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3,692 results on '"Rossolini A"'

1. Video Deblurring by Sharpness Prior Detection and Edge Information

Author

Tian, Yang, Brau, Fabio, Rossolini, Giulio, Buttazzo, Giorgio, and Meng, Hao

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Video deblurring is essential task for autonomous driving, facial recognition, and security surveillance. Traditional methods directly estimate motion blur kernels, often introducing artifacts and leading to poor results. Recent approaches utilize the detection of sharp frames within video sequences to enhance deblurring. However, existing datasets rely on fixed number of sharp frames, which may be too restrictive for some applications and may introduce a bias during model training. To address these limitations and enhance domain adaptability, this work first introduces GoPro Random Sharp (GoProRS), a new dataset where the the frequency of sharp frames within the sequence is customizable, allowing more diverse training and testing scenarios. Furthermore, it presents a novel video deblurring model, called SPEINet, that integrates sharp frame features into blurry frame reconstruction through an attention-based encoder-decoder architecture, a lightweight yet robust sharp frame detection and an edge extraction phase. Extensive experimental results demonstrate that SPEINet outperforms state-of-the-art methods across multiple datasets, achieving an average of +3.2% PSNR improvement over recent techniques. Given such promising results, we believe that both the proposed model and dataset pave the way for future advancements in video deblurring based on the detection of sharp frames., Comment: Under review in Pattern Recognition

Published
2025

2. Edge-Only Universal Adversarial Attacks in Distributed Learning

Author

Rossolini, Giulio, Baldi, Tommaso, Biondi, Alessandro, and Buttazzo, Giorgio

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

Distributed learning frameworks, which partition neural network models across multiple computing nodes, enhance efficiency in collaborative edge-cloud systems but may also introduce new vulnerabilities. In this work, we explore the feasibility of generating universal adversarial attacks when an attacker has access to the edge part of the model only, which consists in the first network layers. Unlike traditional universal adversarial perturbations (UAPs) that require full model knowledge, our approach shows that adversaries can induce effective mispredictions in the unknown cloud part by leveraging key features on the edge side. Specifically, we train lightweight classifiers from intermediate features available at the edge, i.e., before the split point, and use them in a novel targeted optimization to craft effective UAPs. Our results on ImageNet demonstrate strong attack transferability to the unknown cloud part. Additionally, we analyze the capability of an attacker to achieve targeted adversarial effect with edge-only knowledge, revealing intriguing behaviors. By introducing the first adversarial attacks with edge-only knowledge in split inference, this work underscores the importance of addressing partial model access in adversarial robustness, encouraging further research in this area.

Published
2024

3. Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost

Author

Nayab, Sania, Rossolini, Giulio, Simoni, Marco, Saracino, Andrea, Buttazzo, Giorgio, Manes, Nicolamaria, and Giacomelli, Fabrizio

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Today's large language models (LLMs) can solve challenging question-answering tasks, and prompt engineering techniques, such as chain-of-thought (CoT), have gained attention for enhancing the explanation and correctness of outputs. However, many models and techniques tend to produce excessively verbose and lengthy answers, leading to issues with both conciseness and generation time. To address this, this paper analyzes the impact of output lengths on LLM inference pipelines by introducing and proposing novel metrics to evaluate the \textit{correct conciseness} of a model and related prompting techniques. Then, we examine the impact of controlling output length through a refined prompt engineering strategy, Constrained-CoT (CCoT), which encourages the model to produce more concise outputs. To better understand the effects of such a prompt, we also introduce two additional scores for analyzing the conciseness, measured in terms of redundancy and information flow in generated answers. Experiments on pretrained LLMs and multiple datasets demonstrate the benefits of the proposed metrics and the effectiveness of CCoT across different models., Comment: Preprint version, under review

Published
2024

5. Management of intra-abdominal infections: recommendations by the Italian council for the optimization of antimicrobial use

Author

Sartelli, Massimo, Tascini, Carlo, Coccolini, Federico, Dellai, Fabiana, Ansaloni, Luca, Antonelli, Massimo, Bartoletti, Michele, Bassetti, Matteo, Boncagni, Federico, Carlini, Massimo, Cattelan, Anna Maria, Cavaliere, Arturo, Ceresoli, Marco, Cipriano, Alessandro, Cortegiani, Andrea, Cortese, Francesco, Cristini, Francesco, Cucinotta, Eugenio, Dalfino, Lidia, De Pascale, Gennaro, De Rosa, Francesco Giuseppe, Falcone, Marco, Forfori, Francesco, Fugazzola, Paola, Gatti, Milo, Gentile, Ivan, Ghiadoni, Lorenzo, Giannella, Maddalena, Giarratano, Antonino, Giordano, Alessio, Girardis, Massimo, Mastroianni, Claudio, Monti, Gianpaola, Montori, Giulia, Palmieri, Miriam, Pani, Marcello, Paolillo, Ciro, Parini, Dario, Parruti, Giustino, Pasero, Daniela, Pea, Federico, Peghin, Maddalena, Petrosillo, Nicola, Podda, Mauro, Rizzo, Caterina, Rossolini, Gian Maria, Russo, Alessandro, Scoccia, Loredana, Sganga, Gabriele, Signorini, Liana, Stefani, Stefania, Tumbarello, Mario, Tumietto, Fabio, Valentino, Massimo, Venditti, Mario, Viaggi, Bruno, Vivaldi, Francesca, Zaghi, Claudia, Labricciosa, Francesco M., Abu-Zidan, Fikri, Catena, Fausto, and Viale, Pierluigi

Published
2024
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6. Attention-Based Real-Time Defenses for Physical Adversarial Attacks in Vision Applications

Author

Rossolini, Giulio, Biondi, Alessandro, and Buttazzo, Giorgio

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Deep neural networks exhibit excellent performance in computer vision tasks, but their vulnerability to real-world adversarial attacks, achieved through physical objects that can corrupt their predictions, raises serious security concerns for their application in safety-critical domains. Existing defense methods focus on single-frame analysis and are characterized by high computational costs that limit their applicability in multi-frame scenarios, where real-time decisions are crucial. To address this problem, this paper proposes an efficient attention-based defense mechanism that exploits adversarial channel-attention to quickly identify and track malicious objects in shallow network layers and mask their adversarial effects in a multi-frame setting. This work advances the state of the art by enhancing existing over-activation techniques for real-world adversarial attacks to make them usable in real-time applications. It also introduces an efficient multi-frame defense framework, validating its efficacy through extensive experiments aimed at evaluating both defense performance and computational cost.

Published
2023

7. TrainSim: A Railway Simulation Framework for LiDAR and Camera Dataset Generation

Author

D'Amico, Gianluca, Marinoni, Mauro, Nesti, Federico, Rossolini, Giulio, Buttazzo, Giorgio, Sabina, Salvatore, and Lauro, Gianluigi

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Systems and Control
Abstract

The railway industry is searching for new ways to automate a number of complex train functions, such as object detection, track discrimination, and accurate train positioning, which require the artificial perception of the railway environment through different types of sensors, including cameras, LiDARs, wheel encoders, and inertial measurement units. A promising approach for processing such sensory data is the use of deep learning models, which proved to achieve excellent performance in other application domains, as robotics and self-driving cars. However, testing new algorithms and solutions requires the availability of a large amount of labeled data, acquired in different scenarios and operating conditions, which are difficult to obtain in a real railway setting due to strict regulations and practical constraints in accessing the trackside infrastructure and equipping a train with the required sensors. To address such difficulties, this paper presents a visual simulation framework able to generate realistic railway scenarios in a virtual environment and automatically produce inertial data and labeled datasets from emulated LiDARs and cameras useful for training deep neural networks or testing innovative algorithms. A set of experimental results are reported to show the effectiveness of the proposed approach., Comment: Under review

Published
2023

8. Treatment of critically ill patients with cefiderocol for infections caused by multidrug-resistant pathogens: review of the evidence.

Author

Viale, Pierluigi, Sandrock, Christian E, Ramirez, Paula, Rossolini, Gian Maria, and Lodise, Thomas P

Subjects
Appropriate antibiotic, Cefiderocol, Critically ill, Dosing, Multidrug-resistant Gram-negative bacteria, Nosocomial pneumonia, Sepsis, Pneumonia & Influenza, Clinical Research, Clinical Trials and Supportive Activities, Patient Safety, Emerging Infectious Diseases, Pneumonia, Hematology, Lung, Antimicrobial Resistance, Infectious Diseases, Prevention, 2.2 Factors relating to the physical environment, 5.1 Pharmaceuticals, Aetiology, Evaluation of treatments and therapeutic interventions, Development of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Infection, Good Health and Well Being, Clinical Sciences, Public Health and Health Services
Abstract

Appropriate antibiotic treatment for critically ill patients with serious Gram-negative infections in the intensive care unit is crucial to minimize morbidity and mortality. Several new antibiotics have shown in vitro activity against carbapenem-resistant Enterobacterales (CRE) and difficult-to-treat resistant Pseudomonas aeruginosa. Cefiderocol is the first approved siderophore beta-lactam antibiotic with potent activity against multidrug-resistant, carbapenem-resistant, difficult-to-treat or extensively drug-resistant Gram-negative pathogens, which have limited treatment options. The spectrum of activity of cefiderocol includes drug-resistant strains of Acinetobacter baumannii, P. aeruginosa, Stenotrophomonas maltophilia, Achromobacter spp. and Burkholderia spp. and CRE that produce serine- and/or metallo-carbapenemases. Phase 1 studies established that cefiderocol achieves adequate concentration in the epithelial lining fluid in the lung and requires dosing adjustment for renal function, including patients with augmented renal clearance and continuous renal-replacement therapy (CRRT); no clinically significant drug-drug interactions are expected. The non-inferiority of cefiderocol versus high-dose, extended-infusion meropenem in all-cause mortality (ACM) rates at day 14 was demonstrated in the randomized, double-blind APEKS-NP Phase 3 clinical study in patients with nosocomial pneumonia caused by suspected or confirmed Gram-negative bacteria. Furthermore, the efficacy of cefiderocol was investigated in the randomized, open-label, pathogen-focused, descriptive CREDIBLE-CR Phase 3 clinical study in its target patient population with serious carbapenem-resistant Gram-negative infections, including hospitalized patients with nosocomial pneumonia, bloodstream infection/sepsis, or complicated urinary tract infections. However, a numerically greater ACM rate with cefiderocol compared with BAT led to the inclusion of a warning in US and European prescribing information. Cefiderocol susceptibility results obtained with commercial tests should be carefully evaluated due to current issues regarding their accuracy and reliability. Since its approval, real-world evidence in patients with multidrug-resistant and carbapenem-resistant Gram-negative bacterial infections suggests that cefiderocol can be efficacious in certain critically ill patient groups, such as those requiring mechanical ventilation for COVID-19 pneumonia with subsequently acquired Gram-negative bacterial superinfection, and patients with CRRT and/or extracorporeal membrane oxygenation. In this article, we review the microbiological spectrum, pharmacokinetics/pharmacodynamics, efficacy and safety profiles and real-world evidence for cefiderocol, and look at future considerations for its role in the treatment of critically ill patients with challenging Gram-negative bacterial infections.

Published
2023

10. Robust-by-Design Classification via Unitary-Gradient Neural Networks

Author

Brau, Fabio, Rossolini, Giulio, Biondi, Alessandro, and Buttazzo, Giorgio

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Mathematics - Differential Geometry, Mathematics - Metric Geometry
Abstract

The use of neural networks in safety-critical systems requires safe and robust models, due to the existence of adversarial attacks. Knowing the minimal adversarial perturbation of any input x, or, equivalently, knowing the distance of x from the classification boundary, allows evaluating the classification robustness, providing certifiable predictions. Unfortunately, state-of-the-art techniques for computing such a distance are computationally expensive and hence not suited for online applications. This work proposes a novel family of classifiers, namely Signed Distance Classifiers (SDCs), that, from a theoretical perspective, directly output the exact distance of x from the classification boundary, rather than a probability score (e.g., SoftMax). SDCs represent a family of robust-by-design classifiers. To practically address the theoretical requirements of a SDC, a novel network architecture named Unitary-Gradient Neural Network is presented. Experimental results show that the proposed architecture approximates a signed distance classifier, hence allowing an online certifiable classification of x at the cost of a single inference., Comment: Under review

Published
2022

12. Characterization of four novel bacteriophages targeting multi-drug resistant Klebsiella pneumoniae strains of sequence type 147 and 307

Author

Greta Ponsecchi, Tommaso Olimpieri, Noemi Poerio, Alberto Antonelli, Marco Coppi, Gustavo Di Lallo, Mariangela Gentile, Eugenio Paccagnini, Pietro Lupetti, Claudio Lubello, Gian Maria Rossolini, Maurizio Fraziano, and Marco Maria D’Andrea

Subjects
phage, Klebsiella pneumoniae, multi-drug resistance, phage-therapy, Klebsiella pneumoniae ST147, Klebsiella pneumoniae ST307, Microbiology, QR1-502
Abstract

The global dissemination of multi-drug resistant (MDR) pathogenic bacteria requires the rapid research and development of alternative therapies that can support or replace conventional antibiotics. Among MDR pathogens, carbapenem-resistant Klebsiella pneumoniae (CR-Kp) are of particular concern due to their extensive resistance profiles, global dissemination in hospital environments, and their major role in some life-threatening infections. Phages, or some of their components, are recognized as one of the potential alternatives that might be helpful to treat bacterial infections. In this study, we have isolated and characterized four lytic bacteriophages targeting K. pneumoniae strains of Sequence Type (ST) 307 or ST147, two predominant high-risk clones of CR-Kp. Phages, designated vB_KpS_GP-1, vB_KpP_GP-2, vB_KpP_GP-4, and vB_KpP_GP-5, were isolated from sewage wastewater samples. The vB_KpS_GP-1 phage was a siphovirus unable to establish lysogeny with its host, while the other three were podoviruses. While 85.7% of K. pneumoniae strains of ST307 were selectively lysed by the phages vB_KpS_GP-1 or vB_KpP_GP-5, the other two phages were able to lyse all the tested strains of ST147 (n = 12). Phages were stable over a broad pH and temperature range and were characterized by burst sizes of 10–100 plaque forming units and latency periods of 10–50 minutes. Genome sequencing confirmed the absence of antibiotic resistance genes, virulence factors or toxins and revealed that two phages were likely members of new genera. Given their strictly lytic nature and high selectivity towards two of the major high-risk clones of K. pneumoniae, cocktails of these phages could represent promising candidates for further evaluation in in vivo experimental models of K. pneumoniae infection.

Published
2024
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13. CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models

Author

Nesti, Federico, Rossolini, Giulio, D'Amico, Gianluca, Biondi, Alessandro, and Buttazzo, Giorgio

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Adversarial examples represent a serious threat for deep neural networks in several application domains and a huge amount of work has been produced to investigate them and mitigate their effects. Nevertheless, no much work has been devoted to the generation of datasets specifically designed to evaluate the adversarial robustness of neural models. This paper presents CARLA-GeAR, a tool for the automatic generation of photo-realistic synthetic datasets that can be used for a systematic evaluation of the adversarial robustness of neural models against physical adversarial patches, as well as for comparing the performance of different adversarial defense/detection methods. The tool is built on the CARLA simulator, using its Python API, and allows the generation of datasets for several vision tasks in the context of autonomous driving. The adversarial patches included in the generated datasets are attached to billboards or the back of a truck and are crafted by using state-of-the-art white-box attack strategies to maximize the prediction error of the model under test. Finally, the paper presents an experimental study to evaluate the performance of some defense methods against such attacks, showing how the datasets generated with CARLA-GeAR might be used in future work as a benchmark for adversarial defense in the real world. All the code and datasets used in this paper are available at http://carlagear.retis.santannapisa.it.

Published
2022

15. Genomic characterization of carbapenemase-producing Klebsiella pneumoniae ST307 revealed multiple introductions in Buenos Aires, Argentina

Author

Francisco González-Espinosa, Vincenzo Di Pilato, Francisco Magariños, Jose Di Conza, Gian Maria Rossolini, Gabriel Gutkind, Marcela Radice, and Daniela Cejas

Subjects
Klebsiella pneumoniae, ST307, KPC, NDM, Argentina, Microbiology, QR1-502
Abstract

ABSTRACT: Objectives: To describe at genomic level nine carbapenemase-producing Klebsiella pneumoniae ST307 (Kp-ST307) clinical isolates recovered in Buenos Aires during 2017 to 2021, investigating their resistome, virulome, and phylogeny. Methods: Antimicrobial susceptibility was determined according to Clinical and Laboratory Standards Intitute (CLSI). Genomic DNA was sequenced by Illumina MiSeq and analysed using SPAdes, PROKKA, and Kleborate. Phylogeny of 355 randomly selected Kp-ST307 genomes and those from nine local isolates was inferred by a maximum-likelihood approach. The tree was visualized using Microreact. Results: Besides resistance to ß-lactams and fluoroquinolones, six out of nine Kp-ST307 were also resistant to ceftazidime/avibactam (CZA). This difficult-to-treat resvistance phenotype was mediated by blaSHV-28 and GyrA-83I/ParC-80I mutations in addition to carbapenemase coding genes. Among CZA susceptible isolates, two of them harboured blaKPC-3 while the other harboured blaKPC-2+blaCTX-M-15. Regarding CZA-resistant isolates, three harboured blaKPC-3+blaNDM-1+blaCMY-6, two carried blaKPC-2+blaNDM-5+blaCTX-M-15, and blaNDM-5+blaCTX-M-15 were detected in the remaining isolate. Furthermore, five colistin-resistant isolates presented a nonsense mutation in mgrB.Global Kp-ST307 isolates were distributed in two deep-branching lineages while local isolates were set in the main clade of the phylogenetic tree. The five isolates from the same hospital, harbouring blaKPC-3 or blaKPC-3+blaNDM-1+blaCMY-6, clustered in a monophyletic subclade with Italian isolates. Also, an isolate harbouring blaKPC-2+blaNDM-5+blaCTX-M-15 recovered in another hospital was closed to this group. The remaining local Kp-ST307 were grouped in other subclades containing isolates of diverse geographical origin. Conclusion: The inferred resistome was consistent with the resistant phenotype. Phylogeny suggested multiple introduction events in our region and a single major introduction in one hospital followed by local spread.

Published
2024
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17. In vitro activity of aztreonam-avibactam and comparators against Metallo-β-Lactamase-producing Enterobacterales from ATLAS Global Surveillance Program, 2016–2020

Author

Gian Maria Rossolini, Francis F. Arhin, and Michal Kantecki

Subjects
Antimicrobial activity, Aztreonam-avibactam, Enterobacterales, ATLAS, MBL-positive, Microbiology, QR1-502
Abstract

Objectives: Metallo-β-lactamase (MBL)-producing Enterobacterales are a major challenge worldwide due to limited treatment options. Aztreonam-avibactam (ATM-AVI), which is under clinical development, has shown activity against MBL-positive isolates. This study evaluated the prevalence of MBL producers and the nature of enzymes among a global collection of clinical isolates of Enterobacterales from the Antimicrobial Testing Leadership and Surveillance program (ATLAS) surveillance program (2016–2020), and the antimicrobial activity of ATM-AVI and comparators against this collection. Methods: Non-duplicate clinical isolates of Enterobacterales (N = 106 686) collected across 63 countries were analysed. Antimicrobial susceptibility was performed using broth microdilution. Minimum inhibitory concentrations (MICs) were interpreted using Clinical and Laboratory Standards Institute and European Committee on Antimicrobial Susceptibility Testing breakpoints. Provisional pharmacokinetic/pharmacodynamic breakpoint of ≤8 mg/L was considered for ATM-AVI. β-lactamase genes were characterized by polymerase chain reaction and sequencing. The Cochran Armitage Trend test was used to determine significant trends in percentage of isolates over time. Results: Overall, MBL-positive isolates were 1.6% of total Enterobacterales isolates globally, with a significant increasing trend observed over time, globally and across regions (P < 0.05). New Delhi MBL (NDM) was the most common MBL (83.3%). ATM-AVI demonstrated potent activity against MBL-positive isolates (MIC ≤8 mg/L: 99.4% isolates inhibited; MIC90, 1 mg/L). Consistent activity was also noted across different regions. Potent activity was demonstrated against different NDM variants and MBL-positive isolates co-carrying other carbapenemases (98.1% and 99.7% isolates inhibited at ≤8 mg/L, respectively). About 0.6% MBL-positive isolates (10/1707) had MICs >8 mg/L for ATM-AVI. Conclusion: ATM-AVI demonstrated potent activity against MBL-positive isolates, including NDM variants and MBL-positive isolates co-carrying other carbapenemases, and may represent a good option for treating infections caused by MBL-positive Enterobacterales.

Published
2024
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19. Defending From Physically-Realizable Adversarial Attacks Through Internal Over-Activation Analysis

Author

Rossolini, Giulio, Nesti, Federico, Brau, Fabio, Biondi, Alessandro, and Buttazzo, Giorgio

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

This work presents Z-Mask, a robust and effective strategy to improve the adversarial robustness of convolutional networks against physically-realizable adversarial attacks. The presented defense relies on specific Z-score analysis performed on the internal network features to detect and mask the pixels corresponding to adversarial objects in the input image. To this end, spatially contiguous activations are examined in shallow and deep layers to suggest potential adversarial regions. Such proposals are then aggregated through a multi-thresholding mechanism. The effectiveness of Z-Mask is evaluated with an extensive set of experiments carried out on models for both semantic segmentation and object detection. The evaluation is performed with both digital patches added to the input images and printed patches positioned in the real world. The obtained results confirm that Z-Mask outperforms the state-of-the-art methods in terms of both detection accuracy and overall performance of the networks under attack. Additional experiments showed that Z-Mask is also robust against possible defense-aware attacks.

Published
2022

21. On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving

Author

Rossolini, Giulio, Nesti, Federico, D'Amico, Gianluca, Nair, Saasha, Biondi, Alessandro, and Buttazzo, Giorgio

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

The existence of real-world adversarial examples (commonly in the form of patches) poses a serious threat for the use of deep learning models in safety-critical computer vision tasks such as visual perception in autonomous driving. This paper presents an extensive evaluation of the robustness of semantic segmentation models when attacked with different types of adversarial patches, including digital, simulated, and physical ones. A novel loss function is proposed to improve the capabilities of attackers in inducing a misclassification of pixels. Also, a novel attack strategy is presented to improve the Expectation Over Transformation method for placing a patch in the scene. Finally, a state-of-the-art method for detecting adversarial patch is first extended to cope with semantic segmentation models, then improved to obtain real-time performance, and eventually evaluated in real-world scenarios. Experimental results reveal that, even though the adversarial effect is visible with both digital and real-world attacks, its impact is often spatially confined to areas of the image around the patch. This opens to further questions about the spatial robustness of real-time semantic segmentation models.

Published
2022

22. On the Minimal Adversarial Perturbation for Deep Neural Networks with Provable Estimation Error

Author

Brau, Fabio, Rossolini, Giulio, Biondi, Alessandro, and Buttazzo, Giorgio

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Mathematics - Differential Geometry
Abstract

Although Deep Neural Networks (DNNs) have shown incredible performance in perceptive and control tasks, several trustworthy issues are still open. One of the most discussed topics is the existence of adversarial perturbations, which has opened an interesting research line on provable techniques capable of quantifying the robustness of a given input. In this regard, the Euclidean distance of the input from the classification boundary denotes a well-proved robustness assessment as the minimal affordable adversarial perturbation. Unfortunately, computing such a distance is highly complex due the non-convex nature of NNs. Despite several methods have been proposed to address this issue, to the best of our knowledge, no provable results have been presented to estimate and bound the error committed. This paper addresses this issue by proposing two lightweight strategies to find the minimal adversarial perturbation. Differently from the state-of-the-art, the proposed approach allows formulating an error estimation theory of the approximate distance with respect to the theoretical one. Finally, a substantial set of experiments is reported to evaluate the performance of the algorithms and support the theoretical findings. The obtained results show that the proposed strategies approximate the theoretical distance for samples close to the classification boundary, leading to provable robustness guarantees against any adversarial attacks., Comment: Under review on IEEE journal Transactions on Pattern Analysis and Machine Intelligence

Published
2022
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26. Potential value of a rapid syndromic multiplex PCR for the diagnosis of native and prosthetic joint infections: a real-world evidence study

Author

S. Pascual, B. Noble, N. Ahmad-Saeed, C. Aldridge, S. Ambretti, S. Amit, R. Annett, S. A. O'Shea, A. M. Barbui, G. Barlow, L. Barrett, M. Berth, A. Bondi, N. Boran, S. E. Boyd, C. Chaves, M. Clauss, P. Davies, I. T. Dianzo-Delgado, J. Esteban, S. Fuchs, L. Friis-Hansen, D. Goldenberger, A. Kraševac Glaser, J. O. Groonroos, I. Hoffmann, T. Hoffmann, H. Hughes, M. Ivanova, P. Jezek, G. Jones, Z. Ceren Karahan, C. Lass-Flörl, F. Laurent, L. Leach, M. L. Horsbøll Pedersen, C. Loiez, M. Lynch, R. J. Maloney, M. Marsh, O. Milburn, S. Mitchell, L. S. P. Moore, L. Moffat, M. Murdjeva, M. E. Murphy, D. Nayar, G. Nigrisoli, F. O'Sullivan, B. Öz, T. Peach, C. Petridou, M. Prinz, M. Rak, N. Reidy, G. M. Rossolini, A.-L. Roux, P. Ruiz-Garbajosa, K. Saeed, L. Salar-Vidal, C. Salas Venero, M. Selvaratnam, E. Senneville, P. Starzengruber, B. Talbot, V. Taylor, R. Trebše, D. Wearmouth, B. Willinger, M. Wouthuyzen-Bakker, B. Couturier, and F. Allantaz

Subjects
Orthopedic surgery, RD701-811
Abstract

Introduction: The BIOFIRE Joint Infection (JI) Panel is a diagnostic tool that uses multiplex-PCR testing to detect microorganisms in synovial fluid specimens from patients suspected of having septic arthritis (SA) on native joints or prosthetic joint infections (PJIs). Methods: A study was conducted across 34 clinical sites in 19 European and Middle Eastern countries from March 2021 to June 2022 to assess the effectiveness of the BIOFIRE JI Panel. Results: A total of 1527 samples were collected from patients suspected of SA or PJI, with an overall agreement of 88.4 % and 85 % respectively between the JI Panel and synovial fluid cultures (SFCs). The JI Panel detected more positive samples and microorganisms than SFC, with a notable difference on Staphylococcus aureus, Streptococcus species, Enterococcus faecalis, Kingella kingae, Neisseria gonorrhoeae, and anaerobic bacteria. The study found that the BIOFIRE JI Panel has a high utility in the real-world clinical setting for suspected SA and PJI, providing diagnostic results in approximately 1 h. The user experience was positive, implying a potential benefit of rapidity of results' turnover in optimising patient management strategies. Conclusion: The study suggests that the BIOFIRE JI Panel could potentially optimise patient management and antimicrobial therapy, thus highlighting its importance in the clinical setting.

Published
2024
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27. Treatment and diagnosis of severe KPC-producing Klebsiella pneumoniae infections: a perspective on what has changed over last decades

Author

Daniele Roberto Giacobbe, Vincenzo Di Pilato, Ilias Karaiskos, Tommaso Giani, Anna Marchese, Gian Maria Rossolini, and Matteo Bassetti

Subjects
BL-BLI, KPC, cefiderocol, ceftazidime-avibactam, imipenem-relebactam, meropenem-vaborbactam, Medicine
Abstract

AbstractAntimicrobial resistance is a global health threat. Among Gram-negative bacteria, resistance to carbapenems, a class of β-lactam antibiotics, is usually a proxy for difficult-to-treat resistance, since carbapenem-resistant organisms are often resistant to many classes of antibiotics. Carbapenem resistance in the Gram-negative pathogen Klebsiella pneumoniae is mostly due to the production of carbapenemases, enzymes able to hydrolyze carbapenems, and K. pneumoniae carbapenemase (KPC)-type enzymes are overall the most prevalent carbapenemases in K. pneumoniae. In the last decade, the management of severe infections due to KPC-producing K. pneumoniae (KPC-Kp) in humans has presented many peculiar challenges to clinicians worldwide. In this perspective, we discuss how the treatment of severe KPC-Kp infections has evolved over the last decades, guided by the accumulating evidence from clinical studies, and how recent advances in diagnostics have allowed to anticipate identification of KPC-Kp in infected patients.KEY MESSAGESIn the last decade, the management of severe infections due to KPC-Kp has presented many peculiar challenges to clinicians worldwideFollowing the introduction in clinical practice of novel β-lactam/β-lactamase inhibitor combinations and novel β-lactams active against KPC-producing bacteria, the management of severe KPC-Kp infections has witnessed a remarkable evolutionTreatment of severe KPC-Kp infections is a highly dynamic process, in which the wise use of novel antimicrobials should be accompanied by a continuous refinement based on evolving clinical evidence and laboratory diagnostics

Published
2023
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28. Effect of antimetabolite regimen on cellular and humoral immune response to SARS-COV-2 vaccination in solid organ transplant recipients

Author

Capone, Manuela, Vanni, Anna, Salvati, Lorenzo, Lamacchia, Giulia, Mazzoni, Alessio, Maggi, Laura, Cosmi, Lorenzo, Liotta, Francesco, Romagnani, Paola, Cirillo, Luigi, Buti, Elisa, Terlizzi, Vito, Azzari, Chiara, Citera, Francesco, Barbati, Federica, Rossolini, Gian Maria, Bresci, Silvia, Borchi, Beatrice, Cavallo, Annalisa, Mencarini, Jessica, Francalanci, Emanuela, Kiros, Seble Tekle, Bartoloni, Alessandro, and Annunziato, Francesco

Published
2024
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31. The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: a genotypic analysis

Author

Walker, Timothy M, Miotto, Paolo, Köser, Claudio U, Fowler, Philip W, Knaggs, Jeff, Iqbal, Zamin, Hunt, Martin, Chindelevitch, Leonid, Farhat, Maha R, Cirillo, Daniela Maria, Comas, Iñaki, Posey, James, Omar, Shaheed V, Peto, Timothy EA, Suresh, Anita, Uplekar, Swapna, Laurent, Sacha, Colman, Rebecca E, Nathanson, Carl-Michael, Zignol, Matteo, Walker, Ann Sarah, Crook, Derrick W, Ismail, Nazir, Rodwell, Timothy C, Consortium, the SeqTreat Consortium CRyPTIC, Walker, A Sarah, Steyn, Adrie JC, Lalvani, Ajit, Baulard, Alain, Christoffels, Alan, Mendoza-Ticona, Alberto, Trovato, Alberto, Skrahina, Alena, Lachapelle, Alexander S, Brankin, Alice, Piatek, Amy, Cruz, Ana Gibertoni, Koch, Anastasia, Cabibbe, Andrea Maurizio, Spitaleri, Andrea, Brandao, Angela P, Chaiprasert, Angkana, Barbova, Anna, Van Rie, Annelies, Ghodousi, Arash, Bainomugisa, Arnold, Mandal, Ayan, Roohi, Aysha, Javid, Babak, Zhu, Baoli, Letcher, Brice, Rodrigues, Camilla, Nimmo, Camus, NATHANSON, Carl-Michael, Duncan, Carla, Coulter, Christopher, Utpatel, Christian, Liu, Chunfa, Grazian, Clara, Kong, Clare, Wilson, Daniel J, Matias, Daniela, Jorgensen, Danielle, Zimenkov, Danila, Chetty, Darren, Moore, David AJ, Clifton, David A, van Soolingen, Dick, Liu, Dongxin, Kohlerschmidt, Donna, Barreira, Draurio, Ngcamu, Dumisani, Lazaro, Elias David Santos, Kelly, Ellis, Borroni, Emanuele, Roycroft, Emma, Andre, Emmanuel, Böttger, Erik C, Robinson, Esther, Menardo, Fabrizio, Mendes, Flavia F, Jamieson, Frances B, Coll, Francesc, Gao, George Fu, Kasule, George W, Rossolini, Gian Maria, Rodger, Gillian, Smith, E Grace, Meintjes, Graeme, Thwaites, Guy, Hoffmann, Harald, Albert, Heidi, Cox, Helen, Laurenson, Ian F, and Arandjelovic, Irena

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Microbiology, Clinical Sciences, Tuberculosis, Infectious Diseases, Rare Diseases, Antimicrobial Resistance, Orphan Drug, Development of treatments and therapeutic interventions, 5.1 Pharmaceuticals, Infection, Good Health and Well Being, Antitubercular Agents, Drug Resistance, Ethambutol, Microbial Sensitivity Tests, Mutation, Mycobacterium tuberculosis, World Health Organization, CRyPTIC Consortium, Seq&Treat Consortium, Immunology, Medical microbiology
Abstract

BackgroundMolecular diagnostics are considered the most promising route to achieving rapid, universal drug susceptibility testing for Mycobacterium tuberculosiscomplex (MTBC). We aimed to generate a WHO endorsed catalogue of mutations to serve as a global standard for interpreting molecular information for drug resistance prediction.MethodsA candidate gene approach was used to identify mutations as associated with resistance, or consistent with susceptibility, for 13 WHO endorsed anti-tuberculosis drugs. 38,215 MTBC isolates with paired whole-genome sequencing and phenotypic drug susceptibility testing data were amassed from 45 countries. For each mutation, a contingency table of binary phenotypes and presence or absence of the mutation computed positive predictive value, and Fisher's exact tests generated odds ratios and Benjamini-Hochberg corrected p-values. Mutations were graded as Associated with Resistance if present in at least 5 isolates, if the odds ratio was >1 with a statistically significant corrected p-value, and if the lower bound of the 95% confidence interval on the positive predictive value for phenotypic resistance was >25%. A series of expert rules were applied for final confidence grading of each mutation.Findings15,667 associations were computed for 13,211 unique mutations linked to one or more drugs. 1,149/15,667 (7·3%) mutations were classified as associated with phenotypic resistance and 107/15,667 (0·7%) were deemed consistent with susceptibility. For rifampicin, isoniazid, ethambutol, fluoroquinolones, and streptomycin, the mutations' pooled sensitivity was >80%. Specificity was over 95% for all drugs except ethionamide (91·4%), moxifloxacin (91·6%) and ethambutol (93·3%). Only two resistance mutations were classified for bedaquiline, delamanid, clofazimine, and linezolid as prevalence of phenotypic resistance was low for these drugs.InterpretationThis first WHO endorsed catalogue of molecular targets for MTBC drug susceptibility testing provides a global standard for resistance interpretation. Its existence should encourage the implementation of molecular diagnostics by National Tuberculosis Programmes.FundingUNITAID, Wellcome, MRC, BMGF.

Published
2022

32. Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks

Author

Nesti, Federico, Rossolini, Giulio, Nair, Saasha, Biondi, Alessandro, and Buttazzo, Giorgio

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep learning and convolutional neural networks allow achieving impressive performance in computer vision tasks, such as object detection and semantic segmentation (SS). However, recent studies have shown evident weaknesses of such models against adversarial perturbations. In a real-world scenario instead, like autonomous driving, more attention should be devoted to real-world adversarial examples (RWAEs), which are physical objects (e.g., billboards and printable patches) optimized to be adversarial to the entire perception pipeline. This paper presents an in-depth evaluation of the robustness of popular SS models by testing the effects of both digital and real-world adversarial patches. These patches are crafted with powerful attacks enriched with a novel loss function. Firstly, an investigation on the Cityscapes dataset is conducted by extending the Expectation Over Transformation (EOT) paradigm to cope with SS. Then, a novel attack optimization, called scene-specific attack, is proposed. Such an attack leverages the CARLA driving simulator to improve the transferability of the proposed EOT-based attack to a real 3D environment. Finally, a printed physical billboard containing an adversarial patch was tested in an outdoor driving scenario to assess the feasibility of the studied attacks in the real world. Exhaustive experiments revealed that the proposed attack formulations outperform previous work to craft both digital and real-world adversarial patches for SS. At the same time, the experimental results showed how these attacks are notably less effective in the real world, hence questioning the practical relevance of adversarial attacks to SS models for autonomous/assisted driving.

Published
2021

33. The Uprise of Human Leishmaniasis in Tuscany, Central Italy: Clinical and Epidemiological Data from a Multicenter Study

Author

Anna Barbiero, Michele Spinicci, Andrea Aiello, Martina Maruotto, Roberta Maria Antonello, Giuseppe Formica, Matteo Piccica, Patrizia Isola, Eva Maria Parisio, Maria Nardone, Silvia Valentini, Valentina Mangano, Tamara Brunelli, Loria Bianchi, Filippo Bartalesi, Cecilia Costa, Margherita Sambo, Mario Tumbarello, Spartaco Sani, Silvia Fabiani, Barbara Rossetti, Cesira Nencioni, Alessandro Lanari, Donatella Aquilini, Giulia Montorzi, Elisabetta Venturini, Luisa Galli, Giada Rinninella, Marco Falcone, Federica Ceriegi, Francesco Amadori, Antonella Vincenti, Pierluigi Blanc, Iacopo Vellere, Danilo Tacconi, Sauro Luchi, Sara Moneta, Daniela Massi, Michela Brogi, Fabio Voller, Fabrizio Gemmi, Gian Maria Rossolini, Maria Grazia Cusi, Fabrizio Bruschi, Alessandro Bartoloni, and Lorenzo Zammarchi

Subjects
leishmania, Italy, epidemiology, phlebo-borne disease, climate change, One Health, Biology (General), QH301-705.5
Abstract

Human leishmaniasis is facing important epidemiological changes in Southern Europe, driven by increased urbanization, climate changes, emerging of new animal reservoirs, shifts in human behavior and a growing population of immunocompromised and elderly individuals. In this evolving epidemiological landscape, we analyzed the clinical and epidemiological characteristics of human leishmaniasis in the Tuscany region of Central Italy. Through a multicentric retrospective analysis, we collected clinical and demographic data about all cases of leishmaniasis recorded between 2018 and 2023. We observed 176 cases of human leishmaniasis, with 128 (72.7%) visceral leishmaniasis (VL) and 47 (26.7%) cutaneous leishmaniasis (CL). Among these, 92.2% of VL and 85.1% of CL cases were autochthonous. The cumulative incidence of autochthonous human leishmaniasis was 0.22 cases per 100,000 inhabitants in 2018, but reached 1.81/100,000 in 2023. We identified three main areas of transmission: around the city of Florence (North-East Tuscany), around Grosseto city (South-West Tuscany) and Elba Island. Our findings confirm that the epidemiology of leishmaniasis is undergoing significant changes in Central Italy. Awareness towards this emerging health threat and surveillance strategies need to be improved in order to reliably assess the disease’s burden. Further research is needed in a “One-Health” perspective, to clarify the epidemiological dynamics at the environmental, reservoir, vector and human levels. The role of climate change and specific climatic factors affecting the epidemiological patterns of human leishmaniasis should be assessed. Further knowledge in these fields would promote targeted control and prevention strategies at regional and national levels.

Published
2024
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36. Increasing the Confidence of Deep Neural Networks by Coverage Analysis

Author

Rossolini, Giulio, Biondi, Alessandro, and Buttazzo, Giorgio

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

The great performance of machine learning algorithms and deep neural networks in several perception and control tasks is pushing the industry to adopt such technologies in safety-critical applications, as autonomous robots and self-driving vehicles. At present, however, several issues need to be solved to make deep learning methods more trustworthy, predictable, safe, and secure against adversarial attacks. Although several methods have been proposed to improve the trustworthiness of deep neural networks, most of them are tailored for specific classes of adversarial examples, hence failing to detect other corner cases or unsafe inputs that heavily deviate from the training samples. This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model robustness against different unsafe inputs. In particular, four coverage analysis methods are proposed and tested in the architecture for evaluating multiple detection logics. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs, introducing limited extra-execution time and memory requirements.

Published
2021

37. Doubts and concerns about COVID-19 uncertainties on imaging data, clinical score, and outcomes

Author

Cosimo Nardi, Andrea Magnini, Linda Calistri, Edoardo Cavigli, Anna Julie Peired, Vieri Rastrelli, Edoardo Carlesi, Giulia Zantonelli, Olga Smorchkova, Lorenzo Cinci, Martina Orlandi, Nicholas Landini, Edoardo Berillo, Chiara Lorini, Jessica Mencarini, Maria Grazia Colao, Leonardo Gori, Valentina Luzzi, Chiara Lazzeri, Elisa Cipriani, Manuela Bonizzoli, Filippo Pieralli, Carlo Nozzoli, Alessandro Morettini, Federico Lavorini, Alessandro Bartoloni, Gian Maria Rossolini, Marco Matucci-Cerinic, Sara Tomassetti, and Stefano Colagrande

Subjects
COVID-19, SARS-CoV-2, Acute respiratory disease, Pneumonia, Computed tomography, Diseases of the respiratory system, RC705-779
Abstract

Abstract Background COVID-19 is a pandemic disease affecting predominantly the respiratory apparatus with clinical manifestations ranging from asymptomatic to respiratory failure. Chest CT is a crucial tool in diagnosing and evaluating the severity of pulmonary involvement through dedicated scoring systems. Nonetheless, many questions regarding the relationship of radiologic and clinical features of the disease have emerged in multidisciplinary meetings. The aim of this retrospective study was to explore such relationship throughout an innovative and alternative approach. Materials and methods This study included 550 patients (range 25–98 years; 354 males, mean age 66.1; 196 females, mean age 70.9) hospitalized for COVID-19 with available radiological and clinical data between 1 March 2021 and 30 April 2022. Radiological data included CO-RADS, chest CT score, dominant pattern, and typical/atypical findings detected on CT examinations. Clinical data included clinical score and outcome. The relationship between such features was investigated through the development of the main four frequently asked questions summarizing the many issues arisen in multidisciplinary meetings, as follows 1) CO-RADS, chest CT score, clinical score, and outcomes; 2) the involvement of a specific lung lobe and outcomes; 3) dominant pattern/distribution and severity score for the same chest CT score; 4) additional factors and outcomes. Results 1) If CT was suggestive for COVID, a strong correlation between CT/clinical score and prognosis was found; 2) Middle lobe CT involvement was an unfavorable prognostic criterion; 3) If CT score 50%, crazy paving as dominant pattern leaded to a 15% increased death rate, stacked up against other patterns, thus almost doubling it; 4) Additional factors usually did not matter, but lymph-nodes and pleural effusion worsened prognosis. Conclusions This study outlined those radiological features of COVID-19 most relevant towards disease severity and outcome with an innovative approach.

Published
2023
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42. Attributable mortality of infections caused by carbapenem-resistant Enterobacterales: results from a prospective, multinational case-control-control matched cohorts study (EURECA)

Author

Serna, Almudena de la, Monteau, Sophie, Palomo, Virginia, Soriano, Elena, Gutierrez, David, Moreno, Elisa, Sojo-Dorado, Jesus, Morales, Isabel, Maldonado, Natalia, Santis, Lucia Valiente de, Ciezar, Antonio Plata, Ruiz Mesa, Juan Diego, Diaz, Beatriz Sobrino, Gomez, Ignacio Marquez, Camacho, Ines Perez, Cano, Angela, Frutos-Adame, Azahara, Guzman-Puche, Julia, Gracia-Ahufinger, Irene, Perez-Nadales, Elena, Torre-Gimenez, Julian, Pyrpasopoulou, Athina, Iosifidis, Elias, Chorafa, Elsa, Radovanovic, Ivana, Petrovic, Sladjana, Cvetkovi, Slavica, Melentijevic, Srdjan-Sanja, Bicmen, Can, Senol, Gunes, Tubau, Fe, Camara, Jordi, Daniel Gumucio, Victor, Bassoulis, Dimitris, Deliolanis, John, Pitiriga, Vassiliki Ch., Triarides, Nikolaos, Argiti, Efstathia, Legakis, Nikolaos J., Margarita, Kyriakidou, Gijón-Cordero, Desirée, Ruiz-Garbajosa, Patricia, Rossolini, Gian Maria, Nica, Maria, Talapan, Daniela, Medić, Deana, Prijić, Sanja Maričić, Caballero, Mireia Cantero, Ramírez, Lina M Parra, Bilgin, Hüseyin, Dalekos, George N., Stefos, Aggelos, Spyridis, Nikolaos, Michos, Athanasios, De Rosa, Francesco Giuseppe, Cavallo, Rossana, Petrosillo, Nicola, Dicaro, Antonio, Landini, Maria Paola, degli Atti, Marta Luisa Ciofi, Masanovic, Mileva, Matkovic, Dusan, Tsiodras, Sotirios, Blasi, Francesco, pasquale, Marta Di, Viscoli, Claudio, Vata, Andrei, Dorneanu, Olivia, Kapisyzi, Perlat, Vince, Adriana, Tsigou, Evdoxia, Maltezos, Efstratios, Komnos, Apostolos, Gogos, Charalampos, Franzetti, Fabio, Antonelli, Massimo, Lupse, Mihaela, Corneci, Dan, Tomescu, Dana, Georgescu, Anca, Bukarica, Ljiljana, Mitrović, Goran, Krstić, Nataša Lukić, Kurti, Arsim, Reuter, Sandra, Díaz-Pollán, Beatriz, Sabater, Julia Origüen, Muñoz, Patricia, Azap, Alpay, ancak, Banu, Sahin, Arife, Akalin, Halis, Paniagua-García, María, Bravo-Ferrer, Jose M., Pérez-Galera, Salvador, Kostyanev, Tomislav, de Kraker, Marlieke E.A., Feifel, Jan, Palacios-Baena, Zaira R., Schotsman, Joost, Cantón, Rafael, Daikos, George L., Carevic, Biljana, Dragovac, Gorana, Tan, Lionel K., Raka, Lul, Hristea, Adriana, Viale, Pierluigi, Akova, Murat, Cano, Ángela, Reguera, Jose María, Bartoloni, Alessandro, Florescu, Simin-Aysel, Benea, Serban, Asensio, Ángel, Korten, Volkan, Grundmann, Hajo, Goossens, Herman, Bonten, Marc J., Gutiérrez-Gutiérrez, Belén, and Rodríguez-Baño, Jesús

Published
2024
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45. Combination regimen of nirmatrelvir/ritonavir and molnupiravir for the treatment of persistent SARS-CoV-2 infection: A case report and a scoping review of the literature

Author

Davide Marangoni, Roberta Maria Antonello, Marco Coppi, Marianna Palazzo, Luca Nassi, Noemi Streva, Laura Povolo, Francesca Malentacchi, Lorenzo Zammarchi, Gian Maria Rossolini, Alessandro Maria Vannucchi, Alessandro Bartoloni, and Michele Spinicci

Subjects
COVID-19, Antiviral, Remdesivir, Relapsing, Association, Infectious and parasitic diseases, RC109-216
Abstract

Immunocompromised patients still experience unpredictable courses of COVID-19, despite that effective vaccines and drugs against SARS-CoV-2 are now available. Antiviral combination regimens may have a role in SARS-CoV-2 infection in immunocompromised hosts, but current knowledge is still limited. We describe the case of a 73-year-old Italian man affected by follicular lymphoma with persistent SARS-CoV-2 infection who was successfully treated with co-administration of oral antivirals (10-day molnupiravir and nirmatrelvir/ritonavir). The therapy was well tolerated both from a clinical and biochemical standpoint, with no signs of toxicity. We also performed a scoping review, to sum up available knowledge on combined antiviral regimens including remdesivir, molnupiravir, or nirmatrelvir/ritonavir. Pending further studies on larger cohorts of patients, our report is consistent with available pre-clinical and clinical data, supporting the possible use of combination therapy in selected difficult-to-treat COVID-19 cases.

Published
2023
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46. Control of mother-to-child transmission of Chagas disease: the Tuscany Region model

Author

Anna Barbiero, Martina Mazzi, Antonia Mantella, Michele Salvatore Trotta, Gian Maria Rossolini, Alberto Antonelli, Patrizia Bordonaro, Maria Grazia Colao, Anna Rosa Speciale, Tullio Di Benedetto, Mariarosaria Di Tommaso, Elisabetta Mantengoli, Felice Petraglia, Luisa Galli, Marco Pezzati, Carlo Dani, Maria José Caldés Pinilla, Cecilia Berni, Bassam Dannaoui, Pedro Albajar Vinas, Alessandro Bartoloni, and Lorenzo Zammarchi

Subjects
chagas disease, pregnancy, screening, latin america, neglected tropical disease, Nursing, RT1-120
Abstract

Chagas disease is an endemic parasitosis in Latin America where the main route of transmission is vectorial. In Europe, due to migration phenomena, Chagas disease cases are increasing and the main way of transmission is mother-to-child, perpetuating the infection from one generation to the other. Congenital Chagas disease is in most cases asymptomatic at birth, but, if not diagnosed and treated early, it puts the child at risk of developing severe cardiac and gastrointestinal problems throughout life. According to the Regional Resolution throughout the territory of Tuscany, pregnant women born in continental Latin America (or born to a mother born in that area) should be offered free of charge serological test for Chagas disease during pregnancy or at delivery, with the main objective of controlling and stopping the transmission of the disease.

Published
2023
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47. Treatment of critically ill patients with cefiderocol for infections caused by multidrug-resistant pathogens: review of the evidence

Author

Pierluigi Viale, Christian E. Sandrock, Paula Ramirez, Gian Maria Rossolini, and Thomas P. Lodise

Subjects
Appropriate antibiotic, Cefiderocol, Critically ill, Dosing, Multidrug-resistant Gram-negative bacteria, Nosocomial pneumonia, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

Abstract Appropriate antibiotic treatment for critically ill patients with serious Gram-negative infections in the intensive care unit is crucial to minimize morbidity and mortality. Several new antibiotics have shown in vitro activity against carbapenem-resistant Enterobacterales (CRE) and difficult-to-treat resistant Pseudomonas aeruginosa. Cefiderocol is the first approved siderophore beta-lactam antibiotic with potent activity against multidrug-resistant, carbapenem-resistant, difficult-to-treat or extensively drug-resistant Gram-negative pathogens, which have limited treatment options. The spectrum of activity of cefiderocol includes drug-resistant strains of Acinetobacter baumannii, P. aeruginosa, Stenotrophomonas maltophilia, Achromobacter spp. and Burkholderia spp. and CRE that produce serine- and/or metallo-carbapenemases. Phase 1 studies established that cefiderocol achieves adequate concentration in the epithelial lining fluid in the lung and requires dosing adjustment for renal function, including patients with augmented renal clearance and continuous renal-replacement therapy (CRRT); no clinically significant drug–drug interactions are expected. The non-inferiority of cefiderocol versus high-dose, extended-infusion meropenem in all-cause mortality (ACM) rates at day 14 was demonstrated in the randomized, double-blind APEKS–NP Phase 3 clinical study in patients with nosocomial pneumonia caused by suspected or confirmed Gram-negative bacteria. Furthermore, the efficacy of cefiderocol was investigated in the randomized, open-label, pathogen-focused, descriptive CREDIBLE–CR Phase 3 clinical study in its target patient population with serious carbapenem-resistant Gram-negative infections, including hospitalized patients with nosocomial pneumonia, bloodstream infection/sepsis, or complicated urinary tract infections. However, a numerically greater ACM rate with cefiderocol compared with BAT led to the inclusion of a warning in US and European prescribing information. Cefiderocol susceptibility results obtained with commercial tests should be carefully evaluated due to current issues regarding their accuracy and reliability. Since its approval, real-world evidence in patients with multidrug-resistant and carbapenem-resistant Gram-negative bacterial infections suggests that cefiderocol can be efficacious in certain critically ill patient groups, such as those requiring mechanical ventilation for COVID-19 pneumonia with subsequently acquired Gram-negative bacterial superinfection, and patients with CRRT and/or extracorporeal membrane oxygenation. In this article, we review the microbiological spectrum, pharmacokinetics/pharmacodynamics, efficacy and safety profiles and real-world evidence for cefiderocol, and look at future considerations for its role in the treatment of critically ill patients with challenging Gram-negative bacterial infections.

Published
2023
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48. Photocatalytic reductive synthesis of α-functionalised amines and ethers

Author

Rossolini, Thomas and Dixon, Darren

Subjects
541, Photoredox Catalysis
Abstract

Chapter 1 introduces fundamental notions of photocatalysis as well as its evolution into mod- ern photoredox catalysis with seminal contributions of the past decade. Chapter 2 describes the development of a photocatalytic three-component one-pot umpolung methodology for the diastereoselective synthesis of 1,3-diamines based on the in situ formation of nucleophilic α-amino radicals species. The method provides access to a broad range of 1,3- diamine products with a wide variety of functionalities and demonstrates its application as an entry point to bioactive molecules. Chapter 3 details the design and application of a reductive synthetic methodolgy for the construction of challenging α-tertiary dialkyl ether compounds from readily accessible starting materials. The coupling approach pivots on a novel photocatalytic single-electron reduction of oxocarbenium ion intermediates. Chapter 4 illustrates our efforts towards the development of a photocatalyst-free approach for the synthesis of biologically relevant α-trifluoromethyl-α-amino acid derivatives. Visible light-mediated homolytic cleavage of substituted radical precursors (RPs) is used to generate open-shell alkyl species which are believed to engage in radical addition reactions.

Published
2021

50. Utility of bronchoalveolar lavage for COVID-19: a perspective from the Dragon consortium

Author

Sara Tomassetti, Luca Ciani, Valentina Luzzi, Leonardo Gori, Marco Trigiani, Leonardo Giuntoli, Federico Lavorini, Venerino Poletti, Claudia Ravaglia, Alfons Torrego, Fabien Maldonado, Robert Lentz, Francesco Annunziato, Laura Maggi, Gian Maria Rossolini, Simona Pollini, Ombretta Para, Greta Ciurleo, Alessandro Casini, Laura Rasero, Alessandro Bartoloni, Michele Spinicci, Mohammed Munavvar, Stefano Gasparini, Camilla Comin, Marco Matucci Cerinic, Anna Peired, Monique Henket, Benoit Ernst, Renaud Louis, Jean-louis Corhay, Cosimo Nardi, and Julien Guiot

Subjects
COVID-19, bronchoalveolar lavage, interstitial pneumonia, infections, interventional pulmonology, Medicine (General), R5-920
Abstract

Diagnosing COVID-19 and treating its complications remains a challenge. This review reflects the perspective of some of the Dragon (IMI 2-call 21, #101005122) research consortium collaborators on the utility of bronchoalveolar lavage (BAL) in COVID-19. BAL has been proposed as a potentially useful diagnostic tool to increase COVID-19 diagnosis sensitivity. In both critically ill and non-critically ill COVID-19 patients, BAL has a relevant role in detecting other infections or supporting alternative diagnoses and can change management decisions in up to two-thirds of patients. BAL is used to guide steroid and immunosuppressive treatment and to narrow or discontinue antibiotic treatment, reducing the use of unnecessary broad antibiotics. Moreover, cellular analysis and novel multi-omics techniques on BAL are of critical importance for understanding the microenvironment and interaction between epithelial cells and immunity, revealing novel potential prognostic and therapeutic targets. The BAL technique has been described as safe for both patients and healthcare workers in more than a thousand procedures reported to date in the literature. Based on these preliminary studies, we recognize that BAL is a feasible procedure in COVID-19 known or suspected cases, useful to properly guide patient management, and has great potential for research.

Published
2024
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