A dual-mode biosensor based on metal organic framework-coated upconversion composites with near-infrared luminescence and peroxidase-like activity for the detection of alkaline phosphatase and glucose.

An abnormal level of alkaline phosphatase (ALP) in serum is related to many diseases, such as breast cancer, prostate cancer, hepatitis, and diabetes. The level of glucose in the blood is related to diabetes or hypoglycemia. Given the close correlation between ALP and glucose in various diseases, it is essential to establish an accurate, sensitive, and selective assay for monitoring the levels of ALP and glucose in serum.

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.

Highly sensitive detection for xanthine by combining single-band red up-conversion nanoparticles and cycle signal amplification strategy based on internal filtration effect.

Up-conversion nanoparticles (UCNPs), especially single-band bright red UCNPs, have better penetration of biological tissues, absorb less lost energy, and have higher sensitivity and accuracy in the determination of actual biological samples in the field of biosensing. Here, a novel colorimetric and fluorescent dual-channel method based upon an internal filtration effect (IFE) quenching mechanism was proposed for the quantitative analysis of xanthine (XA) by using red UCNPs as fluorescence indicator and 3,3',5,5' -tetramethylbenzidine (TMB) as chromogenic substrate. The sensitivity of the detection system was also enhanced by a cycle signal amplification strategy based on the Fenton reaction.

[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.

Predicting the onset temperature (T) of GeSe 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 (T) of GeSe glass transition remains an open challenge. In this paper, a predictive model for the T in GeSe 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 T from the candidate features of GeSe glass system.