Enel (Italy)

Abstract Background: Antibody-drug conjugates (ADCs) such as trastuzumab deruxtecan (T-DXd) and sacituzumab govitecan (SG) have revolutionized breast cancer (BC) treatment, providing superior efficacy that allows for extended treatment durations. Nausea and vomiting (NV) are among the most frequent adverse effects associated with T-DXd and SG, with nausea rates of >70% for T-DXd and >60% for SG in BC clinical trials. According to antiemetic guidelines, patients receiving T-DXd and SG are at moderate-to-high risk for NV and should routinely receive prophylaxis with an NK1 receptor antagonist (RA)-containing regimen. With limited real-world data on antiemetic efficacy in the ADC setting, a survey of international healthcare providers (HCPs) was conducted to assess their experience. Methods: At SABCS 2024, HCPs completed a brief web-based survey to assess their experiences and perceptions of ADC emetogenicity and approaches to antiemetic prophylaxis. Target participants included HCPs familiar with ADCs who manage patients with BC. Eligible participants completed the survey on computers located in the exhibit hall. Results: Of 209 HCPs surveyed, 112 were eligible. Most were oncologists (69%) or hematologist/oncologists (21%) who spent 73% of time in patient care; nearly half were US-based (46%). Among all HCPs, 80% reported using ADCs in practice and/or in clinical trials; 99% had experience with T-DXd and 87% with SG. Among HCPs using T-DXd and SG, 78% and 83%, respectively, perceived them as either highly (32% and 28%) or moderately (46% and 55%) emetogenic. NV reported by HCPs in this survey was much lower than in the T-DXd and SG clinical trials; HCPs reported that 30%, 25% and 15% of their BC patients treated with T-DXd experience N and/or V during the acute (0-24 hours), delayed (days 2-5), and long-delayed (beyond day 5) phases, respectively. Similarly, the reported NV rates were 29%, 26% and 13%, respectively, for SG. HCPs reported that 88% of patients treated with any ADCs in an average month receive antiemetic prophylaxis; however, only 39% of HCPs report exclusively using a guideline-recommended NK1 RA regimen with T-DXd or SG. The majority of HCPs (94%) reported implementing some type of dose adjustments of ADCs due to NV. A third to nearly half of HCPs had at least sometimes implemented an ADC dose reduction or delay due to NV while a quarter reported interrupting or discontinuing ADC treatment (Table). More than half reported using rescue medication at least sometimes for delayed NV. Conclusions: This survey highlights a gap between evidence and HCP perceptions regarding NV with T-DXd and SG, and reveals higher-than-expected dose reductions, likely reflecting suboptimal adherence to antiemetic guidelines. As ADCs gain prominence in BC treatment, these findings underscore the need for education and guideline-implementation to optimize NV prevention. Citation Format: L. S. Schwartzberg, L. Licata, G. Bianchini, Y. H. Park, E. J. Roeland, M. Massagrande, F. Dato, H. Iihara, F. Scotte, K. Jordan, M. Aapro, H. S. Rugo. Examining Antiemetic Management with Antibody-Drug Conjugates (ADCs) in Patients with Breast Cancer: Healthcare Provider (HCP) Insights from a SABCS Survey [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS1-01-13.

The vigorous development of renewable power plants requires the installation of medium-voltage underground cable networks for the wiring of internal skids and the grid connection. Given the complexity of these networks, which are typically extensive and include a high number of joints, the requiblack pre-commissioning tests should follow a pragmatic engineering approach and be completed within a reasonable timeframe. This paper focuses on field testing procedures for medium-voltage cables prior to energization, including electric resistance, oversheath integrity, and insulation integrity tests. For each test, key performance indicators are described in detail, along with acceptable variability ranges to support the accurate interpretation of results in practical applications. The implementation of the tests on the medium-voltage network of a photovoltaic plant in southern Italy is presented as a real-world application of the proposed assessment procedures.

Chronic kidney disease (CKD) has a worthwhile impact on public health, with an estimated global prevalence of 11 to 13%. Uremic sarcopenia is a typical consequence in these patients. It can cause physical disability and reduce autonomy and quality of life. Game therapy has emerged as a promising therapeutic tool in rehabilitation, effectively addressing essential elements such as attention, motivation, and repetitiveness. This study aimed to design and evaluate the feasibility of a biomedical system created to encourage cycling exercise during hemodialysis, incorporating a serious game as a key component. Thirteen patients were involved in the study, 8 women and 5 men. The average age was 47.2 (±12.3) years. Time on hemodialysis was 52.6 (±31.1) months. The assessment protocol includes handgrip strength (HGS) by dynamometry, the 5 times sit-to-stand test (5x SST), and the gait speed test (GST). An average of 20 sessions were carried out twice a week, each taking 20 to 25 minutes. Patients showed significant improvements at the end of treatment, especially in HGS (p=0.05) and 5x SST (p=0.03). Preliminary results on the feasibility of the resource through the Likert scale satisfaction index were: 77% very satisfied, 23% satisfied, and no patient indicated dissatisfaction with participation in the sessions during the study. The good results in the measurements of controlled variables, and the strong patient acceptance shows great promise for the rehabilitation of individuals with kidney disease.

The objective of the paper is to implement the anomaly detection with help of machine learning methods. The archived historic data is collected from a PV plant at Minnesota. The K-Means for clustering the historic data is employed (based on Temperature, Irradiation and inverter module current). Then the clustered data is used for anomaly detection purpose with help of two networks (LSTM, GRU). The hybrid model for clustering (K-mean method) and anomaly detection networks are developed in Python programming language with help of Scikit-learn and TensorFlow modules. The algorithm tested on data collected over a few years of PI data (OSI-soft PI DataLink). The results demonstrate the effectiveness of both methods (LSTM and GRU) in anomaly detection. The proposed model can assist PV plant operators at the site to be able to schedule a maintenance in advance of equipment failure and help to minimize downtime and support for maintenance decision making.

The accessibility of long-duration recorders, adapted to sometimes demanding field conditions, has enabled the deployment of extensive animal population monitoring campaigns through ecoacoustics. The effectiveness of automatic signal detection methods, increasingly based on neural approaches, is frequently evaluated solely through machine learning metrics, while acoustic analysis of performance remains rare. As part of the acoustic monitoring of Rock Ptarmigan populations, we propose here a simple method for acoustic analysis of the detection system's performance. The proposed measure is based on relating the signal-to-noise ratio of synthetic signals to their probability of detection. We show how this measure provides information about the system and allows optimisation of its training. We also show how it enables modelling of the detection distance, thus offering the possibility of evaluating its dynamics according to the sound environment and accessing an estimation of the spatial density of calls.

The rapid expansion of photovoltaics is driven by significant reduction in costs. However, given the surface requirements for photovoltaic development, utilizing water surfaces for floating photovoltaic (FPV) systems presents a promising solution. To enhance the cost-effectiveness of these systems, bifacial modules and tracking systems can be employed. While numerous experimental studies have evaluated the performance of fixed-configuration FPVs, floating tracking configurations remain underexplored. In addition, various simulation tools offer insights into different configurations, but their different assumptions often yield inconsistent results. This study focuses on the experimental evaluation of a horizontal axis tracking bifacial FPV (HT-bFPV) system. Over one year, the HT-bFPV system was monitored at the FPV test bed of “Enel Innovation Hub & Lab” in Catania, Italy. The experimental results were compared with simulated outcomes using two software tools, to assess their precision in calculating the HT-bFPV performances. The results reveal that the module temperature of the HT-bFPV system is 3 °C to 6 °C lower than the temperatures calculated by System Advisor Model and Photovoltaic system software, respectively. The yearly reference yield of 2139 kWh/kW produced a final yield of 1801 kWh/kW. The yearly performance ratio of the HT-bFPV system was 0.86, which improved by 1.8% when adjusted for temperature. The simulation results closely matched the experimental data, validating the system's performance. Furthermore, it was confirmed that the HT-bFPV system can produce up to 13.3% more energy with more potential in sites with higher latitudes compared with a similar fixed system.