Experimental study of the antideuteron source in p-p collisions at LHC energies.

This thesis presents a first analysis of the correlation function for (anti)proton and (anti)deuteron based on a sample of pp collisions data collected in 2022 at √s = 13.6 TeV by the ALICE experiment at CERN. The study of momentum correlations among particles is employed in constraining light antinuclei formation mechanisms and validating models of coalescence of antinucleons into light antinuclei. This is motivated by the fact that a deeper understanding of the origin of cosmic light antinuclei opens the possibility of searching for them as indirect dark matter signals, having the capability to predict the expected signal and background rates. In this work, the femtoscopy technique is applied to measure the correlation functions for proton−proton (p−p) and proton−deuteron (p−d) pairs. The obtained p−p correlation is fitted with the Argonne ν18 plus the Coulomb potentials to extract a preliminary measurement of the proton source radius in minimum bias pp collisions, resulting equal to r = 1.069 ± 0.014 fm. The obtained p−d correlation function is discussed in comparison with the measurement in pp collisions at √s = 13 TeV. The analysis is based on a new framework developed to meet the goals of this thesis. The optimization of the particle identification selection criteria and the pair-building procedure is achieved in this work. The promising result motivates the extensions of the analysis to the complete pp dataset available from the ongoing LHC Run 3

DarkSide-20k: la trasmissione ottica del segnale

Questa tesi descrive lo sviluppo di un sistema di trasmissione ottica di segnali elettrici concepito per il rivelatore dell'esperimento DarkSide-20k, un esperimento di ultima generazione per la ricerca di materia oscura che verrà realizzato presso i Laboratori Nazionali del Gran Sasso. Il sistema sviluppato, chiamato Opto-Link, si basa su una tecnologia innovativa ideata per rispondere alle caratteristiche di DarkSide-20k. Costituito da materiali estremamente radio-puri, il sistema è in grado di funzionare in maniera ottimale alle temperature criogeniche dell'argon liquido usato nell'esperimento per la rivelazione diretta delle WIMPs. I test di laboratorio effettuati hanno permesso la caratterizzazione degli elementi essenziali dell'Opto-Link e la realizzazione di un primo prototipo di trasmissione ottica dei segnali per DarkSide-20k

Longitudinal multi-omics study of a mother-infant dyad from breastfeeding to weaning: an individualized approach to understand the interactions among diet, fecal metabolome and microbiota composition

The development of the human gut microbiota is characterized by a dynamic sequence of events from birth to adulthood, which make the gut microbiota unique for everyone. Its composition and metabolism may play a critical role in the intestinal homeostasis and health. We propose a study on a single mother-infant dyad to follow the dynamics of an infant fecal microbiota and metabolome changes in relation to breast milk composition during the lactation period and evaluate the changes induced by introduction of complementary food during the weaning period. Nuclear Magnetic Resonance (NMR)-based metabolomics was performed on breast milk and, together with 16S RNA targeted-metagenomics analysis, also on infant stool samples of a mother-infant dyad collected over a period running from the exclusive breastfeeding diet to weaning. Breast milk samples and neonatal stool samples were collected from the 4th to the 10th month of life. Both specimens were collected from day 103 to day 175, while from day 219-268 only stool samples were examined. An exploratory and a predictive analysis were carried out by means of Common component and specific weight analysis and multi-block partial least squares discriminant analysis, respectively. Stools collected during breastfeeding and during a mixed fruit/breastfeeding diet were characterized by high levels of fucosyl-oligosaccharides and glycolysis intermediates, including succinate and formate. The transition to a semi-solid food diet was characterized by several changes in fecal parameters: increase in short-chain fatty acids (SCFAs) levels, including acetate, propionate and butyrate, dissapearance of HMOs and the shift in the community composition, mainly occurring within the Firmicutes phylum. The variations in the fecal metabolome reflected the infant's diet transition, while the composition of the microbiota followed a more complex and still unstable behavior

Gut metabolomics profiling of non-small cell lung cancer (NSCLC) patients under immunotherapy treatment

Despite the efficacy of immune checkpoint inhibitors (ICIs) only the 20-30% of treated patients present long term benefits. The metabolic changes occurring in the gut microbiota metabolome are herein proposed as a factor potentially influencing the response to immunotherapy

The effectiveness of Robot-Assisted Gait Training versus conventional therapy on mobility in severely disabled progressIve MultiplE sclerosis patients (RAGTIME): Study protocol for a randomized controlled trial

Background: Gait and mobility impairments affect the quality of life (QoL) of patients with progressive multiple sclerosis (MS). Robot-assisted gait training (RAGT) is an effective rehabilitative treatment but evidence of its superiority compared to other options is lacking. Furthermore, the response to rehabilitation is multidimensional, person-specific and possibly involves functional reorganization processes. The aims of this study are: (1) to test the effectiveness on gait speed, mobility, balance, fatigue and QoL of RAGT compared to conventional therapy (CT) in progressive MS and (2) to explore changes of clinical and circulating biomarkers of neural plasticity. Methods: This will be a parallel-group, randomized controlled trial design with the assessor blinded to the group allocation of participants. Ninety-eight (49 per arm) progressive MS patients (EDSS scale 6-7) will be randomly assigned to receive twelve 2-h training sessions over a 4-week period (three sessions/week) of either: (1) RAGT intervention on a robotic-driven gait orthosis (Lokomat, Hocoma, Switzerland). The training parameters (torque of the knee and hip drives, treadmill speed, body weight support) are set during the first session and progressively adjusted during training progression or (2) individual conventional physiotherapy focusing on over-ground walking training performed with the habitual walking device. The same assessors will perform outcome measurements at four time points: baseline (before the first intervention session); intermediate (after six training sessions); end of treatment (after the completion of 12 sessions); and follow-up (after 3 months from the end of the training program). The primary outcome is gait speed, assessed by the Timed 25-Foot Walk Test. We will also assess walking endurance, balance, depression, fatigue and QoL as well as instrumental laboratory markers (muscle metabolism, cerebral venous hemodynamics, cortical activation) and circulating laboratory markers (rare circulating cell populations pro and anti-inflammatory cytokines/chemokines, growth factors, neurotrophic factors, coagulation factors, other plasma proteins suggested by transcriptomic analysis and metabolic parameters). Discussion: The RAGT training is expected to improve mobility compared to the active control intervention in progressive MS. Unique to this study is the analysis of various potential markers of plasticity in relation with clinical outcomes. Trial registration: ClinicalTrials.gov, identifier: NCT02421731. Registered on 19 January 2015 (retrospectively registered)

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review.

Funder: Alzheimer's Research UKFunder: National Institute for Health and Care Research (NIHR)Funder: National Institute for Health Research (NIHR)Funder: National Health and Medical Research Council (NHMRC)INTRODUCTION: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. METHODS: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. RESULTS: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DISCUSSION: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HIGHLIGHTS: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias