A fluorescent sensor based on single band bright red luminescent core-shell UCNPs for the high-sensitivity detection of glucose and glutathione.

As the reliable biomarkers to evaluate the diabetes and neurological disease, sensitive and accurate detection of glucose and glutathione (GSH) in biological samples is necessary for early precaution and diagnosis of related-diseases. The single red upconversion nanoparticles (UCNPs) especially with core-shell structure can penetrate deeper biological tissues and cause less energy loss and thus have higher sensitivity and accuracy. Additionally, an enzyme-controlled cascade signal amplification (ECSAm) strategy will further enhance sensitivity. Herein, using single red UCNPs with core-shell structure as the luminescent material, a fluorescent sensor based on ECSAm was developed for the highly sensitive and accurate detection of glucose and GSH. Under the optimal conditions, the limits of detection for glucose and GSH by fluorescent method were 0.03 µM and 0.075 µM, separately. This assay was used to analyze the content of glucose and GSH in serum samples, and the obtained data was close to that of commercial blood glucose and GSH detection kit. The developed sensor platform based on single red UCNPs with core-shell structure and ECSAm can be a promising method for the accurate and sensitive detection of glucose and GSH in biological samples.

[Autonomous and controlled motivation fit on college students' physical fitness:based on the polynomial regression and response surface analysis].

OBJECTIVE: Based on the self-determination theory, to explore the relationship between motivation quality and college students' physical fitness and the mediating role of physical activities from the perspective of the coexistence of autonomous motivation(AM) and controlled motivation(CM). METHODS: From October to November 2019, a total of 682 freshmen and sophomores(252 males and 430 females) were recruited with cluster-sampling method from 4 colleges and universities in Wuhu City, filled with questionnaires of Perceived Locus of Causality scale and Godin's leisure-time physical activity questionnaire, and tested physical fitness according to China National Fitness Test Program after 6 weeks. The data were analyzed by polynomial regression combined with response surface analysis and mediation effect test. RESULTS: Physical fitness presented a "convex" curve increase with the consistency of AM and CM(a1=1.547, a2=1.254, P<0.01). The physical fitness of high AM-high CM combination was higher than that of low AM-low CM combination(Z_(hat)=3.111, 95% CI 0.446-5.896). Under the condition of AM and CM differentiation, physical fitness was higher when the discrepancy was such that AM was higher than CM(a3=5.280, P<0.01; a4=0.232, P>0.05). AM positively predicted physical activity in a nonlinear form(ß_( AM)=1.605, ß_(AM)~2=1.602, P<0.01). Physical activity showed a "convex" change with the consistency of AM-CM(a1=0.811, P > 0.05; a2=1.618, P <0.01), but there was no significant difference in the level of physical activity between high and low AM-CM combination(Z_(hat)=1.407, 95% CI-0.084-3.391). Physical activity was higher in high AM-low CM combination than that in high CM-low AM combination(Z_(hat)=5.008, 95% CI 2.348-7.113). Matching of AM and CM influenced college students' physical fitness directly(ß=0.453, P<0.01) and indirectly through physical activity(ß=0.184, 95% CI 0.145-0.240). CONCLUSION: The coexistence of AM and CM effects physical fitness through their consistency and inconsistency matching.

Predicting the onset temperature (Tg) of GexSe1-x glass transition: a feature selection based two-stage support vector regression method.

Despite the usage of both experimental and topological methods, realizing a rapid and accurate measurement of the onset temperature (Tg) of GexSe1-x glass transition remains an open challenge. In this paper, a predictive model for the Tg in GexSe1-x glass system is presented by a machine learning method named feature selection based two-stage support vector regression (FSTS-SVR). Firstly, Pearson correlation coefficient (PCC) is used to select features highly correlated with Tg from the candidate features of GexSe1-x glass system. Secondly, in order to simulate the two-stage characteristic of Tg which is caused by structural variation with a turning point at x = 0.33 via the structural analysis, SVR is utilized to build predictive models for two stages separately and then the two achieved models are synthesized using a minimum error based model for Tg prediction. Compared with the topological and other methods based on SVR, the FSTS-SVR gives the highest predictive accuracy with the root mean square error (RMSE) and mean absolute percentage error (MAPE) of 10.64 K and 2.38%, respectively. This method is also expected to be more efficient for the prediction of Tg of other glass systems with the multi-stage characteristic.