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Introduction: Despite advancements in the control of emesis, chemotherapy-induced nausea and vomiting (CINV) still represent major problems for patients undergoing chemotherapy and can deeply affect quality of life and everyday activities. Objectives: This study aimed to measure the frequency and intensity of CINV and its variation over time, in order to gain a better understanding of the extent to which patients experience it. Methods: One hundred and twenty-one patients treated for gynecological cancer at the San Raffaele Hospital completed the MASCC Antiemesis Tool (MAT) after their first chemotherapy infusion; 85 of these patients completed the questionnaire after the third infusion. Results: The age range of the sample was 3285 years (mean=58.19; SD=13.82). Thirty-five percent of the patients experienced acute nausea after the first infusion and 44% after the third infusion; 48% reported delayed nausea after the first infusion and 58% after the third infusion. Only a small percentage of patients reported acute and delayed vomiting (5-18%). The intensity of nausea was significantly different from 0 in all four cases (p<.001). A significant increase in the intensity of acute nausea (p=.017) was found between the third infusion and the first. Conclusions: The findings suggest that nausea represents an important problem that is still common among cancer patients undergoing chemotherapy; it is possible that an exhaustion of psychosocial resources and the accumulation of the effects of antineoplastic drugs contribute to the increased intensity of acute nausea over time. These should be further investigated in order to properly address these patient issues.

The success of deep learning, a brain-inspired form of AI, has sparked interest in understanding how the brain could similarly learn across multiple layers of neurons. However, the majority of biologically-plausible learning algorithms have not yet reached the performance of backpropagation (BP), nor are they built on strong theoretical foundations. Here, we analyze target propagation (TP), a popular but not yet fully understood alternative to BP, from the standpoint of mathematical optimization. Our theory shows that TP is closely related to Gauss-Newton optimization and thus substantially differs from BP. Furthermore, our analysis reveals a fundamental limitation of difference target propagation (DTP), a well-known variant of TP, in the realistic scenario of non-invertible neural networks. We provide a first solution to this problem through a novel reconstruction loss that improves feedback weight training, while simultaneously introducing architectural flexibility by allowing for direct feedback connections from the output to each hidden layer. Our theory is corroborated by experimental results that show significant improvements in performance and in the alignment of forward weight updates with loss gradients, compared to DTP.