The development of polyoxometalates for olefin oxidation is critical to achieving the green chemical process of the C5 fraction further processing.Di-lacunary silicotungstic anions were easily obtained by continuously...The development of polyoxometalates for olefin oxidation is critical to achieving the green chemical process of the C5 fraction further processing.Di-lacunary silicotungstic anions were easily obtained by continuously adjusting the p H instead of the traditional step-by-step method,which exhibited excellent performance in the catalytic oxidation of cyclopentene(CPE)to aldehydes or alcohols.The 93.69%CPE conversion and 97.15%total product selectivity(41.38%for glutaraldehyde(GA)and 55.77%for 1,2-cyclopentanediol(1,2-diol)were achieved by using H_(2)O_(2)as the oxidant and acetonitrile as the solvent.Through complementary characterization,it was found that the optimized di-lacunary silicotungstic polyoxometalate retained a complete Keggin structure,and exhibited better catalytic activity and stability than the mono-lacunary or saturated silicodecatungstate because it exposed more catalytic active centers.Furthermore,in situ FT-IR spectra was utilized to monitor the reaction process,revealing the formation of the active species W(O_(2))on the di-lacunary silicotungstic polyoxometalate and the intermediate epoxycyclopentane during the catalytic oxidation of cyclopentene.展开更多
Background and Objectives:Gastric cancer(GC)is the fourth leading cause of cancer death worldwide.Pa tients with GC have higher nutritional risk.This study aimed t o construct a nomogram model for predicting pre opera...Background and Objectives:Gastric cancer(GC)is the fourth leading cause of cancer death worldwide.Pa tients with GC have higher nutritional risk.This study aimed t o construct a nomogram model for predicting pre operative nutritional risk in patients with GC in order to assess preoperative nutritional risk in patients more pre cisely.Methods and S tudy Design:Patients diagnosed with GC and undergoing surgical treatment were includ ed in this study.Data was collected through clinical information,laboratory testing,and radiomics-derived char acteristics.L east absolute shrinkage selection operator(LASSO)regression analysis and multi-variable logistic regression were employed to construct a clinical prediction model,which takes the form of a logistic nomogram.The effectiveness of the nomogram model was evaluated using receiver operating characteristic(ROC)curve,calibration curve,and decision curve analysis(DCA).Results:A total of three predictors,namely body mass in dex(BMI),hemoglobin(Hb)and radiomics characteristic score(Radscore)were identified by LASSO regression analysis from a total of 21 variables studied.The model constructed using these three predictors displayed medi um prediction ability.The area under the ROC curve was 0.895(95%CI 0.844-0.945)in the training set,with a cutoff value of 0.651,precision of 0.957,and sensitivity of 0.718.In the validation set,it was 0.880(95%CI 0.806-0.954),with a cutoff value of 0.655,precision of 0.930,and sensitivity of 0.698.DCA also confirmed the clinical benefit of the combined model.Conclusions:This simple and dependable nomogram model for clinical prediction can assist physicians in assessing preoperative nutritional risk in GC patients in a time-efficient and accurate manner to facilitate early identification and diagnosis.展开更多
基金the Science and Technology Project of Maoming(China)(200203094555139)for financial support。
文摘The development of polyoxometalates for olefin oxidation is critical to achieving the green chemical process of the C5 fraction further processing.Di-lacunary silicotungstic anions were easily obtained by continuously adjusting the p H instead of the traditional step-by-step method,which exhibited excellent performance in the catalytic oxidation of cyclopentene(CPE)to aldehydes or alcohols.The 93.69%CPE conversion and 97.15%total product selectivity(41.38%for glutaraldehyde(GA)and 55.77%for 1,2-cyclopentanediol(1,2-diol)were achieved by using H_(2)O_(2)as the oxidant and acetonitrile as the solvent.Through complementary characterization,it was found that the optimized di-lacunary silicotungstic polyoxometalate retained a complete Keggin structure,and exhibited better catalytic activity and stability than the mono-lacunary or saturated silicodecatungstate because it exposed more catalytic active centers.Furthermore,in situ FT-IR spectra was utilized to monitor the reaction process,revealing the formation of the active species W(O_(2))on the di-lacunary silicotungstic polyoxometalate and the intermediate epoxycyclopentane during the catalytic oxidation of cyclopentene.
基金funded by the National Natural Science Foundation of China[grant no.82060430]Guangxi Clinical Research Center for Enhanced Recovery after Surgery+2 种基金Guangxi Science and Technology Base and Talent Project[grant no.AD19245196]the Guangxi Key Research and Development Project[grant no.AB18126058]Guangxi key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer[grant no.YYZS2020003].
文摘Background and Objectives:Gastric cancer(GC)is the fourth leading cause of cancer death worldwide.Pa tients with GC have higher nutritional risk.This study aimed t o construct a nomogram model for predicting pre operative nutritional risk in patients with GC in order to assess preoperative nutritional risk in patients more pre cisely.Methods and S tudy Design:Patients diagnosed with GC and undergoing surgical treatment were includ ed in this study.Data was collected through clinical information,laboratory testing,and radiomics-derived char acteristics.L east absolute shrinkage selection operator(LASSO)regression analysis and multi-variable logistic regression were employed to construct a clinical prediction model,which takes the form of a logistic nomogram.The effectiveness of the nomogram model was evaluated using receiver operating characteristic(ROC)curve,calibration curve,and decision curve analysis(DCA).Results:A total of three predictors,namely body mass in dex(BMI),hemoglobin(Hb)and radiomics characteristic score(Radscore)were identified by LASSO regression analysis from a total of 21 variables studied.The model constructed using these three predictors displayed medi um prediction ability.The area under the ROC curve was 0.895(95%CI 0.844-0.945)in the training set,with a cutoff value of 0.651,precision of 0.957,and sensitivity of 0.718.In the validation set,it was 0.880(95%CI 0.806-0.954),with a cutoff value of 0.655,precision of 0.930,and sensitivity of 0.698.DCA also confirmed the clinical benefit of the combined model.Conclusions:This simple and dependable nomogram model for clinical prediction can assist physicians in assessing preoperative nutritional risk in GC patients in a time-efficient and accurate manner to facilitate early identification and diagnosis.
