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1. Persistent Topological Features in Large Language Models

Author

Gardinazzi, Yuri, Panerai, Giada, Viswanathan, Karthik, Ansuini, Alessio, Cazzaniga, Alberto, and Biagetti, Matteo

Subjects
Computer Science - Computation and Language, Computer Science - Computational Geometry, Computer Science - Machine Learning
Abstract

Understanding the decision-making processes of large language models (LLMs) is critical given their widespread applications. Towards this goal, describing the topological and geometrical properties of internal representations has recently provided valuable insights. For a more comprehensive characterization of these inherently complex spaces, we present a novel framework based on zigzag persistence, a method in topological data analysis (TDA) well-suited for describing data undergoing dynamic transformations across layers. Within this framework, we introduce persistence similarity, a new metric that quantifies the persistence and transformation of topological features such as $p$-cycles throughout the model layers. Unlike traditional similarity measures, our approach captures the entire evolutionary trajectory of these features, providing deeper insights into the internal workings of LLMs. As a practical application, we leverage persistence similarity to identify and prune redundant layers, demonstrating comparable performance to state-of-the-art methods across several benchmark datasets. Additionally, our analysis reveals consistent topological behaviors across various models and hyperparameter settings, suggesting a universal structure in LLM internal representations., Comment: 10+6 pages, 7 figures, 1 table. All comments welcome!

Published
2024

2. The representation landscape of few-shot learning and fine-tuning in large language models

Author

Doimo, Diego, Serra, Alessandro, Ansuini, Alessio, and Cazzaniga, Alberto

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

In-context learning (ICL) and supervised fine-tuning (SFT) are two common strategies for improving the performance of modern large language models (LLMs) on specific tasks. Despite their different natures, these strategies often lead to comparable performance gains. However, little is known about whether they induce similar representations inside LLMs. We approach this problem by analyzing the probability landscape of their hidden representations in the two cases. More specifically, we compare how LLMs solve the same question-answering task, finding that ICL and SFT create very different internal structures, in both cases undergoing a sharp transition in the middle of the network. In the first half of the network, ICL shapes interpretable representations hierarchically organized according to their semantic content. In contrast, the probability landscape obtained with SFT is fuzzier and semantically mixed. In the second half of the model, the fine-tuned representations develop probability modes that better encode the identity of answers, while the landscape of ICL representations is characterized by less defined peaks. Our approach reveals the diverse computational strategies developed inside LLMs to solve the same task across different conditions, allowing us to make a step towards designing optimal methods to extract information from language models.

Published
2024

4. Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels

Author

Uribarri, Gonzalo, Barone, Federico, Ansuini, Alessio, and Fransén, Erik

Subjects
Computer Science - Machine Learning
Abstract

Time Series Classification (TSC) is essential in fields like medicine, environmental science, and finance, enabling tasks such as disease diagnosis, anomaly detection, and stock price analysis. While machine learning models like Recurrent Neural Networks and InceptionTime are successful in numerous applications, they can face scalability issues due to computational requirements. Recently, ROCKET has emerged as an efficient alternative, achieving state-of-the-art performance and simplifying training by utilizing a large number of randomly generated features from the time series data. However, many of these features are redundant or non-informative, increasing computational load and compromising generalization. Here we introduce Sequential Feature Detachment (SFD) to identify and prune non-essential features in ROCKET-based models, such as ROCKET, MiniRocket, and MultiRocket. SFD estimates feature importance using model coefficients and can handle large feature sets without complex hyperparameter tuning. Testing on the UCR archive shows that SFD can produce models with better test accuracy using only 10\% of the original features. We named these pruned models Detach-ROCKET. We also present an end-to-end procedure for determining an optimal balance between the number of features and model accuracy. On the largest binary UCR dataset, Detach-ROCKET improves test accuracy by 0.6\% while reducing features by 98.9\%. By enabling a significant reduction in model size without sacrificing accuracy, our methodology improves computational efficiency and contributes to model interpretability. We believe that Detach-ROCKET will be a valuable tool for researchers and practitioners working with time series data, who can find a user-friendly implementation of the model at \url{https://github.com/gon-uri/detach_rocket}., Comment: 18 pages, 5 figures, 3 tables

Published
2023

6. Emergent representations in networks trained with the Forward-Forward algorithm

Author

Tosato, Niccolò, Basile, Lorenzo, Ballarin, Emanuele, de Alteriis, Giuseppe, Cazzaniga, Alberto, and Ansuini, Alessio

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning
Abstract

The Backpropagation algorithm has often been criticised for its lack of biological realism. In an attempt to find a more biologically plausible alternative, the recently introduced Forward-Forward algorithm replaces the forward and backward passes of Backpropagation with two forward passes. In this work, we show that the internal representations obtained by the Forward-Forward algorithm can organise into category-specific ensembles exhibiting high sparsity - composed of a low number of active units. This situation is reminiscent of what has been observed in cortical sensory areas, where neuronal ensembles are suggested to serve as the functional building blocks for perception and action. Interestingly, while this sparse pattern does not typically arise in models trained with standard Backpropagation, it can emerge in networks trained with Backpropagation on the same objective proposed for the Forward-Forward algorithm. These results suggest that the learning procedure proposed by Forward-Forward may be superior to Backpropagation in modelling learning in the cortex, even when a backward pass is used.

Published
2023

7. Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness

Author

Ballarin, Emanuele, Ansuini, Alessio, and Bortolussi, Luca

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

In this work, we propose a novel adversarial defence mechanism for image classification - CARSO - blending the paradigms of adversarial training and adversarial purification in a synergistic robustness-enhancing way. The method builds upon an adversarially-trained classifier, and learns to map its internal representation associated with a potentially perturbed input onto a distribution of tentative clean reconstructions. Multiple samples from such distribution are classified by the same adversarially-trained model, and an aggregation of its outputs finally constitutes the robust prediction of interest. Experimental evaluation by a well-established benchmark of strong adaptive attacks, across different image datasets, shows that CARSO is able to defend itself against adaptive end-to-end white-box attacks devised for stochastic defences. Paying a modest clean accuracy toll, our method improves by a significant margin the state-of-the-art for CIFAR-10, CIFAR-100, and TinyImageNet-200 $\ell_\infty$ robust classification accuracy against AutoAttack. Code, and instructions to obtain pre-trained models are available at https://github.com/emaballarin/CARSO ., Comment: 21 pages, 1 figure, 15 tables

Published
2023

8. Protein family annotation for the Unified Human Gastrointestinal Proteome by DPCfam clustering

Author

Federico Barone, Elena Tea Russo, Edith Natalia Villegas Garcia, Marco Punta, Stefano Cozzini, Alessio Ansuini, and Alberto Cazzaniga

Subjects
Science
Abstract

Abstract Technological advances in massively parallel sequencing have led to an exponential growth in the number of known protein sequences. Much of this growth originates from metagenomic projects producing new sequences from environmental and clinical samples. The Unified Human Gastrointestinal Proteome (UHGP) catalogue is one of the most relevant metagenomic datasets with applications ranging from medicine to biology. However, the low levels of sequence annotation may impair its usability. This work aims to produce a family classification of UHGP sequences to facilitate downstream structural and functional annotation. This is achieved through the release of the DPCfam-UHGP50 dataset containing 10,778 putative protein families generated using DPCfam clustering, an unsupervised pipeline grouping sequences into single or multi-domain architectures. DPCfam-UHGP50 considerably improves family coverage at protein and residue levels compared to the manually curated repository Pfam. In the hope that DPCfam-UHGP50 will foster future discoveries in the field of metagenomics of the human gut, we release a FAIR-compliant database of our results that is easily accessible via a searchable web server and Zenodo repository.

Published
2024
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9. The geometry of hidden representations of large transformer models

Author

Valeriani, Lucrezia, Doimo, Diego, Cuturello, Francesca, Laio, Alessandro, Ansuini, Alessio, and Cazzaniga, Alberto

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

Large transformers are powerful architectures used for self-supervised data analysis across various data types, including protein sequences, images, and text. In these models, the semantic structure of the dataset emerges from a sequence of transformations between one representation and the next. We characterize the geometric and statistical properties of these representations and how they change as we move through the layers. By analyzing the intrinsic dimension (ID) and neighbor composition, we find that the representations evolve similarly in transformers trained on protein language tasks and image reconstruction tasks. In the first layers, the data manifold expands, becoming high-dimensional, and then contracts significantly in the intermediate layers. In the last part of the model, the ID remains approximately constant or forms a second shallow peak. We show that the semantic information of the dataset is better expressed at the end of the first peak, and this phenomenon can be observed across many models trained on diverse datasets. Based on our findings, we point out an explicit strategy to identify, without supervision, the layers that maximize semantic content: representations at intermediate layers corresponding to a relative minimum of the ID profile are more suitable for downstream learning tasks.

Published
2023

10. Deep artificial neural network for prediction of atrial fibrillation through the analysis of 12-leads standard ECG

Author

Scagnetto, A., Barbati, G., Gandin, I., Cappelletto, C., Baj, G., Cazzaniga, A., Cuturello, F., Ansuini, A., Bortolussi, L., and Di Lenarda, A.

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract

Atrial Fibrillation (AF) is a heart's arrhythmia which, despite being often asymptomatic, represents an important risk factor for stroke, therefore being able to predict AF at the electrocardiogram exam, would be of great impact on actively targeting patients at high risk. In the present work we use Convolution Neural Networks to analyze ECG and predict Atrial Fibrillation starting from realistic datasets, i.e. considering fewer ECG than other studies and extending the maximal distance between ECG and AF diagnosis. We achieved 75.5% (0.75) AUC firstly increasing our dataset size by a shifting technique and secondarily using the dilation parameter of the convolution neural network. In addition we find that, contrarily to what is commonly used by clinicians reporting AF at the exam, the most informative leads for the task of predicting AF are D1 and avR. Similarly, we find that the most important frequencies to check are in the range of 5-20 Hz. Finally, we develop a net able to manage at the same time the electrocardiographic signal together with the electronic health record, showing that integration between different sources of data is a profitable path. In fact, the 2.8% gain of such net brings us to a 78.6% (std 0.77) AUC. In future works we will deepen both the integration of sources and the reason why we claim avR is the most informative lead., Comment: 10 pages, 2 figures, 5 tables

Published
2022

11. Bi-dimensional Assignment in 5G Periodic Scheduling

Author

Ansuini, Giulia, Frangioni, Antonio, Galli, Laura, Nardini, Giovanni, Stea, Giovanni, Vigo, Daniele, Editor-in-Chief, Agnetis, Alessandro, Series Editor, Amaldi, Edoardo, Series Editor, Guerriero, Francesca, Series Editor, Lucidi, Stefano, Series Editor, Messina, Enza, Series Editor, Sforza, Antonio, Series Editor, Cappanera, Paola, editor, Lapucci, Matteo, editor, Schoen, Fabio, editor, Sciandrone, Marco, editor, Tardella, Fabio, editor, and Visintin, Filippo, editor

Published
2023
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12. Hierarchical nucleation in deep neural networks

Author

Doimo, Diego, Glielmo, Aldo, Ansuini, Alessio, and Laio, Alessandro

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Deep convolutional networks (DCNs) learn meaningful representations where data that share the same abstract characteristics are positioned closer and closer. Understanding these representations and how they are generated is of unquestioned practical and theoretical interest. In this work we study the evolution of the probability density of the ImageNet dataset across the hidden layers in some state-of-the-art DCNs. We find that the initial layers generate a unimodal probability density getting rid of any structure irrelevant for classification. In subsequent layers density peaks arise in a hierarchical fashion that mirrors the semantic hierarchy of the concepts. Density peaks corresponding to single categories appear only close to the output and via a very sharp transition which resembles the nucleation process of a heterogeneous liquid. This process leaves a footprint in the probability density of the output layer where the topography of the peaks allows reconstructing the semantic relationships of the categories.

Published
2020

13. Intrinsic dimension of data representations in deep neural networks

Author

Ansuini, Alessio, Laio, Alessandro, Macke, Jakob H., and Zoccolan, Davide

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Deep neural networks progressively transform their inputs across multiple processing layers. What are the geometrical properties of the representations learned by these networks? Here we study the intrinsic dimensionality (ID) of data-representations, i.e. the minimal number of parameters needed to describe a representation. We find that, in a trained network, the ID is orders of magnitude smaller than the number of units in each layer. Across layers, the ID first increases and then progressively decreases in the final layers. Remarkably, the ID of the last hidden layer predicts classification accuracy on the test set. These results can neither be found by linear dimensionality estimates (e.g., with principal component analysis), nor in representations that had been artificially linearized. They are neither found in untrained networks, nor in networks that are trained on randomized labels. This suggests that neural networks that can generalize are those that transform the data into low-dimensional, but not necessarily flat manifolds., Comment: NeurIPS 2019

Published
2019

14. Observing the Population Dynamics in GE by means of the Intrinsic Dimension

Author

Medvet, Eric, Bartoli, Alberto, Ansuini, Alessio, and Tarlao, Fabiano

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

We explore the use of Intrinsic Dimension (ID) for gaining insights in how populations evolve in Evolutionary Algorithms. ID measures the minimum number of dimensions needed to accurately describe a dataset and its estimators are being used more and more in Machine Learning to cope with large datasets. We postulate that ID can provide information about population which is complimentary w.r.t.\ what (a simple measure of) diversity tells. We experimented with the application of ID to populations evolved with a recent variant of Grammatical Evolution. The preliminary results suggest that diversity and ID constitute two different points of view on the population dynamics., Comment: Evolutionary Machine Learning workshop at International Conference on Parallel Problem Solving from Nature (EML@PPSN), 2018, Coimbra (Portugal)

Published
2018

20. Investigating Similarity Metrics for Convolutional Neural Networks in the Case of Unstructured Pruning

Author

Ansuini, Alessio, Medvet, Eric, Pellegrino, Felice Andrea, Zullich, Marco, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, De Marsico, Maria, editor, Sanniti di Baja, Gabriella, editor, and Fred, Ana, editor

Published
2020
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21. Recurrent pericarditis in a patient with Bardet–Biedl syndrome: A case report

Author

Angela Mauro, Francesca Casini, Emanuele Chittano Congedo, Sara L’assainato, Francesca Pinto, Valentina Ansuini, Ruggiero Mascolo, Alice Pedroli, and Luca Bernardo

Subjects
Bardet–Biedl syndrome, recurrent pericarditis, anakinra, autoinflammatory disease, children, Pediatrics, RJ1-570
Abstract

Bardet–Biedl syndrome is a rare autosomal recessive disorder characterized by rod-cone dystrophy, renal dysfunction, obesity, learning difficulties, hypogonadism, polydactyl, and many other minor features that can affect the cardiovascular, locomotive, neurological, and endocrine systems. We report the case of a 16-year-old boy affected by Bardet–Biedl syndrome who presented with recurrent pericarditis with an optimal response to treatment with Anakinra. To our knowledge, this is the first description of an association between Bardet–Biedl syndrome and recurrent pericarditis.

Published
2022
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22. Enhancing predictions of protein stability changes induced by single mutations using MSA-based language models.

Author

Cuturello, Francesca, Celoria, Marco, Ansuini, Alessio, and Cazzaniga, Alberto

Subjects
LANGUAGE models, PROTEIN models, SEQUENCE alignment, AMINO acids, BIOLOGY, PROTEIN stability
Abstract

Motivation Protein language models offer a new perspective for addressing challenges in structural biology, while relying solely on sequence information. Recent studies have investigated their effectiveness in forecasting shifts in thermodynamic stability caused by single amino acid mutations, a task known for its complexity due to the sparse availability of data, constrained by experimental limitations. To tackle this problem, we introduce two key novelties: leveraging a protein language model that incorporates Multiple Sequence Alignments to capture evolutionary information, and using a recently released mega-scale dataset with rigorous data preprocessing to mitigate overfitting. Results We ensure comprehensive comparisons by fine-tuning various pretrained models, taking advantage of analyses such as ablation studies and baselines evaluation. Our methodology introduces a stringent policy to reduce the widespread issue of data leakage, rigorously removing sequences from the training set when they exhibit significant similarity with the test set. The MSA Transformer emerges as the most accurate among the models under investigation, given its capability to leverage co-evolution signals encoded in aligned homologous sequences. Moreover, the optimized MSA Transformer outperforms existing methods and exhibits enhanced generalization power, leading to a notable improvement in predicting changes in protein stability resulting from point mutations. Availability and implementation Code and data at https://github.com/RitAreaSciencePark/PLM4Muts. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Identifying the signature of prospective motor control in children with autism

Author

Andrea Cavallo, Luca Romeo, Caterina Ansuini, Francesca Battaglia, Lino Nobili, Massimiliano Pontil, Stefano Panzeri, and Cristina Becchio

Subjects
Medicine, Science
Abstract

Abstract Failure to develop prospective motor control has been proposed to be a core phenotypic marker of autism spectrum disorders (ASD). However, whether genuine differences in prospective motor control permit discriminating between ASD and non-ASD profiles over and above individual differences in motor output remains unclear. Here, we combined high precision measures of hand movement kinematics and rigorous machine learning analyses to determine the true power of prospective movement data to differentiate children with autism and typically developing children. Our results show that while movement is unique to each individual, variations in the kinematic patterning of sequential grasping movements genuinely differentiate children with autism from typically developing children. These findings provide quantitative evidence for a prospective motor control impairment in autism and indicate the potential to draw inferences about autism on the basis of movement kinematics.

Published
2021
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26. Gastrointestinal Involvement in Children with Systemic Lupus Erythematosus

Author

Angela Mauro, Teresa Giani, Clelia Di Mari, Martina Sandini, Antonella Talenti, Valentina Ansuini, Luigi Biondi, Giovanni Di Nardo, and Luca Bernardo

Subjects
childhood-onset systemic lupus erythematosus, children, gastrointestinal involvement, autoimmune hepatitis, pancreatitis, mesenteric vasculitis, Pediatrics, RJ1-570
Abstract

Systemic lupus erythematosus (SLE) is a systemic autoimmune disorder. When it presents before the age of 18 years (childhood-onset systemic lupus erythematosus, cSLE), the disease course tends to be more severe with a higher rate of organ involvement and requires an early diagnosis. Gastrointestinal involvement in cSLE is rare and scarcely reported in the literature. Any organ of the gastrointestinal system may be affected, either as a direct consequence of the disease, as a subsequent complication, or as an adverse drug event. Abdominal pain is the most common GI symptom, it can be diffuse or well localized, and can underline different conditions such as hepatitis, pancreatitis, appendicitis, peritonitis, or enteritis. cSLE may have an alteration of the intestinal barrier with features of protein-losing enteropathy or, in genetically predisposed patients, may develop associated autoimmune disorders such as Coeliac Disease or Autoimmune Hepatitis. The aim of this manuscript is to provide a narrative review of gastrointestinal manifestations in cSLE focused on hepatic, pancreatic, and intestinal involvement. A comprehensive literature search based on the PubMed database was performed.

Published
2023
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32. One hand, two hands, two people: Prospective sensorimotor control in children with autism

Author

Caterina Ansuini, Jessica Podda, Francesca Maria Battaglia, Edvige Veneselli, and Cristina Becchio

Subjects
Neurophysiology and neuropsychology, QP351-495
Abstract

Where grasps are made reveals how grasps are planned. The grasp height effect predicts that, when people take hold of an object to move it to a new position, the grasp height on the object is inversely related to the height of the target position. In the present study, we used this effect as a window into the prospective sensorimotor control of children with autism spectrum disorders without accompanying intellectual impairment. Participants were instructed to grasp a vertical cylinder and move it from a table (home position) to a shelf of varying height (target position). Depending on the conditions, they performed the task using only one hand (unimanual), two hands (bimanual), or with the help of a co-actor (joint). Comparison between the performance of typically developing children and children with autism revealed no group difference across tasks. We found, however, a significant influence of IQ on grasp height modulation in both groups. These results provide clear evidence against a general prospective sensorimotor planning deficit and suggest that at least some form of higher order planning is present in autism without accompanying intellectual impairment. Keywords: Autism, Prospective control, Grasp height effect, Sensorimotor, Kinematics, Joint action

Published
2018
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37. CARSO: Counter-Adversarial Recall of Synthetic Observations

Author

Ballarin, Emanuele, Ansuini, Alessio, and Bortolussi, Luca

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Cryptography and Security (cs.CR), Machine Learning (cs.LG)
Abstract

In this paper, we propose a novel adversarial defence mechanism for image classification -- CARSO -- inspired by cues from cognitive neuroscience. The method is synergistically complementary to adversarial training and relies on knowledge of the internal representation of the attacked classifier. Exploiting a generative model for adversarial purification, conditioned on such representation, it samples reconstructions of inputs to be finally classified. Experimental evaluation by a well-established benchmark of varied, strong adaptive attacks, across diverse image datasets and classifier architectures, shows that CARSO is able to defend the classifier significantly better than state-of-the-art adversarial training alone -- with a tolerable clean accuracy toll. Furthermore, the defensive architecture succeeds in effectively shielding itself from unforeseen threats, and end-to-end attacks adapted to fool stochastic defences. Code and pre-trained models are available at https://github.com/emaballarin/CARSO ., 20 pages, 5 figures, 10 tables; Update: removed visual artifacts from some figures, fixed typos/capitalisation, typographic/pagination improvements

Published
2023

38. Protein family annotation for the Unified Human Gastrointestinal Proteome by DPCfam clustering

Author

Federico Barone, Elena Tea Russo, Edith Natalia Villegas Garcia, Marco Punta, Stefano Cozzini, Alessio Ansuini, and Alberto Cazzaniga

Abstract

Technological advances in massively parallel sequencing have led to an exponential growth in the number of known protein sequences. Much of this growth originates from metagenomic projects producing new sequences from environmental and clinical samples. The Unified Human Gastrointestinal Proteome (UHGP) catalogue is one of the most relevant metagenomic datasets with applications ranging from medicine to biology. However, the lack of sequence annotation impairs its usability. This work aims to produce a family classification of UHGP sequences to facilitate downstream structural and functional annotation. This is achieved through the release of theDPCfam-UHGP50 datasetcontaining 10,778 putative protein families generated using DPCfam clustering, an unsupervised pipeline grouping sequences into multi-domain architectures. DPCfam-UHGP50 considerably improves family coverage at protein and residue levels compared to the manually curated repository Pfam. It is our hope that DPCfam-UHGP50 will foster future discoveries in the field of metagenomics of the human gut by the release of a FAIR-compliant database easily accessible via a searchable web server and Zenodo repository.

Published
2023

41. The geometry of hidden representations of protein language models

Author

Lucrezia Valeriani, Francesca Cuturello, Alessio Ansuini, and Alberto Cazzaniga

Abstract

Protein language models (pLMs) transform their input into a sequence of hidden representations whose geometric behavior changes across layers. Looking at fundamental geometric properties such as the intrinsic dimension and the neighbor composition of these representations, we observe that these changes highlight a pattern characterized by three distinct phases. This phenomenon emerges across many models trained on diverse datasets, thus revealing a general computational strategy learned by pLMs to reconstruct missing parts of the data. These analyses show the existence of low-dimensional maps that encode evolutionary and biological properties such as remote homology and structural information. Our geometric approach sets the foundations for future systematic attempts to understand thespaceof protein sequences with representation learning techniques.

Published
2022

44. An Unsupervised Machine Learning Method Stratifies Chronic Lymphocytic Leukemia Patients in Novel Categories with Different Risk of Early Treatment

Author

Cuturello, Francesca, primary, Pozzo, Federico, additional, Villegas Garcia, Edith Natalia, additional, Rossi, Francesca Maria, additional, Degan, Massimo, additional, Nanni, Paola, additional, Cattarossi, Ilaria, additional, Zaina, Eva, additional, Varaschin, Paola, additional, Braida, Alessandra, additional, Berton, Michele, additional, Zannier, Laura, additional, Vit, Filippo, additional, Tissino, Erika, additional, Bittolo, Tamara, additional, Laureana, Roberta, additional, D'Arena, Giovanni, additional, Laurenti, Luca, additional, Tafuri, Agostino, additional, Olivieri, Jacopo, additional, Zaja, Francesco, additional, Chiarenza, Annalisa, additional, Del Principe, Maria Ilaria, additional, Bomben, Riccardo, additional, Zucchetto, Antonella, additional, Cozzini, Stefano, additional, Ansuini, Alessio, additional, Cazzaniga, Alberto, additional, and Gattei, Valter, additional

Published
2022
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48. Tocilizumab for patients with COVID-19 pneumonia. The single-arm TOCIVID-19 prospective trial

Author

Perrone, F, Piccirillo, M, Ascierto, P, Salvarani, C, Parrella, R, Marata, A, Popoli, P, Ferraris, L, Marrocco-trischitta, M, Ripamonti, D, Binda, F, Bonfanti, P, Squillace, N, Castelli, F, Muiesan, M, Lichtner, M, Calzetti, C, Salerno, N, Atripaldi, L, Cascella, M, Costantini, M, Dolci, G, Facciolongo, N, Fraganza, F, Massari, M, Montesarchio, V, Mussini, C, Negri, E, Botti, G, Cardone, C, Gargiulo, P, Gravina, A, Schettino, C, Arenare, L, Chiodini, P, Gallo, C, Vitale, M, Trojaniello, C, Palla, M, Bianchi, A, De Feo, G, Miscio, L, Froldi, M, Menicanti, L, Cuppone, M, Gobbo, G, Baldessari, C, Valenti, V, Castelvecchio, S, Poli, F, Giacomazzi, F, Piccinni, R, Annnunziata, M, Biondi, A, Bussolari, C, Mazzoleni, M, Giachi, A, Filtz, A, Manini, A, Poletti, E, Masserini, F, Conforti, F, Gaudiano, G, Favero, V, Moroni, A, Viva, T, Fancoli, F, Ferrari, D, Niro, D, Resta, M, Ballotta, A, Poli, M, Ranucci, M, Tebaldi, A, Gritti, G, Pasulo, L, Gaglio, L, Del Fabbro, R, Alborghetti, L, Giustinetti, G, Columpsi, P, Cazzaniga, M, Capici, S, Sala, L, Di Sciacca, R, Mosca, G, Pirozzi, M, Franceschini, F, Roccaro, A, Salvetti, M, Paini, A, Corda, L, Ricci, C, Tomasoni, L, Nasta, P, Lorenzotti, S, Odolini, S, Foca, E, Roldan, E, Metra, M, Magrini, S, Borghetti, P, Latronico, N, Piva, S, Filippini, M, Tomasoni, G, Zuccala, F, Cattaneo, S, Scolari, F, Bossini, N, Gaggiotti, M, Properzi, M, Del Giudice, E, Marocco, R, Carraro, A, Del Borgo, C, Belvisi, V, Tieghi, T, De Masi, M, Zuccala, P, Fabietti, P, Vetica, A, Mercurio, V, Fondaco, L, Kertusha, B, Curtolo, A, Lubrano, R, Zotti, M, Puorto, A, Ciuffreda, M, Sarni, A, Monteforte, G, Romeo, D, Viola, E, Damiani, C, Barone, A, Mantovani, B, Di Sanzo, D, Gentili, V, Carletti, M, Aiuti, M, Gallo, A, Meliante, P, Martellucci, S, Riggio, O, Cardinale, V, Ridola, L, Bragazzi, M, Gioia, S, Valenzi, E, Graziosi, C, Bina, N, Fasolo, M, Ricci, S, Gioacchini, M, Lucci, A, Corso, L, Tornese, D, Nijhawan, P, Equitani, F, Cosentino, C, 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C., Ascierto P. A., Salvarani C., Parrella R., Marata A. M., Popoli P., Ferraris L., Marrocco-trischitta M. M., Ripamonti D., Binda F., Bonfanti P., Squillace N., Castelli F., Muiesan M. L., Lichtner M., Calzetti C., Salerno N. D., Atripaldi L., Cascella M., Costantini M., Dolci G., Facciolongo N. C., Fraganza F., Massari M., Montesarchio V., Mussini C., Negri E. A., Botti G., Cardone C., Gargiulo P., Gravina A., Schettino C., Arenare L., Chiodini P., Gallo C., Vitale M. G., Trojaniello C., Palla M., Bianchi A. A. M., De Feo G., Miscio L., Froldi M., Menicanti L., Cuppone M. T., Gobbo G., Baldessari C., Valenti V., Castelvecchio S., Poli F., Giacomazzi F., Piccinni R., Annnunziata M. L., Biondi A., Bussolari C., Mazzoleni M., Giachi A., Filtz A., Manini A., Poletti E., Masserini F., Conforti F., Gaudiano G., Favero V., Moroni A., Viva T., Fancoli F., Ferrari D., Niro D., Resta M., Ballotta A., Poli M. D., Ranucci M., Tebaldi A., Gritti G., Pasulo L., Gaglio L., Del Fabbro R., Alborghetti L., Giustinetti G., Columpsi P., Cazzaniga M., Capici S., Sala L., Di Sciacca R., Mosca G., Pirozzi M. R., Franceschini F., Roccaro A., Salvetti M., Paini A., Corda L., Ricci C., Tomasoni L., Nasta P., Lorenzotti S., Odolini S., Foca E., Roldan E. Q., Metra M., Magrini S., Borghetti P., Latronico N., Piva S., Filippini M., Tomasoni G., Zuccala F., Cattaneo S., Scolari F., Bossini N., Gaggiotti M., Properzi M., Del Giudice E., Marocco R., Carraro A., Del Borgo C., Belvisi V., Tieghi T., De Masi M., Zuccala P., Fabietti P., Vetica A., Mercurio V. S., Fondaco L., Kertusha B., Curtolo A., Lubrano R., Zotti M. G., Puorto A., Ciuffreda M., Sarni A., Monteforte G., Romeo D., Viola E., Damiani C., Barone A., Mantovani B., Di Sanzo D., Gentili V., Carletti M., Aiuti M., Gallo A., Meliante P. G., Martellucci S., Riggio O., Cardinale V., Ridola L., Bragazzi M. 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B., Montineri A., Manuele R., Bonfante S., Aquilini D., Prozzo A., Santopuoli D., Di Rosa Z., Alborghetti A., Peci P., Bakhtadze N., Pandini C. S., Ashofarir N., Casella G., Spagnolli W., Urru S., Marchesoni I., Caminiti G., Argilloni E., Danieli E., Ghirardi G., Antonioli C. M., Lipari A., Zavarise P., Kokaly F., Polati E., Gottin L., Lucernoni P., De Conti F., Marcon E., Pontali E., Vacca E. B., Saffioti C., Zunino A., Pognuz E. R., Berlot G., Saltori M., Tedesco A., Agostini C., Di Rosolini M. A., Marino F., Bellinzona G., Grassi W., Di Carlo M., Scimonello G., Nonini S., Mondino M., Mantovani L. F., Tenti E., Tropea C. M. G., Di Stefano D. E., Guelfi P., Dagna L., Morgana G., Montemurro L., Girelli D., Crisafulli E., Maroccia A., Cemuschi A. M., Bernasconi M., Zummo U., Barbato V., Bevilacqua S., Buonfanti G., Canzanella G., De Matteis G., Florio M., Martino M., Ribecco M. T., Romano F., Savio A., Sparavigna L., Curvietto M., Citarella M., Nava V., Maggioni P., Magni M., Iommelli C., Bianco A., Corsini R., Valli L., Ruggieri M. P., Mancini A., Melica T., Ferrari A., Cicognini D., Delliponti M., Zuccarini A., Ciani S., Raffaeli D., Donati L., Cannizzo S., Lui S., Santini L., Roncaglia E., Mighali P., Eisendle F., Cerino G., Citterio C., Di Nunzio C., Lamonica S., Resimini S., Sarteschi G., Pavei C., Battistini N., Gazzola E., Miceli M., Pontiggia S., Lonati V., Giannandrea G., Sortino C., Ravani S., Uggeri C., Jocolle G., Bare C., Baroni I., De Candia D., Fiorini B., Chierico K., Romeo F., Bottega R., Boccasile L., Corsaro A., Spadoni C., Ria E., Chiari S., Ercolino G., Dell'uomo V., Viri S., Minato M., Gazzola L., Dorina B., Gianelli D., Maspero S., Farinazzo M., Zanini P., Sangiovanni A., Del Giudice A., Dragonetti M. M., Bordignon S., Machiavelli A. M., Chiodelli G., Spatarella M., Zenoni D., Beretta F. N., Santilli G., Badagliacca R., Angileri M., Giannelli L., Campomori A., Maimone P., Fadda A., Faoro S., Pisterna A., Cacopardo B., Marino A., Pampaloni A., Celesia B. M., Cinnella G., Labella D., Caporusso R. R., Danzi M., Fiscon M., Malena M., Fendt D., Nardi S., Stobbione P., Savi M. L., De Monte A., Scala A., Liberato N. L., Luchi S., Vincenti A., Cabrini L., Pinelli G., Brugioni L., Potenza D., Numis F. G., Porta G., D'amico M., Iengo B., Angarano G., Saracino A., Blasi L., De Negri P., Angelici S., Farina A., Martino G. P., Bitti G., Tedeschi A., De Ponti S., Agostinone A., Parruti G., Consorte A., Frattari A., Filippelli A., Pagliano P., Masullo A., Sellitto C., Reta M., Rossi N., Raumer L., Andreassi S., Brancaleoni P., Carai A., Salerno A. M., Marinangeli F., Mariani R., Ciccone A., Meschini C., Santoboni G., Angrisani C., Micarelli D., Tarquini G., Fregoni V., Volta C. A., Cherubini A., Del Prete M. S., Ciarrochi E., Tasca F., Ballarin A., Bianchin A., Flocco R., Cuzzone V., Carpinteri M., Gallotti P., Torre F., Zannetti P., Crapis M., Venturini S., Barattini M., Gori G., Mastroianni A., De Stefano G., Gilio M., Rapisarda G., Gulisano L., Granata M. L., Saglimbene S., Montalto M. T., Grasso I., De Luca S., Magro G., Messina F., Scapino B., Abrate P., Francisco C., Pesce L., Navarra M., Agosti M., Pagani S., Piluso M., Ricioppo A., Tognella S., Rovere P., Vincenzi M., Ghirardi L., Generali D., Ingrosso M., Desiderio E., Molaro R., Vitiello S., Lancione L., Paone T. C., Meli A., Mainardi S., Rastellino V., Ursillo A., Grigoli P. D., Bovetto E., Stefanetto I. M., Mazzola F., Daniele A., Bisio C., Delnero P., Morando G., Nava A., Francesco L., Fiammengo F., Regis M., Roccatello D., Sabato E., Liccardi M. M., Bretto C., Lutri L., Castenetto E., Roberti G., Guidi M. F., Bini F., Zappa M. C., Trequattrini T., Rivitti R., Vigliarolo R., Succu A., Lilli M., Serao M., Giogre G., Ruggieri A., Flores K., Vairo G., Satira R., Lingua A., Spina R., Nicastri E., Maffongelli G., Barreca F., Scollet S., Franchi F., Fabbri C., Minuz P., Dalbeni A., Zanatta P., Gelormini D., Mandelli A., Galderisi F., Zoia E., Marchi M. R., Neves N. D. A., Carbone G., Di Caterino E., Petrone A., Usai C. A., Bandiera F., Monti R., Hofer A., Castiglione G., Angeletti C., Tarsia P., Veronese L., Artoni P. D., Larussa D., Fumagalli R., Brioschi P., Cerutti A., Pasquino P., Gilberto F., Cantadori L., Tomasoni L. R., Coppola N., Spolveri S., Pollastri C., Fico L., Principi T., Pierantozzi S., Fontana C. C., Lubrano G., Martinelli L., Navalesi P., Serra E., Cogi E., Manzi A., Furino E., Dasseni N., Gentilini C., Benatti E., Pignatti A., Aiello G., Milia M., Covesnon M. G., Brianti A., Francesco C., Ilaria B., Pagnozzi F., Mietta S., Rossi A., Maroni L., Borroni V., Bellintani C., Sgarabotto C., Bizzotto G., Bucci L., Spagnuolo G., Agostini M., Caria F. C., Testa F., De Palma R., Murdaca G., Zanolini G., Sala N., Righini E., Pontremoli R., Aondio G., Riccardi F., De Cristoforo M. G., De Michele F., Storti A., Perra R., Deidda S., Enrica C., Valastro F., Pierfranceschi M. G., De Gennaro F., Nardecchia A. L., Castellini M., Buetto G., Ippoliti G., Sicheri D., Bottoli M. G., De Arroyabe B. M. L., Versaci A., Pallotti G., Civita M., Grio M., Liuzzi N., Molino P., Pastorelli M., Ricchiardi A., Varbella F., Zeme A. D., Sighieri C., Portale G., Olivetti A., Pagnoni C., Moschini G., Boni S., Guerra A., Scudellari R., Vella S., Inchiostro S., Piazza O., Guarino S., Aldegheri G., Napoli G., Morettini A., Caldini E., Menicacci L., Pieralli F., Torrini M., Poggesi L., Visetti E. M., Mangano C., Visconti S., Maietta P., Banfi E., Cartella S., Venturi B., Nuceri A., Chiesa E., Pacentra E., Panzolato G., Giannotti M., Bianchi C., Pietrangelo A., Para O., Rutili M. S., Russo R., Lanfranco M., Scalabrino E., Tafuri A., Chiarello T., Perfetti E., Cancanelli L., Otero M., Pannella G., Bellucci F., Ferrero G., Vico C., Stillante M. S., D'andrea G., Amoroso F., Arcidiacono A., Bella A. M., Belsito A., Berte Y., Carubia G., Caruso M. G., Casella O., Chiereleson F., Costa C., De Franco D., Germana G., Messina A., Musumeci D., Noto C., Valenti M., Sorrentino C., Panico R., Schettino G., Piccoli J., Pepe A., De Rosa F., Ottaviano M., Marrazzo G., Raponi G., Diberardino S., Bausi S., Marini S. F., Giubellino E., Innocenti G., Gugliemi G., Maccari D., Baciu I., Perrone, F, Piccirillo, M, Ascierto, P, Salvarani, C, Parrella, R, Marata, A, Popoli, P, Ferraris, L, Marrocco-trischitta, M, Ripamonti, D, Binda, F, Bonfanti, P, Squillace, N, Castelli, F, Muiesan, M, Lichtner, M, Calzetti, C, Salerno, N, Atripaldi, L, Cascella, M, Costantini, M, Dolci, G, Facciolongo, N, Fraganza, F, Massari, M, Montesarchio, V, Mussini, C, Negri, E, Botti, G, Cardone, C, Gargiulo, P, Gravina, A, Schettino, C, Arenare, L, Chiodini, P, Gallo, C, Vitale, M, Trojaniello, C, Palla, M, Bianchi, A, De Feo, G, Miscio, L, Froldi, M, Menicanti, L, Cuppone, M, Gobbo, G, Baldessari, C, Valenti, V, Castelvecchio, S, Poli, F, Giacomazzi, F, Piccinni, R, Annnunziata, M, Biondi, A, Bussolari, C, Mazzoleni, M, Giachi, A, Filtz, A, Manini, A, Poletti, E, Masserini, F, Conforti, F, Gaudiano, G, Favero, V, Moroni, A, Viva, T, Fancoli, F, Ferrari, D, Niro, D, Resta, M, Ballotta, A, Poli, M, Ranucci, M, Tebaldi, A, 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A., Titolo G., Mensi G., Vuotto P., Gasperini B., Mancini M., Pasquini Z., Spanu P., Clementi S., Pierini S., Bokor D., Gori D., Ciofetti M., Caimi M., Bettazzi L., Allevi E., Furiani S., Capitanio C., Mastropasqua B., Fara C., Pulitano G., Matsuno J. S., Porta F. D., Dolfini V., Beyene N. B., Bezzi M., Novali M., Viale P., Tedeschi S., Pascale R., Bruno R., Di Filippo A., Sachs M., Oggionni T., Di Stefano M., Mengoli C., Facchini C., Daniele D. N., Frausini G., Mucci L., Tedesco S., Girolimetti R., Manfredini E., Di Carlo A. M., Espinosa E., Dennetta D., Ticinesi A., Meschi T., Nouvenne A., Claudio N., Vitale F., Saracco M., Codeluppi M., Fronti E., Ferrante P., Nespola G. A., Francisci D., Tosti A., Carbonelli C. M., Greco A., Tinti M. G., Stellini R., Appiani C., Reghenzi P., Poletti V., Ravaglia C., Tacconi D., Malcontenti C., Sainaghi P. P., Landi R., Vassia V., Rizzi E., Bellan M., Rossati A., Castello L., Mastroianni C. M., Russo G., Fabio T., Serino F. S., Brollo L., Momesso E., Turati M. L., Monforte A. D., Marchetti G., Boni F., Teopompi E., Trenti C., Boracchia L., Minelli E., Ghidoni G., Matei A., Caruso A., Arcoleo G., Camarda G., Catalano F., Spatafora M., Bettega D., Andreoni M., Teti E., Sarmati L., Di Lorenzo A., Celeste M., Baratto F., Monticelli J., Criveller P., Andrea A., Anselmo Riccio, Castellano M., Cappelli C., Corvini F., Zanini B., Crippa M., Ronconi M., Costa R., Casella S., Brentana L., Bernardi L., Frascati A., Panese S., Presotto F., Michieletto L., Bernardi C., Fusar M., Agnoletti V., Farina M., Lavorini F., Ginanni R., Palmieri F., Mosti S., Amaglio A., Cattaneo A., Cirri S., Montisci A., Gallazzi C., Cosseta D., Baronio B., Rampa L., Maggi P., Messina V., Berlendis M., Sabatti M. C., Palumbo M., Mazzone A., Faggioli P., Bussini L., Fornaro G., Volpato F., Imperiale D., Manno E., Ferreri E., Martelli D., Verhovez A., Giorgis S., Faccio L., Quadri R. D., Negro C., Converso M., Bosco F., Amadasi S., Prandini P., Cocchi S., Manfrin V., Del Punta V., Mazzola G., Sportato G., Romagnoli M., Cristini F., Facondini F., Perin T., Boschi A., Meschiari M., Guaraldi G., Modica S., Moneta S., Boccalatte D., Marchetti V., Ebbreo G., Dale M., Tura P., Rizzoni D., Boari G. E. M., Bonetti S., Marini E., Daniele I., Grossi P. A., Delfrate N. W., Bernhart O., Spizzo G., Mahlknecht K., Volkl T., Di Pietro M. A., Trezzi M., Monacci C., Peris A., Bonizzoli M., Cavanna L., Moroni C., Stroppa E. M., Savio M. C., Gatti F., Bartolaminelli C., Petrosillo N., Donno D. R., Taglietti F., Topino S., Chinello P., Galati V., D'offizi G., Taibi C., Cimolato B., Moroni F., Palagano N., Pelagatti L., Cristiana S., Landini G., Amitrano M., Raimondo M., Mangiacapra S., Romano A., Atteno M., Blanc P., Suardi L. R., Pallotto C., Casinelli K., Uccella I., Harari S., Caminati A., Lipani F., Di Perri G., Calcagno A., Calleri G., Montrucchio C., Caputo A. M., Cozzio S., Donne L. D., Bassetti M., Malgorzata M., Nicolini L. A., Russo C., Sepulcri C., Beltramini S., Mina F., Puoti M., Gandino A., Langer T., D'amico F., Rocchetti C., Cettolo F., Gabriele F., Bocchi P., Cioni G., Cappi C., Corcione S., De Rosa F. G., Scabini S., Canta F., Pinna S. M., Pensa A., Rocco M., Cirasa M. T., Spinicci M., Mencarini J., Zammarchi L., Giovanni C., Sciole K., Bassi F., Bianchi M., Frigerio S., Spaziani S., Nucera A., Rizzardini G., Cossu M. V., Antivalle M., Carpinteri G., Macheda S., Labate D., Bottiroli M., Erne E. M., Cristina Z., Di Biase V., Malberti F., Montani G., Poisa P., Bettini D., Cauda R., Ciccullo A., Riccardi N., Angheben A., Turrini M., Clerici R., Gardellini A., Liparulo L., Rossini T., Ucciferri C., Cipollone F., Vecchiet J., Nico A., Marra L., Leone A., Sdanganelli A., Palmiotti G. A., D'alagni G., Santantonio T. A., Caputo S. L., Bottalico I., Ponticiello A., Di Perna F., Bernardi E., Beltrame A., Bravi S., David M., Bernardi P., Galante D., Uccelli M. C., Prestini K., Drera M., Zini E., Peregrinelli A., Blanzuoli L., Benedetti V., Calvi R., Scaglione N., Nallino G., Bonazzi M., Crespi T., Masolin T., Regazzetti A., Cerri M. C., Maffezzini E., Piazza M., Papetti C., Filippi C. D., Roveda E., Cipolla G., Scozzafava M., Crepaldi M., Henchi S., Vanoni N., Repossi A., Vezzoli M., Scorletti E., Perugini O., Pasini S. M., Pacetti V., Ferrari L., de Paduanis G. A., Del Duca S., Dell'ara F., Brocchieri A., Minoja G., Storti E., Pitagora L., Costa I., Delfanti F., Orlandi M., Ruggeri R., Ruggieri L., Livigni S., Silengo D., Ageno W., Pedrini L., Artiol S., Morbidoni L., De Donno G., Ravagnani V., Inglese F., Scotton P. G., Costantini P., Delucchi M., Clini E., Ansuini A., Marco B., Giuseppe L., Vincenzo B., Rastelli G., Doria A., Vianello A., Cattelan A. M., Bindoli S., Felicietti M., Canetta C., Scartabellati A., Accordino S., Ferrara M., Cocco L., Cirillo F., Pace E., De Caro M., Alberico M., Benigni G., Damiano T., Fusco P., Iuorio A., Torretta G., Racagni M., Muttini S., Sala G., Ghiringhelli P., Chiumiento F., Baccari L., Luca B., Bocchi F., Benatti F., Catellani J., Coppola M., Papi A., Bosco E., Lazzeri C., Cesira N., Puttini C., Carli T., Croci L., Corridi M., Arlotti M., Guerrini G., Cola L., Romanelli M., Bonifazi M., Gasparini S., Mei F., Cerutti E., Lacedonia D., Santoro A., Guidelli G. M., Greco S., Castellan A., Infantino G., Camici L., Frigieri F. C., Pavoni V., Migliori L., Rossetti B., Montagnini F., Mauro I., Genovese E., Capuozzo A., Vitiello L., Sirignano E., Gnesin P., Servillo G., Marinelli A., Pasero D., Casadio L., Babudieri S., Madeddu G., De Vito A., Ranghitta M., Passalacqua R., Antonio F., Gentile I., Buonomo A. R., Scotto R., Zappulo E., Dell'aquila G., Bianchetti A., Guerini F., Vallone A., Oppedisano P., Pusterla L., Giglio O., Sartori E., Zanardini C., Gatti P., Vincenzo V., Piconi S., Molteni C., Dognini G., Cosimo F., Guarneri L., Pulvirenti F., Mondino V., Traballi G., Iemoli E., Grisolia A., Giorgi R., Nucera G., Raffaelli V., Marino P., Negro E., Serati L., Silvia T., Iacobello C., Strano G., Boglione L., Catania A., Gipponi P., Di Cato L., Panaccione A., Vitale G., Crippa I. A., Giacomini M., Basile A., Andrea B., Tundo P., Buzzigoli S., Palmiero G., Magnaca A., Silva M., Ricci M., Crespi S., Pasquino B., Consales G., Bragantini D., Mastroianni F., Righetti G., Scarafino A., Bitetto M., Franzetti F., Piga S., Delmonte V., Carbonara S., Losappio R., Dejaco C., Mastroianni C., Del Bono V., Gilioli F., Barzan D., De Struppi S., Carlotto A., Guadagnin M. L., Girardis M., Bertellini E., Dentali F., Foresta G., Baratta A., Viviani R., Agrati A. M., Perego G. B., Montineri A., Manuele R., Bonfante S., Aquilini D., Prozzo A., Santopuoli D., Di Rosa Z., Alborghetti A., Peci P., Bakhtadze N., Pandini C. S., Ashofarir N., Casella G., Spagnolli W., Urru S., Marchesoni I., Caminiti G., Argilloni E., Danieli E., Ghirardi G., Antonioli C. M., Lipari A., Zavarise P., Kokaly F., Polati E., Gottin L., Lucernoni P., De Conti F., Marcon E., Pontali E., Vacca E. B., Saffioti C., Zunino A., Pognuz E. R., Berlot G., Saltori M., Tedesco A., Agostini C., Di Rosolini M. A., Marino F., Bellinzona G., Grassi W., Di Carlo M., Scimonello G., Nonini S., Mondino M., Mantovani L. F., Tenti E., Tropea C. M. G., Di Stefano D. E., Guelfi P., Dagna L., Morgana G., Montemurro L., Girelli D., Crisafulli E., Maroccia A., Cemuschi A. M., Bernasconi M., Zummo U., Barbato V., Bevilacqua S., Buonfanti G., Canzanella G., De Matteis G., Florio M., Martino M., Ribecco M. T., Romano F., Savio A., Sparavigna L., Curvietto M., Citarella M., Nava V., Maggioni P., Magni M., Iommelli C., Bianco A., Corsini R., Valli L., Ruggieri M. P., Mancini A., Melica T., Ferrari A., Cicognini D., Delliponti M., Zuccarini A., Ciani S., Raffaeli D., Donati L., Cannizzo S., Lui S., Santini L., Roncaglia E., Mighali P., Eisendle F., Cerino G., Citterio C., Di Nunzio C., Lamonica S., Resimini S., Sarteschi G., Pavei C., Battistini N., Gazzola E., Miceli M., Pontiggia S., Lonati V., Giannandrea G., Sortino C., Ravani S., Uggeri C., Jocolle G., Bare C., Baroni I., De Candia D., Fiorini B., Chierico K., Romeo F., Bottega R., Boccasile L., Corsaro A., Spadoni C., Ria E., Chiari S., Ercolino G., Dell'uomo V., Viri S., Minato M., Gazzola L., Dorina B., Gianelli D., Maspero S., Farinazzo M., Zanini P., Sangiovanni A., Del Giudice A., Dragonetti M. M., Bordignon S., Machiavelli A. M., Chiodelli G., Spatarella M., Zenoni D., Beretta F. N., Santilli G., Badagliacca R., Angileri M., Giannelli L., Campomori A., Maimone P., Fadda A., Faoro S., Pisterna A., Cacopardo B., Marino A., Pampaloni A., Celesia B. M., Cinnella G., Labella D., Caporusso R. R., Danzi M., Fiscon M., Malena M., Fendt D., Nardi S., Stobbione P., Savi M. L., De Monte A., Scala A., Liberato N. L., Luchi S., Vincenti A., Cabrini L., Pinelli G., Brugioni L., Potenza D., Numis F. G., Porta G., D'amico M., Iengo B., Angarano G., Saracino A., Blasi L., De Negri P., Angelici S., Farina A., Martino G. P., Bitti G., Tedeschi A., De Ponti S., Agostinone A., Parruti G., Consorte A., Frattari A., Filippelli A., Pagliano P., Masullo A., Sellitto C., Reta M., Rossi N., Raumer L., Andreassi S., Brancaleoni P., Carai A., Salerno A. M., Marinangeli F., Mariani R., Ciccone A., Meschini C., Santoboni G., Angrisani C., Micarelli D., Tarquini G., Fregoni V., Volta C. A., Cherubini A., Del Prete M. S., Ciarrochi E., Tasca F., Ballarin A., Bianchin A., Flocco R., Cuzzone V., Carpinteri M., Gallotti P., Torre F., Zannetti P., Crapis M., Venturini S., Barattini M., Gori G., Mastroianni A., De Stefano G., Gilio M., Rapisarda G., Gulisano L., Granata M. L., Saglimbene S., Montalto M. T., Grasso I., De Luca S., Magro G., Messina F., Scapino B., Abrate P., Francisco C., Pesce L., Navarra M., Agosti M., Pagani S., Piluso M., Ricioppo A., Tognella S., Rovere P., Vincenzi M., Ghirardi L., Generali D., Ingrosso M., Desiderio E., Molaro R., Vitiello S., Lancione L., Paone T. C., Meli A., Mainardi S., Rastellino V., Ursillo A., Grigoli P. D., Bovetto E., Stefanetto I. M., Mazzola F., Daniele A., Bisio C., Delnero P., Morando G., Nava A., Francesco L., Fiammengo F., Regis M., Roccatello D., Sabato E., Liccardi M. M., Bretto C., Lutri L., Castenetto E., Roberti G., Guidi M. F., Bini F., Zappa M. C., Trequattrini T., Rivitti R., Vigliarolo R., Succu A., Lilli M., Serao M., Giogre G., Ruggieri A., Flores K., Vairo G., Satira R., Lingua A., Spina R., Nicastri E., Maffongelli G., Barreca F., Scollet S., Franchi F., Fabbri C., Minuz P., Dalbeni A., Zanatta P., Gelormini D., Mandelli A., Galderisi F., Zoia E., Marchi M. R., Neves N. D. A., Carbone G., Di Caterino E., Petrone A., Usai C. A., Bandiera F., Monti R., Hofer A., Castiglione G., Angeletti C., Tarsia P., Veronese L., Artoni P. D., Larussa D., Fumagalli R., Brioschi P., Cerutti A., Pasquino P., Gilberto F., Cantadori L., Tomasoni L. R., Coppola N., Spolveri S., Pollastri C., Fico L., Principi T., Pierantozzi S., Fontana C. C., Lubrano G., Martinelli L., Navalesi P., Serra E., Cogi E., Manzi A., Furino E., Dasseni N., Gentilini C., Benatti E., Pignatti A., Aiello G., Milia M., Covesnon M. G., Brianti A., Francesco C., Ilaria B., Pagnozzi F., Mietta S., Rossi A., Maroni L., Borroni V., Bellintani C., Sgarabotto C., Bizzotto G., Bucci L., Spagnuolo G., Agostini M., Caria F. C., Testa F., De Palma R., Murdaca G., Zanolini G., Sala N., Righini E., Pontremoli R., Aondio G., Riccardi F., De Cristoforo M. G., De Michele F., Storti A., Perra R., Deidda S., Enrica C., Valastro F., Pierfranceschi M. G., De Gennaro F., Nardecchia A. L., Castellini M., Buetto G., Ippoliti G., Sicheri D., Bottoli M. G., De Arroyabe B. M. L., Versaci A., Pallotti G., Civita M., Grio M., Liuzzi N., Molino P., Pastorelli M., Ricchiardi A., Varbella F., Zeme A. D., Sighieri C., Portale G., Olivetti A., Pagnoni C., Moschini G., Boni S., Guerra A., Scudellari R., Vella S., Inchiostro S., Piazza O., Guarino S., Aldegheri G., Napoli G., Morettini A., Caldini E., Menicacci L., Pieralli F., Torrini M., Poggesi L., Visetti E. M., Mangano C., Visconti S., Maietta P., Banfi E., Cartella S., Venturi B., Nuceri A., Chiesa E., Pacentra E., Panzolato G., Giannotti M., Bianchi C., Pietrangelo A., Para O., Rutili M. S., Russo R., Lanfranco M., Scalabrino E., Tafuri A., Chiarello T., Perfetti E., Cancanelli L., Otero M., Pannella G., Bellucci F., Ferrero G., Vico C., Stillante M. S., D'andrea G., Amoroso F., Arcidiacono A., Bella A. M., Belsito A., Berte Y., Carubia G., Caruso M. G., Casella O., Chiereleson F., Costa C., De Franco D., Germana G., Messina A., Musumeci D., Noto C., Valenti M., Sorrentino C., Panico R., Schettino G., Piccoli J., Pepe A., De Rosa F., Ottaviano M., Marrazzo G., Raponi G., Diberardino S., Bausi S., Marini S. F., Giubellino E., Innocenti G., Gugliemi G., Maccari D., and Baciu I.

Abstract

Background: Tocilizumab blocks pro-inflammatory activity of interleukin-6 (IL-6), involved in pathogenesis of pneumonia the most frequent cause of death in COVID-19 patients. Methods: A multicenter, single-arm, hypothesis-driven trial was planned, according to a phase 2 design, to study the effect of tocilizumab on lethality rates at 14 and 30 days (co-primary endpoints, a priori expected rates being 20 and 35%, respectively). A further prospective cohort of patients, consecutively enrolled after the first cohort was accomplished, was used as a secondary validation dataset. The two cohorts were evaluated jointly in an exploratory multivariable logistic regression model to assess prognostic variables on survival. Results: In the primary intention-to-treat (ITT) phase 2 population, 180/301 (59.8%) subjects received tocilizumab, and 67 deaths were observed overall. Lethality rates were equal to 18.4% (97.5% CI: 13.6-24.0, P = 0.52) and 22.4% (97.5% CI: 17.2-28.3, P < 0.001) at 14 and 30 days, respectively. Lethality rates were lower in the validation dataset, that included 920 patients. No signal of specific drug toxicity was reported. In the exploratory multivariable logistic regression analysis, older age and lower PaO2/FiO2 ratio negatively affected survival, while the concurrent use of steroids was associated with greater survival. A statistically significant interaction was found between tocilizumab and respiratory support, suggesting that tocilizumab might be more effective in patients not requiring mechanical respiratory support at baseline. Conclusions: Tocilizumab reduced lethality rate at 30 days compared with null hypothesis, without significant toxicity. Possibly, this effect could be limited to patients not requiring mechanical respiratory support at baseline. Registration EudraCT (2020-001110-38); clinicaltrials.gov (NCT04317092).

Published
2020

49. An Unsupervised Machine Learning Method Stratifies Chronic Lymphocytic Leukemia Patients in Novel Categories with Different Risk of Early Treatment

Author

Francesca Cuturello, Federico Pozzo, Edith Natalia Villegas Garcia, Francesca Maria Rossi, Massimo Degan, Paola Nanni, Ilaria Cattarossi, Eva Zaina, Paola Varaschin, Alessandra Braida, Michele Berton, Laura Zannier, Filippo Vit, Erika Tissino, Tamara Bittolo, Roberta Laureana, Giovanni D'Arena, Luca Laurenti, Agostino Tafuri, Jacopo Olivieri, Francesco Zaja, Annalisa Chiarenza, Maria Ilaria Del Principe, Riccardo Bomben, Antonella Zucchetto, Stefano Cozzini, Alessio Ansuini, Alberto Cazzaniga, and Valter Gattei

Subjects
Immunology, Cell Biology, Hematology, Biochemistry
Published
2022

50. DKC1 Overexpression Induces a More Aggressive Cellular Behavior and Increases Intrinsic Ribosomal Activity in Immortalized Mammary Gland Cells

Author

Yuko Nobe, Marianna Penzo, Sara Di Viggiano, Ilaria Gandin, Alessio Ansuini, Carmine Onofrillo, Federico Zacchini, Lorenzo Montanaro, Ania Naila Guerrieri, Toshiaki Isobe, Masato Taoka, Davide Treré, Guerrieri, A. N., Zacchini, F., Onofrillo, C., Di Viggiano, S., Penzo, M., Ansuini, A., Gandin, I., Nobe, Y., Taoka, M., Isobe, T., Trere, D., Montanaro, L., Guerrieri, Ania Naila, Zacchini, Federico, Onofrillo, Carmine, Di Viggiano, Sara, Penzo, Marianna, Ansuini, Alessio, Gandin, Ilaria, Nobe, Yuko, Taoka, Masato, Isobe, Toshiaki, Treré, Davide, and Montanaro, Lorenzo

Subjects
0301 basic medicine, Cancer Research, SnoRNA, dyskerin, Translational efficiency, Nucleolus, Prognosi, RRNA, Biology, Dyskerin, snoRNA, Ribosome, lcsh:RC254-282, Article, 03 medical and health sciences, Telomerase RNA component, 0302 clinical medicine, Breast cancer, breast cancer, Downregulation and upregulation, medicine, Pseudouridylation, Small nucleolar RNA, rRNA, DKC1, Prognosis, medicine.disease, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, 030104 developmental biology, Oncology, 030220 oncology & carcinogenesis, pseudouridylation, Cancer research, prognosis, Dyskeratosis congenita, prognosi
Abstract

Simple Summary Dyskerin is a nucleolar protein involved in the modification and processing of ribosomal RNA and in the stabilization of the telomerase RNA component. In several human tumors, including breast cancer, dyskerin overexpression is found related to patients’ worse prognosis. Our aim was to study this phenomenon at the molecular and cellular levels. We firstly confirmed the correlation between high dyskerin expression with patients’ shorter survival. Then, through the generation of cellular models of increased dyskerin expression, we found that increasing dyskerin levels conferred a more aggressive phenotype and increased intrinsic ribosomal activity only in cells derived from normal breast epithelium. Our study provides evidence on the prognostic and bio-pathological relevance of the overexpression of dyskerin in breast carcinoma. A possible mechanistic explanation of the effects of dyskerin overexpression, involving specific ribosomal RNA modification and consequent increased ribosomal activity, is also provided. Abstract Dyskerin is a nucleolar protein involved in the small nucleolar RNA (snoRNA)-guided pseudouridylation of specific uridines on ribosomal RNA (rRNA), and in the stabilization of the telomerase RNA component (hTR). Loss of function mutations in DKC1 causes X-linked dyskeratosis congenita, which is characterized by a failure of proliferating tissues and increased susceptibility to cancer. However, several tumors show dyskerin overexpression. We observed that patients with primary breast cancers with high dyskerin levels are more frequently characterized by shorter survival rates and positive lymph node status than those with tumors with a lower dyskerin expression. To functionally characterize the effects of high dyskerin expression, we generated stably overexpressing DKC1 models finding that increased dyskerin levels conferred a more aggressive cellular phenotype in untransformed immortalized MCF10A cells. Contextually, DKC1 overexpression led to an upregulation of some snoRNAs, including SNORA67 and a significantly increased U1445 modification on 18S rRNA, the known target of SNORA67. Lastly, we found that dyskerin overexpression strongly enhanced the synthetic activity of ribosomes increasing translational efficiency in MCF10A. Altogether, our results indicate that dyskerin may sustain the neoplastic phenotype from an early stage in breast cancer endowing ribosomes with an augmented translation efficiency.

Published
2020

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