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Background: Music Therapy (MT) is a non-pharmacological, art-based intervention that employs music experiences within a therapeutic alliance to attend to clients' physical, emotional, cognitive, and social requirements. This is the first study aiming at investigating the impact of MT on the psychological facets of children suffering from cancer., Methods: The study, combining the AQR and m-YPAS assessment tools, evaluated behavioral, sound-musical, and interactive parameters in pediatric oncology patients undergoing MT sessions during hospitalization. Fifty patients admitted to the Paediatric Oncology and Haematology Unit at Policlinico S. Orsola Hospital in Bologna, Italy, were enrolled, irrespective of their treatment regimen. Data collection occurred on the first day of the MT session between 3 p.m. and 5 p.m., with observations conducted by independent observers. In addition to traditional statistical analysis, network analysis was used to explore the combined interactions of all parameters, effectively discerning the distinctive roles played by each one during therapy sessions and their influence on all others., Results: Network analysis highlighted distinct patterns of interactions among parameters during the various sessions, emphasizing the role of positive emotions and a calm setting, the child's ability to take the initiative in sessions, their sense of agency, and the parent's role in guiding them. Significant differences were recorded at each time point between all variables considered., Conclusions: The results of this innovative study may pave the way for future multicenter studies aimed at further exploring the role of MT in children undergoing both curative and palliative treatments for cancer.

Large high-quality datasets are essential for building powerful artificial intelligence (AI) algorithms capable of supporting advancement in cardiac clinical research. However, researchers working with electrocardiogram (ECG) signals struggle to get access and/or to build one. The aim of the present work is to shed light on a potential solution to address the lack of large and easily accessible ECG datasets. Firstly, the main causes of such a lack are identified and examined. Afterward, the potentials and limitations of cardiac data generation via deep generative models (DGMs) are deeply analyzed. These very promising algorithms have been found capable not only of generating large quantities of ECG signals but also of supporting data anonymization processes, to simplify data sharing while respecting patients' privacy. Their application could help research progress and cooperation in the name of open science. However several aspects, such as a standardized synthetic data quality evaluation and algorithm stability, need to be further explored., Competing Interests: Declaration of competing interest None Declared, (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Aims: Brugada syndrome (BrS) is an inherited disease associated with an increased risk of ventricular arrhythmias. Recent studies have reported the presence of an altered atrial phenotype characterized by abnormal P-wave parameters. The aim of this study was to identify BrS based exclusively on P-wave features through an artificial intelligence (AI)-based model., Methods and Results: Continuous 5 min 12-lead ECG recordings were obtained in sinus rhythm from (i) patients with spontaneous or ajmaline-induced BrS and no history of AF and (ii) subjects with suspected BrS and negative ajmaline challenge. The recorded ECG signals were processed and divided into epochs of 15 s each. Within these epochs, P-waves were first identified and then averaged. From the averaged P-waves, a total of 67 different features considered relevant to the classification task were extracted. These features were then used to train nine different AI-based supervised classifiers. A total of 2228 averaged P-wave observations, resulting from the analysis of 33 420 P-waves, were obtained from 123 patients (79 BrS+ and 44 BrS-). Averaged P-waves were divided using a patient-wise split, allocating 80% for training and 20% for testing, ensuring data integrity and reducing biases in AI-based model training. The BrS+ patients presented with longer P-wave duration (136 ms vs. 124 ms, P < 0.001) and higher terminal force in lead V1 (2.5 au vs. 1.7 au, P < 0.01) compared with BrS- subjects. Among classifiers, AdaBoost model had the highest values of performance for all the considered metrics, reaching an accuracy of over 81% (sensitivity 86%, specificity 73%)., Conclusion: An AI machine-learning model is able to identify patients with BrS based only on P-wave characteristics. These findings confirm the presence of an atrial hallmark and open new horizons for AI-guided BrS diagnosis., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Hospitalization for breast surgery is a distressing experience for women. This study investigated the impact of music therapy (MT), an integrative approach that is characterized by the establishment of a therapeutic relationship between patients and a certified music therapist, through different musical interventions targeted to the specific needs of the patients. The impact of two different MT experiences was compared on anxiety and distressing emotions., Methods: One hundred fifty-one patients during hospitalization for breast surgery were randomly assigned to two music therapy treatment arms: individual/receptive (MTri) vs. group/active-receptive integrated (MTiGrp). Stress, depression, anger, and need for help were measured with the emotion thermometers (ET) and State Trait Anxiety Inventory Y-1 form (STAY-Y1). Data were collected before and after the MT intervention., Results: Both types of MT interventions were effective in reducing all the variables: stress, depression, anger, and anxiety (T Student p‹0.01). Patients' perception of help received was correlated with a significant reduction in anxiety and distressing emotions during hospitalization for breast surgery., Conclusion: Considerations regarding the implementation of MT interventions in clinical practice are discussed. In individual receptive MT, there was a significant decrease in anxiety levels, whereas in the integrated MT group, there was a higher perception of help received and use of inter-individual resources., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The tendency to have better control of the flow of electrons in a channel of field-effect transistors (FETs) did lead to the design of two gates in junction field-effect transistors, field plates in a variety of metal semiconductor field-effect transistors and high electron mobility transistors, and finally a gate wrapping around three sides of a narrow fin-shaped channel in a FinFET. With the enhanced control, performance trends of all FETs are still challenged by carrier mobility dependence on the strengths of the electrical field along the channel. However, in cases when the ratio of FinFET volume to its surface dramatically decreases, one should carefully consider the surface boundary conditions of the device. Moreover, the inherent non-planar nature of a FinFET demands 3D modeling for accurate analysis of the device performance. Using the Silvaco modeling tool with quantization effects, we modeled a physical FinFET described in the work of Hisamoto et al. (IEEE Tran. Elec. Devices 47:12, 2000) in 3D. We compared it with a 2D model of the same device. We demonstrated that 3D modeling produces more accurate results. As 3D modeling results came close to experimental measurements, we made the next step of the study by designing a dual-gate FinFET biased at Vg1 >Vg2. It is shown that the dual-gate FinFET carries higher transconductance than the single-gate device.