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•Clinical Connectome Fingerprint (CCF) analysis extracts subject-specific connectome features in multiple sclerosis (MS)•CCF shows a lower similarity between functional connectomes ...(FCs) of the same patient and among the FCs of the MS group.•The identifiability parameters (extracted from the CCF) are able to predict MS-related fatigue.
Brain connectome fingerprinting is progressively gaining ground in the field of brain network analysis. It represents a valid approach in assessing the subject-specific connectivity and, according to recent studies, in predicting clinical impairment in some neurodegenerative diseases. Nevertheless, its performance, and clinical utility, in the Multiple Sclerosis (MS) field has not yet been investigated.
We conducted the Clinical Connectome Fingerprint (CCF) analysis on source-reconstructed magnetoencephalography signals in a cohort of 50 subjects: twenty-five MS patients and twenty-five healthy controls.
All the parameters of identifiability, in the alpha band, were reduced in patients as compared to controls. These results implied a lower similarity between functional connectomes (FCs) of the same patient and a reduced homogeneity among FCs in the MS group. We also demonstrated that in MS patients, reduced identifiability was able to predict, fatigue level (assessed by the Fatigue Severity Scale).
These results confirm the clinical usefulness of the CCF in both identifying MS patients and predicting clinical impairment. We hope that the present study provides future prospects for treatment personalization on the basis of individual brain connectome.
Executive function disorders are more bound to occur in children with specific learning disorders (SLD). In this population deficits in executive functioning are associated with worse clinical ...conditions, maladaptive behaviors, and problems with school achievement. Here, are reported the effects of the training targeting these higher-order processes through computer games in a child with a diagnosis of SLD treated in our rehabilitation center. Results show an improvement after a 6-month training program for all the executive domains.
To determine the efficacy of L-carnitine in reducing hyperammonaemia and improving neuropsychological performance in patients with hepatic cirrhosis and subclinical hepatic encephalopathy (SHE).
...Randomised, parallel group, controlled trial.
The study enrolled 31 patients with hepatic cirrhosis resulting from hepatitis C and/or hepatitis B, alcohol abuse and other causes. Patients randomised to active treatment, received oral L-carnitine 6 g/day in two divided doses for 4 weeks. Diagnosis of SHE was based on psychometric tests (subtests of the Wechsler Adult Intelligence Scale-Revised and the Halstead-Reitan Neuropsychological Test Battery) carried out at beginning and end of study. Serum ammonia levels were measured before treatment and weekly thereafter.
A total of 27 patients completed the study. Sixteen patients received L-carnitine and 11 served as controls (no treatment). L-carnitine caused rapid and significant reductions in ammonia levels, sustained over the 4-week treatment period (mean reductions 60.1 and 1.4 (μmol/L in the treated and control groups, respectively). Normal ammonia levels were attained in 14 of 16 patients receiving L-carnitine. Based on psychometric test results, seven patients (43.7%) in the L-carnitine group and five (45.5%) in the control group had SHE at baseline. L-carnitine treatment for 4 weeks caused a net overall improvement in psychometric test results compared with controls. No clinically significant adverse events were reported and all patients receiving L-carnitine reported subjective improvements in their condition.
RESULTS of this preliminary study indicate that L-carnitine reduces hyperammonaemia and improves the clinical symptoms of SHE in patients with hepatic cirrhosis.
The anthracyclines, doxorubicin and daunorubicin, are antibiotics effective in the treatment of many malignancies. However, their usefulness is limited by the development of potentially fatal ...cardiotoxicity. Cardiac monitoring by a noninvasive test capable of identifying patients at high risk of cardiac damage, before the ejection fraction deteriorates would have clinical utility. Electrocardiograms and echocardiograms are routinely utilized for noninvasive assessment of myocardial function. However, of the ECG abnormalities described, none has been noted to be of consistent predictive value for cardiotoxicity. The aim of this study was to assess the effects of doxorubicin on ventricular repolarization time indexes, as they have been shown to be effective in the identification of electrical myocardial instability and, hence, in the identification of risk for either arrhythmia or heart failure. For this reason, electrocardiograms were compared in 35 cancer patients at the first presentation (drug-free state) and after 29.4 +/- 37.65 weeks of treatment with doxorubicin. The results of the present study showed that after only a short period of treatment with doxorubicin there was a significant increase in ventricular recovery time dispersion indexes (QTc, JT, and JTc dispersion, and their "adjusted" values). Thus, increased regional variation in ventricular repolarization could be, in the absence of a significant modification of the echocardiographic parameters, an early marker of an electropathy, due to the early cardiotoxic action of doxorubicin on myocardial cells, eventually leading to heart failure.
Environmental time series are often affected by missing data, namely data unavailability at certain time points. This paper presents the Iterated Imputation and Prediction algorithm, that allows the ...prediction of time series with missing data. The algorithm uses iteratively the Correlation Dimension Estimation of the underlying dynamic system generating the time series to fix the model order (i.e., how many past samples are required to model the time series accurately), and the Support Vector Machine Regression to estimate the skeleton of time series. Experimental validation of the algorithm on three environmental time series with missing data, expressing the concentration of Ozone in three European sites, shows a small average percentage prediction error for all time series on the test set.
•The paper presents Iterated Imputation and Prediction (IIP) algorithm for the missing data time series prediction .•IIP uses Correlation Dimension and Support Vector Machine Regression to estimate the model order and the skeleton of time series.•Correlation Dimension is estimated with the proposed Grassberger-Procaccia-Hough algorithm.
Globally, the number of internet users increases every year. As a matter of fact, we use technological devices to surf the internet, for online shopping, or just to relax and keep our relationships ...by spending time on social networks. By doing any of those actions, we release information that can be used in many ways, such as targeted advertising via cookies but also abused by malicious users for scams or theft. On the other hand, many detection systems have been developed with the aim to counteract malicious actions. In particular, special attention has been paid to the malware, designed to perpetrate malicious actions inside software systems and widespread through internet networks or e-mail messages. In this paper, we propose a deep learning model aimed to detect ransomware. We propose a set of experiments aimed to demonstrate that the proposed method obtains good accuracy during the training and test phases across a dataset of over 15,000 elements. Moreover, to improve our results and interpret the output obtained from the models, we have also exploited the Gradient-weighted Class Activation Mapping.
The ARGO-USV (Unmanned Surface Vehicle for ARchaeological GeO-application) is a technological project involving a marine drone aimed at devising an innovative methodology for marine geological and ...geomorphological investigations in shallow areas, usually considered critical areas to be investigated, with the help of traditional vessels. The methodological approach proposed in this paper has been implemented according to a multimodal mapping technique involving the simultaneous and integrated use of both optical and geoacoustic sensors. This approach has been enriched by tools based on artificial intelligence (AI), specifically intended to be installed onboard the ARGO-USV, aimed at the automatic recognition of submerged targets and the physical characterization of the seabed. This technological project is composed of a main command and control system and a series of dedicated sub-systems successfully tested in different operational scenarios. The ARGO drone is capable of acquiring and storing a considerable amount of georeferenced data during surveys lasting a few hours. The transmission of all acquired data in broadcasting allows the cooperation of a multidisciplinary team of specialists able to analyze specific datasets in real time. These features, together with the use of deep-learning-based modules and special attention to green-compliant construction phases, are the particular aspects that make ARGO-USV a modern and innovative project, aiming to improve the knowledge of wide coastal areas while minimizing the impact on these environments. As a proof-of-concept, we present the extensive mapping and characterization of the seabed from a geoarchaeological survey of the underwater Roman harbor of Puteoli in the Gulf of Naples (Italy), demonstrating that deep learning techniques can work synergistically with seabed mapping methods.
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline with loss of memory. In the last years there has been a great interest on the early phases of AD, trying to ...identify the pathogenic mechanisms of AD and define early treatment modalities. In particular, Mild Cognitive Impairment (MCI) is attractive because it represents a transitional state between normal aging and dementia, although not all MCI patients automatically convert to AD. The neurotrophin brain-derived neurotrophic factor (BDNF) is critical for survival and function of neurons that degenerate in AD and represents a potential neuroprotective agent. However, opposite data on serum levels of BDNF have been reported in AD patients, probably reflecting differences in patient recruitment and stage of the disease. Thus, in this study we measured BDNF serum levels in AD patients (with different degree of severity), MCI patients and healthy subjects. We found that serum BNDF levels were significantly increased in MCI and AD patients when compared to healthy subjects and this increase in AD patients was neither dependent on illness severity, nor on treatment with Acetylcholinesterase inhibitors and/or antidepressant medications. Our findings indicate that BDNF serum levels increase in MCI and AD patients, supporting the hypothesis of an upregulation of BDNF in both preclinical phase of dementia (MCI) and clinical stages of AD. Other studies are necessary to establish a direct link between BDNF peripheral levels and AD longitudinal course, as well as the role of other factors, such as blood cell activation, in determining these events.
