The Role of Nutritionists in Primary Health Care: A Case of Narok County Referral Hospital in Narok County

Introduction: The study identifies the perception of users in devolved in primary Health care (PHC) with regards to the role of nutritionists. This study also aims at acknowledging the importance of the nutritionist’s work for their health, and verifies their expectations in relation to it. Methods: Semi-structured individual interviews were applied to users of Primary Health Care, under follow up or otherwise with the unit nutritionist. The interviews were transcribed and submitted to a discursive textual analysis. Results: The nutritionist was correctly associated to food, feeding and nutrition, although the boundaries of their function are subtle, suggesting a weak professional identity. The work developed has always been positively evaluated and recognized as important for the users’ health. The expectations related to the practice point to the need of humanized and integral care, including more actions for the collectivity – community and team work – in accordance with present health policies. Conclusions: To meet expectations and advance in the professionalization process it is necessary to listen and fulfill the demands, as well as assess the education process and daily practice, taking into account the new paradigms of primary care and health promotion. Keywords: Nutritionist, Primary Health Care, Perception, Narok County. DOI: 10.7176/JHMN/68-08 Publication date: November 30th 201

Improving Food Security Through Conservation of the Mau Ecosystem in Narok County, Kenya

The world is experiencing intense hunger; Food production is becoming less each day as global populations continue to grow. Mau and other ecosystems destruction have increased global climate change. The only remaining approach to food security sustenance is conservation of ecosystem globally. Kenya in the year 2017 had declared drought a national disaster with half of the country experiencing intense draught. Recently the Kenyan government has put more effort by issuing a gazette notice to stop any forest degradation activity. The study purpose is to close the gap left by other studies on the effectiveness increasing forest cover towards improved food security. The study also compares approaches used by different countries to mitigate against the destruction of ecosystem while ensuring adequate food production for growing population. The study employed descriptive survey design. The target population were 100 respondents. The study found out ways to ensure healthy population through sustainability of food production, while conserving ecosystems in the rising global climate change. The study took into consideration the Mau ecosystems in Narok, Kenya. Quantitative and qualitative approaches in data collection, analysis and presentations were adopted. Data was analyzed using SPSS Version 20 and presented using frequencies and percentages. The study main findings and conclusions has unmasked several challenges experienced in conservation, restoration and protection of the Mau ecosystem. The major recommendation drawn from this Research shows that human activities in the ecosystem, directly and indirectly, contributes to decline in food security, with major implications for people's livelihoods and wellbeing, particularly for the poor. Keywords: Food security, Mau ecosystems, Narok County, Population, Food production. DOI: 10.7176/JBAH/9-22-03 Publication date: November 30th 201

White matter microstructure of patients with neurofibromatosis type 1 and its relation to inhibitory control

Neuro Imaging Researc

Clusterwise Independent Component Analysis (C-ICA): using fMRI resting state networks to cluster subjects and find neurofunctional subtypes

Background: FMRI resting state networks (RSNs) are used to characterize brain disorders. They also show extensive heterogeneity across patients. Identifying systematic differences between RSNs in patients, i.e. discovering neurofunctional subtypes, may further increase our understanding of disease heterogeneity. Currently, no methodology is available to estimate neurofunctional subtypes and their associated RSNs simultaneously.New method: We present an unsupervised learning method for fMRI data, called Clusterwise Independent Component Analysis (C-ICA). This enables the clustering of patients into neurofunctional subtypes based on differences in shared ICA-derived RSNs. The parameters are estimated simultaneously, which leads to an improved estimation of subtypes and their associated RSNs.Results: In five simulation studies, the C-ICA model is successfully validated using both artificially and realistically simulated data (N = 30-40). The successful performance of the C-ICA model is also illustrated on an empirical data set consisting of Alzheimer's disease patients and elderly control subjects (N = 250). C-ICA is able to uncover a meaningful clustering that partially matches (balanced accuracy = .72) the diagnostic labels and identifies differences in RSNs between the Alzheimer and control cluster. Comparison with other methods: Both in the simulation study and the empirical application, C-ICA yields better results compared to competing clustering methods (i.e., a two step clustering procedure based on single subject ICA's and a Group ICA plus dual regression variant thereof) that do not simultaneously estimate a clustering and associated RSNs. Indeed, the overall mean adjusted Rand Index, a measure for cluster recovery, equals 0.65 for C-ICA and ranges from 0.27 to 0.46 for competing methods.Conclusions: The successful performance of C-ICA indicates that it is a promising method to extract neuro-functional subtypes from multi-subject resting state-fMRI data. This method can be applied on fMRI scans of patient groups to study (neurofunctional) subtypes, which may eventually further increase understanding of disease heterogeneity.Multivariate analysis of psychological dat

Analyzing hierarchical multi-view MRI data with StaPLR: an application to Alzheimer's disease classification

Multivariate analysis of psychological dat

Grey-matter network disintegration as predictor of cognitive and motor function with aging

Neuro Imaging Researc

Grey-matter network disintegration as predictor of cognitive and motor function with aging

Loss of grey-matter volume with advancing age affects the entire cortex. It has been suggested that atrophy occurs in a network-dependent manner with advancing age rather than in independent brain areas. The relationship between networks of structural covariance (SCN) disintegration and cognitive functioning during normal aging is not fully explored. We, therefore, aimed to (1) identify networks that lose GM integrity with advancing age, (2) investigate if age-related impairment of integrity in GM networks associates with cognitive function and decreasing fine motor skills (FMS), and (3) examine if GM disintegration is a mediator between age and cognition and FMS. T1-weighted scans of n = 257 participants (age range: 20–87) were used to identify GM networks using independent component analysis. Random forest analysis was implemented to examine the importance of network integrity as predictors of memory, executive functions, and FMS. The associations between GM disintegration, age and cognitive performance, and FMS were assessed using mediation analyses. Advancing age was associated with decreasing cognitive performance and FMS. Fourteen of 20 GM networks showed integrity changes with advancing age. Next to age and education, eight networks (fronto-parietal, fronto-occipital, temporal, limbic, secondary somatosensory, cuneal, sensorimotor network, and a cerebellar network) showed an association with cognition and FMS (up to 15.08%). GM networks partially mediated the effect between age and cognition and age and FMS. We confirm an age-related decline in cognitive functioning and FMS in non-demented community-dwelling subjects and showed that aging selectively affects the integrity of GM networks. The negative effect of age on cognition and FMS is associated with distinct GM networks and is partly mediated by their disintegration.Multivariate analysis of psychological dat

Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease

AbstractMagnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N=77) from the prospective registry on dementia study and controls (N=173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification

Analyzing hierarchical multi-view MRI Data With StaPLR An Application to Alzheimer's disease classification: an application to Alzheimer's disease classification

Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure. We also introduce a new view importance measure for StaPLR, which allows us to compare the importance of views at any level of the hierarchy. We apply our extended StaPLR algorithm to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which derived MRI measures are most important for classification, and it outperforms elastic net regression in classification performance. Horizon 2020(H2020)101041064Multivariate analysis of psychological dat

Investigating the genetic and environmental basis of head micromovements during MRI

Introduction Head motion during magnetic resonance imaging is heritable. Further, it shares phenotypical and genetic variance with body mass index (BMI) and impulsivity. Yet, to what extent this trait is related to single genetic variants and physiological or behavioral features is unknown. We investigated the genetic basis of head motion in a meta-analysis of genome-wide association studies. Further, we tested whether physiological or psychological measures, such as respiratory rate or impulsivity, mediated the relationship between BMI and head motion.Methods We conducted a genome-wide association meta-analysis for mean and maximal framewise head displacement (FD) in seven population neuroimaging cohorts (UK Biobank, LIFE-Adult, Rotterdam Study cohort 1-3, Austrian Stroke Prevention Family Study, Study of Health in Pomerania; total N = 35.109). We performed a pre-registered analysis to test whether respiratory rate, respiratory volume, self-reported impulsivity and heart rate mediated the relationship between BMI and mean FD in LIFE-Adult.Results No variant reached genome-wide significance for neither mean nor maximal FD. Neither physiological nor psychological measures mediated the relationship between BMI and head motion.Conclusion Based on these findings from a large meta-GWAS and pre-registered follow-up study, we conclude that the previously reported genetic correlation between BMI and head motion relies on polygenic variation, and that neither psychological nor simple physiological parameters explain a substantial amount of variance in the association of BMI and head motion. Future imaging studies should thus rigorously control for head motion at acquisition and during preprocessing