1,721,963 research outputs found
Computational methodologies for radiogenomics and digital pathology
No full textThe primary objective of this thesis is to showcase computational methodologies for Radiogenomics and Digital Pathology.
Radiogenomics seeks to establish connections between a lesion's phenotypic features and its genotypic traits, relying on quantitative insights referred to as radiomics features.
Digital Pathology, initially centered on digitizing classical histopathology, has expanded its scope. It now encompasses a broad range of image processing algorithms for analyzing acquired images.
Both these disciplines heavily rely on Machine Learning (ML) and Deep Learning (DL) techniques. DL, in particular, has revolutionized medical image analysis, significantly boosting performance in classification, detection, and various medical domains. The thesis focuses on creating accessible, interpretable end-to-end pipelines utilizing ML and DL frameworks, drawing data from both public repositories and local hospitals.
In the field of Radiogenomics, the research activities have focused on analyzing lung cancer cases. As a first step, a model that classifies lung adenocarcinomas from other types of lesions using radiomic features extracted from Computed Tomography (CT) images has been developed. Subsequently, a system that classifies the mutational status of two crucial genes in cases of lung adenocarcinomas, KRAS and EGFR, also based on radiomic features extracted from CT images has been built. Predicting the mutational status of these genes is indeed crucial in clinical settings as it enables physicians to tailor a personalized treatment plan for the patient.
In the domain of Digital Pathology, the research has concentrated on two studies: the first one assessed the impact of unpaired image-to-image translation (UI2IT) architectures for normalizing hematoxylin and eosin stained images in the classification of histopathological tissue of patients with colorectal cancer. The UI2IT architectures were compared with classical histological image normalization techniques, revealing enhanced classifying performance when images were normalized using a UI2IT model as opposed to classic techniques.
The second study involved the development of a system that, starting from Periodic Acid-Schiff Whole Slide Images, segments glomeruli and classifies glomerular lesions according to the Oxford classification in patients with IgA nephropathy. Object detection architectures, particularly Mask R-CNN and Cascade Mask R-CNN, were employed for the segmentation module, while the classification part was achieved through convolutional neural networks. Additionally, the intraclass correlation coefficient was computed for glomerular lesion classification between annotations by an expert pathologist and the classification model results. For each lesion, at least one of the models surpassed the minimum ICC threshold as set by the Oxford classification. The developed tool has been made available on GitHub along with the trained models for use with other images.
The thesis's structure is as follows: Chapter 1 introduces the thesis's goals and contributions. Chapter 2 provides a comprehensive overview of the methodologies employed, delving into various ML and DL methodologies and defining Radiomics features. Chapter 3 details the thesis's contributions in Radiogenomics, specifically focusing on lung adenocarcinoma radiomic characterization and the prediction of EGFR and KRAS gene mutational status in lung adenocarcinoma. Chapter 4 outlines the contributions in Digital Pathology, encompassing the role of unpaired image-to-image translation stain color normalization in colorectal cancer histology classification, and the segmentation of glomeruli and Oxford classification of MESC lesions for IgA nephropathy. Lastly, Chapter 5 summarizes the work accomplished in this thesis and offers insights into potential future works
Reversal of doxorubicin and cisplatin resistance in vivo in murine leukemias by the calcium antagonist RO 11-2933
No full textThe ability of RO 11-2933 to modulate in vivo Doxorubicin and Cisplatin antitumor activity has been evaluated in sensitive and resistant P388 and L1210 murine leukemias. A reversal of both Doxorubicin or Cisplatin resistance has been observed when P388/Dx or L1210/CP tumor bearing mice received multiple treatments of the antitumor agent plus 30 mg/Kg of RO 11-2933. No modification of Doxorubicin or Cisplatin effect has been observed in sensitive tumors. The results obtained indicate that RO 11-2933 might represent a promising agent for the reversal of multidrug resistance
Traumi oculari in età scolare
No full textTramite questa rewiev della letteratura internazionale, abbiamo focalizzato l’attenzione su alcuni dati salienti in merito ai traumi oculari che si verificano in età scolare. Secondo una divisione per età e esesso, i ragazzi tra i 5 e i 10 anni sono i più soggetti ai traumi oculari. La scuola, ovunque nel mondo, risulta in assoluto il luogo più sicuro a differenza dell’alto rischio di lesioni che possono verificarsi in casa. Un trauma oculare può provocare danni funzionali su tutte le strutture anatomiche, in realtà le lesioni del cristallino sono quelle con peggior prognosi finale. Un dato ottimista è rappresentato dalla alta percentuale di recupero con acuità visiva finale non invalidante. Anche in questo ambito la prevenzione, con la diffusione ai genitori di precise regole comportamentali da adottare nella supervisione dei propri figli, potrebbe ridurre ulteriormente il numero dei traumi oculari
AIDS: esiste ancora? Storia e Prevenzione
No full textAldo Mazzoni – Roberto Manfredi
AIDS: esiste ancora?
Storia e prevenzione.
collana « Segmenti»
pp. 112 - € 10,00 - formato 11,5 x 19
ISBN 978-88-7094-673-4
L'Aids esiste ancora? Nessuno parla più della diffusione di questa malattia, anzi secondo alcuni i farmaci retrovirali sarebbero capaci di curare i suoi effetti, un tempo terribili e mortali. In realtà ogni giorno in Italia sono diagnosticati centinaia di nuovi casi di infezione e dopo anni di terapie si continua a morire di Aids, non solo nell'Africa sub-sahariana, ma anche in Europa.
Il primo dicembre di ogni anno viene celebrata la giornata mondiale di sensibilizzazione e di raccolta fondi per la prevenzione e la cura dell'Aids, indetta dall’Organizzazione Mondiale della Sanità.
Questo contributo vuole far riflettere sul modo in cui storicamente è comparsa questa epidemia e sui reali ed efficaci modi di prevenirla, che non sono certamente la promiscuità sessuale.
Libro di buona divulgazione.
Il 1° dicembre ricorre la giornata mondiale sull'Aids.
Autori: Aldo Mazzoni è stato per anni ordinario di Microbiologia all'Università di Bologna nonché cultore di Bioetica. Roberto Manfredi è infettivologo e professore associato di Malattie Infettive presso l'Università di Bologna
Advanced computational approaches for EEG-Based decoding of neurodegenerative diseases
No full textL'obiettivo della tesi di dottorato è quello di illustrare i lavori di ricerca svolti per la progettazione e lo sviluppo di framework computazionali avanzati per l'analisi dei segnali elettroencefalografici (EEG), al fine di migliorare la diagnosi precoce delle malattie neurodegenerative (ND). La demenza è una delle principali cause di disabilità e mortalità a livello globale, e l'identificazione delle sue fasi iniziali rimane una sfida critica sia per scopi prognostici che terapeutici. La moderna concettualizzazione delle ND, in particolare della malattia di Alzheimer, configura il declino cognitivo come un continuum, lungo il quale popolazioni con una compensazione funzionale ancora sufficiente potrebbero costituire target ideali per i trial clinici precoci. In questo contesto, i segnali EEG possono fornire biomarcatori non invasivi e a basso costo, con il potenziale di catturare disfunzioni neurali associate alla neurodegenerazione. Tuttavia, la complessità intrinseca e la variabilità dell’EEG comportano sfide significative per un'interpretazione e un'analisi accurate.
La tesi affronta il modo in cui i modelli di Deep Learning (DL), in particolare i Transformers, e le tecniche di interpretabilità possano essere utilizzati per una classificazione robusta dei dati EEG, offrendo spunti sui cambiamenti cognitivi nelle fasi precliniche e prodromiche e superando la necessità di competenze specifiche per l'estrazione di caratteristiche consistenti e affidabili. Inoltre, vengono esplorati altri approcci che avanzano l'integrazione tra neuroscienze computazionali e Machine Learning (ML), includendo la modellazione biofisica della modulazione neurale in risposta a stimoli specifici.
In particolare, la prima parte del lavoro presenta un nuovo framework di Deep Learning basato sui segnali, progettato per distinguere tra il declino cognitivo soggettivo (SCD) e l’impairment cognitivo lieve (MCI) utilizzando EEG resting-state. I metodi mirano a catturare i segni prodromici della malattia di Alzheimer attraverso un modello di Transformer basato sul meccanismo di self-attention. Per migliorare l'affidabilità e la traslabilità clinica, il framework descritto è stato integrato con strumenti di interpretabilità. Nello specifico, il ruolo del meccanismo di self-attention all'interno dei modelli Transformer è stato esplorato sistematicamente per spiegare i processi decisionali, fornendo maggiore trasparenza sul focus dei modelli sui segnali in ingresso per differenziare SCD da MCI e dimostrando che queste informazioni potrebbero essere utilizzate per guidare l'identificazione di biomarcatori di compromissione cognitiva nei segnali EEG a riposo.
La seconda parte del lavoro di ricerca è focalizzata sull’implementazione di metodi computazionali per l'analisi delle risposte evocate, cioè dei potenziali evento-correlati (ERP) e della (de)sicronizzazione evento-correlata (ERD/ERS), nella neurodegenerazione. Vengono analizzati il meccanismo di risonanza motoria nelle fasi precoci della malattia di Parkinson, la modellazione causale dinamica per la classificazione degli ERP e gli effetti degli stimoli sensoriali sulle risposte elettrofisiologiche in uno scenario di interazione uomo-robot.The aim of this Ph.D. thesis is to illustrate the research works conducted to design and develop advanced computational frameworks for analyzing electroencephalographic (EEG) signals to improve the early diagnosis of neurodegenerative diseases (NDs). Dementia is one of the leading causes of disability and death worldwide, and the detection of its initial phases remains a critical challenge both for prognostic and therapeutic purposes. The modern conceptualization of NDs, and particularly of Alzheimer’s disease, assumes cognitive decline to develop as a continuum, along which populations with still sufficient functional compensation could be targeted for early clinical trials. In this context, EEG signals can provide non-invasive and cost-effective biomarkers, holding potential for capturing neural dysfunctions associated with neurodegeneration. Nonetheless, the inherent complexity and variability of EEG result in significant challenges for accurate interpretation and analysis.
This thesis addresses how Deep Learning (DL) models, particularly Transformers, and interpretability techniques can be leveraged for robust classification of EEG data, offering insights into subtle cognitive changes in preclinical and prodromal stages and overcoming the need for domain-specific expertise to extract consistent and reliable features. Furthermore, other approaches advancing the integration of computational neuroscience with Machine Learning (ML), including biophysical modeling of neural modulation in response to specific stimuli, are explored.
In particular, the first part of the work presents a novel signal-based Deep Learning framework for distinguishing between subjective cognitive decline (SCD) and mild cognitive impairment (MCI) using resting-state EEG. The methods aim to capture prodromal signs of Alzheimer’s disease through a state-of-the-art Transformer model based on the mechanism of self-attention. To enhance clinical trustworthiness and translatability, the previously described method is then integrated with interpretability tools. Specifically, the role of self-attention within Transformer models is systematically explored to explain decision-making processes, providing greater transparency into the models’ focus on the input signals for differentiating SCD from MCI and proving that this information could be used to guide the identification of biomarkers of cognitive impairment in resting-state EEG.
The second part of the research work presents computational methods for analyzing evoked responses, namely event-related potentials (ERP) and event-related (de)synchronization (ERD/ERS), in neurodegeneration, exploring motor resonance in early Parkinson’s disease, dynamic causal modeling for ERP classification, and the effects of sensory stimuli on electrophysiological responses in a Human-Robot Interaction scenario
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Partecipazioni pubbliche locali e regionali e art. 13 del D.L. n. 223/2006 (c.d. Decreto Bersani)
No full textVariations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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