In pancreatic cancer patients, chemotherapy reshapes the gene expression profile and antigen receptor repertoire of T lymphocytes and enhances their effector response to tumor-associated antigens.

Introduction: Pancreatic Ductal Adenocarcinoma (PDA) is one of the most aggressive malignancies with a 5-year survival rate of 13%. Less than 20% of patients have a resectable tumor at diagnosis due to the lack of distinctive symptoms and reliable biomarkers. PDA is resistant to chemotherapy (CT) and understanding how to gain an anti-tumor effector response following stimulation is, therefore, critical for setting up an effective immunotherapy.

[Synchronies and asynchronies in the development of COVID-19 pandemic in Italy].

Background: the study of the possible determinants of the rise and fall of infections can be of great relevance, as was experienced during the COVID-19 pandemic. One of the methods to understand whether determinants are simultaneous or develop through contiguity between different areas is the study of the diagnostic replication index RDt among regions.

Objectives: to introduce the analysis of RDt variability and the subsequent application of a recently introduced functional clustering method as highly useful procedures for recognizing the presence of clusters with similar trends in epidemic curves.

The mutation process on the ancestral line under selection.

We consider the Moran model of population genetics with two types, mutation, and selection, and investigate the line of descent of a randomly-sampled individual from a contemporary population. We trace this ancestral line back into the distant past, far beyond the most recent common ancestor of the population (thus connecting population genetics to phylogeny), and analyse the mutation process along this line. To this end, we use the pruned lookdown ancestral selection graph (Lenz et al.

A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development.

Background: Artificial Intelligence entails the application of computer algorithms to the huge and heterogeneous amount of morphodynamic data produced by Time-Lapse Technology. In this context, Machine Learning (ML) methods were developed in order to assist embryologists with automatized and objective predictive models able to standardize human embryo assessment. In this study, we aimed at developing a novel ML-based strategy to identify relevant patterns associated with the prediction of blastocyst development stage on day 5.

Modulation of faecal miRNAs highlights the preventive effects of a Mediterranean low-inflammatory dietary intervention.

Background: Dietary interventions have been proposed as therapeutic approaches for several diseases, including cancer. A low-inflammatory Mediterranean dietary intervention, conducted as a pilot study in subjects with Familial Adenomatous Polyposis (FAP), reduced markers of local and systemic inflammation. We aim to determine whether this diet may modulate faecal microRNA (miRNA) and gene expression in the gut.