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Background and Purpose: Inhibition of PDE5 improves synaptic plasticity and memory via enhancing cGMP expression, thus activating the cGMP/cAMP response element binding protein (CREB) signalling pathway. This study investigated the effects of a PDE5 inhibitor on scopolamine-induced cognitive dysfunction, using memory-related behavioural tests and biochemical assays.Experimental Approach: In mice were pretreated with PDE5 inhibitor, amnesia was induced by scopolamine. The learning and memory abilities of mice were tested using the Morris water maze test, the Y-maze test, the passive avoidance test and the novel object recognition test in sequence. Expression of memory-related bio-molecules and oxidative stress parameters in brain tissue were measured using Western blot and spectrophotometry respectively.Key Results: KJH-1002, a novel and potent inhibitor of PDE5 (IC50 0.059 ± 0.04 nmol·L-1 ), was synthesized. In the behavioural tests, it markedly improved the memory performance impaired by scopolamine, indicating a restoration of cognitive function in the mice. Moreover, KJH-1002 increased cGMP levels in the cortex and the scopolamine-reduced expression of phosphorylated CREB, Levels of ERK 1/2, Akt and brain-derived neurotrophic factor in the cortex and hippocampus were restored by KJH-1002 treatment. In addition, KJH-1002 administration increased the activities of SOD, glutathione peroxidase and glutathione reductase, and decreased the level of malondialdehyde.Conclusion and Implications: KJH-1002 restored cognitive function in scopolamine-induced amnesia mice by activating the cGMP/CREB signalling pathway and attenuating oxidative stress. The beneficial effects of KJH-1002 on cognition indicate its potential as a therapeutic candidate for Alzheimer's disease. [ABSTRACT FROM AUTHOR]

This research investigated the relation between racial categorization and implicit racial bias in majority and minority children. Chinese and Indian 3- to 7-year-olds from Singapore (N = 158) categorized Chinese and Indian faces by race and had their implicit and explicit racial biases measured. Majority Chinese children, but not minority Indian children, showed implicit bias favoring own race. Regardless of ethnicity, children's racial categorization performance correlated positively with implicit racial bias. Also, Chinese children, but not Indian children, displayed explicit bias favoring own race. Furthermore, children's explicit bias was unrelated to racial categorization performance and implicit bias. The findings support a perceptual-social linkage in the emergence of implicit racial bias and have implications for designing programs to promote interracial harmony. [ABSTRACT FROM AUTHOR]

Survival regression is commonly applied in biomedical studies or clinical trials, and evaluating their predictive performance plays an essential role for model diagnosis and selection. The presence of censored data, particularly if informative, may pose more challenges for the assessment of predictive accuracy. Existing literature mainly focuses on prediction for survival probabilities with limitation work for survival time. In this work, we focus on accuracy measures of predicted survival times adjusted for a potentially informative censoring mechanism (ie, coarsening at random (CAR); non-CAR) by adopting the technique of inverse probability of censoring weighting. Our proposed predictive metric can be adaptive to various survival regression frameworks including but not limited to accelerated failure time models and proportional hazards models. Moreover, we provide the asymptotic properties of the inverse probability of censoring weighting estimators under CAR. We consider the settings of high-dimensional data under CAR or non-CAR for extensions. The performance of the proposed method is evaluated through extensive simulation studies and analysis of real data from the Critical Assessment of Microarray Data Analysis. [ABSTRACT FROM AUTHOR]

Generalized estimating equations (GEE) is a general statistical method to fit marginal models for longitudinal data in biomedical studies. The variance-covariance matrix of the regression parameter coefficients is usually estimated by a robust "sandwich" variance estimator, which does not perform satisfactorily when the sample size is small. To reduce the downward bias and improve the efficiency, several modified variance estimators have been proposed for bias-correction or efficiency improvement. In this paper, we provide a comprehensive review on recent developments of modified variance estimators and compare their small-sample performance theoretically and numerically through simulation and real data examples. In particular, Wald tests and t-tests based on different variance estimators are used for hypothesis testing, and the guideline on appropriate sample sizes for each estimator is provided for preserving type I error in general cases based on numerical results. Moreover, we develop a user-friendly R package "geesmv" incorporating all of these variance estimators for public usage in practice. [ABSTRACT FROM AUTHOR]