Scientific Research and University

Rome, Italy

Scientific Research and University

Rome, Italy

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News Article | April 17, 2017
Site: cen.acs.org

Geochemists searching for evidence of ancient life often look to collections of round, twisted, and loopy mineral precipitates they find in soil or water samples as possible microbial fossils. A team led by Juan Manuel Garcia-Ruiz at the Spanish Higher Council of Scientific Research and University of Granada and Oliver Steinbock at Florida State University, however, have now made these types of inorganic structures without the help of biology (Sci. Adv. . . .


Melillo P.,Scientific Research and University | Melillo P.,The Second University of Naples | Orrico A.,Scientific Research and University | Orrico A.,The Second University of Naples | And 7 more authors.
BMC Medical Informatics and Decision Making | Year: 2015

Background: Falls in the elderly is a major problem. Although falls have a multifactorial etiology, a commonly cited cause of falls in older people is poor vision. This study proposes a method to discriminate fallers and non-fallers among ophthalmic patients, based on data-mining algorithms applied to health and socio-demographic information. Methods: A group of 150 subjects aged 55 years and older, recruited at the Eye Clinic of the Second University of Naples, underwent a baseline ophthalmic examination and a standardized questionnaire, including lifestyles, general health, social engagement and eyesight problems. A subject who reported at least one fall within one year was considered as faller, otherwise as non-faller. Different tree-based data-mining algorithms (i.e., C4.5, Adaboost and Random Forest) were used to develop automatic classifiers and their performances were evaluated by assessing the receiver-operator characteristics curve estimated with the 10-fold-crossvalidation approach. Results: The best predictive model, based on Random Forest, enabled to identify fallers with a sensitivity and specificity rate of 72.6% and 77.9%, respectively. The most informative variables were: intraocular pressure, best corrected visual acuity and the answers to the total difficulty score of the Activities of Daily Vision Scale (a questionnaire for the measurement of visual disability). Conclusions: The current study confirmed that some ophthalmic features (i.e. cataract surgery, lower intraocular pressure values) could be associated with a lower fall risk among visually impaired subjects. Finally, automatic analysis of a combination of visual function parameters (either self-evaluated either by ophthalmological test) and other health information, by data-mining algorithms, could be a feasible tool for identifying fallers among ophthalmic patients. © 2015 Melillo et al.;.


Melillo P.,The Second University of Naples | Melillo P.,Scientific Research and University | Izzo R.,University of Naples Federico II | Orrico A.,The Second University of Naples | And 7 more authors.
PLoS ONE | Year: 2015

Background There is consensus that Heart Rate Variability is associated with the risk of vascular events. However, Heart Rate Variability predictive value for vascular events is not completely clear. The aim of this study is to develop novel predictive models based on data-mining algorithms to provide an automatic risk stratification tool for hypertensive patients. Methods A database of 139 Holter recordings with clinical data of hypertensive patients followed up for at least 12 months were collected ad hoc. Subjects who experienced a vascular event (i. e., myocardial infarction, stroke, syncopal event) were considered as high-risk subjects. Several data-mining algorithms (such as support vector machine, tree-based classifier, artificial neural network) were used to develop automatic classifiers and their accuracy was tested by assessing the receiver-operator characteristics curve. Moreover, we tested the echographic parameters, which have been showed as powerful predictors of future vascular events. Results The best predictive model was based on random forest and enabled to identify high-risk hypertensive patients with sensitivity and specificity rates of 71.4% and 87.8%, respectively. The Heart Rate Variability based classifier showed higher predictive values than the conventional echographic parameters, which are considered as significant cardiovascular risk factors. Conclusions Combination of Heart Rate Variability measures, analyzed with data-mining algorithm, could be a reliable tool for identifying hypertensive patients at high risk to develop future vascular events. © 2015 Melillo et al.


Melillo P.,The Second University of Naples | Melillo P.,Scientific Research and University | Scala P.,Scientific Research and University | De Luca N.,University of Naples Federico II | And 2 more authors.
IFMBE Proceedings | Year: 2015

This paper proposes an automatic classifier for risk assessment of developing vascular events in hypertensive patients. The proposed classifier separates lower-risk patients from higher-risk ones, using linear and nonlinear Heart Rate Variability (HRV) measures. Higher risk patients were those having a clinical vascular event (e.g. myocardial infarction, syncope, stroke or transient ischemic attack) within one year after the Holter recording. A database of Holter recordings with clinical data of patients followed up for at least 12 months were collected a hoc. 17 out of 142 patients had one of the following vascular events: 11 myocardial infarctions, 3 strokes, 2 syncopal events. An ensemble tree-based algorithm suitable for imbalanced dataset, called Rusboost, was adopted to develop the classifier. The proposed classifier achieved sensitivity and specificity rate of 71% and 66%, respectively, in identifying higher risk patients. The abnormal HRV revealed by the proposed classifier represented a strong risk factor of vascular events within one year among hypertensive patients (odds ratio higher than 4). Finally, the proposed system outperformed the classification based on carotid intima media thickness measurement, which is a proven power predictor of future vascular events. © Springer International Publishing Switzerland 2015.


Melillo P.,The Second University of Naples | Melillo P.,Scientific Research and University | Jovic A.,University of Zagreb | De Luca N.,University of Naples Federico II | And 3 more authors.
IFMBE Proceedings | Year: 2015

Accidental falls in elderly is a major problem. This paper presents the preliminary results of a retrospective study investigating association between Heart Rate Variability (HRV) measures and risk of falling, analyzing 168 clinical 24- hour ECG recording from hypertensive patients, 47 of them experienced at least one fall in the three months before/after the registration. Several HRV patterns, based on 68 linear and non-linear HRV measures, were analyzed in relation to falls using advanced statistical and data mining methods. The results demonstrated that there is a significant association between a depressed HRV and the risk of falling, suggesting that a depressed HRV could be a new independent risk factor for falls with an odds ratio of 5.12 (CI 95% 1.42-18.41; p<0.01). © Springer International Publishing Switzerland 2015.


PubMed | The Second University of Naples, University of Naples Federico II, University of Warwick and Scientific Research and University
Type: Journal Article | Journal: PloS one | Year: 2015

There is consensus that Heart Rate Variability is associated with the risk of vascular events. However, Heart Rate Variability predictive value for vascular events is not completely clear. The aim of this study is to develop novel predictive models based on data-mining algorithms to provide an automatic risk stratification tool for hypertensive patients.A database of 139 Holter recordings with clinical data of hypertensive patients followed up for at least 12 months were collected ad hoc. Subjects who experienced a vascular event (i.e., myocardial infarction, stroke, syncopal event) were considered as high-risk subjects. Several data-mining algorithms (such as support vector machine, tree-based classifier, artificial neural network) were used to develop automatic classifiers and their accuracy was tested by assessing the receiver-operator characteristics curve. Moreover, we tested the echographic parameters, which have been showed as powerful predictors of future vascular events.The best predictive model was based on random forest and enabled to identify high-risk hypertensive patients with sensitivity and specificity rates of 71.4% and 87.8%, respectively. The Heart Rate Variability based classifier showed higher predictive values than the conventional echographic parameters, which are considered as significant cardiovascular risk factors.Combination of Heart Rate Variability measures, analyzed with data-mining algorithm, could be a reliable tool for identifying hypertensive patients at high risk to develop future vascular events.

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