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Abstract Background The population longitudinal study named “The Conselice Study” has been the focus of the present investigation. 65 years old or older participants of this population study on brain aging were followed up for 5 years: 937 subjects completed the follow-up. Relationships of 46 genetic, phenotypic, clinical and nutritional factors on incident cognitive decline and incident dementia cases were investigated. Results A new statistical approach, called the Auto Contractive Map (AutoCM) was applied to find relationship between variables and a possible hierarchy in the relevance of each variable with incident dementia. This method, based on an artificial adaptive system, was able to define the association strength of each variable with all the others. Moreover, few variables resulted to be aggregation points in the variable connectivity map related to cognitive decline and dementia. Gene variants and cognate phenotypic variables showed differential degrees of relevance to brain aging and dementia. A risk map for age associated cognitive decline and dementia has been constructed and will be presented and discussed. Conclusion This map of variables may be use to identify subjects with increased risk of developing cognitive decline end/or dementia and provide pivotal information for early intervention protocols for prevention of dementia.

Abstract An individual patient is not the average representative of the population. Rather he or she is a person with unique characteristics. An intervention may be effective for a population but not necessarily for the individual patient. The recommendation of a guideline may not be right for a particular patient because it is not what he or she wants, and implementing the recommendation will not necessarily mean a favourable outcome. The author will describe a reconfiguration of medical thought which originates from non linear dynamics and chaos theory. The coupling of computer science and these new theoretical bases coming from complex systems mathematics allows the creation of “intelligent” agents able to adapt themselves dynamically to problem of high complexity: the Artificial Adaptive Systems, which include Artificial Neural Networks( ANNs ) and Evolutionary Algorithms ( EA). ANNs and EA are able to reproduce the dynamical interaction of multiple factors simultaneously, allowing the study of complexity; they can also help medical doctors in making decisions under extreme uncertainty and to draw conclusions on individual basis and not as average trends. These tools can allow a more efficient Technology Transfer from the Science of Medicine to the Real World overcoming many obstacles responsible for the present translational failure. They also contribute to a new holistic vision of the human subject contrasting the statistical reductionism which tends to squeeze or even delete the single subject sacrificing him to his group of belongingness. A remarkable contribution to this individual approach comes from Fuzzy Logic, according to which there are no sharp limits between opposite things, like health and disease. This approach allows to partially escape from probability theory trap in situations where is fundamental to express a judgment based on a single case and favours a novel humanism directed to the management of the patient as individual subject.

Abstract Background Multiple Sclerosis (MS) is a multi-factorial disease, where a single biomarker unlikely can provide comprehensive information. Moreover, due to the non-linearity of biomarkers, traditional statistic is both unsuitable and underpowered to dissect their relationship. Patients affected with primary (PP=14), secondary (SP=33), benign (BB=26), relapsing-remitting (RR=30) MS, and 42 sex and age matched healthy controls were studied. We performed a depth immune-phenotypic and functional analysis of peripheral blood mononuclear cell (PBMCs) by flow-cytometry. Semantic connectivity maps (AutoCM) were applied to find the natural associations among immunological markers. AutoCM is a special kind of Artificial Neural Network able to find consistent trends and associations among variables. The matrix of connections, visualized through minimum spanning tree, keeps non linear associations among variables and captures connection schemes among clusters. Results Complex immunological relationships were shown to be related to different disease courses. Low CD4IL25+ cells level was strongly related (link strength, ls=0.81) to SP MS. This phenotype was also associated to high CD4ROR+ cells levels (ls=0.56). BB MS was related to high CD4+IL13 cell levels (ls=0.90), as well as to high CD14+IL6 cells percentage (ls=0.80). RR MS was strongly (ls=0.87) related to CD4+IL25 high cell levels, as well indirectly to high percentages of CD4+IL13 cells. In this latter strong (ls=0.92) association could be confirmed the induction activity of the former cells (CD4+IL25) on the latter (CD4+IL13). Another interesting topographic data was the isolation of Th9 cells (CD4IL9) from the main part of the immunological network related to MS, suggesting a possible secondary role of this new described cell phenotype in MS disease. Conclusions This novel application of non-linear mathematical techniques suggests peculiar immunological signatures for different MS phenotypes. Notably, the immune-network displayed by this new method, rather than a single marker, might be viewed as the right target of immunotherapy. Furthermore, this new statistical technique could be also employed to increase the knowledge of other age-related multifactorial disease in which complex immunological networks play a substantial role.

Abstract Background Inadequate blood pressure control and poor adherence to treatment remain among the major limitations in the management of hypertensive patients, particularly of those at high risk of cardiovascular events. Preliminary evidence suggests that home blood pressure telemonitoring (HBPT) might help increasing the chance of achieving blood pressure targets and improve patient’s therapeutic adherence. However, all these potential advantages of HBPT have not yet been fully investigated. Methods/design The purpose of this open label, parallel group, randomized, controlled study is to assess whether, in patients with high cardiovascular risk (treated or untreated essential arterial hypertension - both in the office and in ambulatory conditions over 24 h - and metabolic syndrome), long-term (48 weeks) blood pressure control is more effective when based on HBPT and on the feedback to patients by their doctor between visits, or when based exclusively on blood pressure determination during quarterly office visits (conventional management (CM)). A total of 252 patients will be enrolled and randomized to usual care (n=84) or HBPT (n=168). The primary study endpoint will be the rate of subjects achieving normal daytime ambulatory blood pressure targets ( Discussion The TELEBPMET Study will show whether HBPT is effective in improving blood pressure control and related medical and economic outcomes in hypertensive patients with metabolic syndrome. It will also provide a comprehensive understanding of the psychological determinants of medication adherence and blood pressure control of these patients. Trial registration Clinical Trials.gov: NCT01541566

Abstract Background There is compelling evidence that psychological factors may have the same or even greater impact on the possibility of adverse events on cardiac diseases (CD) than other traditional clinical risk factors. Anxiety and depression are predictors of short- and long-term adverse outcomes, increased risk for higher rates of in-hospital complications, re-infarction, malignant arrhythmias, and mortality in CD patients. Despite researchers finding that cognitive behavior therapy (CBT) reduced depressive and anxiety symptoms, the fact that such results are maintained only in the short term and the lack of maintenance of the long-term affects the absence of changes in lifestyles, preventing the possibility of a wide generalization of results. Recently wellbeing therapy (WBT) has been proposed as a useful approach to improve healthy lifestyle behaviors and reduce psychological distress. Methods/design The present randomized controlled study will test WBT, in comparison with CBT, as far as the reduction of symptoms of depression, anxiety and psychological distress, and the improvement of lifestyle behaviors and quality of life in cardiac patients are concerned. Moreover, innovations in communication technologies allow patients to be constantly followed in real life. Therefore WBT based on personalized mobile technology will allow the testing of its effectiveness in comparison with usual WBT. Discussion The present study is a large outpatient study on the treatment of co-morbid depression, anxiety, and psychological distress in cardiac patients. The most important issues of this study are its randomized design, the focus on promotion of health-related behaviors, and the use of innovative technologies supporting patients’ wellbeing in real life and in a continuous way. First results are expected in 2012. Trial registration ClinicalTrials.gov Identifier: NCT01543815.

Abstract Background 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. 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 (MTHFR 677C>T and 1298A>C, MTRR 66A>G, MTR 2756A>G, RFC1 80G>A and TYMS 28bp repeats and 1494 6bp deletion). Data were analysed using TWIST system in combination with supervised artificial neural networks, and a semantic connectivity map. Results TWIST system selected 6 variables (BNMN frequency, MTHFR 677TT, RFC1 80AA, TYMS 1494 6bp +/+, TYMS 28bp 3R/3R and MTR 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). Conclusions Overall, the study suggests a link between polymorphisms in folate metabolic genes and DS risk in Italian women.

Abstract Background Changes and relationships of components of the cytokine and IGF systems have been shown in placenta and cord serum of fetal growth restricted (FGR) compared with normal newborns (AGA). This study aimed to analyse a data set of clinical and biochemical data in FGR and AGA newborns to assess if a mathematical model existed and was capable of identifying these two different conditions in order to identify the variables which had a mathematically consistent biological relevance to fetal growth. Methods Whole villous tissue was collected at birth from FGR (N = 20) and AGA neonates (N = 28). Total RNA was extracted, reverse transcribed and then real-time quantitative (TaqMan) RT-PCR was performed to quantify cDNA for IGF-I, IGF-II, IGFBP-1, IGFBP-2 and IL-6. The corresponding proteins with TNF-α in addition were assayed in placental lysates using specific kits. The data were analysed using Artificial Neural Networks (supervised networks), and principal component analysis and connectivity map. Results The IGF system and IL-6 allowed to predict FGR in approximately 92% of the cases and AGA in 85% of the cases with a low number of errors. IGF-II, IGFBP-2, and IL-6 content in the placental lysates were the most important factors connected with FGR. The condition of being FGR was connected mainly with the IGF-II placental content, and the latter with IL-6 and IGFBP-2 concentrations in placental lysates. Conclusion These results suggest that further research in humans should focus on these biochemical data. Furthermore, this study offered a critical revision of previous studies. The understanding of this system biology is relevant to the development of future therapeutical interventions possibly aiming at reducing IL-6 and IGFBP-2 concentrations preserving IGF bioactivity in both placenta and fetus.

Abstract Background 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 Results 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. Conclusion 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.

Abstract 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.

Abstract Background The PGWBI is a 22-item health-related Quality of Life (HRQoL) questionnaire developed in US which produces a self-perceived evaluation of psychological well-being expressed by a summary score. The PGWBI has been validated and used in many countries on large samples of the general population and on specific patient groups. Recently a study was carried out in Italy to reduce the number of items of the original questionnaire, yielding the creation of a shorter validated version of the questionnaire (PGWB-S). The purpose of the present paper is to describe the methods adopted and to report and discuss the relevance of results. Methods Data for this study were collected from 4 different population samples: two general population samples a student and a patient sample. On the basis of the results of the first (development) sample population, six relevant items were identified statistically from the original questionnaire and grouped to assemble a new summary scale. Following the newly created 6-item questionnaire was administered in three independent population samples. Descriptive statistics, correlation coefficients, univariate and multivariate regression analyses were used to compare the performance of the long and short questionnaire, within and between population samples and across relevant subgroups. A further independent sample extracted by an ongoing cancer clinical trial served as final validation step. Results Overall, the questionnaires were administered to 1443 subjects. Six items were selected by a step-wise approach to explain 90% of the variance of the summary measure of the original questionnaire. Response rates reached 100%, while missing items were not observed. University students (n = 400) showed the highest mean value of the summary measure (75.3); while the patient sample (n = 28) had the lowest score (71.5). The correlation coefficients between the summary measures and the single items according to the different studies were satisfactory, reaching the highest estimates in the student sample. The internal consistency showed high values of the Cronbach's alpha coefficient (range 0.80 – 0.92) for all three study samples, coming close to the value of the coefficient established for the original questionnaire (0.94). A cross-validation in an independent sample of 755 cancer patients confirmed the item selection procedure and amount of variance explained by the new shorter questionnaire (ranging from 90. 2 to 95.1 %, across age and sex strata). Conclusion The newly identified PGWB-S showed good acceptability and validity for the use in various settings in Italy. The translation of the PGWB-S into different languages, and its use in other linguistic settings will add evidence about its cross-cultural validity.

Abstract Background In recent years a number of algorithms for cardiovascular risk assessment has been proposed to the medical community. These algorithms consider a number of variables and express their results as the percentage risk of developing a major fatal or non-fatal cardiovascular event in the following 10 to 20 years Discussion The author has identified three major pitfalls of these algorithms, linked to the limitation of the classical statistical approach in dealing with this kind of non linear and complex information. The pitfalls are the inability to capture the disease complexity, the inability to capture process dynamics, and the wide confidence interval of individual risk assessment. Artificial Intelligence tools can provide potential advantage in trying to overcome these limitations. The theoretical background and some application examples related to artificial neural networks and fuzzy logic have been reviewed and discussed. Summary The use of predictive algorithms to assess individual absolute risk of cardiovascular future events is currently hampered by methodological and mathematical flaws. The use of newer approaches, such as fuzzy logic and artificial neural networks, linked to artificial intelligence, seems to better address both the challenge of increasing complexity resulting from a correlation between predisposing factors, data on the occurrence of cardiovascular events, and the prediction of future events on an individual level.

Abstract Background The concept of risk has pervaded medical literature in the last decades and has become a familiar topic, and the concept of probability, linked to binary logic approach, is commonly applied in epidemiology and clinical medicine. The application of probability theory to groups of individuals is quite straightforward but can pose communication challenges at individual level. Few articles by the way have tried to focus the concept of "risk" at the individual subject level rather than at population level. Discussion The author has reviewed the conceptual framework which has led to the use of probability theory in the medical field in a time when the principal causes of death were represented by acute disease often of infective origin. In the present scenario, in which chronic degenerative disease dominate and there are smooth transitions between health and disease the use of fuzzy logic rather than binary logic would be more appropriate. The use of fuzzy logic in which more than two possible truth-value assignments are allowed overcomes the trap of probability theory when dealing with uncertain outcomes, thereby making the meaning of a certain prognostic statement easier to understand by the patient. Summary At individual subject level the recourse to the term plausibility, related to fuzzy logic, would help the physician to communicate to the patient more efficiently in comparison with the term probability, related to binary logic. This would represent an evident advantage for the transfer of medical evidences to individual subjects.

Abstract Background Previous studies have shown that in platelets of mild Alzheimer Disease (AD) patients there are alterations of specific APP forms, paralleled by alteration in expression level of both ADAM 10 and BACE when compared to control subjects. Due to the poor linear relation among each key-element of beta-amyloid cascade and the target diagnosis, the use of systems able to afford non linear tasks, like artificial neural networks (ANNs), should allow a better discriminating capacity in comparison with classical statistics. Objective To evaluate the accuracy of ANNs in AD diagnosis. Methods 37 mild-AD patients and 25 control subjects were enrolled, and APP, ADM10 and BACE measures were performed. Fifteen different models of feed-forward and complex-recurrent ANNs (provided by Semeion Research Centre), based on different learning laws (back propagation, sine-net, bi-modal) were compared with the linear discriminant analysis (LDA). Results The best ANN model correctly identified mild AD patients in the 94% of cases and the control subjects in the 92%. The corresponding diagnostic performance obtained with LDA was 90% and 73%. Conclusion This preliminary study suggests that the processing of biochemical tests related to beta-amyloid cascade with ANNs allows a very good discrimination of AD in early stages, higher than that obtainable with classical statistics methods.

Abstract Background Use of synbiotic preparations as dietary supplement is believed to be a valid approach to restore and maintain colonic microflora. However, only few papers have been published on the assessment of these food supplements and none of them have used molecular biology techniques to evaluate the effects of the probiotic components. Methods Twelve healthy volunteers were recruited. Faecal samples were taken before and at various time points during the administration period and at day 3 in the post-treatment period. Stool culture were performed and amplified ribosomal DNA restriction analysis was used to detect L. paracasei, the major bacterial component of the synbiotic products. Results An increase of at least 1 log of L. paracasei-like bacteria was observed in all subjects. An increase of as much as 3 log was seen in subjects who had a low number of L. paracasei-like lactobacilli at the baseline. The counts of L. paracasei-like lactobacilli were found to persist for at least 3 days after discontinuation of intake in healthy volunteers in 7 subjects. Genetic analysis showed that the maiority of vancomicin insensitive lactobacilli were real L. paracasei, as the strains administered with the tested product. Conclusion This study has shown that the strains of L paracasei administered with a synbiotic dietary supplement are able to survive through the gastrointestinal tract and to persist for at least a few days. It was also shown the efficacy of a synbiotic preparation to positively affect the microflora of healthy volunteers.

Carotid artery intima media thickness (cIMT) and carotid plaque are measures of subclinical atherosclerosis associated with ischemic stroke and coronary heart disease (CHD). Here, we undertake meta-analyses of genome-wide association studies (GWAS) in 71,128 individuals for cIMT, and 48,434 individuals for carotid plaque traits. We identify eight novel susceptibility loci for cIMT, one independent association at the previously-identified PINX1 locus, and one novel locus for carotid plaque. Colocalization analysis with nearby vascular expression quantitative loci (cis-eQTLs) derived from arterial wall and metabolic tissues obtained from patients with CHD identifies candidate genes at two potentially additional loci, ADAMTS9 and LOXL4. LD score regression reveals significant genetic correlations between cIMT and plaque traits, and both cIMT and plaque with CHD, any stroke subtype and ischemic stroke. Our study provides insights into genes and tissue-specific regulatory mechanisms linking atherosclerosis both to its functional genomic origins and its clinical consequences in humans.

The aim of this paper is to present a shortened version of the SIS (support intensity scale) obtained by the application of mathematical models and instruments, adopting special algorithms based on the most recent developments in artificial adaptive systems. All the variables of SIS applied to 1,052 subjects with ID (intellectual disabilities) involved in the validation of the Italian version of SIS, were analyzed with the aforementioned artificial adaptive systems. This study has identified 56 items, whose responses are able to explain up to 89% of sample variance. Secondly, these same variables have been analyzed by means of specific semantic networks, in order to demonstrate the plenty of scientific suggestions emerging from such an approach to statistic processing. (Contains 5 figures and 3 tables.)

This study aimed to investigate the role of the thymus in influencing long-term outcomes of COVID-19 by comparing the thymic appearance in patients with and without COVID-19 pulmonary sequelae at chest computed tomography (CT). A total of 102 adult patients previously hospitalized for COVID-19 underwent a follow-up chest CT three months after discharge. Pulmonary sequelae and thymic appearance were independently assessed by two experienced radiologists. The thymus was detectable in 55/102 patients (54%), with only 7/55 (13%) having any kind of pulmonary sequelae, compared to 33 out of 47 (70%, p < 0.001) in patients without thymic visibility, as confirmed in age-stratified analysis and at logistic regression analysis, where thymic involution had a 9.3 odds ratio (95% CI 3.0–28.2, p < 0.001) for the development of pulmonary sequelae. These results support the hypothesis that thymic reactivation plays a protective role against adverse long-term outcomes of COVID-19. [ABSTRACT FROM AUTHOR]

The term "toe walking" describes walking on the toes with a lack of heel strike upon initiation of the stance phase of gait. In individuals with autism spectrum disorder (ASD), this phenomenon, or "tip‐toe behavior" (TTB), can be present in a substantial proportion of subjects even during standing. In this study, we investigated TTB in 50 persons with ASD (age range 4–26 years). We evaluated TTB through an observational/report‐based assessment protocol. Subsequently, we employed a new structured video‐based coding protocol based on standardized video recordings, focusing on static and dynamic conditions. Finally, the findings of the two protocols were compared. Twenty‐four subjects with TTB were identified and classified according to three functional groups: TTB1, present only during running (6 subjects); TTB2, present during walking and running (11 subjects); and TTB3, present during standing, walking, and running (7 subjects). Moreover, we found that TTB3 subjects exhibited a significantly higher quantity of TTB compared with subjects in the TTB1 and TTB2 groups during both standing and walking tests. Additionally, a high quantity of TTB in the static test was found to be related to a high quantity of TTB in the dynamic test. Variables such as age, autism severity, intellectual disability, and gender were not significantly associated with the mean percent of TTB both in static and dynamic tests in multivariate analysis. This structured video‐based coding approach appears feasible and useful for assessing TTB in individuals with ASD and it has the potential to provide insights into TTB trajectories and aid in designing possible interventions. Lay Summary: Toe walking can be observed during the gait of individuals with autism spectrum disorder (ASD). As this motor behavior can also be present during standing, we used the term tip‐toe behavior (TTB). TTB is an understudied sensorimotor phenomenon in ASD, and there is a lack of quantitative measures of TTB in ASD. This study proposed two standardized clinical tests to quantify TTB during walking and standing. These TTB measures could aid in understanding the natural history of TTB and in making decisions about TTB interventions. [ABSTRACT FROM AUTHOR]