Authenticity Markers of Aged Red Wines from Aglianico, Uva di Troia, Negroamaro and Primitivo Grapes.

The wide ampelographic treasure of Italian wine grape varieties is driving research towards suitable approaches for the varietal authenticity control of wine. In this paper, Aglianico, Negroamaro, Primitivo and Uva di Troia red wines, which were produced experimentally by single-grape winemaking from non-aromatic grapes native to southern Italy, were analyzed with respect to berry markers, namely anthocyanins, hydroxycinnamic acids (HPLC-DAD), shikimic acid (HPLC-UV) and glycosidic aroma precursors (GC-MS). The study confirms that, just as for the berries, useful varietal authenticity markers for red wine, even after aging, turn out to be hydroxycinnamic acids, relative amounts of acylated forms of anthocyanins, and shikimic acid, together with some grape glycosidic precursors from terpenes and C13- norisoprenoids. Principal Component Analysis was used as a valuable tool to highlight the results.

Explainable 3D CNN based on baseline breast DCE-MRI to give an early prediction of pathological complete response to neoadjuvant chemotherapy.

BACKGROUND: So far, baseline Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has played a key role for the application of sophisticated artificial intelligence-based models using Convolutional Neural Networks (CNNs) to extract quantitative imaging information as earlier indicators of pathological Complete Response (pCR) achievement in breast cancer patients treated with neoadjuvant chemotherapy (NAC). However, these models did not exploit the DCE-MRI exams in their full geometry as 3D volume but analysed only few individual slices independently, thus neglecting the depth information. METHOD: This study aimed to develop an explainable 3D CNN, which fulfilled the task of pCR prediction before the beginning of NAC, by leveraging the 3D information of post-contrast baseline breast DCE-MRI exams. Specifically, for each patient, the network took in input a 3D sequence containing the tumor region, which was previously automatically identified along the DCE-MRI exam. A visual explanation of the decision-making process of the network was also provided. RESULTS: To the best of our knowledge, our proposal is competitive than other models in the field, which made use of imaging data alone, reaching a median AUC value of 81.8%, 95%CI [75.3%; 88.3%], a median accuracy value of 78.7%, 95%CI [74.8%; 82.5%], a median sensitivity value of 69.8%, 95%CI [59.6%; 79.9%] and a median specificity value of 83.3%, 95%CI [82.6%; 84.0%], respectively. The median and CIs were computed according to a 10-fold cross-validation scheme for 5 rounds. CONCLUSION: Finally, this proposal holds high potential to support clinicians on non-invasively early pursuing or changing patient-centric NAC pathways.