ObjectiveGastric cancer (GC) seriously affects human health and life, and research has shown that it is closely related to the serine/glycine metabolism. The proliferation ability of tumor cells is greatly influenced by the metabolism of serine and glycine. The aim of this study was to investigate the molecular mechanism of serine/glycine metabolism can affect the proliferation of gastric cancer cells. MethodsIn this work, a stable metabolic dynamic model of gastric cancer cells was established via a large-scale metabolic network dynamic modeling method in terms of a potential landscape description of stochastic and non-gradient systems. Based on the regulation of the model, a quantitative analysis was conducted to investigate the dynamic mechanism of serine/glycine metabolism affecting the proliferation of gastric cancer cells. We introduced random noise to the kinetic equations of the general metabolic network, and applied stochastic kinetic decomposition to obtain the Lyapunov function of the metabolic network parameter space. A stable metabolic network was achieved by further reducing the change in the Lyapunov function tied to the stochastic fluctuations. ResultsDespite the unavailability of a large number of dynamic parameters, we were able to successfully construct a dynamic model for the metabolic network in gastric cancer cells. When extracellular serine is available, the model preferentially consumes serine. In addition, when the conversion rate of glycine to serine increases, the model significantly upregulates the steady-state fluxes of S-adenosylmethionine (SAM) and S-adenosyl homocysteine (SAH). ConclusionIn this paper, we provide evidence supporting the preferential uptake of serine by gastric cancer cells and the important role of serine/glycine conversion rate in SAM generation, which may affect the proliferation ability of gastric cancer cells by regulating the cellular methylation process. This provides a new idea and direction for targeted cancer therapy based on serine/glycine metabolism.

Using the liquid chromatography-tandem mass spectrometry (LC-MS/MS) to determine the plasma level of Lyso-GL3 in patients with Fabry disease and to analyze the clinical application of the method.

Based on the genomic information of Emericella sp. 1454, in conjunction with literature analysis of its secondary metabolite emestrin, this study identified the biosynthetic precursors of emestrin and enhanced its production by supplementing the culture medium with these precursors. In this study, it was found for the first time that the addition of biosynthetic precursor, reduced glutathione, to the culture medium significantly increased emestrin yield. By incorporating 1.5 g of reduced glutathione into 50 g of rice culture medium and fermenting for 15 days, a yield of 30.82 mg of emestrin was obtained, which marked an 11.71-fold increase compared to the original fermentation approach. The method is both simple and cost-effective, establishing a solid foundation for the efficient synthesis of emestrin and similar compounds. Additionally, it serves as an important reference for enhancing the production of other epipolythiodioxopiperazine compounds.

AIM:Bone morphogenetic protein 7(BMP7)reduces the expression of Yes-related protein 1(YAP1)by down-regulating Ajuba level and decreasing extracellular matrix(ECM)deposition.This study aimed to inves-tigate the influence of these factors on modifying the degree of renal fibrosis in rats with diabetic nephropathy.METH-ODS:Eighteen Sprague-Dawley(SD)rats were randomly divided into three groups:the normal control(NC)group,the diabetes mellitus(DM)group,and the DM group treated with BMP7 overexpressing adeno-associated virus(DM+rAAV-BMP7).Each group consisted of six rats.Diabetic kidney disease(DKD)was established in the DM and DM+rAAV-BMP7 groups by injecting 55 mg/kg streptozotocin(STZ)via the tail vein.NRK-52E cells were divided into three groups:the normal glucose(NG)group,the high glucose(HG)group,and the high glucose group treated with recombinant hu-man BMP7(HG+rhBMP7)group.Pathological changes in renal tissues were observed using hematoxylin and eosin(HE)and Sirius red staining.Immunohistochemical staining was performed to examine the expression sites of Ajuba and YAP1 in the renal cortex.Western blot analysis was conducted to determine the expression levels of BMP7,Ajuba,YAP1,colla-gen type Ⅲ(Col-Ⅲ),and fibronectin(FN)in the rat renal cortex and NRK-52E cells.RT-qPCR was used to measure the mRNA levels of Ajuba and YAP1 in the rat renal cortex.RESULTS:Biochemical indices revealed significantly ele-vated levels of blood glucose,serum creatinine,triglycerides,total cholesterol,and 24-hour urinary protein in the DM group compared to the NC group(P<0.05).In the DM+rAAV-BMP7 group,the levels of serum creatinine,24-hour uri-nary protein,triglycerides,and total cholesterol were lower than those in the DM group(P<0.05).Pathological staining demonstrated that the renal interstitium of the DM group exhibited inflammatory cell infiltration,fibrous tissue,collagen fi-ber deposition,disordered renal tubule arrangement,atrophy,and vacuolar degeneration,which were ameliorated in the DM+rAAV-BMP7 group.Immunohistochemistry revealed that Ajuba and YAP1 were mainly expressed in the cytoplasm and nucleus,with high expression in the cytoplasm of the DM group,which was significantly decreased in the DM+rAAV-BMP7 group.Western blot results indicated that the protein levels of FN,Col-Ⅲ,Ajuba,and YAP1 were up-regulated in the DM and the HG groups(P<0.05),but significantly down-regulated in the DM+rAAV-BMP7 group(P<0.05).RT-qP-CR results demonstrated that the mRNA levels of Ajuba and YAP1 were higher in the DM group and significantly lower in the DM+rAAV-BMP7 group(P<0.05).CONCLUSION:The overexpression of BMP7 can ameliorate renal fibrosis in rats with DKD.This effect is likely mediated by the down-regulation of Ajuba,reduction of YAP1 expression,and subse-quent inhibition of ECM deposition.

Objective To explore the efficacy of T-cell spot test of tuberculosis infection(T-SPOT.TB)in the differential diagnosis of spinal tuberculosis(STB),and optimize diagnostic efficacy through the optimal cut-off value of receiver operating characteristic(ROC)curve.Methods Clinical data of patients with spinal infection in a hospi-tal from January 2010 to May 2019 were collected,including preoperative T-SPOT.TB test results,white blood cell count,C-reactive protein,erythrocyte sedimentation rate,procalcitonin,and tuberculosis antibodies,etal.Clinical diagnosis was conducted based on diagnostic criteria.The sensitivity and specificity of T-SPOT.TB in preoperative diagnosis of STB and other spinal infection was analyzed,and the diagnostic efficacy of the optimized T-SPOT.TB indicators was evaluated.Results A total of 132 patients were included in this study,out of whom 78 patients(59.09％)were diagnosed with STB,and 54(40.91％)were diagnosed with non-tuberculosis(non-TB)spinal in-fection.The sensitivity and specificity of T-SPOT.TB in differential diagnosis of STB were 67.68％and 66.67％,respectively.Univariate logistic regression analysis showed that compared with non-TB spinal infection,the OR va-lue of T-SPOT.TB test in diagnosing STB was 4.188(95％CI:1.847-9.974,P＜0.001).The optimized T-SPOT.TB evaluation index through ROC curve to determine the optimal cut-off values of ESAT-6,CFP-10,and CFP-10+ESAT-6 for differential diagnosis of STB and non-TB spinal infection were 12.5,19.5,and 36,respec-tively,and area under curve(AUC)values were 0.765 6,0.741 5,and 0.778 6,respectively,all with good diag-nostic efficacy.CFP-10+ESAT-6 had the highest AUC.CFP-10+ESAT-6 specific spot count had higher efficacy in the diagnosis of STB,with a diagnostic accuracy of 75.56％,higher than 67.42％of pre-optimized T-SPOT.TB.Conclusion T-SPOT.TB test has high diagnostic efficacy in differentiating STB from non-TB spinal infection.Posi-tivity in T-SPOT.TB test,especially with spot count of CFP-10+ESAT-6 over 36,indicates a higher likelihood of STB.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.