Health-related quality of life in patients with neuroendocrine neoplasms: a two-wave longitudinal study

Purpose: Scientific knowledge on health-related quality of life (HRQoL) in patients with neuroendocrine neoplasm (NEN) is still limited and longitudinal assessment of HRQoL over the time in NEN patients are scarce. The current study aimed to assess the role of clinical severity and heterogeneity of NEN, as well as resilience, in the HRQoL of NEN patients over the course of a year. Methods: 39 consecutive NEN patients (25 men and 14 women) aged from 29 to 73 years participated in a longitudinal Italian multicentric study. The main outcome measure concerned the severity and heterogeneity of NEN, HRQoL, and resilience. Results: Over the course of a year, higher levels of the global health (GH) were associated to the absence of distant metastases, while the presence of metastases with higher levels of fatigue, diarrhea, and financial difficulties. Higher levels of resilience are still associated with better GH and lower levels of fatigue, diarrhea, and financial difficulties, but no longer with constipation. Furthermore, patients with gastroenteropancreatic NEN still have higher scores on constipation, but not on GH, fatigue, diarrhea, and financial difficulties. Patients with hereditary NEN continue to have greater GH than those with a sporadic NEN and lower fatigue, diarrhea, and financial difficulties. Conclusion: These findings showed that the effects of severity and clinical heterogeneity of the NEN on HRQoL may change over time. This evidence should lead clinicians to monitor the HRQoL of NEN patients throughout the course of the disease and psychologists to implement evidence-based resilience interventions

Quality of Life in Patients with Neuroendocrine Neoplasms: The Role of Severity, Clinical Heterogeneity and Resilience

Context Although health-related quality of life (HRQoL) is a fundamental outcome in oncological clinical trials, its evaluation in the neuroendocrine neoplasm (NEN) research field is still limited. Objectives This study assessed the role of clinical severity (i.e., presence or absence of metastasis and lines of therapies) and heterogeneity (i.e., primary site, types of therapy, biology and surgery) of NEN in relation to HRQoL, as well as resilience as a moderator between clinical severity and HRQoL. Design Cross-sectional multicentric study. Setting Italian university hospitals. Patients 99 Italian patients (53 men and 46 women) with a NEN ranged in age from 22 to 79 years old. Main Outcome Measure Severity and heterogeneity of NENs, HRQoL and resilience. Results The presence of metastasis and a greater number of therapies affected the global health and some physical symptoms. Resilience was associated with global health, functional status and some physical symptoms, and moderated the impact of metastases on constipation and of the multiple therapies on diarrhea and financial problems. Patients with NEN in districts other than the gastro-entero-pancreatic system and those in follow-up perceived fewer physical symptoms than their counterparts. Patients with a sporadic NEN perceived their functional status, global health and disease-related worries as better than those with a hereditary NEN. Patients who underwent surgery were lower in constipation than their counterparts. Conclusion These findings highlight the need to assess the relationships between the clinical severity and heterogeneity of NEN with HRQoL and the role of resilience in improving patients’ HRQoL

Impact characterization on thin structures using machine learning approaches

Machine learning algorithms are trained and compared to identify and to characterise the impact on typical aerospace panels of different geometry. Experimental activities are conducted to build a proper impacts’ dataset. Polynomial regression algorithm and artificial neural network are applied and optimised to panels without stringer to test their capability to identify the impacts. Subsequently, the algorithms are applied to panels reinforced with stringers that represent a significant increase of complexity in terms of dynamic features of the system to test: the focus is not only on the impact position's detection but also on the event's severity. After the identification of the best algorithm, the corresponding machine learning model is deployed on an ARM processor mini-computer, implementing an impact detection system, able to be installed on board an aerial vehicle, making it a smart aircraft equipped with an artificial intelligence decision-making system

Impact characterization on RC airplane model in operation using machine learning

Structural Health Monitoring represents a growing field of great interest for aerospace engineering. This manuscript proposes an on-working SHM method for impact detection on RC airplane by ultrasounds, that is based on Machine Learning algorithms (polynomial regression and neural networks) and is useful to establish critical and dangerous operational conditions. The proposed method can be used to detect impact events both in metallic or composite structures, it is specifically designed to be used on typical fuselage and wing panels and is based on the propagation of Lamb waves in the structure on which PZT sensors are bonded for receiving signals. Algorithms are implemented in order to evaluate the impact location by post-processing the acquired signals. Several test cases are numerically studied before being tested in laboratory and reproduced on-working conditions. A good agreement between the numerical, laboratory and in-flight results is achieved

Machine Learning regression models diagnosis for structural health monitoring

This work aims at determining the location of low speed impact events on thin aluminium panels, specifically designed to be used on typical aircraft fuselage and wing panels, by processing the acoustic emission signals. The detection principle is based on the propagation of the first antisymmetric lamb wave (A0 mode) in the panel on which four PZT sensors are bonded to receive the signals. The impact location is assessed with the use of a supervised machine learning algorithm that is based on linear regression, appropriately designated to post-process the acquired signals. Some experimental cases are reported in order to investigate the optimal kind and amount of training data to improve the performance of the algorithm and therefore the accuracy of the impact location estimation