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1. High-Fidelity 3D Lung CT Synthesis in ARDS Swine Models Using Score-Based 3D Residual Diffusion Models

Author

Yoon, Siyeop, Oh, Yujin, Li, Xiang, Xin, Yi, Cereda, Maurizio, and Li, Quanzheng

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

Acute respiratory distress syndrome (ARDS) is a severe condition characterized by lung inflammation and respiratory failure, with a high mortality rate of approximately 40%. Traditional imaging methods, such as chest X-rays, provide only two-dimensional views, limiting their effectiveness in fully assessing lung pathology. Three-dimensional (3D) computed tomography (CT) offers a more comprehensive visualization, enabling detailed analysis of lung aeration, atelectasis, and the effects of therapeutic interventions. However, the routine use of CT in ARDS management is constrained by practical challenges and risks associated with transporting critically ill patients to remote scanners. In this study, we synthesize high-fidelity 3D lung CT from 2D generated X-ray images with associated physiological parameters using a score-based 3D residual diffusion model. Our preliminary results demonstrate that this approach can produce high-quality 3D CT images that are validated with ground truth, offering a promising solution for enhancing ARDS management., Comment: 5 page, 3 figures, Submitted to SPIE 2025-Medical Imaging

Published
2024

2. The 2023 Dengue Outbreak in Lombardy, Italy: A One-Health Perspective

Author

Rovida, Francesca, Faccini, Marino, Grané, Carla Molina, Cassaniti, Irene, Senatore, Sabrina, Rossetti, Eva, Scardina, Giuditta, Piazza, Manuela, Campanini, Giulia, Lilleri, Daniele, Paolucci, Stefania, Ferrari, Guglielmo, Piralla, Antonio, Defilippo, Francesco, Lelli, Davide, Moreno, Ana, Vezzosi, Luigi, Attanasi, Federica, Marzia, Soresini, Manuela, Barozzi, Cerutti, Lorenzo, Paglia, Stefano, Regazzetti, Angelo, Marcacci, Maurilia, Di Donato, Guido, Farioli, Marco, Manica, Mattia, Poletti, Piero, Lavazza, Antonio, Bonini, Maira, Merler, Stefano, Baldanti, Fausto, Cereda, Danilo, and network, Lombardy Dengue

Subjects
Quantitative Biology - Populations and Evolution
Abstract

Introduction. Here we reported the virological, entomological and epidemiological characteristics of the large autochthonous outbreak of dengue (DENV) occurred in a small village of the Lombardy region (Northern Italy) during summer 2023. Methods. After the diagnosis of the first autochthonous case on 18 August 2023, public health measures, including epidemiological investigation and vector control measures, were carried out. A serological screening for DENV antibodies detection was offered to the population. In the case of positive DENV IgM, a second sample was collected to detect DENV RNA and verify seroconversion. Entomological and epidemiological investigations were also performed. A modeling analysis was conducted to estimate the dengue generation time, transmission potential, distance of transmission, and assess diagnostic delays. Results. Overall, 416 subjects participated to the screening program and 20 were identified as DENV-1 cases (15 confirmed and 5 probable). In addition, DENV-1 infection was diagnosed in 24 symptomatic subjects referred to the local Emergency Room Department for suggestive symptoms and 1 case was identified through blood donation screening. The average generation time was estimated to be 18.3 days (95% CI: 13.1-23.5 days). R0 was estimated at 1.31 (95% CI: 0.76-1.98); 90% of transmission occurred within 500m. Entomological investigations performed in 46 pools of mosquitoes revealed the presence of only one positive pool for DENV-1. Discussion. This report highlights the importance of synergic surveillance, including virological, entomological and public health measures to control the spread of arboviral infections.

Published
2024

3. Training on the Fly: On-device Self-supervised Learning aboard Nano-drones within 20 mW

Author

Cereda, Elia, Giusti, Alessandro, and Palossi, Daniele

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Systems and Control
Abstract

Miniaturized cyber-physical systems (CPSes) powered by tiny machine learning (TinyML), such as nano-drones, are becoming an increasingly attractive technology. Their small form factor (i.e., ~10cm diameter) ensures vast applicability, ranging from the exploration of narrow disaster scenarios to safe human-robot interaction. Simple electronics make these CPSes inexpensive, but strongly limit the computational, memory, and sensing resources available on board. In real-world applications, these limitations are further exacerbated by domain shift. This fundamental machine learning problem implies that model perception performance drops when moving from the training domain to a different deployment one. To cope with and mitigate this general problem, we present a novel on-device fine-tuning approach that relies only on the limited ultra-low power resources available aboard nano-drones. Then, to overcome the lack of ground-truth training labels aboard our CPS, we also employ a self-supervised method based on ego-motion consistency. Albeit our work builds on top of a specific real-world vision-based human pose estimation task, it is widely applicable for many embedded TinyML use cases. Our 512-image on-device training procedure is fully deployed aboard an ultra-low power GWT GAP9 System-on-Chip and requires only 1MB of memory while consuming as low as 19mW or running in just 510ms (at 38mW). Finally, we demonstrate the benefits of our on-device learning approach by field-testing our closed-loop CPS, showing a reduction in horizontal position error of up to 26% vs. a non-fine-tuned state-of-the-art baseline. In the most challenging never-seen-before environment, our on-device learning procedure makes the difference between succeeding or failing the mission., Comment: This paper has been accepted for publication in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. Copyright 2024 IEEE

Published
2024

4. Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan.

Author

Rezoagli, Emanuele, Xin, Yi, Signori, Davide, Sun, Wenli, Gerard, Sarah, Delucchi, Kevin, Magliocca, Aurora, Vitale, Giovanni, Giacomini, Matteo, Mussoni, Linda, Montomoli, Jonathan, Subert, Matteo, Ponti, Alessandra, Spadaro, Savino, Poli, Giancarla, Casola, Francesco, Herrmann, Jacob, Foti, Giuseppe, Calfee, Carolyn, Laffey, John, Bellani, Giacomo, and Cereda, Maurizio

Subjects
Artificial intelligence, COVID-19, Computed tomography, Latent class analysis, Respiratory failure, Subphenotypes, Humans, COVID-19, Tomography, X-Ray Computed, Female, Male, Middle Aged, Phenotype, Lung, Aged, Respiratory Insufficiency, Cohort Studies, Adult
Abstract

BACKGROUND: Automated analysis of lung computed tomography (CT) scans may help characterize subphenotypes of acute respiratory illness. We integrated lung CT features measured via deep learning with clinical and laboratory data in spontaneously breathing subjects to enhance the identification of COVID-19 subphenotypes. METHODS: This is a multicenter observational cohort study in spontaneously breathing patients with COVID-19 respiratory failure exposed to early lung CT within 7 days of admission. We explored lung CT images using deep learning approaches to quantitative and qualitative analyses; latent class analysis (LCA) by using clinical, laboratory and lung CT variables; regional differences between subphenotypes following 3D spatial trajectories. RESULTS: Complete datasets were available in 559 patients. LCA identified two subphenotypes (subphenotype 1 and 2). As compared with subphenotype 2 (n = 403), subphenotype 1 patients (n = 156) were older, had higher inflammatory biomarkers, and were more hypoxemic. Lungs in subphenotype 1 had a higher density gravitational gradient with a greater proportion of consolidated lungs as compared with subphenotype 2. In contrast, subphenotype 2 had a higher density submantellar-hilar gradient with a greater proportion of ground glass opacities as compared with subphenotype 1. Subphenotype 1 showed higher prevalence of comorbidities associated with endothelial dysfunction and higher 90-day mortality than subphenotype 2, even after adjustment for clinically meaningful variables. CONCLUSIONS: Integrating lung-CT data in a LCA allowed us to identify two subphenotypes of COVID-19, with different clinical trajectories. These exploratory findings suggest a role of automated imaging characterization guided by machine learning in subphenotyping patients with respiratory failure. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04395482. Registration date: 19/05/2020.

Published
2024

5. Fusing Multi-sensor Input with State Information on TinyML Brains for Autonomous Nano-drones

Author

Crupi, Luca, Cereda, Elia, and Palossi, Daniele

Subjects
Computer Science - Robotics
Abstract

Autonomous nano-drones (~10 cm in diameter), thanks to their ultra-low power TinyML-based brains, are capable of coping with real-world environments. However, due to their simplified sensors and compute units, they are still far from the sense-and-act capabilities shown in their bigger counterparts. This system paper presents a novel deep learning-based pipeline that fuses multi-sensorial input (i.e., low-resolution images and 8x8 depth map) with the robot's state information to tackle a human pose estimation task. Thanks to our design, the proposed system -- trained in simulation and tested on a real-world dataset -- improves a state-unaware State-of-the-Art baseline by increasing the R^2 regression metric up to 0.10 on the distance's prediction.

Published
2024

6. On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors

Author

Cereda, Elia, Rusci, Manuele, Giusti, Alessandro, and Palossi, Daniele

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract

Sub-\SI{50}{\gram} nano-drones are gaining momentum in both academia and industry. Their most compelling applications rely on onboard deep learning models for perception despite severe hardware constraints (\ie sub-\SI{100}{\milli\watt} processor). When deployed in unknown environments not represented in the training data, these models often underperform due to domain shift. To cope with this fundamental problem, we propose, for the first time, on-device learning aboard nano-drones, where the first part of the in-field mission is dedicated to self-supervised fine-tuning of a pre-trained convolutional neural network (CNN). Leveraging a real-world vision-based regression task, we thoroughly explore performance-cost trade-offs of the fine-tuning phase along three axes: \textit{i}) dataset size (more data increases the regression performance but requires more memory and longer computation); \textit{ii}) methodologies (\eg fine-tuning all model parameters vs. only a subset); and \textit{iii}) self-supervision strategy. Our approach demonstrates an improvement in mean absolute error up to 30\% compared to the pre-trained baseline, requiring only \SI{22}{\second} fine-tuning on an ultra-low-power GWT GAP9 System-on-Chip. Addressing the domain shift problem via on-device learning aboard nano-drones not only marks a novel result for hardware-limited robots but lays the ground for more general advancements for the entire robotics community., Comment: \c{opyright} 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Published
2024

7. Medical occurrence and safety of SARS-CoV-2 vaccination outside of the hospital setting

Author

Ferrari, Fiorenza, Sodi, Federico, Madotto, Fabiana, Carlesso, Eleonora, Florio, Gaetano, Pelliccia, Maria Rosa, Laquintana, Dario, Bisesti, Alberto, Piatti, Alessandra, Letzgus, Matteo, Tiwana, Navpreet, Jachetti, Alessandro, Mancarella, Marta, Cereda, Danilo, Leoni, Olivia, Borriello, Catia Rosanna, Chiappa, Laura, Sottocorno, Marcello, Costantino, Giorgio, Zanella, Alberto, and Grasselli, Giacomo

Published
2024
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8. Predicting the Risk of Morbidity by GLIM-Based Nutritional Assessment and Body Composition Analysis in Oncologic Abdominal Surgery in the Context of Enhanced Recovery Programs: The PHase Angle Value in Abdominal Surgery (PHAVAS) Study

Author

Sandini, Marta, Gianotti, Luca, Paiella, Salvatore, Bernasconi, Davide P., Roccamatisi, Linda, Famularo, Simone, Donadon, Matteo, Di Lucca, Gabriele, Cereda, Marco, Baccalini, Edoardo, Capretti, Giovanni, Nappo, Gennaro, Casirati, Amanda, Braga, Marco, Zerbi, Alessandro, Torzilli, Guido, Bassi, Claudio, Salvia, Roberto, Cereda, Emanuele, and Caccialanza, Riccardo

Published
2024
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9. A Sim-to-Real Deep Learning-based Framework for Autonomous Nano-drone Racing

Author

Lamberti, Lorenzo, Cereda, Elia, Abbate, Gabriele, Bellone, Lorenzo, Morinigo, Victor Javier Kartsch, Barcis, Michał, Barcis, Agata, Giusti, Alessandro, Conti, Francesco, and Palossi, Daniele

Subjects
Computer Science - Robotics, Electrical Engineering and Systems Science - Image and Video Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

Autonomous drone racing competitions are a proxy to improve unmanned aerial vehicles' perception, planning, and control skills. The recent emergence of autonomous nano-sized drone racing imposes new challenges, as their ~10cm form factor heavily restricts the resources available onboard, including memory, computation, and sensors. This paper describes the methodology and technical implementation of the system winning the first autonomous nano-drone racing international competition: the IMAV 2022 Nanocopter AI Challenge. We developed a fully onboard deep learning approach for visual navigation trained only on simulation images to achieve this goal. Our approach includes a convolutional neural network for obstacle avoidance, a sim-to-real dataset collection procedure, and a navigation policy that we selected, characterized, and adapted through simulation and actual in-field experiments. Our system ranked 1st among seven competing teams at the competition. In our best attempt, we scored 115m of traveled distance in the allotted 5-minute flight, never crashing while dodging static and dynamic obstacles. Sharing our knowledge with the research community, we aim to provide a solid groundwork to foster future development in this field., Comment: 8 pages, 10 Figures, 3 Tables, This paper has been accepted for publication in the IEEE Robotics and Automation Letters (RAL). Copyright 2023 IEEE

Published
2023
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10. Leveraging SAR and Optical Remote Sensing for Enhanced Biomass Estimation in the Amazon with Random Forest and XGBoost Models

Author

R. Antunes, L. Junior, G. Costa, R. Feitosa, E. D. S. Bias, A. Cereda Junior, C. Almeida, L. E. C. La Rosa, P. Happ, and L. Chiamulera

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

This study addresses the challenge of estimating above-ground biomass (AGB) in the Amazon rainforest by developing a reference geographical database, which provides the ground truth, and comparing the relative importance of using Synthetic Aperture Radar (SAR) and optical remote sensing data to automatically infer AGB. In the experiments reported in this article, we assessed how those two remote sensing data sources impact the accuracy of AGB estimates produced by regression models built with Random Forest (RF) and Extreme Gradient Boosting (XGBoost). The research involved compiling a comprehensive database from many available forest inventories, integrating parcel- and tree-level data to enable precise biomass estimation. The methodology included setting up a spatial data analysis environment, standardizing data, and implementing an experimental protocol with feature selection and leave-one-out cross-validation. The results demonstrate that both kinds of data, i.e., SAR and optical, and their combination can be used for estimating AGB, providing valuable insights for forest management and climate change mitigation efforts. The reference database is available upon request to the corresponding authors.

Published
2024
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11. Inferring disease course from differential exon usage in the wide titinopathy spectrum

Author

Maria Francesca Di Feo, Ali Oghabian, Ella Nippala, Mathias Gautel, Heinz Jungbluth, Francesca Forzano, Edoardo Malfatti, Claudia Castiglioni, Ilona Krey, David Gomez Andres, Angela F. Brady, Maria Iascone, Anna Cereda, Lidia Pezzani, Daniel Natera De Benito, Andres Nascimiento Osorio, Berta Estévez Arias, Sergei A. Kurbatov, Tania Attie‐Bitach, Sheela Nampoothiri, Erin Ryan, Michelle Morrow, Svetlana Gorokhova, Brigitte Chabrol, Juha Sinisalo, Heli Tolppanen, Johanna Tolva, Francina Munell, Jessica Camacho Soriano, Maria Angeles Sanchez Duran, Mridul Johari, Homa Tajsharghi, Peter Hackman, Bjarne Udd, and Marco Savarese

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Objective Biallelic titin truncating variants (TTNtv) have been associated with a wide phenotypic spectrum, ranging from complex prenatal muscle diseases with dysmorphic features to adult‐onset limb‐girdle muscular dystrophy, with or without cardiac involvement. Given the size and complexity of TTN, reaching an unequivocal molecular diagnosis and precise disease prognosis remains challenging. Methods In this case series, 12 unpublished cases and one already published case with biallelic TTNtv were collected from multiple international medical centers between November 2022 and September 2023. TTN mutations were detected through exome or genome sequencing. Information about familial and personal clinical history was collected in a standardized form. RNA‐sequencing and analysis of TTN exon usage were performed on an internal sample cohort including postnatal skeletal muscles, fetal skeletal muscles, postnatal heart muscles, and fetal heart muscles. In addition, publicly available RNA‐sequencing data was retrieved from ENCODE. Results We generated new RNA‐seq data on TTN exons and identified genotype–phenotype correlations with prognostic implications for each titinopathy patient (whether worsening or improving in prenatal and postnatal life) using percentage spliced in (PSI) data for the involved exons. Interestingly, thanks to exon usage, we were also able to rule out a titinopathy diagnosis in one prenatal case. Interpretation This study demonstrates that exon usage provides valuable insights for a more exhaustive clinical interpretation of TTNtv; additionally, it may serve as a model for implementing personalized medicine in many other genetic diseases, since most genes undergo alternative splicing.

Published
2024
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12. Impact of PRECEDE–PROCEED Model Audits in Cancer Screening Programs in Lombardy Region: Supporting Equity and Quality Improvement

Author

Stefano Odelli, Margherita Zeduri, Maria Rosa Schivardi, Davide Archi, Liliana Coppola, Roberto Genco Russo, Maristella Moscheni, Elena Tettamanzi, Fabio Terragni, Michela Viscardi, Valentina Vitale, Anna Odone, Danilo Cereda, and Silvia Deandrea

Subjects
PRECEDE–PROCEED model, cancer screening, audit, Lombardy region, equity, quality improvement, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Background: Health disparities related to socio-economic factors impact access to preventive health interventions. The PRECEDE–PROCEED model, a multidimensional approach to health promotion, has been adapted to optimise cancer screening programs in Lombardy, Italy, addressing these disparities. Methods: This study evaluated the application of systemic audits based on the PRECEDE–PROCEED model across Lombardy cancer screening programs. A systematic region-wide audit was performed in 2019, and follow-up audits were performed in 2022–2023. Data were collected using structured analysis methodologies, including epidemiological, behavioural, and organisational assessments. Results: The 2019 audit showed strengths in participation and quality standards but identified challenges in cervical cancer screening coverage and waiting times for assessments. Improvements plans included the digitisation of processes and stakeholder engagement. The 2022–2023 audits reported increased coverage for breast and colorectal screenings, but a slight decline in participation rates and examination coverage. Organisational improvements were noted, yet gaps in training and equity-targeted actions remained. Conclusion: The PRECEDE–PROCEED model audits led to notable improvements in the quality and equity of cancer screening programs in Lombardy. Sustained focus on digital integration, continuous re-training, and targeted equity interventions is essential for further progress.

Published
2024
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13. Sim-to-Real Vision-depth Fusion CNNs for Robust Pose Estimation Aboard Autonomous Nano-quadcopter

Author

Crupi, Luca, Cereda, Elia, Giusti, Alessandro, and Palossi, Daniele

Subjects
Computer Science - Robotics
Abstract

Nano-quadcopters are versatile platforms attracting the interest of both academia and industry. Their tiny form factor, i.e., $\,$10 cm diameter, makes them particularly useful in narrow scenarios and harmless in human proximity. However, these advantages come at the price of ultra-constrained onboard computational and sensorial resources for autonomous operations. This work addresses the task of estimating human pose aboard nano-drones by fusing depth and images in a novel CNN exclusively trained in simulation yet capable of robust predictions in the real world. We extend a commercial off-the-shelf (COTS) Crazyflie nano-drone -- equipped with a 320$\times$240 px camera and an ultra-low-power System-on-Chip -- with a novel multi-zone (8$\times$8) depth sensor. We design and compare different deep-learning models that fuse depth and image inputs. Our models are trained exclusively on simulated data for both inputs, and transfer well to the real world: field testing shows an improvement of 58% and 51% of our depth+camera system w.r.t. a camera-only State-of-the-Art baseline on the horizontal and angular mean pose errors, respectively. Our prototype is based on COTS components, which facilitates reproducibility and adoption of this novel class of systems.

Published
2023

14. Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones

Author

Cereda, Elia, Giusti, Alessandro, and Palossi, Daniele

Subjects
Computer Science - Robotics, Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

Palm-sized nano-drones are an appealing class of edge nodes, but their limited computational resources prevent running large deep-learning models onboard. Adopting an edge-fog computational paradigm, we can offload part of the computation to the fog; however, this poses security concerns if the fog node, or the communication link, can not be trusted. To tackle this concern, we propose a novel distributed edge-fog execution scheme that validates fog computation by redundantly executing a random subnetwork aboard our nano-drone. Compared to a State-of-the-Art visual pose estimation network that entirely runs onboard, a larger network executed in a distributed way improves the $R^2$ score by +0.19; in case of attack, our approach detects it within 2s with 95% probability., Comment: This paper has been accepted for publication in the EWSN 2023 conference. \copyright 2023 ACM

Published
2023

15. ARF1-related disorder: phenotypic and molecular spectrum.

Author

de Sainte Agathe, Jean-Madeleine, Pode-Shakked, Ben, Naudion, Sophie, Michaud, Vincent, Arveiler, Benoit, Fergelot, Patricia, Delmas, Jean, Keren, Boris, Poirsier, Céline, Alkuraya, Fowzan, Tabarki, Brahim, Bend, Eric, Davis, Kellie, Bebin, Martina, Thompson, Michelle, Bryant, Emily, Wagner, Matias, Hannibal, Iris, Lenberg, Jerica, Krenn, Martin, Wigby, Kristen, Friedman, Jennifer, Iascone, Maria, Cereda, Anna, Miao, Térence, LeGuern, Eric, Sherr, Elliott, Caluseriu, Oana, Tidwell, Timothy, Bayrak-Toydemir, Pinar, Hagedorn, Caroline, Brugger, Melanie, Vill, Katharina, Morneau-Jacob, Francois-Dominique, Chung, Wendy, Weaver, Kathryn, Owens, Joshua, Husami, Ammar, Chaudhari, Bimal, Stone, Brandon, Burns, Katie, Li, Rachel, de Lange, Iris, Biehler, Margaux, Ginglinger, Emmanuelle, Gérard, Bénédicte, Stottmann, Rolf, Trimouille, Aurélien, and Argilli, Emanuela

Subjects
epilepsy, human genetics, sequence analysis, DNA, Humans, Brain, Genotype, Intellectual Disability, Microcephaly, Periventricular Nodular Heterotopia, Phenotype, Seizures
Abstract

PURPOSE: ARF1 was previously implicated in periventricular nodular heterotopia (PVNH) in only five individuals and systematic clinical characterisation was not available. The aim of this study is to provide a comprehensive description of the phenotypic and genotypic spectrum of ARF1-related neurodevelopmental disorder. METHODS: We collected detailed phenotypes of an international cohort of individuals (n=17) with ARF1 variants assembled through the GeneMatcher platform. Missense variants were structurally modelled, and the impact of several were functionally validated. RESULTS: De novo variants (10 missense, 1 frameshift, 1 splice altering resulting in 9 residues insertion) in ARF1 were identified among 17 unrelated individuals. Detailed phenotypes included intellectual disability (ID), microcephaly, seizures and PVNH. No specific facial characteristics were consistent across all cases, however microretrognathia was common. Various hearing and visual defects were recurrent, and interestingly, some inflammatory features were reported. MRI of the brain frequently showed abnormalities consistent with a neuronal migration disorder. CONCLUSION: We confirm the role of ARF1 in an autosomal dominant syndrome with a phenotypic spectrum including severe ID, microcephaly, seizures and PVNH due to impaired neuronal migration.

Published
2023

17. RAS-ON inhibition overcomes clinical resistance to KRAS G12C-OFF covalent blockade

Author

Nokin, Marie-Julie, Mira, Alessia, Patrucco, Enrico, Ricciuti, Biagio, Cousin, Sophie, Soubeyran, Isabelle, San José, Sonia, Peirone, Serena, Caizzi, Livia, Vietti Michelina, Sandra, Bourdon, Aurelien, Wang, Xinan, Alvarez-Villanueva, Daniel, Martínez-Iniesta, María, Vidal, August, Rodrigues, Telmo, García-Macías, Carmen, Awad, Mark M., Nadal, Ernest, Villanueva, Alberto, Italiano, Antoine, Cereda, Matteo, Santamaría, David, and Ambrogio, Chiara

Published
2024
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19. Derivation and external validation of predictive models for invasive mechanical ventilation in intensive care unit patients with COVID-19

Author

Maia, Gabriel, Martins, Camila Marinelli, Marques, Victoria, Christovam, Samantha, Prado, Isabela, Moraes, Bruno, Rezoagli, Emanuele, Foti, Giuseppe, Zambelli, Vanessa, Cereda, Maurizio, Berra, Lorenzo, Rocco, Patricia Rieken Macedo, Cruz, Mônica Rodrigues, Samary, Cynthia dos Santos, Guimarães, Fernando Silva, and Silva, Pedro Leme

Published
2024
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20. Huntington’s disease cellular phenotypes are rescued non-cell autonomously by healthy cells in mosaic telencephalic organoids

Author

Galimberti, Maura, Nucera, Maria R., Bocchi, Vittoria D., Conforti, Paola, Vezzoli, Elena, Cereda, Matteo, Maffezzini, Camilla, Iennaco, Raffaele, Scolz, Andrea, Falqui, Andrea, Cordiglieri, Chiara, Cremona, Martina, Espuny-Camacho, Ira, Faedo, Andrea, Felsenfeld, Dan P., Vogt, Thomas F., Ranzani, Valeria, Zuccato, Chiara, Besusso, Dario, and Cattaneo, Elena

Published
2024
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22. Breast cancer patient-derived organoids for the investigation of patient-specific tumour evolution

Author

Mazzucchelli, Serena, Signati, Lorena, Messa, Letizia, Franceschini, Alma, Bonizzi, Arianna, Castagnoli, Lorenzo, Gasparini, Patrizia, Consolandi, Clarissa, Mangano, Eleonora, Pelucchi, Paride, Cifola, Ingrid, Camboni, Tania, Severgnini, Marco, Villani, Laura, Tagliaferri, Barbara, Carelli, Stephana, Pupa, Serenella M., Cereda, Cristina, and Corsi, Fabio

Published
2024
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24. Goodbye Hartmann trial: a prospective, international, multicenter, observational study on the current use of a surgical procedure developed a century ago

Author

Perrone, Gennaro, Giuffrida, Mario, Abu-Zidan, Fikri, Kruger, Vitor F., Livrini, Marco, Petracca, Gabriele Luciano, Rossi, Giorgio, Tarasconi, Antonio, Tian, Brian W. C. A., Bonati, Elena, Mentz, Ricardo, Mazzini, Federico N., Campana, Juan P., Gasser, Elisabeth, Kafka-Ritsch, Reinhold, Felsenreich, Daniel M., Dawoud, Christopher, Riss, Stefan, Gomes, Carlos Augusto, Gomes, Felipe Couto, Gonzaga, Ricardo Alessandro Teixeira, Canton, Cassio Alfred Brattig, Pereira, Bruno Monteiro, Fraga, Gustavo P., Zem, Leticia Gonçalves, Cordeiro-Fonseca, Vinicius, de Mesquita Tauil, Renato, Atanasov, Boyko, Belev, Nikolay, Kovachev, Nikola, Meléndez, L. Juan José, Dimova, Ana, Dimov, Stefan, Zelić, Zdravko, Augustin, Goran, Bogdanić, Branko, Morić, Trpimir, Chouillard, Elie, Bajul, Melinda, De Simone, Belinda, Panis, Yves, Esposito, Francesco, Notarnicola, Margherita, Lauka, Lelde, Fabbri, Anna, Hentati, Hassen, Fnaiech, Iskander, Aurélien, Venara, Bougard, Marie, Roulet, Maxime, Demetrashvili, Zaza, Pipia, Irakli, Merabishvili, Giorgi, Bouliaris, Konstantinos, Koukoulis, Georgios, Doudakmanis, Christos, Xenaki, Sofia, Chrysos, Emmanuel, Kokkinakis, Stamatios, Vassiliu, Panteleimon, Michalopoulos, Nikolaos, Margaris, Ioannis, Kechagias, Aristotelis, Avgerinos, Konstantinos, Katunin, Jevgeni, Lostoridis, Eftychios, Nagorni, Eleni-Aikaterini, Pujante, Antonio, Mulita, Francesk, Maroulis, Ioannis, Vailas, Michail, Marinis, Athanasios, Siannis, Ioannis, Bourbouteli, Eirini, Manatakis, Dimitrios K., Tasis, Nikolaos, Acheimastos, Vasileios, Maria, Sotiropoulou, Stylianos, Kapiris, Kuzeridis, Harilaos, Korkolis, Dimitrios, Fradelos, Evangelos, Kavalieratos, George, Petropoulou, Thalia, Polydorou, Andreas, Papacostantinou, Ioannis, Triantafyllou, Tania, Kimpizi, Despina, Theodorou, Dimitrios, Toutouzas, Konstantinos, Chamzin, Alexandros, Frountzas, Maximos, Schizas, Dimitrios, Karavokyros, Ioannis, Syllaios, Athanasios, Charalabopoulos, Alexandros, Boura, Maria, Baili, Efstratia, Ioannidis, Orestis, Loutzidou, Lydia, Anestiadou, Elissavet, Tsouknidas, Ioannis, Petrakis, Georgios, Polenta, Eleni, Bains, Lovenish, Gupta, Rahul, Singh, Sudhir K., Khanduri, Archana, Bala, Miklosh, Kedar, Asaf, Pisano, Marcello, Podda, Mauro, Pisanu, Adolfo, Martines, Gennaro, Trigiante, Giuseppe, Lantone, Giuliano, Agrusa, Antonino, Di Buono, Giuseppe, Buscemi, Salvatore, Veroux, Massimiliano, Gioco, Rossella, Veroux, Gastone, Oragano, Luigi, Zonta, Sandro, Lovisetto, Federico, Feo, Carlo V., Pesce, Antonio, Fabbri, Nicolò, Lantone, Giulio, Marino, Fabio, Perrone, Fabrizio, Vincenti, Leonardo, Papagni, Vincenzo, Picciariello, Arcangelo, Rossi, Stefano, Picardi, Biagio, Del Monte, Simone Rossi, Visconti, Diego, Osella, Giulia, Petruzzelli, Luca, Pignata, Giusto, Andreuccetti, Jacopo, D’Alessio, Rossella, Buonfantino, Massimo, Guaitoli, Eleonora, Spinelli, Stefano, Sampietro, Gianluca Matteo, Corbellini, Carlo, Lorusso, Leonardo, Frontali, Alice, Pezzoli, Isabella, Bonomi, Alessandro, Chierici, Andrea, Cotsoglou, Christian, Manca, Giuseppe, Delvecchio, Antonella, Musa, Nicola, Casati, Massimiliano, Letizia, Laface, Abate, Emmanuele, Ercolani, Giorgio, D’Acapito, Fabrizio, Solaini, Leonardo, Guercioni, Gianluca, Cicconi, Simone, Sasia, Diego, Borghi, Felice, Giraudo, Giorgio, Sena, Giuseppe, Castaldo, Pasquale, Cardamone, Eugenia, Portale, Giuseppe, Zuin, Matteo, Spolverato, Ylenia, Esposito, Marialusia, Isernia, Roberta Maria, Di Salvo, Maria, Manunza, Romina, Esposito, Giuseppe, Agus, Marcello, Asti, Emanuele Luigi Giuseppe, Bernardi, Daniele Tiziano, Tonucci, Tommaso Panici, Luppi, Davide, Casadei, Massimiliano, Bonilauri, Stefano, Pezzolla, Angela, Panebianco, Annunziata, Laforgia, Rita, De Luca, Maurizio, Zese, Monica, Parini, Dario, Jovine, Elio, De Sario, Giuseppina, Lombardi, Raffaele, Aprea, Giovanni, Palomba, Giuseppe, Capuano, Marianna, Argenio, Giulio, Orio, Gianluca, Armellino, Mariano Fortunato, Troian, Marina, Guerra, Martina, Nagliati, Carlo, Biloslavo, Alan, Germani, Paola, Aizza, Giada, Monsellato, Igor, Chahrour, Ali Chaouki, Anania, Gabriele, Bombardini, Cristina, Bagolini, Francesco, Sganga, Gabriele, Fransvea, Pietro, Bianchi, Valentina, Boati, Paolo, Ferrara, Francesco, Palmieri, Francesco, Cianci, Pasquale, Gattulli, Domenico, Restini, Enrico, Cillara, Nicola, Cannavera, Alessandro, Nita, Gabriela Elisa, Sarnari, Jlenia, Roscio, Francesco, Clerici, Federico, Scandroglio, Ildo, Berti, Stefano, Cadeo, Alessandro, Filippelli, Alice, Conti, Luigi, Grassi, Carmine, Cattaneo, Gaetano Maria, Pighin, Marina, Papis, Davide, Gambino, Giovanni, Bertino, Vanessa, Schifano, Domenico, Prando, Daniela, Fogato, Luisella, Cavallo, Fabio, Ansaloni, Luca, Picheo, Roberto, Pontarolo, Nicholas, Depalma, Norma, Spampinato, Marcello, D’Ugo, Stefano, Lepre, Luca, Capponi, Michela Giulii, Campa, Rossella Domenica, Sarro, Giuliano, Dinuzzi, Vincenza Paola, Olmi, Stefano, Uccelli, Matteo, Ferrari, Davide, Inama, Marco, Moretto, Gianluigi, Fontana, Michele, Favi, Francesco, Picariello, Erika, Rampini, Alessia, Barberis, Andrea, Azzinnaro, Antonio, Oliva, Alba, Totaro, Luigi, Benzoni, Ilaria, Ranieri, Valerio, Capolupo, Gabriella Teresa, Carannante, Filippo, Caricato, Marco, Ronconi, Maurizio, Casiraghi, Silvia, Casole, Giovanni, Pantalone, Desire, Alemanno, Giovanni, Scheiterle, Maximilian, Ceresoli, Marco, Cereda, Marco, Fumagalli, Chiara, Zanzi, Federico, Bolzon, Stefano, Guerra, Enrico, Lecchi, Francesca, Cellerino, Paola, Ardito, Antonella, Scaramuzzo, Rosa, Balla, Andrea, Lepiane, Pasquale, Tartaglia, Nicola, Ambrosi, Antonio, Pavone, Giovanna, Palini, Gian Marco, Veneroni, Simone, Garulli, Gianluca, Ricci, Claudio, Torre, Beatrice, Russo, Iris Shari, Rottoli, Matteo, Tanzanu, Marta, Belvedere, Angela, Milone, Marco, Manigrasso, Michele, De Palma, Giovanni Domenico, Piccoli, Micaela, Pattacini, Gianmaria Casoni, Magnone, Stefano, Bertoli, Paolo, Pisano, Michele, Massucco, Paolo, Palisi, Marco, Luzzi, Andrea-Pierre, Fleres, Francesco, Clarizia, Guglielmo, Spolini, Alessandro, Kobe, Yoshiro, Toma, Takayuki, Shimamura, Fumihiko, Parker, Robert, Ranketi, Sinkeet, Mitei, Mercy, Svagzdys, Saulius, Pauzas, Henrikas, Zilinskas, Justas, Poskus, Tomas, Kryzauskas, Marius, Jakubauskas, Matas, Zakaria, Andee Dzulkarnaen, Zakaria, Zaidi, Wong, Michael Pak-Kai, Jusoh, Asri Che, Zakaria, Muhammad Nazreen, Cruz, Daniel Rios, Elizalde, Aurea Barbara Rodriguez, Reynaud, Alejandro Bañon, Hernandez, Edgard Efren Lozada, Monroy, Jose maria Victor Palomo, Hinojosa-Ugarte, Diego, Quiodettis, Martha, Du Bois, María Esther, Latorraca, José, Major, Piotr, Pędziwiatr, Michał, Pisarska-Adamczyk, Magdalena, Walędziak, Maciej, Kwiatkowski, Andrzej, Czyżykowski, Łukasz, da Costa, Silvia Dantas, Pereira, Bela, Ferreira, Ana Rita Oliveira, Almeida, Filipe, Rocha, Ricardo, Carneiro, Carla, Perez, Diego Pita, Carvas, João, Rocha, Catarina, Ferreira, Cátia, Marques, Rita, Fernandes, Urânia, Leao, Pedro, Goulart, André, Pereira, Rita Gonçalves, Patrocínio, Sara Daniela Direito, de Mendonça, Nuno Gonçalo Gonçalves, Manso, Maria Isabel Cerqueira, Morais, Henrique Manuel Cardoso, Cardoso, Paulo Sebastião, Calu, Valentin, Miron, Adrian, Toma, Elena Adelina, Gachabayov, Mahir, Abdullaev, Abakar, Litvin, Andrey, Nechay, Taras, Tyagunov, Alexander, Yuldashev, Anvar, Bradley, Alison, Wilson, Michael, Panyko, Arpád, Látečková, Zuzana, Lacko, Vladimír, Lesko, Dusan, Soltes, Marek, Radonak, Jozef, Turrado-Rodriguez, Victor, Termes-Serra, Roser, Morales-Sevillano, Xavier, Lapolla, Pierfrancesco, Mingoli, Andrea, Brachini, Gioia, Degiuli, Maurizio, Sofia, Silvia, Reddavid, Rossella, de Manzoni Garberini, Andrea, Buffone, Angelica, del Pozo, Eduardo Perea, Aparicio-Sánchez, Daniel, Dos Barbeito, Sandra, Estaire-Gómez, Mercedes, Vitón-Herrero, Rebeca, de los Ángeles Gil Olarte-Marquez, Mª, Gil-Martínez, José, Alconchel, Felipe, Nicolás-López, Tatiana, Rahy-Martin, Aida Cristina, Pelloni, María, Bañolas-Suarez, Raquel, Mendoza-Moreno, Fernando, Nisa, Francisca García-Moreno, Díez-Alonso, Manuel, Rodas, María Elisa Valle, Agundez, María Carmona, Andrés, María Inmaculada Pérez, Moreira, Claudia Cristina Lopes, Perez, Aintzane Lizarazu, Ponce, Iñigo Augusto, González-Castillo, Ana María, Membrilla-Fernández, Estela, Salvans, Silvia, Serradilla-Martín, Mario, Pardo, Pablo Sancho, Rivera-Alonso, Daniel, Dziakova, Jana, Huguet, Jose Mugüerza, Valle, Naila Pagès, Ruiz, Enrique Colás, Valcárcel, Cristina Rey, Moreno, Cristina Ruiz, Salazar, Yeniffer Tatiana Moreno, García, Juan Jesús Rubio, Micó, Silvia Sevila, López, Joaquín Ruiz, Farré, Silvia Pérez, Gomez, Maite Santamaria, Petit, Nuria Mestres, Titos-García, Alberto, Aranda-Narváez, Jose Manuel, Romacho-López, Laura, Sánchez-Guillén, Luis, Aranaz-Ostariz, Veronica, Bosch-Ramírez, Marina, Martínez-Pérez, Aleix, Martínez-López, Elías, Sebastián-Tomás, Juan Carlos, Jimenez-Riera, Granada, Jimenez-Vega, Javier, Cuellar, Jose Aurelio Navas, Campos-Serra, Andrea, Muñoz-Campaña, Anna, Gràcia-Roman, Raquel, Alegre, Javier Martínez, Pinto, Francisca Lima, O’Sullivan, Sara Nuñez, Antona, Francisco Blanco, Jiménez, Beatriz Muñoz, López-Sánchez, Jaime, Carmona, Zahira Gómez, Fernández, Rocio Torres, Sierra, Isabel Blesa, de León, Laura Román García, Moreno, Verónica Polaino, Iglesias, Eva, Cumplido, Paola Lora, Bravo, Altea Arango, Simó, Ignacio Rey, Domínguez, Carlota López, Caamaño, Aloia Guerreiro, Lozano, Rafael Calleja, Martínez, Manuel Durán, Torres, Álvaro Naranjo, de Quiros, Javier Tomas Morales Bernaldo, Pellino, Gianluca, Cloquell, Miriam Moratal, Moller, Elsa García, Jalal-Eldin, Sami, Abdoun, Ahmed K., Hamid, Hytham K. S., Lohsiriwat, Varut, Mongkhonsupphawan, Aitsariya, Baraket, Oussama, Ayed, Karim, Abbassi, Imed, Ali, Ali Ben, Ammar, Houssem, Kchaou, Ali, Tlili, Ahmed, Zribi, Imen, Colak, Elif, Polat, Suleyman, Koylu, Zehra Alan, Guner, Ali, Usta, Mehmet Arif, Reis, Murat Emre, Mantoglu, Baris, Gonullu, Emre, Akin, Emrah, Altintoprak, Fatih, Bayhan, Zulfu, Firat, Necattin, Isik, Arda, Memis, Ufuk, Bayrak, Mehmet, Altıntaş, Yasemin, Kara, Yasin, Bozkurt, Mehmet Abdussamet, Kocataş, Ali, Das, Koray, Seker, Ahmet, Ozer, Nazmi, Atici, Semra Demirli, Tuncer, Korhan, Kaya, Tayfun, Ozkan, Zeynep, Ilhan, Onur, Agackiran, Ibrahim, Uzunoglu, Mustafa Yener, Demirbas, Eren, Altinel, Yuksel, Meric, Serhat, Hacım, Nadir Adnan, Uymaz, Derya Salim, Omarov, Nail, Balık, Emre, Tebala, Giovanni D., Khalil, Hany, Rana, Mridul, Khan, Mansoor, Florence, Charlotte, Swaminathan, Christie, Leo, Cosimo Alex, Liasis, Lampros, Watfah, Josef, Trostchansky, Ivan, Delgado, Edward, Pontillo, Marcelo, Latifi, Rifat, Coimbra, Raul, Edwards, Sara, Lopez, Ana, Velmahos, George, Dorken, Ander, Gebran, Anthony, Palmer, Amanda, Oury, Jeffrey, Bardes, James M., Seng, Sirivan Suon, Coffua, Lauren S., Ratnasekera, Asanthi, Egodage, Tanya, Echeverria-Rosario, Karla, Armento, Isabella, Napolitano, Lena M., Sangji, Naveen F., Hemmila, Mark, Quick, Jacob A., Austin, Tyler R., Hyman, Theodore S., Curtiss, William, McClure, Amanda, Cairl, Nicholas, Biffl, Walter L., Truong, Hung P., Schaffer, Kathryn, Reames, Summer, Banchini, Filippo, Capelli, Patrizio, Coccolini, Federico, Sartelli, Massimo, Bravi, Francesca, Vallicelli, Carlo, Agnoletti, Vanni, Baiocchi, Gian Luca, and Catena, Fausto

Published
2024
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25. Diving into the proteomic atlas of SARS-CoV-2 infected cells

Author

Carregari, Victor C., Reis-de-Oliveira, Guilherme, Crunfli, Fernanda, Smith, Bradley J., de Souza, Gabriela Fabiano, Muraro, Stéfanie Primon, Saia-Cereda, Veronica M., Vendramini, Pedro H., Baldasso, Paulo A., Silva-Costa, Lícia C., Zuccoli, Giuliana S., Brandão-Teles, Caroline, Antunes, André, Valença, Aline F., Davanzo, Gustavo G., Virgillio-da-Silva, João Victor, dos Reis Araújo, Thiago, Guimarães, Raphael Campos, Chaim, Felipe David Mendonça, Chaim, Elinton Adami, Kawagosi Onodera, Carolina Mie, Ludwig, Raissa Guimarães, Saccon, Tatiana Dandolini, Damásio, André R. L., Leiria, Luiz Osório S., Vinolo, Marco Aurélio R., Farias, Alessandro S., Moraes-Vieira, Pedro M., Mori, Marcelo A., Módena, José Luiz P., and Martins-de-Souza, Daniel

Published
2024
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29. Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors

Author

Bonato, Stefano, Lambertenghi, Stefano Carlo, Cereda, Elia, Giusti, Alessandro, and Palossi, Daniele

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract

Precise relative localization is a crucial functional block for swarm robotics. This work presents a novel autonomous end-to-end system that addresses the monocular relative localization, through deep neural networks (DNNs), of two peer nano-drones, i.e., sub-40g of weight and sub-100mW processing power. To cope with the ultra-constrained nano-drone platform, we propose a vertically-integrated framework, from the dataset collection to the final in-field deployment, including dataset augmentation, quantization, and system optimizations. Experimental results show that our DNN can precisely localize a 10cm-size target nano-drone by employing only low-resolution monochrome images, up to ~2m distance. On a disjoint testing dataset our model yields a mean R2 score of 0.42 and a root mean square error of 18cm, which results in a mean in-field prediction error of 15cm and in a closed-loop control error of 17cm, over a ~60s-flight test. Ultimately, the proposed system improves the State-of-the-Art by showing long-endurance tracking performance (up to 2min continuous tracking), generalization capabilities being deployed in a never-seen-before environment, and requiring a minimal power consumption of 95mW for an onboard real-time inference-rate of 48Hz., Comment: \c{opyright} 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Published
2023
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30. Deep Neural Network Architecture Search for Accurate Visual Pose Estimation aboard Nano-UAVs

Author

Cereda, Elia, Crupi, Luca, Risso, Matteo, Burrello, Alessio, Benini, Luca, Giusti, Alessandro, Pagliari, Daniele Jahier, and Palossi, Daniele

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract

Miniaturized autonomous unmanned aerial vehicles (UAVs) are an emerging and trending topic. With their form factor as big as the palm of one hand, they can reach spots otherwise inaccessible to bigger robots and safely operate in human surroundings. The simple electronics aboard such robots (sub-100mW) make them particularly cheap and attractive but pose significant challenges in enabling onboard sophisticated intelligence. In this work, we leverage a novel neural architecture search (NAS) technique to automatically identify several Pareto-optimal convolutional neural networks (CNNs) for a visual pose estimation task. Our work demonstrates how real-life and field-tested robotics applications can concretely leverage NAS technologies to automatically and efficiently optimize CNNs for the specific hardware constraints of small UAVs. We deploy several NAS-optimized CNNs and run them in closed-loop aboard a 27-g Crazyflie nano-UAV equipped with a parallel ultra-low power System-on-Chip. Our results improve the State-of-the-Art by reducing the in-field control error of 32% while achieving a real-time onboard inference-rate of ~10Hz@10mW and ~50Hz@90mW., Comment: \c{opyright} 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Published
2023
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31. RAS-ON inhibition overcomes clinical resistance to KRAS G12C-OFF covalent blockade

Author

Marie-Julie Nokin, Alessia Mira, Enrico Patrucco, Biagio Ricciuti, Sophie Cousin, Isabelle Soubeyran, Sonia San José, Serena Peirone, Livia Caizzi, Sandra Vietti Michelina, Aurelien Bourdon, Xinan Wang, Daniel Alvarez-Villanueva, María Martínez-Iniesta, August Vidal, Telmo Rodrigues, Carmen García-Macías, Mark M. Awad, Ernest Nadal, Alberto Villanueva, Antoine Italiano, Matteo Cereda, David Santamaría, and Chiara Ambrogio

Subjects
Science
Abstract

Abstract Selective KRASG12C inhibitors have been developed to covalently lock the oncogene in the inactive GDP-bound state. Two of these molecules, sotorasib and adagrasib, are approved for the treatment of adult patients with KRASG12C-mutated previously treated advanced non-small cell lung cancer. Drug treatment imposes selective pressures leading to the outgrowth of drug-resistant variants. Mass sequencing from patients’ biopsies identified a number of acquired KRAS mutations -both in cis and in trans- in resistant tumors. We demonstrate here that disease progression in vivo can also occur due to adaptive mechanisms and increased KRAS-GTP loading. Using the preclinical tool tri-complex KRASG12C-selective covalent inhibitor, RMC-4998 (also known as RM-029), that targets the active GTP-bound (ON) state of the oncogene, we provide a proof-of-concept that the clinical stage KRASG12C(ON) inhibitor RMC-6291 alone or in combination with KRASG12C(OFF) drugs can be an alternative potential therapeutic strategy to circumvent resistance due to increased KRAS-GTP loading.

Published
2024
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32. Derivation and external validation of predictive models for invasive mechanical ventilation in intensive care unit patients with COVID-19

Author

Gabriel Maia, Camila Marinelli Martins, Victoria Marques, Samantha Christovam, Isabela Prado, Bruno Moraes, Emanuele Rezoagli, Giuseppe Foti, Vanessa Zambelli, Maurizio Cereda, Lorenzo Berra, Patricia Rieken Macedo Rocco, Mônica Rodrigues Cruz, Cynthia dos Santos Samary, Fernando Silva Guimarães, and Pedro Leme Silva

Subjects
COVID-19, SOFA score, SpO2, Multiple logistic regression, Invasive mechanical ventilation, External validation, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

Abstract Background This study aimed to develop prognostic models for predicting the need for invasive mechanical ventilation (IMV) in intensive care unit (ICU) patients with COVID-19 and compare their performance with the Respiratory rate-OXygenation (ROX) index. Methods A retrospective cohort study was conducted using data collected between March 2020 and August 2021 at three hospitals in Rio de Janeiro, Brazil. ICU patients aged 18 years and older with a diagnosis of COVID-19 were screened. The exclusion criteria were patients who received IMV within the first 24 h of ICU admission, pregnancy, clinical decision for minimal end-of-life care and missing primary outcome data. Clinical and laboratory variables were collected. Multiple logistic regression analysis was performed to select predictor variables. Models were based on the lowest Akaike Information Criteria (AIC) and lowest AIC with significant p values. Assessment of predictive performance was done for discrimination and calibration. Areas under the curves (AUC)s were compared using DeLong’s algorithm. Models were validated externally using an international database. Results Of 656 patients screened, 346 patients were included; 155 required IMV (44.8%), 191 did not (55.2%), and 207 patients were male (59.8%). According to the lowest AIC, arterial hypertension, diabetes mellitus, obesity, Sequential Organ Failure Assessment (SOFA) score, heart rate, respiratory rate, peripheral oxygen saturation (SpO2), temperature, respiratory effort signals, and leukocytes were identified as predictors of IMV at hospital admission. According to AIC with significant p values, SOFA score, SpO2, and respiratory effort signals were the best predictors of IMV; odds ratios (95% confidence interval): 1.46 (1.07–2.05), 0.81 (0.72–0.90), 9.13 (3.29–28.67), respectively. The ROX index at admission was lower in the IMV group than in the non-IMV group (7.3 [5.2–9.8] versus 9.6 [6.8–12.9], p

Published
2024
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33. Combination of the PARPi and ARSi in advanced castration resistant prostate cancer: a review of the recent phase III trials

Author

Martina Panebianco, Vittore Cereda, and Mario Rosario D’Andrea

Subjects
prostate cancer, parpi, arsi, combination strategies, castration resistant, brca, Internal medicine, RC31-1245
Abstract

Tumors with an impaired ability to repair DNA double-strand breaks by homologous recombination, including those with alterations in breast cancer 1 and 2 (BRCA1 and BRCA2) genes, are very sensitive to blocking DNA single-strand repair by inhibition of the poly (ADP-ribose) polymerase (PARP) enzyme. This provides the basis for a synthetic deadly strategy in the treatment of different types of cancer, such as prostate cancer (PCa). The phase 3 PROfound study was the first to lead to olaparib approval in patients with metastatic castration resistant PCa (mCRPC) and BRCA genes mutations. In recent years, the benefit of combination therapy consisted of a PARP inhibitor (PARPi) plus an androgen receptor signalling inhibitor (ARSi), was evaluated as first-line treatment of mCRPC, regardless of the mutational state of genes, participating in the homologous recombination repair (HRR). This review explores the role of PARPi in PCa and analyses the data of latest clinical trials exploring the PARPi—ARSi combinations, and how these results could change our clinical practice.

Published
2024
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34. Huntington’s disease cellular phenotypes are rescued non-cell autonomously by healthy cells in mosaic telencephalic organoids

Author

Maura Galimberti, Maria R. Nucera, Vittoria D. Bocchi, Paola Conforti, Elena Vezzoli, Matteo Cereda, Camilla Maffezzini, Raffaele Iennaco, Andrea Scolz, Andrea Falqui, Chiara Cordiglieri, Martina Cremona, Ira Espuny-Camacho, Andrea Faedo, Dan P. Felsenfeld, Thomas F. Vogt, Valeria Ranzani, Chiara Zuccato, Dario Besusso, and Elena Cattaneo

Subjects
Science
Abstract

Abstract Huntington’s disease (HD) causes selective degeneration of striatal and cortical neurons, resulting in cell mosaicism of coexisting still functional and dysfunctional cells. The impact of non-cell autonomous mechanisms between these cellular states is poorly understood. Here we generated telencephalic organoids with healthy or HD cells, grown separately or as mosaics of the two genotypes. Single-cell RNA sequencing revealed neurodevelopmental abnormalities in the ventral fate acquisition of HD organoids, confirmed by cytoarchitectural and transcriptional defects leading to fewer GABAergic neurons, while dorsal populations showed milder phenotypes mainly in maturation trajectory. Healthy cells in mosaic organoids restored HD cell identity, trajectories, synaptic density, and communication pathways upon cell-cell contact, while showing no significant alterations when grown with HD cells. These findings highlight cell-type-specific alterations in HD and beneficial non-cell autonomous effects of healthy cells, emphasizing the therapeutic potential of modulating cell-cell communication in disease progression and treatment.

Published
2024
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35. Sarcopenic obesity and health outcomes: An umbrella review of systematic reviews with meta‐analysis

Author

Nicola Veronese, Francesco Saverio Ragusa, Francesco Pegreffi, Ligia J. Dominguez, Mario Barbagallo, Michela Zanetti, and Emanuele Cereda

Subjects
meta‐analysis, obesity, sarcopenia, sarcopenic obesity, umbrella review, Diseases of the musculoskeletal system, RC925-935, Human anatomy, QM1-695
Abstract

Abstract Many studies support the idea that sarcopenic obesity (SO) could be considered a potential risk factor for negative health outcomes. These results have been inconsistent, and no umbrella reviews exist regarding this topic. Several databases until November 2023 were searched for systematic reviews with meta‐analysis of observational studies (cross‐sectional, case–control and prospective). For each association, random‐effects summary effect sizes with correspondent 95% confidence intervals (CIs) were evaluated using the GRADE tool. Among the 213 papers initially screened, nine systematic reviews with meta‐analysis were included, for a total of 384 710 participants. In cross‐sectional and case–control studies, 30 different outcomes were analysed, and 18 were statistically significant. In any population addressed in cross‐sectional and case–control studies, compared with non‐SO, SO increased the prevalence of cognitive impairment (k = 3; odds ratio [OR] = 3.46; 95% CI: 2.24–5.32; high certainty of evidence), coronary artery disease (k = 2; OR = 2.48; 95% CI: 1.85–3.31) and dyslipidaemia (k = 3; OR = 2.50; 95% CI: 1.51–4.15). When compared with sarcopenia or obesity, the results were conflicting. In prospective studies, the association between SO—compared with non‐SO—and other negative outcomes was supported by low/very low certainty of evidence and limited to a few conditions. Besides, no comparison with sarcopenia or obesity was provided. Finally, only a few studies have considered muscle function/physical performance in the diagnostic workup. SO could be considered a risk factor only for a few conditions, with the literature mainly based on cross‐sectional and case–control studies. Future studies with clear definitions of SO are needed for quantifying the importance of SO—particularly when compared with the presence of only sarcopenia or obesity—and the weight of muscle function/physical performance in its definition.

Published
2024
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36. A compartmental model for smoking dynamics in Italy: a pipeline for inference, validation, and forecasting under hypothetical scenarios

Author

Alessio Lachi, Cecilia Viscardi, Giulia Cereda, Giulia Carreras, and Michela Baccini

Subjects
Compartmental models, Smoking dynamics, Tobacco control policies, Global sensitivity analysis, Parametric bootstrap, Cross validation, Medicine (General), R5-920
Abstract

Abstract We propose a compartmental model for investigating smoking dynamics in an Italian region (Tuscany). Calibrating the model on local data from 1993 to 2019, we estimate the probabilities of starting and quitting smoking and the probability of smoking relapse. Then, we forecast the evolution of smoking prevalence until 2043 and assess the impact on mortality in terms of attributable deaths. We introduce elements of novelty with respect to previous studies in this field, including a formal definition of the equations governing the model dynamics and a flexible modelling of smoking probabilities based on cubic regression splines. We estimate model parameters by defining a two-step procedure and quantify the sampling variability via a parametric bootstrap. We propose the implementation of cross-validation on a rolling basis and variance-based Global Sensitivity Analysis to check the robustness of the results and support our findings. Our results suggest a decrease in smoking prevalence among males and stability among females, over the next two decades. We estimate that, in 2023, 18% of deaths among males and 8% among females are due to smoking. We test the use of the model in assessing the impact on smoking prevalence and mortality of different tobacco control policies, including the tobacco-free generation ban recently introduced in New Zealand.

Published
2024
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37. Healthcare-associated infections and antimicrobial use in acute care hospitals: a point prevalence survey in Lombardy, Italy, in 2022

Author

Antonio Antonelli, Maria Elena Ales, Greta Chiecca, Zeno Dalla Valle, Emanuele De Ponti, Danilo Cereda, Lucia Crottogini, Cristina Renzi, Carlo Signorelli, and Matteo Moro

Subjects
Point prevalence survey, Healthcare associated infection, Antimicrobial use, Antimicrobial stewardship, Surveillance, Infection prevention and control, Infectious and parasitic diseases, RC109-216
Abstract

Abstract Background Healthcare-Associated Infections (HAIs) are a global public health issue, representing a significant burden of disease that leads to prolonged hospital stays, inappropriate use of antimicrobial drugs, intricately linked to the development of resistant microorganisms, and higher costs for healthcare systems. The study aimed to measure the prevalence of HAIs, the use of antimicrobials, and assess healthcare- and patient-related risk factors, to help identify key intervention points for effectively reducing the burden of HAIs. Methods A total of 28 acute care hospitals in the Lombardy region, Northern Italy, participated in the third European Point Prevalence Survey (PPS-3) coordinated by ECDC for the surveillance of HAIs in acute care hospitals (Protocol 6.0). Results HAIs were detected in 1,259 (10.1%, 95% CI 9.6–10.7%) out of 12,412 enrolled patients. 1,385 HAIs were reported (1.1 HAIs per patient on average). The most common types of HAIs were bloodstream infections (262 cases, 18.9%), urinary tract infections (237, 17.1%), SARS-CoV-2 infections (236, 17.0%), pneumonia and lower respiratory tract infections (231, 16.7%), and surgical site infections (152, 11.0%). Excluding SARS-CoV-2 infections, the overall prevalence of HAIs was 8.4% (95% CI 7.9–8.9%). HAIs were significantly more frequent in patients hospitalized in smaller hospitals and in intensive care units (ICUs), among males, advanced age, severe clinical condition and in patients using invasive medical devices. Overall, 5,225 patients (42.1%, 95% CI 41.3–43.0%) received systemic antimicrobial therapy. According to the WHO’s AWaRe classification, the Access group accounted for 32.7% of total antibiotic consumption, while Watch and Reserve classes accounted for 57.0% and 5.9% respectively. From a microbiological perspective, investigations were conducted on only 64% of the HAIs, showing, however, a significant pattern of antibiotic resistance. Conclusions The PPS-3 in Lombardy, involving data collection on HAIs and antimicrobial use in acute care hospitals, highlights the crucial need for a structured framework serving both as a valuable benchmark for individual hospitals and as a foundation to effectively channel interventions to the most critical areas, prioritizing future regional health policies to reduce the burden of HAIs.

Published
2024
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38. Breast cancer patient-derived organoids for the investigation of patient-specific tumour evolution

Author

Serena Mazzucchelli, Lorena Signati, Letizia Messa, Alma Franceschini, Arianna Bonizzi, Lorenzo Castagnoli, Patrizia Gasparini, Clarissa Consolandi, Eleonora Mangano, Paride Pelucchi, Ingrid Cifola, Tania Camboni, Marco Severgnini, Laura Villani, Barbara Tagliaferri, Stephana Carelli, Serenella M. Pupa, Cristina Cereda, and Fabio Corsi

Subjects
Breast cancer, Patient-derived organoids, Tumour evolution, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282, Cytology, QH573-671
Abstract

Abstract Background A reliable preclinical model of patient-derived organoids (PDOs) was developed in a case study of a 69-year-old woman diagnosed with breast cancer (BC) to investigate the tumour evolution before and after neoadjuvant chemotherapy and surgery. The results were achieved due to the development of PDOs from tissues collected before (O-PRE) and after (O-POST) treatment. Methods PDO cultures were characterized by histology, immunohistochemistry (IHC), transmission electron microscopy (TEM), scanning electron microscopy (SEM), confocal microscopy, flow cytometry, real-time PCR, bulk RNA-seq, single-cell RNA sequencing (scRNA-seq) and drug screening. Results Both PDO cultures recapitulated the histological and molecular profiles of the original tissues, and they showed typical mammary gland organization, confirming their reliability as a personalized in vitro model. Compared with O-PRE, O-POST had a greater proliferation rate with a significant increase in the Ki67 proliferation index. Moreover O-POST exhibited a more stem-like and aggressive phenotype, with increases in the CD24low/CD44low and EPCAMlow/CD49fhigh cell populations characterized by increased tumour initiation potential and multipotency and metastatic potential in invasive lobular carcinoma. Analysis of ErbB receptor expression indicated a decrease in HER-2 expression coupled with an increase in EGFR expression in O-POST. In this context, deregulation of the PI3K/Akt signalling pathway was assessed by transcriptomic analysis, confirming the altered transcriptional profile. Finally, transcriptomic single-cell analysis identified 11 cell type clusters, highlighting the selection of the luminal component and the decrease in the number of Epithelial–mesenchymal transition cell types in O-POST. Conclusion Neoadjuvant treatment contributed to the enrichment of cell populations with luminal phenotypes that were more resistant to chemotherapy in O-POST. PDOs represent an excellent 3D cell model for assessing disease evolution.

Published
2024
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41. Impaired neutrophil-mediated cell death drives Ewing’s Sarcoma in the background of Down syndrome

Author

Serena Peirone, Elisa Tirtei, Anna Campello, Caterina Parlato, Simonetta Guarrera, Katia Mareschi, Elena Marini, Sebastian Dorin Asaftei, Luca Bertero, Mauro Papotti, Francesca Priante, Sarah Perrone, Matteo Cereda, and Franca Fagioli

Subjects
Ewing Sarcoma, Down syndrome, paediatric bone sarcoma, genomics, transcriptomics, neutrophils, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

IntroductionEwing Sarcoma (EWS) has been reported in seven children with Down syndrome (DS). To date, a detailed assessment of this solid tumour in DS patients is yet to be made.MethodsHere, we characterise a chemo-resistant mediastinal EWS in a 2-year-old DS child, the youngest ever reported case, by exploiting sequencing approaches.ResultsThe tumour showed a neuroectodermal development driven by the EWSR1-FLI1 fusion. The inherited myeloperoxidase deficiency of the patient caused failure of neutrophil-mediated cell death and promoted genomic instability.DiscussionIn this context, the tumour underwent genome-wide near haploidisation resulting in a massive overexpression of pro-inflammatory cytokines. Recruitment of defective neutrophils fostered rapid evolution of this EWS.

Published
2024
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42. Bioimpedance-derived body composition parameters predict mortality and dose-limiting toxicity: the multicenter ONCO-BIVA study

Author

Caccialanza, Riccardo, Cereda, Emanuele, Casirati, Amanda, Crotti, Silvia, Masi, Sara, Klersy, Catherine, Ferrari, Alessandra, Pedrazzoli, Paolo, Gavazzi, Cecilia, Vandoni, Giulia, Farina, Gabriella, La Verde, Nicla, Zagonel, Vittorina, Nardi, Maria Teresa, Baldan, Ilaria, Di Costanzo, Francesco, Mascheroni, Annalisa, Trestini, Ilaria, Valoriani, Filippo, Lucchin, Lucio, Aprile, Giuseppe, De Francesco, Antonella, Agnello, Elena, Giovanardi, Filippo, Cereda, E., Casirati, A., Klersy, C., Nardi, M., Vandoni, G., Agnello, E., Crotti, S., Masi, S., Ferrari, A., Pedrazzoli, P., and Caccialanza, R.

Published
2024
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44. Dyspnea and Respiratory Distress in the Neuro ICU

Author

Cohen, Samuel M., Hatton, Kevin W., Cereda, Maurizio, Mahanna Gabrielli, Elizabeth, editor, O'Phelan, Kristine H., editor, Kumar, Monisha A., editor, Levine, Joshua, editor, Le Roux, Peter, editor, Gabrielli, Andrea, editor, and Layon, A. Joseph, editor

Published
2024
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45. Emotions in Practice: Studying Lectures and Seminars in On-Line and Offline Education

Author

Barpi, Fabrizio, Cereda, Ambrogia, De Blasio, Antonella, Vinci, Fiorella, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Casalino, Gabriella, editor, Di Fuccio, Raffaele, editor, Fulantelli, Giovanni, editor, Raviolo, Paolo, editor, Rivoltella, Pier Cesare, editor, Taibi, Davide, editor, and Toto, Giusi Antonia, editor

Published
2024
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46. Validation of the Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT) questionnaire for adults

Author

Solmi, Marco, Thompson, Trevor, Estradé, Andrés, Agorastos, Agorastos, Radua, Joaquim, Cortese, Samuele, Dragioti, Elena, Leisch, Friedrich, Vancampfort, Davy, Thygesen, Lau Caspar, Aschauer, Harald, Schlögelhofer, Monika, Aschauer, Elena, Schneeberger, Andres, Huber, Christian G, Hasler, Gregor, Conus, Philippe, Do Cuénod, Kim Q, von Känel, Roland, Arrondo, Gonzalo, Fusar-Poli, Paolo, Gorwood, Philip, Llorca, Pierre-Michel, Krebs, Marie-Odile, Scanferla, Elisabetta, Kishimoto, Taishiro, Rabbani, Golam, Skonieczna-Żydecka, Karolina, Brambilla, Paolo, Favaro, Angela, Takamiya, Akihiro, Zoccante, Leonardo, Colizzi, Marco, Bourgin, Julie, Kamiński, Karol, Moghadasin, Maryam, Seedat, Soraya, Matthews, Evan, Wells, John, Vassilopoulou, Emilia, Gadelha, Ary, Su, Kuan-Pin, Kwon, Jun Soo, Kim, Minah, Lee, Tae Young, Papsuev, Oleg, Manková, Denisa, Boscutti, Andrea, Gerunda, Cristiano, Saccon, Diego, Righi, Elena, Monaco, Francesco, Croatto, Giovanni, Cereda, Guido, Demurtas, Jacopo, Brondino, Natascia, Veronese, Nicola, Enrico, Paolo, Politi, Pierluigi, Ciappolino, Valentina, Pfennig, Andrea, Bechdolf, Andreas, Meyer-Lindenberg, Andreas, Kahl, Kai G, Domschke, Katharina, Bauer, Michael, Koutsouleris, Nikolaos, Winter, Sibylle, Borgwardt, Stefan, Bitter, Istvan, Balazs, Judit, Czobor, Pál, Unoka, Zsolt, Mavridis, Dimitris, Tsamakis, Konstantinos, Bozikas, Vasilios P, Tunvirachaisakul, Chavit, Maes, Michael, Rungnirundorn, Teerayuth, Supasitthumrong, Thitiporn, Haque, Ariful, Brunoni, Andre R, Costardi, Carlos Gustavo, Schuch, Felipe Barreto, Polanczyk, Guilherme, Luiz, Jhoanne Merlyn, Fonseca, Lais, Aparicio, Luana V, Valvassori, Samira S, Nordentoft, Merete, Vendsborg, Per, Hoffmann, Sofie Have, Sehli, Jihed, Sartorius, Norman, Heuss, Sabina, Guinart, Daniel, Hamilton, Jane, Kane, John, Rubio, Jose, and Sand, Michael

Subjects
Clinical and Health Psychology, Health Sciences, Psychology, Mental Health, Behavioral and Social Science, Brain Disorders, Depression, Mental health, Good Health and Well Being, Humans, Adult, Reproducibility of Results, Pandemics, COVID-19, Surveys and Questionnaires, Outcome Assessment, Health Care, Factor Analysis, Statistical, Psychometrics, Covid-19, Pandemic, COH-FIT, Survey: P-factor: well-being: mental health: psychiatry: psychometric, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundThe Collaborative Outcome study on Health and Functioning during Infection Times (COH-FIT; www.coh-fit.com) is an anonymous and global online survey measuring health and functioning during the COVID-19 pandemic. The aim of this study was to test concurrently the validity of COH-FIT items and the internal validity of the co-primary outcome, a composite psychopathology "P-score".MethodsThe COH-FIT survey has been translated into 30 languages (two blind forward-translations, consensus, one independent English back-translation, final harmonization). To measure mental health, 1-4 items ("COH-FIT items") were extracted from validated questionnaires (e.g. Patient Health Questionnaire 9). COH-FIT items measured anxiety, depressive, post-traumatic, obsessive-compulsive, bipolar and psychotic symptoms, as well as stress, sleep and concentration. COH-FIT Items which correlated r ≥ 0.5 with validated companion questionnaires, were initially retained. A P-score factor structure was then identified from these items using exploratory factor analysis (EFA) and confirmatory factor analyses (CFA) on data split into training and validation sets. Consistency of results across languages, gender and age was assessed.ResultsFrom >150,000 adult responses by May 6th, 2022, a subset of 22,456 completed both COH-FIT items and validated questionnaires. Concurrent validity was consistently demonstrated across different languages for COH-FIT items. CFA confirmed EFA results of five first-order factors (anxiety, depression, post-traumatic, psychotic, psychophysiologic symptoms) and revealed a single second-order factor P-score, with high internal reliability (ω = 0.95). Factor structure was consistent across age and sex.ConclusionsCOH-FIT is a valid instrument to globally measure mental health during infection times. The P-score is a valid measure of multidimensional mental health.

Published
2023

49. Goodbye Hartmann trial: a prospective, international, multicenter, observational study on the current use of a surgical procedure developed a century ago

Author

Gennaro Perrone, Mario Giuffrida, Fikri Abu-Zidan, Vitor F. Kruger, Marco Livrini, Gabriele Luciano Petracca, Giorgio Rossi, Antonio Tarasconi, Brian W. C. A. Tian, Elena Bonati, Ricardo Mentz, Federico N. Mazzini, Juan P. Campana, Elisabeth Gasser, Reinhold Kafka-Ritsch, Daniel M. Felsenreich, Christopher Dawoud, Stefan Riss, Carlos Augusto Gomes, Felipe Couto Gomes, Ricardo Alessandro Teixeira Gonzaga, Cassio Alfred Brattig Canton, Bruno Monteiro Pereira, Gustavo P. Fraga, Leticia Gonçalves Zem, Vinicius Cordeiro-Fonseca, Renato de Mesquita Tauil, Boyko Atanasov, Nikolay Belev, Nikola Kovachev, L. Juan José Meléndez, Ana Dimova, Stefan Dimov, Zdravko Zelić, Goran Augustin, Branko Bogdanić, Trpimir Morić, Elie Chouillard, Melinda Bajul, Belinda De Simone, Yves Panis, Francesco Esposito, Margherita Notarnicola, Lelde Lauka, Anna Fabbri, Hassen Hentati, Iskander Fnaiech, Venara Aurélien, Marie Bougard, Maxime Roulet, Zaza Demetrashvili, Irakli Pipia, Giorgi Merabishvili, Konstantinos Bouliaris, Georgios Koukoulis, Christos Doudakmanis, Sofia Xenaki, Emmanuel Chrysos, Stamatios Kokkinakis, Panteleimon Vassiliu, Nikolaos Michalopoulos, Ioannis Margaris, Aristotelis Kechagias, Konstantinos Avgerinos, Jevgeni Katunin, Eftychios Lostoridis, Eleni-Aikaterini Nagorni, Antonio Pujante, Francesk Mulita, Ioannis Maroulis, Michail Vailas, Athanasios Marinis, Ioannis Siannis, Eirini Bourbouteli, Dimitrios K. Manatakis, Nikolaos Tasis, Vasileios Acheimastos, Sotiropoulou Maria, Kapiris Stylianos, Harilaos Kuzeridis, Dimitrios Korkolis, Evangelos Fradelos, George Kavalieratos, Thalia Petropoulou, Andreas Polydorou, Ioannis Papacostantinou, Tania Triantafyllou, Despina Kimpizi, Dimitrios Theodorou, Konstantinos Toutouzas, Alexandros Chamzin, Maximos Frountzas, Dimitrios Schizas, Ioannis Karavokyros, Athanasios Syllaios, Alexandros Charalabopoulos, Maria Boura, Efstratia Baili, Orestis Ioannidis, Lydia Loutzidou, Elissavet Anestiadou, Ioannis Tsouknidas, Georgios Petrakis, Eleni Polenta, Lovenish Bains, Rahul Gupta, Sudhir K. Singh, Archana Khanduri, Miklosh Bala, Asaf Kedar, Marcello Pisano, Mauro Podda, Adolfo Pisanu, Gennaro Martines, Giuseppe Trigiante, Giuliano Lantone, Antonino Agrusa, Giuseppe Di Buono, Salvatore Buscemi, Massimiliano Veroux, Rossella Gioco, Gastone Veroux, Luigi Oragano, Sandro Zonta, Federico Lovisetto, Carlo V. Feo, Antonio Pesce, Nicolò Fabbri, Giulio Lantone, Fabio Marino, Fabrizio Perrone, Leonardo Vincenti, Vincenzo Papagni, Arcangelo Picciariello, Stefano Rossi, Biagio Picardi, Simone Rossi Del Monte, Diego Visconti, Giulia Osella, Luca Petruzzelli, Giusto Pignata, Jacopo Andreuccetti, Rossella D’Alessio, Massimo Buonfantino, Eleonora Guaitoli, Stefano Spinelli, Gianluca Matteo Sampietro, Carlo Corbellini, Leonardo Lorusso, Alice Frontali, Isabella Pezzoli, Alessandro Bonomi, Andrea Chierici, Christian Cotsoglou, Giuseppe Manca, Antonella Delvecchio, Nicola Musa, Massimiliano Casati, Laface Letizia, Emmanuele Abate, Giorgio Ercolani, Fabrizio D’Acapito, Leonardo Solaini, Gianluca Guercioni, Simone Cicconi, Diego Sasia, Felice Borghi, Giorgio Giraudo, Giuseppe Sena, Pasquale Castaldo, Eugenia Cardamone, Giuseppe Portale, Matteo Zuin, Ylenia Spolverato, Marialusia Esposito, Roberta Maria Isernia, Maria Di Salvo, Romina Manunza, Giuseppe Esposito, Marcello Agus, Emanuele Luigi Giuseppe Asti, Daniele Tiziano Bernardi, Tommaso Panici Tonucci, Davide Luppi, Massimiliano Casadei, Stefano Bonilauri, Angela Pezzolla, Annunziata Panebianco, Rita Laforgia, Maurizio De Luca, Monica Zese, Dario Parini, Elio Jovine, Giuseppina De Sario, Raffaele Lombardi, Giovanni Aprea, Giuseppe Palomba, Marianna Capuano, Giulio Argenio, Gianluca Orio, Mariano Fortunato Armellino, Marina Troian, Martina Guerra, Carlo Nagliati, Alan Biloslavo, Paola Germani, Giada Aizza, Igor Monsellato, Ali Chaouki Chahrour, Gabriele Anania, Cristina Bombardini, Francesco Bagolini, Gabriele Sganga, Pietro Fransvea, Valentina Bianchi, Paolo Boati, Francesco Ferrara, Francesco Palmieri, Pasquale Cianci, Domenico Gattulli, Enrico Restini, Nicola Cillara, Alessandro Cannavera, Gabriela Elisa Nita, Jlenia Sarnari, Francesco Roscio, Federico Clerici, Ildo Scandroglio, Stefano Berti, Alessandro Cadeo, Alice Filippelli, Luigi Conti, Carmine Grassi, Gaetano Maria Cattaneo, Marina Pighin, Davide Papis, Giovanni Gambino, Vanessa Bertino, Domenico Schifano, Daniela Prando, Luisella Fogato, Fabio Cavallo, Luca Ansaloni, Roberto Picheo, Nicholas Pontarolo, Norma Depalma, Marcello Spampinato, Stefano D’Ugo, Luca Lepre, Michela Giulii Capponi, Rossella Domenica Campa, Giuliano Sarro, Vincenza Paola Dinuzzi, Stefano Olmi, Matteo Uccelli, Davide Ferrari, Marco Inama, Gianluigi Moretto, Michele Fontana, Francesco Favi, Erika Picariello, Alessia Rampini, Andrea Barberis, Antonio Azzinnaro, Alba Oliva, Luigi Totaro, Ilaria Benzoni, Valerio Ranieri, Gabriella Teresa Capolupo, Filippo Carannante, Marco Caricato, Maurizio Ronconi, Silvia Casiraghi, Giovanni Casole, Desire Pantalone, Giovanni Alemanno, Maximilian Scheiterle, Marco Ceresoli, Marco Cereda, Chiara Fumagalli, Federico Zanzi, Stefano Bolzon, Enrico Guerra, Francesca Lecchi, Paola Cellerino, Antonella Ardito, Rosa Scaramuzzo, Andrea Balla, Pasquale Lepiane, Nicola Tartaglia, Antonio Ambrosi, Giovanna Pavone, Gian Marco Palini, Simone Veneroni, Gianluca Garulli, Claudio Ricci, Beatrice Torre, Iris Shari Russo, Matteo Rottoli, Marta Tanzanu, Angela Belvedere, Marco Milone, Michele Manigrasso, Giovanni Domenico De Palma, Micaela Piccoli, Gianmaria Casoni Pattacini, Stefano Magnone, Paolo Bertoli, Michele Pisano, Paolo Massucco, Marco Palisi, Andrea-Pierre Luzzi, Francesco Fleres, Guglielmo Clarizia, Alessandro Spolini, Yoshiro Kobe, Takayuki Toma, Fumihiko Shimamura, Robert Parker, Sinkeet Ranketi, Mercy Mitei, Saulius Svagzdys, Henrikas Pauzas, Justas Zilinskas, Tomas Poskus, Marius Kryzauskas, Matas Jakubauskas, Andee Dzulkarnaen Zakaria, Zaidi Zakaria, Michael Pak-Kai Wong, Asri Che Jusoh, Muhammad Nazreen Zakaria, Daniel Rios Cruz, Aurea Barbara Rodriguez Elizalde, Alejandro Bañon Reynaud, Edgard Efren Lozada Hernandez, Jose maria Victor Palomo Monroy, Diego Hinojosa-Ugarte, Martha Quiodettis, María Esther Du Bois, José Latorraca, Piotr Major, Michał Pędziwiatr, Magdalena Pisarska-Adamczyk, Maciej Walędziak, Andrzej Kwiatkowski, Łukasz Czyżykowski, Silvia Dantas da Costa, Bela Pereira, Ana Rita Oliveira Ferreira, Filipe Almeida, Ricardo Rocha, Carla Carneiro, Diego Pita Perez, João Carvas, Catarina Rocha, Cátia Ferreira, Rita Marques, Urânia Fernandes, Pedro Leao, André Goulart, Rita Gonçalves Pereira, Sara Daniela Direito Patrocínio, Nuno Gonçalo Gonçalves de Mendonça, Maria Isabel Cerqueira Manso, Henrique Manuel Cardoso Morais, Paulo Sebastião Cardoso, Valentin Calu, Adrian Miron, Elena Adelina Toma, Mahir Gachabayov, Abakar Abdullaev, Andrey Litvin, Taras Nechay, Alexander Tyagunov, Anvar Yuldashev, Alison Bradley, Michael Wilson, Arpád Panyko, Zuzana Látečková, Vladimír Lacko, Dusan Lesko, Marek Soltes, Jozef Radonak, Victor Turrado-Rodriguez, Roser Termes-Serra, Xavier Morales-Sevillano, Pierfrancesco Lapolla, Andrea Mingoli, Gioia Brachini, Maurizio Degiuli, Silvia Sofia, Rossella Reddavid, Andrea de Manzoni Garberini, Angelica Buffone, Eduardo Perea del Pozo, Daniel Aparicio-Sánchez, Sandra Dos Barbeito, Mercedes Estaire-Gómez, Rebeca Vitón-Herrero, Mª de los Ángeles Gil Olarte-Marquez, José Gil-Martínez, Felipe Alconchel, Tatiana Nicolás-López, Aida Cristina Rahy-Martin, María Pelloni, Raquel Bañolas-Suarez, Fernando Mendoza-Moreno, Francisca García-Moreno Nisa, Manuel Díez-Alonso, María Elisa Valle Rodas, María Carmona Agundez, María Inmaculada Pérez Andrés, Claudia Cristina Lopes Moreira, Aintzane Lizarazu Perez, Iñigo Augusto Ponce, Ana María González-Castillo, Estela Membrilla-Fernández, Silvia Salvans, Mario Serradilla-Martín, Pablo Sancho Pardo, Daniel Rivera-Alonso, Jana Dziakova, Jose Mugüerza Huguet, Naila Pagès Valle, Enrique Colás Ruiz, Cristina Rey Valcárcel, Cristina Ruiz Moreno, Yeniffer Tatiana Moreno Salazar, Juan Jesús Rubio García, Silvia Sevila Micó, Joaquín Ruiz López, Silvia Pérez Farré, Maite Santamaria Gomez, Nuria Mestres Petit, Alberto Titos-García, Jose Manuel Aranda-Narváez, Laura Romacho-López, Luis Sánchez-Guillén, Veronica Aranaz-Ostariz, Marina Bosch-Ramírez, Aleix Martínez-Pérez, Elías Martínez-López, Juan Carlos Sebastián-Tomás, Granada Jimenez-Riera, Javier Jimenez-Vega, Jose Aurelio Navas Cuellar, Andrea Campos-Serra, Anna Muñoz-Campaña, Raquel Gràcia-Roman, Javier Martínez Alegre, Francisca Lima Pinto, Sara Nuñez O’Sullivan, Francisco Blanco Antona, Beatriz Muñoz Jiménez, Jaime López-Sánchez, Zahira Gómez Carmona, Rocio Torres Fernández, Isabel Blesa Sierra, Laura Román García de León, Verónica Polaino Moreno, Eva Iglesias, Paola Lora Cumplido, Altea Arango Bravo, Ignacio Rey Simó, Carlota López Domínguez, Aloia Guerreiro Caamaño, Rafael Calleja Lozano, Manuel Durán Martínez, Álvaro Naranjo Torres, Javier Tomas Morales Bernaldo de Quiros, Gianluca Pellino, Miriam Moratal Cloquell, Elsa García Moller, Sami Jalal-Eldin, Ahmed K. Abdoun, Hytham K. S. Hamid, Varut Lohsiriwat, Aitsariya Mongkhonsupphawan, Oussama Baraket, Karim Ayed, Imed Abbassi, Ali Ben Ali, Houssem Ammar, Ali Kchaou, Ahmed Tlili, Imen Zribi, Elif Colak, Suleyman Polat, Zehra Alan Koylu, Ali Guner, Mehmet Arif Usta, Murat Emre Reis, Baris Mantoglu, Emre Gonullu, Emrah Akin, Fatih Altintoprak, Zulfu Bayhan, Necattin Firat, Arda Isik, Ufuk Memis, Mehmet Bayrak, Yasemin Altıntaş, Yasin Kara, Mehmet Abdussamet Bozkurt, Ali Kocataş, Koray Das, Ahmet Seker, Nazmi Ozer, Semra Demirli Atici, Korhan Tuncer, Tayfun Kaya, Zeynep Ozkan, Onur Ilhan, Ibrahim Agackiran, Mustafa Yener Uzunoglu, Eren Demirbas, Yuksel Altinel, Serhat Meric, Nadir Adnan Hacım, Derya Salim Uymaz, Nail Omarov, Emre Balık, Giovanni D. Tebala, Hany Khalil, Mridul Rana, Mansoor Khan, Charlotte Florence, Christie Swaminathan, Cosimo Alex Leo, Lampros Liasis, Josef Watfah, Ivan Trostchansky, Edward Delgado, Marcelo Pontillo, Rifat Latifi, Raul Coimbra, Sara Edwards, Ana Lopez, George Velmahos, Ander Dorken, Anthony Gebran, Amanda Palmer, Jeffrey Oury, James M. Bardes, Sirivan Suon Seng, Lauren S. Coffua, Asanthi Ratnasekera, Tanya Egodage, Karla Echeverria-Rosario, Isabella Armento, Lena M. Napolitano, Naveen F. Sangji, Mark Hemmila, Jacob A. Quick, Tyler R. Austin, Theodore S. Hyman, William Curtiss, Amanda McClure, Nicholas Cairl, Walter L. Biffl, Hung P. Truong, Kathryn Schaffer, Summer Reames, Filippo Banchini, Patrizio Capelli, Federico Coccolini, Massimo Sartelli, Francesca Bravi, Carlo Vallicelli, Vanni Agnoletti, Gian Luca Baiocchi, and Fausto Catena

Subjects
Hartmann’s procedure, Ostomy, Emergency surgery, Resection, Primary anastomosis, Left side, Surgery, RD1-811, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

Abstract Background Literature suggests colonic resection and primary anastomosis (RPA) instead of Hartmann’s procedure (HP) for the treatment of left-sided colonic emergencies. We aim to evaluate the surgical options globally used to treat patients with acute left-sided colonic emergencies and the factors that leading to the choice of treatment, comparing HP and RPA. Methods This is a prospective, international, multicenter, observational study registered on ClinicalTrials.gov. A total 1215 patients with left-sided colonic emergencies who required surgery were included from 204 centers during the period of March 1, 2020, to May 31, 2020. with a 1-year follow-up. Results 564 patients (43.1%) were females. The mean age was 65.9 ± 15.6 years. HP was performed in 697 (57.3%) patients and RPA in 384 (31.6%) cases. Complicated acute diverticulitis was the most common cause of left-sided colonic emergencies (40.2%), followed by colorectal malignancy (36.6%). Severe complications (Clavien-Dindo ≥ 3b) were higher in the HP group (P

Published
2024
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50. Diving into the proteomic atlas of SARS-CoV-2 infected cells

Author

Victor C. Carregari, Guilherme Reis-de-Oliveira, Fernanda Crunfli, Bradley J. Smith, Gabriela Fabiano de Souza, Stéfanie Primon Muraro, Veronica M. Saia-Cereda, Pedro H. Vendramini, Paulo A. Baldasso, Lícia C. Silva-Costa, Giuliana S. Zuccoli, Caroline Brandão-Teles, André Antunes, Aline F. Valença, Gustavo G. Davanzo, João Victor Virgillio-da-Silva, Thiago dos Reis Araújo, Raphael Campos Guimarães, Felipe David Mendonça Chaim, Elinton Adami Chaim, Carolina Mie Kawagosi Onodera, Raissa Guimarães Ludwig, Tatiana Dandolini Saccon, André R. L. Damásio, Luiz Osório S. Leiria, Marco Aurélio R. Vinolo, Alessandro S. Farias, Pedro M. Moraes-Vieira, Marcelo A. Mori, José Luiz P. Módena, and Daniel Martins-de-Souza

Subjects
Medicine, Science
Abstract

Abstract The COVID-19 pandemic was initiated by the rapid spread of a SARS-CoV-2 strain. Though mainly classified as a respiratory disease, SARS-CoV-2 infects multiple tissues throughout the human body, leading to a wide range of symptoms in patients. To better understand how SARS-CoV-2 affects the proteome from cells with different ontologies, this work generated an infectome atlas of 9 cell models, including cells from brain, blood, digestive system, and adipocyte tissue. Our data shows that SARS-CoV-2 infection mainly trigger dysregulations on proteins related to cellular structure and energy metabolism. Despite these pivotal processes, heterogeneity of infection was also observed, highlighting many proteins and pathways uniquely dysregulated in one cell type or ontological group. These data have been made searchable online via a tool that will permit future submissions of proteomic data ( https://reisdeoliveira.shinyapps.io/Infectome_App/ ) to enrich and expand this knowledgebase.

Published
2024
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