Peripheral Circulating Blood Cells Deviation Based on Tumor Inflammatory Microenvironment Activity in Resected Upstaged Lung Adenocarcinomas.

: The tumour inflammatory microenvironment (TIME) reflects a selective activation of the central immune system (IS), particularly T-cells expansion, which leads to immune cells migrating to the target, such as lung cancer, via the bloodstream and lymphatic vessels. In this study, the aim is to investigate whether the distribution of peripheral blood cells varies based on the immune status of patients with lung adenocarcinoma. : This is a single-center retrospective study conducted in the Thoracic Surgery Unit of the University of Padua (Italy) between 1 January 2016 and 1 April 2024.

Towards an objective theory of subjective liking: A first step in understanding the sense of beauty.

The study of the electroencephalogram signals recorded from subjects during an experience is a way to understand the brain processes that underlie their physical and emotional involvement. Such signals have the form of time series, and their analysis could benefit from applying techniques that are specific to this kind of data. Neuroaesthetics, as defined by Zeki in 1999, is the scientific approach to the study of aesthetic perceptions of art, music, or any other experience that can give rise to aesthetic judgments, such as liking or disliking a painting.

The voice of COVID-19: Breath and cough recording classification with temporal decision trees and random forests.

Symbolic learning is the logic-based approach to machine learning, and its mission is to provide algorithms and methodologies to extract logical information from data and express it in an interpretable way. Interval temporal logic has been recently proposed as a suitable tool for symbolic learning, specifically via the design of an interval temporal logic decision tree extraction algorithm. In order to improve their performances, interval temporal decision trees can be embedded into interval temporal random forests, mimicking the corresponding schema at the propositional level.

Predicting treatment recommendations in postmenopausal osteoporosis.

We designed, implemented, and tested a clinical decision support system at the Research Center for the Study of Menopause and Osteoporosis within the University of Ferrara (Italy). As an independent module of our system, we implemented an original machine learning system for rule extraction, enriched with a hierarchical extraction methodology and a novel rule evaluation technique. Such a module is used in everyday operation protocol, and it allows physicians to receive suggestions for prevention and treatment of osteoporosis.

Correlation between IL-31 and sCD40L plasma levels in Fingolimod-treated patients with Relapsing-Remitting Multiple Sclerosis (RRMS).

Introduction: Multiple Sclerosis (MS) is a chronic, autoimmune, demyelinating disease of the central nervous system (CNS). Currently, several protocols are described for the different phases of MS. In this longitudinal study, we aim to quantify the concentration of plasma cytokines of MS patients treated with Fingolimod alone or after Glatiramer Acetate (GA) or Interferon-beta (IFN-β), in order to compeer both treatments and describes if it is possible to use them as biomarkers.