Efficacy of Spectral Signatures for the Automatic Classification of Abnormal Ventricular Potentials in Substrate-Guided Mapping Procedures

Several peculiar spectral signatures of post-ischaemic ventricular tachycardia (VT) electrograms (EGMs) have been recently published in the scientific literature. However, despite they were claimed as potentially useful for the automatic identification of arrhythmogenic targets for the VT treatment by trans-catheter ablation, their exploitation in machine learning (ML) applications has been not assessed yet. The aim of this work is to investigate the impact of the information retrieved from these frequency-domain signatures in modelling supervised ML tools for the identification of physiological and abnormal ventricular potentials (AVPs). As such, 1504 bipolar intracardiac EGMs from nine electroanatomic mapping procedures of post-ischaemic VT patients were retrospectively labelled as AVPs or physiological by an expert electrophysiologist. In order to assess the efficacy of the proposed spectral features for AVPs recognition, two different classifiers were adopted in a 10-time 10-fold cross-validation scheme. In both classifiers, the adoption of spectral signatures led to recognition accuracy values above 81%, suggesting that the use of the frequency-domain characteristics of these signals can be successfully considered for the computer-aided recognition of AVPs in substrate-guided mapping procedures

Exploring Transfer Learning for Ventricular Tachycardia Electrophysiology Studies

Arrhythmogenic sites in post-ischemic ventricular tachycardia (VT) are usually identified by looking for abnormal ventricular potentials (AVPs) in intracardiac electrograms (EGMs). Unfortunately, the accurate recognition of AVPs is a challenging problem for different reasons, including the intrinsic variability in the A VP waveform. Given the high performance of deep neural networks in several scenarios, in this work, we explored the use of transfer learning (TL) for AVPs detection in intracardiac electrophysiology. A balanced set of 1504 bipolar intracardiac EGMs was collected from nine post-ischemic VT patients. The time-frequency representation was generated for each EGM by computing the synchrosqueezed wavelet transform to be used in the re-training of the convolutional neural network. The proposed approach allows obtaining high recognition results, above 90% for all the investigated performance indexes, demonstrating the effectiveness of deep learning in the recognition of AVPs in post-ischemic VT EGMs and paving the way for its use in supporting clinicians in targeting arrhythmogenic sites. In addition, this study further confirms the efficacy of the TL approach even in case of limited dataset sizes

Fingerprint Presentation Attacks: Tackling the Ongoing Arms Race in Biometric Authentication

The widespread use of Automated Fingerprint Identification Systems (AFIS) in consumer electronics opens for the development of advanced presentation attacks, i.e. procedures designed to bypass an AFIS using a forged fingerprint. As a consequence, AFIS are often equipped with a fingerprint presentation attack detection (FPAD) module, to recognize live fingerprints from fake replicas, in order to both minimize the risk of unauthorized access and avoid pointless computations. The ongoing arms race between attackers and detector designers demands a comprehensive understanding of both the defender’s and attacker’s perspectives to develop robust and efficient FPAD systems. This paper proposes a dual-perspective approach to FPAD, which encompasses the presentation of a new technique for carrying out presentation attacks starting from perturbed samples with adversarial techniques and the presentation of a new detection technique based on an adversarial data augmentation strategy. In this case, attack and defence are based on the same assumptions demonstrating that this dual research approach can be exploited to enhance the overall security of fingerprint recognition systems against spoofing attacks

Effects of metformin and exercise training, alone or in association, on cardio-pulmonary performance and quality of life in insulin resistance patients

BACKGROUND: Metformin (MET) therapy exerts positive effects improving glucose tolerance and preventing the evolution toward diabetes in insulin resistant patients. It has been shown that adding MET to exercise training does not improve insulin sensitivity. The aim of this study was to determine the effect of MET and exercise training alone or in combination on maximal aerobic capacity and, as a secondary end-point on quality of life indexes in individuals with insulin resistance. METHODS: 75 insulin resistant patients were enrolled and subsequently assigned to MET (M), MET with exercise training (MEx), and exercise training alone (Ex). 12-weeks of supervised exercise-training program was carried out in both Ex and MEx groups. Cardiopulmonary exercise test and SF-36 to evaluate Health-Related Quality of Life (HRQoL) was performed at basal and after 12-weeks of treatment. RESULTS: Cardiopulmonary exercise test showed a significant increase of peak VO2 in Ex and MEx whereas M showed no improvement of peak VO2 (∆ VO2 [CI 95%] Ex +0.26 [0.47 to 0.05] l/min; ∆ VO2 MEx +0.19 [0.33 to 0.05] l/min; ∆ VO2 M -0.09 [-0.03 to -0.15] l/min; M vs E p < 0.01; M vs MEx p < 0.01; MEx vs Ex p = ns). SF-36 highlighted a significant increase in general QoL index in the MEx (58.3 ± 19 vs 77.3 ± 16; p < 0.01) and Ex (62.1 ± 17 vs 73.7 ± 12; p < 0.005) groups. CONCLUSIONS: We evidenced that cardiopulmonary negative effects showed by MET therapy may be counterbalanced with the combination of exercise training. Given that exercise training associated with MET produced similar effects to exercise training alone in terms of maximal aerobic capacity and HRQoL, programmed exercise training remains the first choice therapy in insulin resistant patients

Adenosine A2A Receptors and A2A Receptor Heteromers as Key Players in Striatal Function

A very significant density of adenosine A2A receptors (A2ARs) is present in the striatum, where they are preferentially localized postsynaptically in striatopallidal medium spiny neurons (MSNs). In this localization A2ARs establish reciprocal antagonistic interactions with dopamine D2 receptors (D2Rs). In one type of interaction, A2AR and D2R are forming heteromers and, by means of an allosteric interaction, A2AR counteracts D2R-mediated inhibitory modulation of the effects of NMDA receptor stimulation in the striatopallidal neuron. This interaction is probably mostly responsible for the locomotor depressant and activating effects of A2AR agonist and antagonists, respectively. The second type of interaction involves A2AR and D2R that do not form heteromers and takes place at the level of adenylyl cyclase (AC). Due to a strong tonic effect of endogenous dopamine on striatal D2R, this interaction keeps A2AR from signaling through AC. However, under conditions of dopamine depletion or with blockade of D2R, A2AR-mediated AC activation is unleashed with an increased gene expression and activity of the striatopallidal neuron and with a consequent motor depression. This interaction is probably the main mechanism responsible for the locomotor depression induced by D2R antagonists. Finally, striatal A2ARs are also localized presynaptically, in cortico-striatal glutamatergic terminals that contact the striato-nigral MSN. These presynaptic A2ARs heteromerize with A1 receptors (A1Rs) and their activation facilitates glutamate release. These three different types of A2ARs can be pharmacologically dissected by their ability to bind ligands with different affinity and can therefore provide selective targets for drug development in different basal ganglia disorders

Effects of metformin and exercise training, alone or in combination, on cardiac function in individuals with insulin resistance

Introduction: In patients affected by insulin resistance (IR), metformin (MET) therapy has been shown to exert its positive effects by improving glucose tolerance and preventing the evolution to diabetes. Recently, it was shown that the addition of metformin to physical training did not improve sensitivity to insulin or peak oxygen consumption (peak VO2). The purpose of this study was to establish the effect of metformin and exercise, separately or in combination, on systolic left ventricular (LV) function in individuals with IR. Methods: Seventy-five patients with IR were enrolled and subsequently assigned to MET, combination MET and exercise, or exercise alone. The LV systolic and diastolic functions were evaluated with standard echocardiography tissue Doppler imaging (TDI) and speckle tracking echocardiography at baseline and after 12 weeks of treatment. Results: MET, administered alone or in association with exercise, improved longitudinal LV function, as evidenced by an increase in systolic (S) wave on TDI, alongside increases in longitudinal global strain and strain rate in comparison to the group undergoing physical training alone. The traditional echocardiographic parameters showed no statistically significant differences among the three groups before or after the different cycles of therapy. Conclusions: Treatment with MET, either with or without exercise, but not exercise alone, produced a significant increase in global longitudinal LV systolic function at rest. These findings validate the observation that the use of MET alone or in association with exercise has a crucial role to counteract the negative effects of IR on cardiovascular function

Approaches to ‘vulnerability’ in eight European disaster management systems

Orru, K., Hansson, S., Gabel, F., Tammpuu, P., Krüger, M., Savadori, L., Meyer, S.F., Torpan, S., Jukarainen, P., Schieffelers, A., Lovasz, G. and Rhinard, M. (2022), Approaches to ‘vulnerability’ in eight European disaster management systems. Disasters, 46: 742-767. https://doi.org/10.1111/disa.12481While social vulnerability in the face of disasters has received increasing academic attention, relatively little is known about the extent to which that knowledge is reflected in practice by institutions involved in disaster management. In this study, we chart the practitioners’ approaches to disaster vulnerability in eight European countries: Germany, Italy, Belgium, Hungary, Sweden, Norway, Finland, and Estonia. The study draws from a comparative document analysis and 95 interviews with disaster managers and reveals significant differences across countries in terms of the ontology of vulnerability, its sources, reduction strategies, and the allocation of related duties. To advance the debate and provide conceptual clarity, we put forward a model for explicating different understandings of vulnerability along the dimensions of human agency and technological structures as well as social support through private relations and state actors.acceptedVersio

Leveraging Artificial Intelligence to Fight (Cyber)Bullying for Human Well-being: The BullyBuster Project

Bullying and cyberbullying are phenomena which, due to their growing diffusion, have become a real social emergency. In this context, artificial intelligence can be a powerful weapon to identify episodes of violence and fight bullying both in the virtual and in the real world. Through machine learning, it is possible to detect the language patterns used by bullies and their victims and develop rules to detect cyberbullying content automatically. The BullyBuster project merges the know-how of four interdisciplinary research groups to develop a framework useful for maintaining psycho-physical well-being in educational contexts

Vulnerability and vulnerable groups from an intersectionality perspective

In general, the identification and protection of vulnerable groups in the case of hazards or when a crisis unfolds is an issue that any crisis and disaster risk management should address, since people have different levels of exposure to hazards and crises. In this article, we promote the application of the intersectionality perspective in the study of vulnerable groups, and we call for intersectionality as a guiding principle in risk and crisis management, to provide a better and more nuanced picture of vulnerabilities and vulnerable groups. This can help national and local authorities and agencies to formulate specific guides, to hire staff with the skills necessary to meet particular needs, and to inform vulnerable groups in a particular way, taking into account the differences that may coexist within the same group. Intersectionality allows us to read vulnerability not as the characteristic of some socio-demographic groups. It is rather the result of different and interdependent societal stratification processes that result in multiple dimensions of marginalisation. In this vein, we argue that research should focus on 1) self-perceived vulnerability of individuals and an intersectionality approach to unpack vulnerable groups; 2) cases of crises according to the level and/or likelihood of individual exposure to hazards, to better nuance issues of vulnerability.publishedVersio