The Anatolian Clay Sealing: The First Quality Assurance System of Human History?

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The single individual in medicine: how to escape from the probability theory trap

Doctors and their patients are always concerned with the likely outcome of an existing disease and the risk of future diseases, but there are many problems in interpreting for the individual data derived from populations. Yet recent developments in mathematics and science should allow us to do much better

Well-Being in Alpine Space: How Subjective Determinants Affect Urban and Rural Areas. A Case Study Analysis in South Tyrol, Italy

The paper analyzes urban-rural difference on the individual psychological well-being of residents living in the Autonomous Province of Alto Adige, region on the border between Italy and Austria. Data comes from a cross-sectional survey undertaken in 2010 on a statistical representative sample, based on the PGWBI, an instrument specifically used to measure individual subjective well-being. The study examines the influence of socio-demographic factors, as well as cultural determinants, on the PGWBI. Urban inhabitants were found to perceive higher level of psychological well-being compared to rural ones, while the determinants affecting individual subjective had a greater impact on the rural one

Neuropathological findings processed by artificial neural networks (ANNs) can perfectly distinguish Alzheimer\u27s patients from controls in the Nun Study

BACKGROUND: Many reports have described that there are fewer differences in AD brain neuropathologic lesions between AD patients and control subjects aged 80 years and older, as compared with the considerable differences between younger persons with AD and controls. In fact some investigators have suggested that since neurofibrillary tangles (NFT) can be identified in the brains of non-demented elderly subjects they should be considered as a consequence of the aging process. At present, there are no universally accepted neuropathological criteria which can mathematically differentiate AD from healthy brain in the oldest old. The aim of this study is to discover the hidden and non-linear associations among AD pathognomonic brain lesions and the clinical diagnosis of AD in participants in the Nun Study through Artificial Neural Networks (ANNs) analysis. METHODS: The analyses were based on 26 clinically- and pathologically-confirmed AD cases and 36 controls who had normal cognitive function. The inputs used for the analyses were just NFT and neuritic plaques counts in neocortex and hippocampus, for which, despite substantial differences in mean lesions counts between AD cases and controls, there was a substantial overlap in the range of lesion counts. RESULTS: By taking into account the above four neuropathological features, the overall predictive capability of ANNs in sorting out AD cases from normal controls reached 100%. The corresponding accuracy obtained with Linear Discriminant Analysis was 92.30%. These results were consistently obtained in ten independent experiments. The same experiments were carried out with ANNs on a subgroup of 13 non severe AD patients and on the same 36 controls. The results obtained in terms of prediction accuracy with ANNs were exactly the same. Input relevance analysis confirmed the relative dominance of NFT in neocortex in discriminating between AD patients and controls and indicated the lesser importance played by NP in the hippocampus. CONCLUSION: The results of this study suggest that: a) cortical NFT represent the key variable in AD neuropathology; b) the neuropathologic profile of AD subjects is complex, however, c) ANNs can analyze neuropathologic features and differentiate AD cases from controls

Artificial Neural Networks Link One-Carbon Metabolism to Gene-Promoter Methylation in Alzheimer's Disease

Background: There is increasing interest in DNA methylation studies in Alzheimer's disease (AD), but little is still known concerning the relationship between gene-promoter methylation and circulating biomarkers of one-carbon metabolism in patients. Objective: To detect the connections among circulating folate, homocysteine (hcy) and vitamin B12 levels and promoter methylation levels of PSEN1, BACE1, DNMT1, DNMT3A, DNMT3B, and MTHFR genes in blood DNA. Methods: We applied a data mining system called Auto Contractive Map to an existing database of 100 AD and 100 control individuals. Results: Low vitamin B12 was linked to the AD condition, to low folates, and to high hcy. Low PSEN1 methylation was linked to low folate levels as well as to low promoter methylation of BACE1 and DNMTs genes. Low hcy was linked to controls, to high folates and vitamin B12, as well as to high methylation levels of most of the studied genes. Conclusions: The present pilot study suggests that promoter methylation levels of the studied genes are linked to circulating levels of folates, hcy, and vitamin B12

Cities, the Urban Green Environment, and Individual Subjective Well-Being: The Case of Milan, Italy

This paper evaluates the independent effect of the spatial proximity of green urban areas upon the individual subjective well-being of the Milan population (Italy). The methodology is based on a survey undertaken in 2010 using a sample of 1,000 of Milan citizens. Univariate and multivariate analyses and GIS localization have been employed in order to rank the major individual well-being determinants and the relationship between citizens and urban green areas. Results show that the residential proximity of citizens to urban green areas seems to have little bearing on individual subjective well-being

The Implicit Function as Squashing Time Model: A Novel Parallel Nonlinear EEG Analysis Technique Distinguishing Mild Cognitive Impairment and Alzheimer's Disease Subjects with High Degree of Accuracy

Objective. This paper presents the results obtained using a protocol based on special types of artificial neural networks (ANNs) assembled in a novel methodology able to compress the temporal sequence of electroencephalographic (EEG) data into spatial invariants for the automatic classification of mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects. With reference to the procedure reported in our previous study (2007), this protocol includes a new type of artificial organism, named TWIST. The working hypothesis was that compared to the results presented by the workgroup (2007); the new artificial organism TWIST could produce a better classification between AD and MCI. Material and methods. Resting eyes-closed EEG data were recorded in 180 AD patients and in 115 MCI subjects. The data inputs for the classification, instead of being the EEG data, were the weights of the connections within a nonlinear autoassociative ANN trained to generate the recorded data. The most relevant features were selected and coincidently the datasets were split in the two halves for the final binary classification (training and testing) performed by a supervised ANN. Results. The best results distinguishing between AD and MCI were equal to 94.10% and they are considerable better than the ones reported in our previous study (∼92%) (2007). Conclusion. The results confirm the working hypothesis that a correct automatic classification of MCI and AD subjects can be obtained by extracting spatial information content of the resting EEG voltage by ANNs and represent the basis for research aimed at integrating spatial and temporal information content of the EEG