A Cluster Analysis of Oral and Cognitive Health Indicators: An Exploratory Study on Cholinergic Activity as the Link.

INTRODUCTION: Poor oral health has been suggested as a risk factor for cognitive decline. Yet, biologically plausible mechanisms explaining this relationship remain unknown. OBJECTIVES: We aimed (1) to identify oral and cognitive health clustering patterns among middle-aged to elderly Canadians and (2) to investigate the extent to which these patterns could be explained by bone mineral density (BMD), a proxy measure of the cholinergic neurons' activity. METHODS: This cross-sectional study used baseline data from the Comprehensive cohort of the Canadian Longitudinal Study of Aging (CLSA). Oral health was assessed by a self-report questionnaire, and 7 task-based instruments measured cognitive health. We identified oral and cognitive health clusters, our outcome variables, using latent class analysis. Two sets of multivariate logistic regression and 95% confidence intervals were used to investigate whether BMD explains the odds of membership in a certain oral and cognitive health group. The final models were adjusted for socioeconomic, health, and lifestyle factors. RESULTS: Our study sample (N = 25,444: 13,035 males, 12,409 females) was grouped into 5 and 4 clusters based on the oral health status and performance on the cognitive tasks, respectively. After adjusting for all potential covariates, increase in BMD was not associated with higher odds of membership in classes with better oral health (odds ratio [OR] = 1.58 [95% confidence interval {CI}: 0.85-2.92]) and cognitive health (OR = 1.61 [95% CI: 1-2.6]) compared with the groups with the least favorable oral and cognitive health status, respectively. CONCLUSION: Middle-aged and elderly Canadians show different oral and cognitive health profiles, based on their denture-wearing status and performance on cognitive tests. No evidence could be found to support BMD in place of cholinergic neurons' activity as the common explanatory factor behind the association between oral health and cognitive health. KNOWLEDGE TRANSFER STATEMENT: This study is probably the first of its kind to shed light on the cholinergic system as a potential pathway influencing oral and cognitive health. Our findings may support the notion that any potential association between poor oral health and cognitive health might be explained by common contributors, helping clinicians to find the common risk factors for both conditions.

Lungworm of cattle in Malaysia.

Bovine lungworm Dictyocaulus viviparus is highly endemic in temperate regions. However, the occurrence of the lungworm has not been reported in any South East Asian country. The main aim of the present study was to detect the presence of lungworm in cattle in peninsular Malaysia and to examine the morphology of the parasite. A cross-sectional study was carried out in which 602 animals from four large scale government cattle farms and one dairy smallholder farm were sampled. In addition, 283 lungs from 11 abattoirs around the country were examined. Faecal samples were examined using the Baermann technique while post-mortem examination was performed on the lungs. Approximately 5% of faecal samples and 1% of lungs were positive for lungworm. Based on the morphology of adult lungworm, eggs and first stage larvae, Malaysian bovine lungworms were D. viviparus.

Neural subnet design by direct polynomial mapping.

In a recent paper by M. Chen and M. Maury (1990), it was shown that multilayer perceptron neural networks can be used to form products of any number of inputs, thereby constructively proving universal approximation. This result is extended, and a method for the analysis and synthesis of single-input, single-output neural subnetworks is described. Given training samples of a function to be approximated, a feedforward neural network is designed which implements a polynomial approximation of the function with arbitrary accuracy. For comparison, example subnets are designed by classical backpropagation training and by mapping. The examples illustrate that the mapped subnets avoid local minima which backpropagation-trained subnets get trapped in and that the mapping approach is much faster.