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

Capacités morphogènes des cellules d’éponges dissociées

The reconstitution of functional sponges from aggregates, some built from non-fractionated suspensions, others from purified archaeocytes, has been studied using electron microscopy in process of time.The reorganization of aggregates made from complete suspensions mainly consists in a gathering of cells keeping their initial differentiation into functional structures. During restructuration, cellular debris resulting from dissociation and surnumerary healthy cells are phagocytized by archaeocytes.The evolution of archaeocyte aggregates points out the totipotency of these cells, since they appear to be able to differentiate into all sponge cell types. Nevertheless, the anomalies appearing during the sponge reconstitution, which mainly consist in a cell type population ratio desequilibrium, suggest that some morphogenetic regulation mechanisms are lost

Toward a precision, complexity-informed cultural policy design: Structural bottlenecks to culture-led development in Skaraborg, Sweden

We analyze the spatial-temporal dynamics of cultural vibrancy in the Swedish sub-region of Skaraborg. Our database consists of 4170 geo-localized cultural activities and facilities, mapped between October 2013 and March 2014. We make use of the TWC methodology for the dynamic simulation of the evolution of geo-localized activity starting from an observed distribution of events, and of the AutoCM ANN architecture to understand how cultural variables are related to the rest of the Skaraborg socio-economy. We find that cultural vibrancy in Skaraborg is likely characterized by a 'flaring' pattern of initial, widespread activity followed by a re-concentration into the main local urban hubs. The deep reason behind this unsuccessful developmental trajectory is the lack of centrality of cultural production in the local socio-economy, and of integration across cultural production sectors. This is in turn due also to structural bottlenecks of a non-cultural nature such as insufficient access of women to higher education. We make a case for the necessity to develop a new precision cultural policy design approach founded upon the science of complexity for both policy design and assessment, and we provide and illustrate a first technical toolkit to this purpose.(c) 2022 Elsevier B.V. All rights reserved

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>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</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>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.</p> <p>Conclusion</p> <p>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.</p

A nonlinear, data-driven, ANNs-based approach to culture-led development policies in rural areas: The case of Gjakove and Pee districts, Western Kosovo

We develop a computational approach to the analysis of cultural vibrancy and to the role of the cultural and cre-ative sectors in the socio-economic organization of two districts of Western Kosovo, Gjakove and Pee. Our anal-ysis is built on a geolocalized mapping of the cultural activities and facilities, and on the main socio-economic variables for the two districts, and makes use of innovative data analysis techniques: Theory of Impossible Words (TIW), the Topological Weighted Centroid (TWC), and the AutoCM ANN. We find that the dynamics of cul-tural vibrancy of the territory is mainly driven by the competing attraction pulls of the nearby countries of Serbia and Albania, that also form the region's main and often conflicting ethnicities, and that such dynamics are likely to further polarize in the future. We also find that the cultural system plays a marginal role in the territory's socio-economic organization. This situation makes a case for a more active role of cultural policy in shaping future local developmental models in rural areas and in acting as an agent of social cohesion.(c) 2022 Elsevier Ltd. All rights reserved

Polymorphisms in folate-metabolizing genes, chromosome damage, and risk of Down syndrome in Italian women: identification of key factors using artificial neural networks

<p>Abstract</p> <p>Background</p> <p>Studies in mothers of Down syndrome individuals (MDS) point to a role for polymorphisms in folate metabolic genes in increasing chromosome damage and maternal risk for a Down syndrome (DS) pregnancy, suggesting complex gene-gene interactions. This study aimed to analyze a dataset of genetic and cytogenetic data in an Italian group of MDS and mothers of healthy children (control mothers) to assess the predictive capacity of artificial neural networks assembled in TWIST system in distinguish consistently these two different conditions and to identify the variables expressing the maximal amount of relevant information to the condition of being mother of a DS child.</p> <p>The dataset consisted of the following variables: the frequency of chromosome damage in peripheral lymphocytes (BNMN frequency) and the genotype for 7 common polymorphisms in folate metabolic genes (<it>MTHFR </it>677C>T and 1298A>C, <it>MTRR </it>66A>G, <it>MTR </it>2756A>G, <it>RFC1 </it>80G>A and <it>TYMS </it>28bp repeats and 1494 6bp deletion). Data were analysed using TWIST system in combination with supervised artificial neural networks, and a semantic connectivity map.</p> <p>Results</p> <p>TWIST system selected 6 variables (BNMN frequency, <it>MTHFR </it>677TT, <it>RFC1 </it>80AA, <it>TYMS </it>1494 6bp +/+, <it>TYMS </it>28bp 3R/3R and <it>MTR </it>2756AA genotypes) that were subsequently used to discriminate between MDS and control mothers with 90% accuracy. The semantic connectivity map provided important information on the complex biological connections between the studied variables and the two conditions (being MDS or control mother).</p> <p>Conclusions</p> <p>Overall, the study suggests a link between polymorphisms in folate metabolic genes and DS risk in Italian women.</p

Networks in coronary heart disease genetics as a step towards systems epidemiology

We present the use of innovative machine learning techniques in the understanding of Coronary Heart Disease (CHD) through intermediate traits, as an example of the use of this class of methods as a first step towards a systems epidemiology approach of complex diseases genetics. Using a sample of 252 middle-aged men, of which 102 had a CHD event in 10 years follow-up, we applied machine learning algorithms for the selection of CHD intermediate phenotypes, established markers, risk factors, and their previously associated genetic polymorphisms, and constructed a map of relationships between the selected variables. Of the 52 variables considered, 42 were retained after selection of the most informative variables for CHD. The constructed map suggests that most selected variables were related to CHD in a context dependent manner while only a small number of variables were related to a specific outcome. We also observed that loss of complexity in the network was linked to a future CHD event. We propose that novel, non-linear, and integrative epidemiological approaches are required to combine all available information, in order to truly translate the new advances in medical sciences to gains in preventive measures and patients care.British Heart Foundation; European Commission; British Medical Research Council; the US National Institutes of Health and Du Pont Pharma, Wilmington

New application of intelligent agents in sporadic amyotrophic lateral sclerosis identifies unexpected specific genetic background

<p>Abstract</p> <p>Background</p> <p>Few genetic factors predisposing to the sporadic form of amyotrophic lateral sclerosis (ALS) have been identified, but the pathology itself seems to be a true multifactorial disease in which complex interactions between environmental and genetic susceptibility factors take place. The purpose of this study was to approach genetic data with an innovative statistical method such as artificial neural networks to identify a possible genetic background predisposing to the disease. A DNA multiarray panel was applied to genotype more than 60 polymorphisms within 35 genes selected from pathways of lipid and homocysteine metabolism, regulation of blood pressure, coagulation, inflammation, cellular adhesion and matrix integrity, in 54 sporadic ALS patients and 208 controls. Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis</p> <p>Results</p> <p>Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis. An unexpected discovery of a strong genetic background in sporadic ALS using a DNA multiarray panel and analytical processing of the data with advanced artificial neural networks was found. The predictive accuracy obtained with Linear Discriminant Analysis and Standard Artificial Neural Networks ranged from 70% to 79% (average 75.31%) and from 69.1 to 86.2% (average 76.6%) respectively. The corresponding value obtained with Advanced Intelligent Systems reached an average of 96.0% (range 94.4 to 97.6%). This latter approach allowed the identification of seven genetic variants essential to differentiate cases from controls: apolipoprotein E arg158cys; hepatic lipase -480 C/T; endothelial nitric oxide synthase 690 C/T and glu298asp; vitamin K-dependent coagulation factor seven arg353glu, glycoprotein Ia/IIa 873 G/A and E-selectin ser128arg.</p> <p>Conclusion</p> <p>This study provides an alternative and reliable method to approach complex diseases. Indeed, the application of a novel artificial intelligence-based method offers a new insight into genetic markers of sporadic ALS pointing out the existence of a strong genetic background.</p