ROC curve analysis and Kaplan-Meier survival curves.Additionally,univariate and multivariate COX regression analyses were performed to identify independent risk factors for MM prognosis.Finally,KEGG and GO enrichment analyses were performed using the DAVID online platform.Results The level of ELAVL1 expression was significantly higher in newly diagnosed MM patients and refractory/relapsed MM patients than in the healthy controls(P＜0.001).Moreover,ELAVL1 expression was positively correlated with the International Staging System(ISS)stage of MM(P＜0.01).Furthermore,qRT-PCR validation confirmed that ELAVL1 expression was elevated in the 28 newly diagnosed MM patients compared to the 20 healthy controls(P＜0.001).ROC curve analysis demonstrated that ELAVL1 could effectively differentiate between newly diagnosed MM patients,healthy controls,and MGUS patients(P＜0.001 and P=0.000 2,respectively).Survival analysis revealed that high ELAVL1 expression was associated with shorter progression-free survival(P=0.0141)and overall survival(P=0.008 0).Univariate and multivariate COX regression analyses identified high ELAVL1 expression as an independent risk factor for poor MM prognosis(P=0.005 0).KEGG analysis suggested that ELAVL1 might be involved in the Hippo and MAPK signaling pathways.Conclusion High ELAVL1 expression in MM may serve as a biomarker for diagnosis and poor prognosis.ELAVL1 may promote MM initiation and progression via the Hippo and MAPK signaling pathways.

Objective To investigate the expression of ferritinophagy-related gene ELAV-like RNA binding protein 1(ELAVL1)in multiple myeloma(MM)and elucidate its diagnostic and prognostic value for MM.Methods First,we analyzed ELAVL1 expression level in healthy controls and MM patients using data from the GEO and TCGA databases.Subsequently,bone marrow specimens were collected from 28 newly diagnosed MM patients and 20 healthy controls,and qRT-PCR was employed to validate ELAVL1 expression.The diagnostic and prognostic potential of ELAVL1 was assessed using ROC curve analysis and Kaplan-Meier survival curves.Additionally,univariate and multivariate COX regression analyses were performed to identify independent risk factors for MM prognosis.Finally,KEGG and GO enrichment analyses were performed using the DAVID online platform.Results The level of ELAVL1 expression was significantly higher in newly diagnosed MM patients and refractory/relapsed MM patients than in the healthy controls(P＜0.001).Moreover,ELAVL1 expression was positively correlated with the International Staging System(ISS)stage of MM(P＜0.01).Furthermore,qRT-PCR validation confirmed that ELAVL1 expression was elevated in the 28 newly diagnosed MM patients compared to the 20 healthy controls(P＜0.001).ROC curve analysis demonstrated that ELAVL1 could effectively differentiate between newly diagnosed MM patients,healthy controls,and MGUS patients(P＜0.001 and P=0.000 2,respectively).Survival analysis revealed that high ELAVL1 expression was associated with shorter progression-free survival(P=0.0141)and overall survival(P=0.008 0).Univariate and multivariate COX regression analyses identified high ELAVL1 expression as an independent risk factor for poor MM prognosis(P=0.005 0).KEGG analysis suggested that ELAVL1 might be involved in the Hippo and MAPK signaling pathways.Conclusion High ELAVL1 expression in MM may serve as a biomarker for diagnosis and poor prognosis.ELAVL1 may promote MM initiation and progression via the Hippo and MAPK signaling pathways.

An UHPLC-Q-exactive orbitrap MS method for the simultaneous determination of 19 chemical components in Qilong Zhuang'er oral liquid was established and the quality differences between different batches of samples was compared by chemometric analysis to provide a basis for the quality evaluation of the preparation. The contents of allantoin, L-proline, pyroglutamic acid, hordenine, adenosine, L-phenylalanine, guanosine, L-tryptophan, caffeic acid, calycosin-7-glucoside, verbascoside, isoacteoside, ononin, calycosin, 3-hydroxy-9,10-dimethoxyptercarpan, formononetin, atractylenolide III, atractylenolide II and astragaloside A were analyzed by cluster heat map, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) using Hiplot platform and Markerlynx^XS software to comprehensively evaluate the quality difference of different batches of Qilong Zhuang'er oral liquid. The 19 chemical compounds showed good linearity in their respective concentration ranges (r ≥ 0.999). The RSD of precision, repeatability and stability (24 h) tests were all less than 1.94%. The average recovery was 97.24%-102.75% (RSD < 2.74%, n = 6). The 10 batches of samples were divided into two categories by cluster heat map and PCA analysis. 3-Hydroxy-9,10-dimethoxyptercarpan, atractylenolide III, calycosin, atractylenolide II, formononetin, allantoin and caffeic acid were identified as differential markers by PLS-DA. The established multi component quantitative method of Qilong Zhuang'er oral liquid combined with chemometric analysis can provide reference for the quality evaluation of the preparation.

Objective:To rapidly recognize and identify the chemical constituents in caulis of Erycibe schmidtii by ultra-high performance liquid chromatography coupled with Q-Exactive Focus mass spectrometry (UPLC-Q-Exactive Focus-MS/MS). Method:Taking 80% methanol extract of E. schmidtii caulis as the test solution, the chemical constituents in caulis of E. schmidtii were analyzed and identified. Thermo Accucure aQ C₁₈ column (2.1 mm×150 mm, 2.6 μm) was used for chromatographic separation with the mobile phase of methanol (A)-0.1% formic acid solution (B) for gradient elution (0-12 min, 5%-25%A; 12-20 min, 25%-30%A; 20-28 min, 30%-38%A; 28-40 min, 38%-42%A). Positive and negative ion monitoring modes and heated electrospray ion source (HESI) were used for mass spectrographic analysis. The scanning range was m/z 80-1 200. Result:A total of 42 chemical constituents from caulis of E. schmidtii were identified, including 12 coumarins, 14 chlorogenic acids, 1 tropane alkaloid, 1 amide and 14 esterified glycosides. Conclusion:Chemical constituents in caulis of E. schmidtii can be quickly and fully identified by UPLC-Q-Exactive Focus-MS/MS. Among them, 11 compounds are unambiguously identified by comparing with reference standards, 31 compounds are reported for the first time in this herb, 2 compounds are reported for the first time in Erycibe plants. This paper can provide the important basis for study on pharmacodynamic material base and substitute development of E. schmidtii caulis.