Browsing by Author "Yang, Ying"

Background: Singapore has a fast-growing aged population and increasing numbers of dementia patients. The heavy burden of dementia nursing care imposes challenges on the nursing workforce. The aim of this study is to describe the current situation of nurses’ dementia care training.

Methods: This study reviewed literature and policy documents to investigate the nurse’s profile, nurse education mechanisms, and the social and political support for nurse training in Singapore. Additionally, by conducting in-depth interviews with 17 nurses, this study investigated the current dementia care training status in selected nursing facilities, and obtained nurses’ views on desired training, barriers and challenges for obtaining dementia care training.

Results: This study found that Singapore has established a mature nurse education model consists of advanced school education and well-designed continuing education. A supportive social environment was established to cultivate nurses and enhance nursing professionalism. Nurses expressed that existing dementia care training is insufficient with low frequency, unsatisfied curriculum contents, and unreasonable candidate selection. Besides, the shortage of workforce, the high turnover rate and overlook of nurses’ needs in designing course content were viewed as challenges for providing and obtaining training.

Conclusions: This study portrays the situation of nurses’ professional training on dementia care in Singapore. The findings emphasize the value of fostering nurses’ dementia care training with higher frequency, need-oriented contents and reasonable candidate selection method. Results generated in this study lay the foundation for future research, policy analysis, and regulation establishment, hence, to improve nurse training and to ensure responsive dementia care services.

Long-term plasticity, the long-lasting, activity-dependent change in synaptic efficacy, is a fundamental property of the nervous system. Presynaptic forms of long-term plasticity are widely expressed throughout the brain, having been described in regions such as the cortex, cerebellum, hippocampus, thalamus, amygdala and striatum. Presynaptic long-term potentiation (LTP) is associated with an increase in presynaptic release probability, but further evidence of the cellular basis for the change in release probability is not known. At the molecular level, presynaptic LTP is known to require protein kinase A, the synaptic vesicle protein, Rab3A, and the active zone protein, RIM1alpha. RIM1alpha, a presynaptic scaffold protein, binds to many molecules with known functions at different stages of the neurotransmitter release process and the synaptic vesicle cycle. Understanding which interactions of RIM1alpha mediate presynaptic LTP would shed light on the molecular and cellular mechanisms for presynaptic long-term plasticity.

Here I developed a novel platform to achieve robust acute genetic

manipulation of presynaptic proteins at hippocampal mossy fiber synapses, where presynaptic LTP is expressed. With this platform, I perform structure-function analysis of RIM1alpha in presynaptic LTP. I find that RIM1alpha phosphorylation by PKA at serine 413 is not required for mossy fiber LTP, nor does RIM1alpha-Rab3A interation. These findings suggest that RIM1alpha, Rab3A and PKA signaling, instead of functioning synergistically, may represent separate requirements for presynaptic long-term plasticity. I then tested whether Munc13-1, a priming protein, is an effector for RIM1alpha in presynaptic LTP and provide the first evidence for the involvement of Munc13-1 in presynaptic long-term synaptic plasticity. I further demonstrate that the interaction between RIM1alpha and Munc13-1 is required for this plasticity. These results further our understanding of the molecular mechanisms of presynaptic plasticity and suggest that modulation of vesicle priming may provide the cellular substrate for expression of LTP at mossy fiber synapses.

Functional magnetic resonance imaging (fMRI) is a major neuroimaging methodology and have greatly facilitate basic cognitive neuroscience research. However, there are multiple statistical challenges in the analysis of fMRI data, including, dimension reduction, multiple testing and inter-dependence of the MRI responses. In this thesis, a spatial Bayesian variable selection (BVS) model is proposed for the analysis of multi-subject fMRI data. The BVS framework simultaneously account for uncertainty in model specific parameters as well as the model selection process, solving the multiple testing problem. A spatial prior incorporate the spatial relationship of the MRI response, accounting for their inter-dependence. Compared to the non-spatial BVS model, the spatial BVS model enhances the sensitivity and accuracy of identifying activated voxels.