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1. Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

Author

Bassi, Pedro R. A. S., Li, Wenxuan, Tang, Yucheng, Isensee, Fabian, Wang, Zifu, Chen, Jieneng, Chou, Yu-Cheng, Kirchhoff, Yannick, Rokuss, Maximilian, Huang, Ziyan, Ye, Jin, He, Junjun, Wald, Tassilo, Ulrich, Constantin, Baumgartner, Michael, Roy, Saikat, Maier-Hein, Klaus H., Jaeger, Paul, Ye, Yiwen, Xie, Yutong, Zhang, Jianpeng, Chen, Ziyang, Xia, Yong, Xing, Zhaohu, Zhu, Lei, Sadegheih, Yousef, Bozorgpour, Afshin, Kumari, Pratibha, Azad, Reza, Merhof, Dorit, Shi, Pengcheng, Ma, Ting, Du, Yuxin, Bai, Fan, Huang, Tiejun, Zhao, Bo, Wang, Haonan, Li, Xiaomeng, Gu, Hanxue, Dong, Haoyu, Yang, Jichen, Mazurowski, Maciej A., Gupta, Saumya, Wu, Linshan, Zhuang, Jiaxin, Chen, Hao, Roth, Holger, Xu, Daguang, Blaschko, Matthew B., Decherchi, Sergio, Cavalli, Andrea, Yuille, Alan L., and Zhou, Zongwei

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

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across various out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms on three test sets. In addition, we also evaluated pre-existing AI frameworks--which, differing from algorithms, are more flexible and can support different algorithms--including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain., Comment: Accepted to NeurIPS-2024

Published
2024

2. Label Critic: Design Data Before Models

Author

Bassi, Pedro R. A. S., Wu, Qilong, Li, Wenxuan, Decherchi, Sergio, Cavalli, Andrea, Yuille, Alan, and Zhou, Zongwei

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

As medical datasets rapidly expand, creating detailed annotations of different body structures becomes increasingly expensive and time-consuming. We consider that requesting radiologists to create detailed annotations is unnecessarily burdensome and that pre-existing AI models can largely automate this process. Following the spirit don't use a sledgehammer on a nut, we find that, rather than creating annotations from scratch, radiologists only have to review and edit errors if the Best-AI Labels have mistakes. To obtain the Best-AI Labels among multiple AI Labels, we developed an automatic tool, called Label Critic, that can assess label quality through tireless pairwise comparisons. Extensive experiments demonstrate that, when incorporated with our developed Image-Prompt pairs, pre-existing Large Vision-Language Models (LVLM), trained on natural images and texts, achieve 96.5% accuracy when choosing the best label in a pair-wise comparison, without extra fine-tuning. By transforming the manual annotation task (30-60 min/scan) into an automatic comparison task (15 sec/scan), we effectively reduce the manual efforts required from radiologists by an order of magnitude. When the Best-AI Labels are sufficiently accurate (81% depending on body structures), they will be directly adopted as the gold-standard annotations for the dataset, with lower-quality AI Labels automatically discarded. Label Critic can also check the label quality of a single AI Label with 71.8% accuracy when no alternatives are available for comparison, prompting radiologists to review and edit if the estimated quality is low (19% depending on body structures).

Published
2024

3. The need to implement FAIR principles in biomolecular simulations

Author

Amaro, Rommie, Åqvist, Johan, Bahar, Ivet, Battistini, Federica, Bellaiche, Adam, Beltran, Daniel, Biggin, Philip C., Bonomi, Massimiliano, Bowman, Gregory R., Bryce, Richard, Bussi, Giovanni, Carloni, Paolo, Case, David, Cavalli, Andrea, Chang, Chie-En A., Cheatham III, Thomas E., Cheung, Margaret S., Chipot, Cris, Chong, Lillian T., Choudhary, Preeti, Cisneros, Gerardo Andres, Clementi, Cecilia, Collepardo-Guevara, Rosana, Coveney, Peter, Covino, Roberto, Crawford, T. Daniel, Peraro, Matteo Dal, de Groot, Bert, Delemotte, Lucie, De Vivo, Marco, Essex, Jonathan, Fraternali, Franca, Gao, Jiali, Gelpí, Josep Lluís, Gervasio, Francesco Luigi, Gonzalez-Nilo, Fernando Danilo, Grubmüller, Helmut, Guenza, Marina, Guzman, Horacio V., Harris, Sarah, Head-Gordon, Teresa, Hernandez, Rigoberto, Hospital, Adam, Huang, Niu, Huang, Xuhui, Hummer, Gerhard, Iglesias-Fernández, Javier, Jensen, Jan H., Jha, Shantenu, Jiao, Wanting, Jorgensen, William L., Kamerlin, Shina Caroline Lynn, Khalid, Syma, Laughton, Charles, Levitt, Michael, Limongelli, Vittorio, Lindahl, Erik, Lindorff-Larsen, Kresten, Loverde, Sharon, Lundborg, Magnus, Luo, Yun Lyna, Luque, Francisco Javier, Lynch, Charlotte I., MacKerell, Alexander, Magistrato, Alessandra, Marrink, Siewert J., Martin, Hugh, McCammon, J. Andrew, Merz, Kenneth, Moliner, Vicent, Mulholland, Adrian, Murad, Sohail, Naganathan, Athi N., Nangia, Shikha, Noe, Frank, Noy, Agnes, Oláh, Julianna, O'Mara, Megan, Ondrechen, Mary Jo, Onuchic, José N., Onufriev, Alexey, Osuna, Silvia, Panchenko, Anna R., Pantano, Sergio, Parish, Carol, Parrinello, Michele, Perez, Alberto, Perez-Acle, Tomas, Perilla, Juan R., Pettitt, B. Montgomery, Pietropalo, Adriana, Piquemal, Jean-Philip, Poma, Adolfo, Praprotnik, Matej, Ramos, Maria J., Ren, Pengyu, Reuter, Nathalie, Roitberg, Adrian, Rosta, Edina, Rovira, Carme, Roux, Benoit, Röthlisberger, Ursula, Sanbonmatsu, Karissa Y., Schlick, Tamar, Shaytan, Alexey K., Simmerling, Carlos, Smith, Jeremy C., Sugita, Yuji, Świderek, Katarzyna, Taiji, Makoto, Tao, Peng, Tikhonova, Irina G., Tirado-Rives, Julian, Tunón, Inaki, Van Der Kamp, Marc W., Van der Spoel, David, Velankar, Sameer, Voth, Gregory A., Wade, Rebecca, Warshel, Ariel, Welborn, Valerie Vaissier, Wetmore, Stacey, Wong, Chung F., Yang, Lee-Wei, Zacharias, Martin, and Orozco, Modesto

Subjects
Quantitative Biology - Biomolecules
Abstract

This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necessity of a collaborative effort to create, establish, and sustain a database that allows findability, accessibility, interoperability, and reusability of molecular dynamics simulation data. Such a development would democratize the field and significantly improve the impact of MD simulations on life science research. This will transform our working paradigm, pushing the field to a new frontier. We invite you to support our initiative at the MDDB community (https://mddbr.eu/community/)

Published
2024

4. Explanation is All You Need in Distillation: Mitigating Bias and Shortcut Learning

Author

Bassi, Pedro R. A. S., Cavalli, Andrea, and Decherchi, Sergio

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Bias and spurious correlations in data can cause shortcut learning, undermining out-of-distribution (OOD) generalization in deep neural networks. Most methods require unbiased data during training (and/or hyper-parameter tuning) to counteract shortcut learning. Here, we propose the use of explanation distillation to hinder shortcut learning. The technique does not assume any access to unbiased data, and it allows an arbitrarily sized student network to learn the reasons behind the decisions of an unbiased teacher, such as a vision-language model or a network processing debiased images. We found that it is possible to train a neural network with explanation (e.g by Layer Relevance Propagation, LRP) distillation only, and that the technique leads to high resistance to shortcut learning, surpassing group-invariant learning, explanation background minimization, and alternative distillation techniques. In the COLOURED MNIST dataset, LRP distillation achieved 98.2% OOD accuracy, while deep feature distillation and IRM achieved 92.1% and 60.2%, respectively. In COCO-on-Places, the undesirable generalization gap between in-distribution and OOD accuracy is only of 4.4% for LRP distillation, while the other two techniques present gaps of 15.1% and 52.1%, respectively.

Published
2024

5. Targeting senescence induced by age or chemotherapy with a polyphenol-rich natural extract improves longevity and healthspan in mice

Author

Zumerle, Sara, Sarill, Miles, Saponaro, Miriam, Colucci, Manuel, Contu, Liliana, Lazzarini, Edoardo, Sartori, Roberta, Pezzini, Camilla, Rinaldi, Anna, Scanu, Anna, Sgrignani, Jacopo, Locatelli, Patrizia, Sabbadin, Marianna, Valdata, Aurora, Brina, Daniela, Giacomini, Isabella, Rizzo, Beatrice, Pierantoni, Alessandra, Sharifi, Saman, Bressan, Silvia, Altomare, Claudia, Goshovska, Yulia, Giraudo, Chiara, Luisetto, Roberto, Iaccarino, Luca, Torcasio, Cristina, Mosole, Simone, Pasquini, Emiliano, Rinaldi, Andrea, Pellegrini, Laura, Peron, Gregorio, Fassan, Matteo, Masiero, Stefano, Giori, Andrea Maria, Dall’Acqua, Stefano, Auwerx, Johan, Cippà, Pietro, Cavalli, Andrea, Bolis, Marco, Sandri, Marco, Barile, Lucio, Montopoli, Monica, and Alimonti, Andrea

Published
2024
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6. Faster ISNet for Background Bias Mitigation on Deep Neural Networks

Author

Bassi, Pedro R. A. S., Decherchi, Sergio, and Cavalli, Andrea

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

Bias or spurious correlations in image backgrounds can impact neural networks, causing shortcut learning (Clever Hans Effect) and hampering generalization to real-world data. ISNet, a recently introduced architecture, proposed the optimization of Layer-Wise Relevance Propagation (LRP, an explanation technique) heatmaps, to mitigate the influence of backgrounds on deep classifiers. However, ISNet's training time scales linearly with the number of classes in an application. Here, we propose reformulated architectures whose training time becomes independent from this number. Additionally, we introduce a concise and model-agnostic LRP implementation. We challenge the proposed architectures using synthetic background bias, and COVID-19 detection in chest X-rays, an application that commonly presents background bias. The networks hindered background attention and shortcut learning, surpassing multiple state-of-the-art models on out-of-distribution test datasets. Representing a potentially massive training speed improvement over ISNet, the proposed architectures introduce LRP optimization into a gamut of applications that the original model cannot feasibly handle.

Published
2024
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7. Machine learning for precision diagnostics of autoimmunity

Author

Kruta, Jan, Carapito, Raphael, Trendelenburg, Marten, Martin, Thierry, Rizzi, Marta, Voll, Reinhard E., Cavalli, Andrea, Natali, Eriberto, Meier, Patrick, Stawiski, Marc, Mosbacher, Johannes, Mollet, Annette, Santoro, Aurelia, Capri, Miriam, Giampieri, Enrico, Schkommodau, Erik, and Miho, Enkelejda

Published
2024
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11. Improving deep neural network generalization and robustness to background bias via layer-wise relevance propagation optimization

Author

Bassi, Pedro R. A. S., Dertkigil, Sergio S. J., and Cavalli, Andrea

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Features in images' backgrounds can spuriously correlate with the images' classes, representing background bias. They can influence the classifier's decisions, causing shortcut learning (Clever Hans effect). The phenomenon generates deep neural networks (DNNs) that perform well on standard evaluation datasets but generalize poorly to real-world data. Layer-wise Relevance Propagation (LRP) explains DNNs' decisions. Here, we show that the optimization of LRP heatmaps can minimize the background bias influence on deep classifiers, hindering shortcut learning. By not increasing run-time computational cost, the approach is light and fast. Furthermore, it applies to virtually any classification architecture. After injecting synthetic bias in images' backgrounds, we compared our approach (dubbed ISNet) to eight state-of-the-art DNNs, quantitatively demonstrating its superior robustness to background bias. Mixed datasets are common for COVID-19 and tuberculosis classification with chest X-rays, fostering background bias. By focusing on the lungs, the ISNet reduced shortcut learning. Thus, its generalization performance on external (out-of-distribution) test databases significantly surpassed all implemented benchmark models., Comment: Text and style improvements. Included reference to the published article (Nature Communications, https://doi.org/10.1038/s41467-023-44371-z)

Published
2022
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12. Esmethadone-HCl (REL-1017): a promising rapid antidepressant

Author

Fava, Maurizio, Stahl, Stephen M., De Martin, Sara, Mattarei, Andrea, Bettini, Ezio, Comai, Stefano, Alimonti, Andrea, Bifari, Francesco, Pani, Luca, Folli, Franco, Guidetti, Clotilde, Furlan, Alberto, Sgrignani, Jacopo, Locatelli, Patrizia, Cavalli, Andrea, O’Gorman, Cedric, Traversa, Sergio, Inturrisi, Charles E., Pappagallo, Marco, and Manfredi, Paolo L.

Published
2023
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15. Watching the release of a photopharmacological drug from tubulin using time-resolved serial crystallography

Author

Wranik, Maximilian, Weinert, Tobias, Slavov, Chavdar, Masini, Tiziana, Furrer, Antonia, Gaillard, Natacha, Gioia, Dario, Ferrarotti, Marco, James, Daniel, Glover, Hannah, Carrillo, Melissa, Kekilli, Demet, Stipp, Robin, Skopintsev, Petr, Brünle, Steffen, Mühlethaler, Tobias, Beale, John, Gashi, Dardan, Nass, Karol, Ozerov, Dmitry, Johnson, Philip J. M., Cirelli, Claudio, Bacellar, Camila, Braun, Markus, Wang, Meitian, Dworkowski, Florian, Milne, Chris, Cavalli, Andrea, Wachtveitl, Josef, Steinmetz, Michel O., and Standfuss, Jörg

Published
2023
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16. A critical view on autoantibodies in lupus nephritis: Concrete knowledge based on evidence

Author

Bruschi, Maurizio, Angeletti, Andrea, Prunotto, Marco, Meroni, Pier Luigi, Ghiggeri, Gian Marco, Moroni, Gabriella, Sinico, Renato Alberto, Franceschini, Franco, Fredi, Micaela, Vaglio, Augusto, Cavalli, Andrea, Scapozza, Leonardo, Patel, Jigar J., Tan, John C., Lo, Ken C., Cavagna, Lorenzo, Petretto, Andrea, Pratesi, Federico, Migliorini, Paola, Locatelli, Francesco, Pazzola, Giulia, Pesce, Giampaola, Giannese, Domenico, Manfredi, Angelo, Ramirez, Giuseppe A., Esposito, Pasquale, Murdaca, Giuseppe, Negrini, Simone, Bui, Federica, Trezzi, Barbara, Emmi, Giacomo, Cavazzana, Ilaria, Binda, Valentina, Fenaroli, Paride, Pisan, Isabella, Montecucco, Carlomaurizio, Santoro, Domenico, Scolari, Francesco, Mescia, Federica, Volpi, Stefano, Mosca, Marta, Tincani, Angela, Ravelli, Angelo, Murtas, Corrado, Candiano, Giovanni, Caridi, Gianluca, La Porta, Edoardo, and Verrina, Enrico

Published
2024
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17. ARN25068, a versatile starting point towards triple GSK-3β/FYN/DYRK1A inhibitors to tackle tau-related neurological disorders

Author

Demuro, Stefania, Sauvey, Conall, Tripathi, Shailesh K, Di Martino, Rita MC, Shi, Da, Ortega, Jose A, Russo, Debora, Balboni, Beatrice, Giabbai, Barbara, Storici, Paola, Girotto, Stefania, Abagyan, Ruben, and Cavalli, Andrea

Subjects
Biomedical and Clinical Sciences, Medicinal and Biomolecular Chemistry, Organic Chemistry, Chemical Sciences, Pharmacology and Pharmaceutical Sciences, Aging, Alzheimer's Disease, Frontotemporal Dementia (FTD), Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Neurodegenerative, Neurosciences, Acquired Cognitive Impairment, Brain Disorders, 5.1 Pharmaceuticals, Development of treatments and therapeutic interventions, Neurological, Binding Sites, Drug Evaluation, Preclinical, Glycogen Synthase Kinase 3 beta, Humans, Microtubules, Models, Molecular, Neurofibrillary Tangles, Neuroprotective Agents, Phosphorylation, Protein Binding, Protein Conformation, Protein Kinase Inhibitors, Protein Serine-Threonine Kinases, Protein-Tyrosine Kinases, Proto-Oncogene Proteins c-fyn, Structure-Activity Relationship, Tauopathies, tau Proteins, Central nervous system, Kinase inhibitors, Multitarget compounds, Docking studies, X-ray studies, Enzymatic and cell-based assays, Tau phosphorylation assay, Selectivity, Medicinal & Biomolecular Chemistry, Pharmacology and pharmaceutical sciences, Medicinal and biomolecular chemistry, Organic chemistry
Abstract

The human kinome plays a crucial role in several pathways. Its dysregulation has been linked to diverse central nervous system (CNS)-related disorders with a drastic impact on the aging population. Among them, tauopathies, such as Alzheimer's Disease (AD) and Frontotemporal Lobar degeneration (FTLD-tau), are neurodegenerative disorders pathologically defined by the presence of hyperphosphorylated tau-positive intracellular inclusions known as neurofibrillary tangles (NFTs). Compelling evidence has reported the great potential of the simultaneous modulation of multiple protein kinases (PKs) involved in abnormal tau phosphorylation through a concerted pharmacological approach to achieve a superior therapeutic effect relative to classic "one target, one drug" approaches. Here, we report on the identification and characterization of ARN25068 (4), a low nanomolar and well-balanced dual GSK-3β and FYN inhibitor, which also shows inhibitory activity against DYRK1A, an emerging target in AD and tauopathies. Computational and X-Ray studies highlight compound 4's typical H-bonding pattern of ATP-competitive inhibitors at the binding sites of all three PKs. In a tau phosphorylation assay on Tau0N4R-TM-tGFP U2OS cell line, 4 reduces the extent of tau phosphorylation, promoting tau-stabilized microtubule bundles. In conclusion, this compound emerges as a promising prototype for further SAR explorations to develop potent and well-balanced triple GSK-3β/FYN/DYRK1A inhibitors to tackle tau hyperphosphorylation.

Published
2022

18. An 19F NMR fragment-based approach for the discovery and development of BRCA2-RAD51 inhibitors to pursuit synthetic lethality in combination with PARP inhibition in pancreatic cancer

Author

Myers, Samuel H., Poppi, Laura, Rinaldi, Francesco, Veronesi, Marina, Ciamarone, Andrea, Previtali, Viola, Bagnolini, Greta, Schipani, Fabrizio, Ortega Martínez, Jose Antonio, Girotto, Stefania, Di Stefano, Giuseppina, Farabegoli, Fulvia, Walsh, Naomi, De Franco, Francesca, Roberti, Marinella, and Cavalli, Andrea

Published
2024
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19. PI3Kδ activation, IL-6 overexpression, and CD37 loss cause resistance to naratuximab emtansine in lymphomas

Author

Arribas, Alberto J., Napoli, Sara, Gaudio, Eugenio, Herbaux, Charles, Cannas, Eleonora, Tarantelli, Chiara, Bordone Pittau, Roberta, Cascione, Luciano, Munz, Nicolas, Aresu, Luca, Sgrignani, Jacopo, Rinaldi, Andrea, Kwee, Ivo, Rossi, Davide, Cavalli, Andrea, Zucca, Emanuele, Stussi, Georg, Stathis, Anastasios, Sloss, Callum, Davids, Matthew S., and Bertoni, Francesco

Published
2024
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20. Autoantibodies against chemokines post-SARS-CoV-2 infection correlate with disease course

Author

Muri, Jonathan, Cecchinato, Valentina, Cavalli, Andrea, Shanbhag, Akanksha A., Matkovic, Milos, Biggiogero, Maira, Maida, Pier Andrea, Moritz, Jacques, Toscano, Chiara, Ghovehoud, Elaheh, Furlan, Raffaello, Barbic, Franca, Voza, Antonio, De Nadai, Guendalina, Cervia, Carlo, Zurbuchen, Yves, Taeschler, Patrick, Murray, Lilly A., Danelon-Sargenti, Gabriela, Moro, Simone, Gong, Tao, Piffaretti, Pietro, Bianchini, Filippo, Crivelli, Virginia, Podešvová, Lucie, Pedotti, Mattia, Jarrossay, David, Sgrignani, Jacopo, Thelen, Sylvia, Uhr, Mario, Bernasconi, Enos, Rauch, Andri, Manzo, Antonio, Ciurea, Adrian, Rocchi, Marco B. L., Varani, Luca, Moser, Bernhard, Bottazzi, Barbara, Thelen, Marcus, Fallon, Brian A., Boyman, Onur, Mantovani, Alberto, Garzoni, Christian, Franzetti-Pellanda, Alessandra, Uguccioni, Mariagrazia, and Robbiani, Davide F.

Published
2023
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22. Efficacy and safety of sparsentan versus irbesartan in patients with IgA nephropathy (PROTECT): 2-year results from a randomised, active-controlled, phase 3 trial

Author

Abbasciano, Isabella, Abrantes, Catarina, Accarino, Simone, Adler, Sharon, Adoberg, Annika, Afsari, Rouzbeh, Ahmad, Syeda, Ahmed, Jafar, Ahn, Wooin, Ajayi, Bamidele, Aksamit, Dariusz, Al Chalabi, Saif, Alamartine, Eric, Alchi, Bassam, Ali, Mohammad, Aliotta, Roberta, Almaani, Salem, Almeida, Catarina, Almeida, Edgar, Alvarez, Francisco de la Prada, Alves, Patricia, Annese, Francesca, Appel, Gerald, Arduan, Alberto Ortiz, Arena, Maria, Arevalo, Marta Calvo, Arfaania, Dariush, Arias, Carlos, Aristoy, Emma Calatayud, Asakiene, Egle, Ashley, Sarah, Assefi, Ali, Atenza, Alba, Auerbach, Asta, Augustyniak-Bartosik, Hanna, Avella, Monroy, Ayling-Smith, Jonathan, Ayoub, Isabelle, Ayvazyan, Christine, Baccaro, Rocco, Bailey, Asha, Baker, Bruce, Balamuthusamy, Saravanan, Ballarin, Jose, Barata, Rui, Barbic, Jerko, Barisic, Dunja, Barratt, Jonathan, Barreto, Jose Carlos de Jesus, Barrios, Clara, Belingheri, Mirco, Benesova, Anna, Bernabeu, Ana Avila, Bernhardt, Wanja, Bhadra, Shamik, Biancone, Luigi, Blanchard, Anne, Boaglio, Elena, Bolignano, Davide, Bomback, Andrew, Bonilla, Gustavo Andres Useche, Bordoli, Monica, Bose, Bhadran, Boudville, Neil, Brandon, Donald, Brown, Karen, Broyet, Christian, Bucknall, Thomas, Buffet, Alexandre, Bumblyte', Inga Arune, Burdese, Manuel, Burgos, Natalia Allende, Burguet, Laure, Burtey, Stephane, Busch, Martin, Cakiroglu, Figen, Campbell, Victoria, Canetta, Pietro, Capaccio, Flavia, Carbonell, Juan, Cardoso, Filipa, Carro, Clara Garcia, Carroll, Robert, Carron, Pierre-Louis, Carullo, Nazareno, Cassia, Matthias, Caster, Dawn, Castillo, Belen Vizcaino, Catucci, Davide, Cavalli, Andrea, Chae, Dong-Wan, Chakera, Aron, Chan, Doris, Chan, Gary Chi Wang, Chan, Anthony Ting Pong, Chan, Lai Wan, Chang, Jae Hyun, Chang, Ming-Shan, Chapman, Fiona, Charytan, Chaim, Chaudhry, Asghar, Cheetham, Melissa, Chen, Cheng-Hsu, Chen, Hsin-Yu, Chen, Chien-Liang, Chen, Hung-Yuan, Chen, Hung-Chun, Chenine, Leila, Cheung, Siu Fai, Cheung, Chee Kay, Chiche-Jourde, Noemie, Chiu, Hsien-Fu, Chiu, Yen-Ling, Chiu, Yi-Wen, Chiu, Ian, Choi, Hoon Young, Chou, Kang-Ju, Chowdhury, Paramit, Chowdhury, Pradip, Chrysochou, Constantina, Chukwu, Chukwuma, Chun, Ka Yeong, Chung, Wookyung, Cieniawski, Dominik, Claisse, Guillaume, Clayton, Philip, Cleary, Catalina Martín, Clement, Nisha, Coates, Toby, Cogno, Chiara, Colla, Loredana, Collins, Michael, Colucci, Marco, Combe, Christian, Comi, Nicolino, Comunale, Roderick, Contreras, Gabriel, Cooper, Bruce, Coppock, Gaia, Costa, Ana Cortesão, Costanzi, Stefano, Cozzolino, Mario, Crail, Susan, Cunha, Liliana, Curry, Elizabeth, Dai, Tiane, Daina, Erica, Daneshvari, Sam, Dasgupta, Indranil, DaSilva, Louise, Davidson, Robert, De Bhailis, Aine Maire, de Précigout, Valérie, Decupere, Marc, Delmas, Yahsou, Deodato, Francesca, Dhaun, Neeraj, Dhaygude, Ajay, Dhelaria, Ranjit, Di Maio, Federica, Diezhandino, Maria Angeles Goicoechea, Dika, Zivka, Dinic, Miriana, Dittmer, Ian, Dobre, Mirela, Doctor, Gabriel, Donati, Gabriele, Doubel, Peter, Douthit, Nicole, Draganova, Diliana, Drexler, Yelena, Drinkovic, Martin, Drinovska, Klara, Dudreuilh, Caroline, Dumond, Clement, Durlen, Ivan, Dussol, Bertrand, Encarnacion, Montserrat Diaz, Endre, Zoltan, Enrique, Jorge, Eqbal, Yusuf, Erlich, Jonathan, Esposito, Vittoria, Esposito, Ciro, Fakih, Faisal, Fang, Hua-Chang, Farinha, Ana, Fearday, Aaron, Felgueiras, Joana de Sousa Soares, Felicjanczuk, Elzbieta, Fernandes, João Carlos, Fernandes, Sara, Fernandes, Adriana, Fernandez, Jose Luño, Fernandez, Victor, Fernando, Mangalee, Figueiredo, Ana Carolina, Firre, Eric, Floege, Jürgen, Flossmann, Oliver, Floyd, Lauren, Fonseca, Nuno, Fonseca, Jose Nuno de Almeida Agapito, Foote, Celine, Forbes, Suzanne, Forcen, Angel, Fornoni, Alessia, Fouassier, David, Fraser, Ian, Fung, Ka Shun Samuel, Furlano, Monica, Furtado, Teresa, Gadh, Rajdeep, Gaisset, Roxane, Gallagher, Martin, Gameiro, Joana, Gangadharan, Muralikrishna, Garcia, Fabiola Alonso, Garcia, Olga Gracia, Garg, Gunjan, Garrouste, Cyril, Gautam, Archana, Geara, Abdallah, Geddes, Colin, Gellineo, Lana, Germain, Michael, Gesualdo, Loreto, Ghalli, Farid, Giaroni, Francesco, Giles, Harold, Gillies, Alastair, Gimeno, Ana Vilar, Girardet, Annette, Glaze, Jeffrey, Goh, Tze, Gois, Mario, Goldsmith, Christopher, Goma, Elena, Gomes, Ana Marta, Goncalves, Luis Pedro Falcao, Goncalves, Sara, Gontarek-Kacprzak, Julita, Gonzalez, Carlos, Gopu, Arun, Gowda, Nayan, Gray, Nicholas, Greco, Barbara, Green, Heather, Greze, Clarisse, Griffin, Sian, Guldris, Secundino Cigarran, Guzman, Ursula Verdalles, Ha, Jeffrey Tsun Kit, Hall, Amanda, Hamilton, Patrick, Han, Seung Hyeok, Haws, Robert, Hayat, Ashik, Heath, Lauren, Heinrichs, Sven, Henderson, Andrew, Henderson, Amy, Hendren, Elizabeth, Heng, Anne-Elisabeth, Heres, Salia Virxinia Pol, Herfurth, Johann Konstantin, Herrero, Evangelina Merida, Ho, Tzung-Yo, Hogan, Jonathan, Hohenstein, Bernd, Hollett, Peter, Horvatic, Ivica, Hsu, Bang-Gee, Hsu, Chia-Tien, Hsu, Chih-Yang, Hsu, Shih-Ping, Huang, Chien-Wei, Hudson, Amy, Hung, Chi-Chih, Hwang, Shang-Jyh, Hwang, Daw-Yang, Hyun, Young Youl, Ilmoja, Madis, Irish, Georgina, Jacob, Radu, Jahan, Sadia, Jain, Arunima, Jamar, Sofie, Jander, Anna, Jang, Hye Ryoun, Janko, Catherine, Jardine, Meg, Jaskowski, Piotr, Jeantet, Guillaume, Jelakovic, Bojan, Jeon, Junseok, Jeong, Jong Cheol, Jesudason, Shilpanjali, Jittirat, Arksarapuk, Jo, Sang-Kyung, Jones, Rachel, Joo, Narae, Jorge, Sofia, Julian, Bruce, Jung, JiYong, Jyothinagaram, Praveena, Kadakia, Jigar, Kamal, Fahameedah, Kanigicherla, Durga, Karanovic, Sandra, Karras, Alexandre, Kasimatis, Theodoros, Kassem, Mohamad, Kattamanchi, Siddhartha, Keller, Christopher, Kentric, Dubravka Trajbar, Kettner, Christiane, Khalil, Kassem, Kim, Dana, Kim, Se Joong, Kim, Yeong Hoon, Kim, Yunmi, Kim, Hyung Wook, Kim, Sunggyun, Kim, Jwa-Kyung, Kim, Aejin, Kim, Myung-Gyu, Kimber, Cassandra, Klamm, Kathryn, Koch, Annegret, Koester, Eric, Kolvald, Kulli, Kon, Sui, Kooienga, Laura Ann, Kotwal, Sradha, Krajewska, Magdalena, Kratka, Karolina, Krishnan, Anoushka, Krishnasamy, Rathika, Krueger, Thilo, Kruljac, Ivan, Kulka, Simone, Kunzendorf, Ulrich, Kuo, Mei-Chuan, Kuo, Hung-Tien, Kuo, Sheng, Kwan, Lorraine Pui Yuen, Kwella, Norbert, Kwella, Bogna, Kyaw, Kay, Labat, Aurore, Lafayette, Richard, Lagatta, Mark, Lam, Chi Kwan Darwin, Lambie, Mark, Lanaret, Camille, Latif, Farah, Laute, Margo, Law, Wai Ping, Lawman, Sarah, Lazo, Mercedes Salgueira, Lee, Hajeong, Lee, Moon Hyoung, Lee, Kyu-Beck, Lee, Hyung-seok, Lee, JungEun, Lee, Hyun Hee, Lee, Po-Tsang, Leis, Liisi, Lemahieu, Wim, Leung, Janson, Li, Yan, Lie, Davina Ngoi Wah, Lifland, Howard, Lilienthal, Kadri, Lim, Wai, Limardo, Monica, Livrozet, Marine, Longhi, Selena, Lopes, Daniela, Lopes, Jose Antonio, Lorde, Nathan, Luxton, Grant, Ma, Mingyao, Madhrira, Machaiah, Madonia, Phillip, Maes, Bart, Mahadevan, Kumaradevan, Mahdi, Amar, Mahdi, Eamon, Maillard, Nicolas, Maixnerova, Dita, Majstorovic Barac, Karolina, Malecki, Robert, Malfait, Thomas, Malvica, Silvia, Mandayam, Sreedhar, Manley, Paul, Mansfield, Nick, Marcantoni, Carmelita, Mariat, Christophe, Marin, Tamara Malek, Marin, Lida Maria Rodas, Mark, Patrick, Marques, Joana, Martinez, Eduardo Gutierrez, Martinez, Stacy, Masset, Catherine, Masson, Ingrid, Mather, Amanda, Maung Myint, Thida Maung, Mayet, Valentin, McCafferty, Kieran, McCarthy, Ellen, McDonald, Stephen, McGreal, Kerri, McMahon, Kellyn, McNally, Andrew, McQuarrie, Emily, Medina, Allyson, Meeus, Gert, Mehta, Kshama, Mendoza, Elder, Menezes, Maria, Meng, Jerry, Messa, Piergiorgio, Messias, Ana, Miarka, Przemyslaw, Miglinas, Marius, Mikacic, Ivana, Mikhail, Ashraf, Milicevic, Martina, Minasian, Raffi, Moeller, Marcus, Mohey, Hesham, Mojarrab, Javad, Mok, Maggie Ming Yee, Moncasi, Eduardo Parra, Moonen, Martial, Morales, Wenceslao Adrian Aguilera, Moreno, Eduardo Verde, Moreno, Antolina, Morris, Adam, Moutzouris, Dimitrios, Moya, Mercedes Gonzalez, Mucha, Simon, Musolino, Michela, 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Carole, Phua, Elaine, Piel, Julia, Pillai, Unnikrishnan, Pilmore, Helen, Plavljanic, Djuro, Plaza, Mercedes Velo, Pollock, Carol, Ponnusamy, Arvind, Ponti, Serena, Powell, Thomas, Pradhan, Nishigandha, Prado, Raul Fernandez, Prkacin, Ingrid, Pruthi, Rishi, Pugh, Dan, Quinn, Sarah, Quintana, Luis F, Raakesh, Lathika, Rabindranath, Kannaiyan, Radermacher, Luc, Radhakrishnan, Jai, Ragy, Omar Sherin Ibrahim, Raichoudhury, Ritesh, Raimundo, Mario, Randone, Paolo, Razavian, Mona, Reichel, Helmut, Reis, Marina, Remuzzi, Giuseppe, Reynolds, Annie, Ricchiuto, Anna, Rico, Ivon Maritza Bobadilla, Rigothier, Claire, Ritchie, Angus, Rivero, Maria Antonieta Azancot, Rizk, Dana, Ro, Han, Rodriguez, Alfred, Rodriguez Garcia, Eva, Roger, Simon, Rony, Mohammad, Rosenberg, Mai, Roueff, Stephane, Rovin, Brad, Roxburgh, Sarah, Ruiz, Mario, Rusinol, Helena Marco, Rychlik, Ivan, Sá, Helena, Salehi, Tania, Sánchez de la Nieta, Maria Dolores, Sanden, Silja, Santockiene, Lina, Santos, Patricia Valerio, Santos, Iara Da Silva, Sargsyan, Mari, Sarnow, Franziska, Sat, Sebahat, Satora, Ewa, Savuk, Ana, Sayer, Matthew, Schlieper, Georg, Schlosser, Mandy, Schmidt-Guertler, Hans, Schockaert, Olivier, Schulte, Kevin, Schulz, Lisa, Selvaskandan, Haresh, Semple, David, Sen, Shaundeep, Seppet, Elviira, Serrano, Antonio Galan, Serre, Jean-Emmanuel, Sevillano, Angel, Shah, Sapna, Shen, Yvonne, Sheridan, Brittany, Shu, Kai-Hsiang, Sightler, Ford, Silva, Sónia, Silva, Arnold, Sinha, Smeeta, Siriwardana, Amanda, Sitas, Zvonimir, Sivo, Carmen, Sizeland, Peter, Sly, Cathylee, Smith, Priscilla, Snow, Brian, Soares, Elsa Sofia Quadrado, Solazzo, Andrea, Solis, Elena Gutierrez, Solomon, Laurence, Song, Young Rim, Sousa, Helena, Spinowitz, Bruce, Stallworthy, Elizabeth, Stamellou, Eleni, Stanczyk, Malgorzata, Stangenberg, Stefanie, Stankovic, Marija, Stankuviene, Asta, Stea, Emma Diletta, Stompor, Tomasz, Storrar, Joshua, Sullivan, James, Sulowicz, Wladyslaw, Suyumova, Irina, Szeto, Cheuk Chun, Tamarit, Esther, Tan, Eddie, Tan, May, Tang, Sydney Chi Wai, Tang, Hon, Teixeira, Catarina, Telling, Kadri, Terrades, Natalia Ramos, Tesar, Vladimir, Tez, Didem, Thervet, Eric, Thibaudin, Damien, Thomas, Mark, Thyagarajan, Nikhil, Tkaczyk, Marcin, Torreggiani, Massimo, Trillini, Matias, Tsai, Shang-Feng, Tsai, Wan-Chuan, Tsai, Jer-Chia, Tseng, Po-Yu, Tumlin, James, Tung, Kuei-Ting, Turnbull, Angus, Udani, Suneel, Uhlinova, Jana, Unt, Kristin, Urciuolo, Federica, Vanacker, An, Vandewiele, Bert, Vaz, Alvaro, Veermae, Kristi, Viaene, Liesbeth, Vickiene, Alvita, Vigano, Sara, Vila, Maria Antonia Munar, Vilayur, Eswari, Villanueva, Caridad Martinez, Villarroya, Cristina Medrano, Villen, Alejandro Soria, Viramontes, Veronica, Vita, Caterina, Vujcic, Dunja, Wahba, Mona, Wan, Susan, Wang, Chih-Hsien, Warling, Xavier, Waters, Gerald, Waugh, Jane, Weiland, Lea, Weiner, Stefan, Weinreich, Thomas, Werth, Stephan Christian, Wickens, Olivia, Wijeratne, Vidu, Wilder, Karen, Willcocks, Lisa, Williams, Allister, Winiarska, Agata, Wirtz, Nikolaus, Wolf, Lothar, Wolf, Gunter, Wong, Muh Geot, Wong, Yick Hei, Wong, Sze Ho Sunny, Workeneh, Biruh, Wu, Ming-Ju, Wu, Hon-Yen, Wyndham, Roger, Yang, Jihyun, Yang, Ju-Yeh, Yeap, Chii, Yim, Ka Fai, Yong, Kenneth, Yoo, Tae-Hyun, Yoon, Songuk, Yu, Tung-Min, Yue, Tak Tai Andrew, Zakari, Michel, Zakauskiene, Urte, Zaoui, Philippe, Zbrzezniak, Justyna, Zielinska, Dorota, Zizzi, Carlotta Federica, Rovin, Brad H, Heerspink, Hiddo J L, Alpers, Charles E, Bieler, Stewart, Diva, Ulysses A, Inrig, Jula K, Kohan, Donald E, Komers, Radko, Małecki, Robert, Mercer, Alex, Noronha, Irene L, Oh, Se Won, Peh, Chen Au, Praga, Manuel, Preciado, Priscila, Rheault, Michelle N, Rote, William E, Tang, Sydney C W, Trachtman, Howard, Trimarchi, Hernán, Tumlin, James A, and Perkovic, Vlado

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2023
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24. Sparsentan in patients with IgA nephropathy: a prespecified interim analysis from a randomised, double-blind, active-controlled clinical trial

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Alarmartine, Eric, Barratt, Jonathan, Chae, Dong Wan, Del Vecchio, Lucia, Floege, Jurgen, Hwang, Shang-Jyh, Jelakovic, Bojan, Maes, Bart, Malecki, Robert, Miglinas, Marius, Nolasco, Fernando Eduardo Barbosa, Praga, Manual, Rabindranath, Kannaiyan, Rosenberg, Mai, Rovin, Brad, Tang, Sydney Chi Wai, Tesar, Vladmir, Wong, Muh Geot, Bose, Bhadran, Gangadharan, Muralikrishna, McDonald, Stephen, Peh, Chen, Jahan, Sadia, Yeap, Chii, Clayton, Philip, Irish, Georgina, Thyagarajan, Nikhil, Hollett, Peter, Krishnasamy, Rathika, Carroll, Robert, Jesudason, Shilpanjali, Crail, Susan, Coates, Toby, Waugh, Jane, Noble, Euan, Mahadevan, Kumaradevan, Campbell, Victoria, Salehi, Tania, Lim, Wai, Boudville, Neil, Chakera, Aron, Chan, Doris, Krishnan, Anoushka, Eqbal, Yusuf, Gillies, Alastair, Vilayur, Eswari, Maung Myint, Thida Maung, Gray, Nicholas, Cheetham, Melissa, Pollock, Carol, Cooper, Bruce, Mather, Amanda, Roxburgh, Sarah, Shen, Yvonne, Stangenberg, Stefanie, Siriwardana, Amanda, O'Lone, Emma, Wan, Susan, Neuen, Brendon, Tsun Kit Ha, Jeffrey, Kim, Dana, Heath, Lauren, Jain, Arunima, Phua, Elaine, Li, Yan, Gallagher, Martin, Jardine, Meg, Ritchie, Angus, Razavian, Mona, Foote, Celine, Wyndham, Roger, Sen, Shaundeep, Endre, Zoltan, Erlich, Jonathan, Fernando, Mangalee, Yong, Kenneth, Luxton, Grant, Kotwal, Sradha, Roger, Simon, Wijeratne, Vidu, Packham, David, Fraser, Ian, Vandewiele, Bert, Laute, Margo, Lemahieu, Wim, Jamar, Sofie, Ombelet, Sara, Meeus, Gert, Decupere, Marc, Schockaert, Olivier, Doubel, Peter, Viaene, Liesbeth, Radermacher, Luc, Masset, Catherine, Moonen, Martial, Firre, Eric, Milicevic, Martina, Warling, Xavier, Vanacker, An, Malfait, Thomas, Durlen, Ivan, Horvatic, Ivica, Savuk, Ana, Gellineo, Lana, Karanovic, Sandra, Dika, Zivka, Plavljanic, Djuro, Mikacic, Ivana, Trajbar Kentric, Dubravka, Barisic, Dunja, Stankovic, Marija, Majstorovic Barac, Karolina, Kruljac, Ivan, Pavlovic, Drasko, Drinkovic, Martin, Prkacin, Ingrid, Barbic, Jerko, Sitas, Zvonimir, Vujcic, Dunja, Rychlik, Ivan, Benesova, Anna, Drinovska, Klara, Kratka, Karolina, Maixnerova, Dita, Ilmoja, Madis, Unt, Kristin, Lilienthal, Kadri, Auerbach, Asta, Leis, Liisi, Piel, Julia, Adoberg, Annika, Kolvald, Kulli, Veermae, Kristi, Telling, Kadri, Seppet, Elviira, Uhlinova, Jana, Zaoui, Philippe, Carron, Pierre-Louis, Masson, Ingrid, Dinic, Miriana, Thibaudin, Damien, Broyet, Christian, Maillard, Nicolas, Mohey, Hesham, Mariat, Christophe, Claisse, Guillaume, Alamartine, Eric, Dussol, Bertrand, Burtey, Stephane, Chiche-Jourde, Noemie, Serre, Jean-Emmanuel, Jeantet, Guillaume, Chenine, Leila, Blanchard, Anne, Roueff, Stephane, Thervet, Eric, Fouassier, David, Buffet, Alexandre, Livrozet, Marine, Gaisset, Roxane, Karras, Alexandre, Heng, Anne-Elisabeth, Garrouste, Cyril, Philipponnet, Carole, Nicolo, Clementine, Atenza, Alba, Lanaret, Camille, Greze, Clarisse, Mayet, Valentin, Dumond, Clement, Delmas, Yahsou, Combe, Christian, Rigothier, Claire, Burguet, Laure, Labat, Aurore, Mucha, Simon, de Précigout, Valérie, Weinreich, Thomas, Reichel, Helmut, Draganova, Diliana, Wolf, Lothar, Hohenstein, Bernd, Heinrichs, Sven, Kulka, Simone, Sat, Sebahat, Weiland, Lea, Krueger, Thilo, Wolf, Gunter, Kettner, Christiane, Schlosser, Mandy, Herfurth, Johann Konstantin, Koch, Annegret, Busch, Martin, Werth, Stephan Christian, Nitschke, Martin, Cakiroglu, Figen, Sarnow, Franziska, Schulz, Lisa, Weiner, Stefan, Wirtz, Nikolaus, Koester, Eric, Moeller, Marcus, Stamellou, Eleni, Sanden, Silja, Schmidt-Guertler, Hans, Bernhardt, Wanja, Patecki, Margret, Schlieper, Georg, Schulte, Kevin, Girardet, Annette, Kunzendorf, Ulrich, Kwan, Lorraine Pui Yuen, Mok, Maggie Ming Yee, Chan, Gary Chi Wang, Ma, Mingyao, Lie, Davina Ngoi Wah, Chan, Anthony Ting Pong, Szeto, Cheuk Chun, Ng, Kit Chung Jack, Cheung, Siu Fai, Yue, Tak Tai Andrew, Fung, Ka Shun Samuel, Tang, Hon, Yim, Ka Fai, Law, Wai Ping, Wong, Yick Hei, Lam, Chi Kwan Darwin, Wong, Sze Ho Sunny, Marcantoni, Carmelita, Aliotta, Roberta, Deodato, Francesca, Patella, Gemma, Comi, Nicolino, Vita, Caterina, Carullo, Nazareno, Bolignano, Davide, Musolino, Michela, Trillini, Matias, Perico, Norberto, Remuzzi, Giuseppe, Daina, Erica, Biancone, Luigi, Colla, Loredana, Burdese, Manuel, Cogno, Chiara, Boaglio, Elena, Abbasciano, Isabella, Zizzi, Carlotta Federica, Randone, Paolo, Napodano, Pietro, Ricchiuto, Anna, Cassia, Matthias, Accarino, Simone, Cozzolino, Mario, Baccaro, Rocco, Costanzi, Stefano, Di Maio, Federica, Arena, Maria, Urciuolo, Federica, Vigano, Sara, Cavalli, Andrea, Limardo, Monica, Bordoli, Monica, Ponti, Serena, Longhi, Selena, Solazzo, Andrea, Giaroni, Francesco, Donati, Gabriele, Torreggiani, Massimo, Catucci, Davide, Colucci, Marco, Esposito, Vittoria, Esposito, Ciro, Gesualdo, Loreto, Capaccio, Flavia, Diletta Stea, Emma, Sivo, Carmen, Annese, Francesca, Papadia, Federica, Messa, Piergiorgio, Belingheri, Mirco, Passerini, Patrizia, Malvica, Silvia, Vickiene, Alvita, Zakauskiene, Urte, Asakiene, Egle, Bumblyte', Inga Arune, Stankuviene, Asta, Santockiene, Lina, Hayat, Ashik, Williams, Allister, Sizeland, Peter, Tan, Eddie, Waters, Gerald, Chan, Lai Wan, Henderson, Andrew, Turnbull, Angus, McNally, Andrew, Reynolds, Annie, Pilmore, Helen, Dittmer, Ian, Manley, Paul, Stallworthy, Elizabeth, Goh, Tze, Semple, David, Collins, Michael, Curry, Elizabeth, Ahmed, Jafar, Nguyen, Thu, Winiarska, Agata, Zbrzezniak, Justyna, Stompor, Tomasz, Krajewska, Magdalena, Augustyniak-Bartosik, Hanna, Zielinska, Dorota, Jander, Anna, Stanczyk, Malgorzata, Tkaczyk, Marcin, Miarka, Przemyslaw, Aksamit, Dariusz, Jaskowski, Piotr, Sulowicz, Wladyslaw, Cieniawski, Dominik, Gontarek-Kacprzak, Julita, Felicjanczuk, Elzbieta, Kwella, Norbert, Kwella, Bogna, Satora, Ewa, Fernandes, João Carlos, Gomes, Ana Marta, Reis, Marina, Lopes, Daniela, Almeida, Catarina, Sá, Helena, Figueiredo, Ana Carolina, Pardinhas, Clara, Almeida, Edgar, Raimundo, Mario, Cortesão Costa, Ana, Falcao Goncalves, Luis Pedro, Fernandes, Sara, Silva, Sónia, Teixeira, Catarina, Fernandes, Adriana, Nolasco, Fernando, Alves, Patricia, Gois, Mario, Fonseca, Nuno, Messias, Ana, Menezes, Maria, Cardoso, Filipa, Sousa, Helena, Marques, Joana, Barata, Rui, Lopes, Jose Antonio, Jorge, Sofia, Gameiro, Joana, de Almeida Agapito Fonseca, Jose Nuno, Goncalves, Sara, Farinha, Ana, Valerio Santos, Patricia, Natario, Ana, de Jesus Barreto, Jose Carlos, Abrantes, Catarina, Quadrado Soares, Elsa Sofia, Soares Felgueiras, Joana de Sousa, Cunha, Liliana, Parreira, Lucia, Furtado, Teresa, Vaz, Alvaro, Oh, Kook-Hwan, Lee, Hajeong, Joong Kim, Se, Jeong, Jong Cheol, Hoon Kim, Yeong, Kim, Yunmi, Park, Hyeong Cheon, Choi, Hoon Young, Wook Kim, Hyung, Lee, Moon Hyoung, Yoon, Songuk, Lee, Kyu-Beck, Hyun, YoungYoul, Yoo, Tae-Hyun, Han, Seung Hyeok, Park, Jung Tak, Kim, Sunggyun, Song, Young Rim, Kim, Jwa-Kyung, Lee, Hyung-seok, Joo, Narae, Lee, JungEun, Ryoun Jang, Hye, Jeon, Junseok, Chung, Wookyung, Lee, HyunHee, Chang, Jae Hyun, Chun, Ka Yeong, Jung, Ji Yong, Ro, Han, Kim, Aejin, Jo, Sang-Kyung, Yang, Jihyun, Kim, Myung-Gyu, Oh, SeWon, Martinez Villanueva, Caridad, Gimeno, Ana Vilar, Andres Useche Bonilla, Gustavo, Tamarit, Esther, Galan Serrano, Antonio, Verde Moreno, Eduardo, Fernandez, Jose Luño, Goicoechea Diezhandino, Maria Angeles, Verdalles Guzman, Ursula, de Jose, Ana Perez, Ortiz Arduan, Alberto, Pérez Gómez, María Vanessa, Martín Cleary, Catalina, Prado, Raul Fernandez, Goma, Elena, Ballarin, Jose, Encarnacion, Montserrat Diaz, Da Silva Santos, Iara, Marco Rusinol, Helena, Furlano, Monica, Arias, Carlos, Barrios, Clara, Garcia, Eva Rodriguez, Sierra Ochoa, Adriana, Vizcaino Castillo, Belen, Pantoja Perez, Jonay, Gonzalez Moya, Mercedes, Sargsyan, Mari, Calatayud Aristoy, Emma, Bernabeu, Ana Avila, Perez Lluna, Leticia, Malek Marin, Tamara, Antonia Munar Vila, Maria, Bobadilla Rico, Ivon Maritza, Allende Burgos, Natalia, Gutierrez Martinez, Eduardo, Gutierrez Solis, Elena, Sevillano, Angel, Merida Herrero, Evangelina, Miquel Blasco Pelicano, Josep, Rodas Marin, Lida Maria, Quintana, Luis F, Antonieta Azancot Rivero, Maria, Ramos Terrades, Natalia, Garcia Carro, Clara, Agraz Pamplona, Irene, Salgueira Lazo, Mercedes, de la Prada Alvarez, Francisco, Alonso Garcia, Fabiola, Adrian Aguilera Morales, Wenceslao, Virxinia Pol Heres, Salia, Forcen, Angel, Parra Moncasi, Eduardo, Medrano Villarroya, Cristina, Soria Villen, Alejandro, Gracia Garcia, Olga, Velo Plaza, Mercedes, Sánchez de la Nieta, Maria Dolores, Calvo Arevalo, Marta, Moreno, Antolina, Cigarran Guldris, Secundino, de Vicente, Manuel Pereira, Munar Vila, Maria Antonia, Hsu, Bang-Gee, Wang, Chih-Hsien, Chen, Cheng-Hsu, Yu, Tung-Min, Wu, Ming-Ju, Tsai, Shang-Feng, Hsu, Chia-Tien, Chiu, Hsien-Fu, Chou, Kang-Ju, Fang, Hua-Chang, Lee, Po-Tsang, Chen, Hsin-Yu, Chen, Chien-Liang, Huang, Chien-Wei, Ou, Shih-Hsiang, Ho, Tzung-Yo, Hsu, Chih-Yang, Chang, Ming-Shan, Chiu, Yen-Ling, Peng, Yu-Sen, Shu, Kai-Hsiang, Pan, Szu-Yu, Hsu, Shih-Ping, Yang, Ju-Yeh, Pai, Mei-Fen, Tseng, Po-Yu, Wu, Hon-Yen, Tsai, Wan-Chuan, Tung, Kuei-Ting, Chen, Hung-Yuan, Chen, Hung-Chun, Kuo, Mei-Chuan, Hwang, Daw-Yang, Chiu, Yi-Wen, Hung, Chi-Chih, Kuo, Hung-Tien, Tsai, Jer-Chia, McCafferty, Kieran, Forbes, Suzanne, Dasgupta, Indranil, Thomas, Mark, Mahdi, Amar, Ajayi, Bamidele, Chowdhury, Paramit, Kasimatis, Theodoros, Moutzouris, Dimitrios, Dudreuilh, Caroline, Pruthi, Rishi, Mansfield, Nick, Doctor, Gabriel, Shah, Sapna, Kon, Sui, Smith, Priscilla, Hamilton, Patrick, Kanigicherla, Durga, Ibrahim Ragy, Omar Sherin, Alchi, Bassam, Flossmann, Oliver, Ghalli, Farid, Lawman, Sarah, Sinha, Smeeta, Chrysochou, Constantina, Chukwu, Chukwuma, Maire De Bhailis, Aine, Al Chalabi, Saif, Hudson, Amy, Gopu, Arun, Wickens, Olivia, Storrar, Joshua, Wahba, Mona, Lorde, Nathan, Rony, Mohammad, Griffin, Sian, Latif, Farah, Ali, Mohammad, DaSilva, Louise, Ayling-Smith, Jonathan, Mahdi, Eamon, Willcocks, Lisa, Jones, Rachel, Cheung, Chee Kay, Selvaskandan, Haresh, Pugh, Dan, Sayer, Matthew, Dhaun, Neeraj, Chapman, Fiona, Mark, Patrick, Geddes, Colin, McQuarrie, Emily, Patel, Rajan, Solomon, Laurence, Ponnusamy, Arvind, Morris, Adam, Okoh, Pedro, Floyd, Lauren, Dhaygude, Ajay, Leung, Janson, Goldsmith, Christopher, Pandya, Bhavna, Tez, Didem, Mikhail, Ashraf, Brown, Karen, Bucknall, Thomas, Lambie, Mark, Comunale, Roderick, Brandon, Donald, Martinez, Stacy, Hall, Amanda, Henderson, Amy, Fearday, Aaron, Douthit, Nicole, Snow, Brian, Silva, Arnold, Sly, Cathylee, Keller, Christopher, Davidson, Robert, Meng, Jerry, Haws, Robert, Kattamanchi, Siddhartha, Mojarrab, Javad, Pillai, Unnikrishnan, Lafayette, Richard, O'Shaughnessy, Michelle, Kamal, Fahameedah, Mehta, Kshama, Baker, Bruce, Ruiz, Mario, Jyothinagaram, Praveena, Peri, Usha, Paxton, William, Tumlin, James, McGreal, Kerri, McCarthy, Ellen, Kimber, Cassandra, Gautam, Archana, Khalil, Kassem, Nguyen, Viet, Minasian, Raffi, Arfaania, Dariush, Daneshvari, Sam, Zakari, Michel, Patrikyan, Artashes, Afsari, Rouzbeh, Ayvazyan, Christine, Fakih, Faisal, Lagatta, Mark, Rodriguez, Alfred, Avella, Jorge Enrique Monroy, Patak, Ramachandra, Kadakia, Jigar, Radhakrishnan, Jai, Appel, Gerald, Ahn, Wooin, Nelson, Bradley, Medina, Allyson, Ahmad, Syeda, Peleg, Yonatan, Clement, Nisha, Chiu, Ian, Hendren, Elizabeth, Bomback, Andrew, Canetta, Pietro, Spinowitz, Bruce, Charytan, Chaim, Parikh, Nishita, Kuo, Sheng, Raichoudhury, Ritesh, Dobre, Mirela, Negrea, Lavinia, Padiyar, Aparna, Jittirat, Arksarapuk, Pradhan, Nishigandha, Dhelaria, Ranjit, Balamuthusamy, Saravanan, Madhrira, Machaiah, Powell, Thomas, Lifland, Howard, Bailey, Asha, Ford Sightler, Sarah Ashley, Suthar, Meera Patel, Green, Heather, Parikh, Samir, Ayoub, Isabelle, Almaani, Salem, Contreras, Gabriel, Fornoni, Alessia, Drexler, Yelena, Geara, Abdallah, Sheridan, Brittany, Coppock, Gaia, Hogan, Jonathan, Gonzalez, Carlos, Bhadra, Shamik, Chowdhury, Pradip, Kyaw, Kay, Tan, May, Raakesh, Lathika, Mendoza, Elder, Viramontes, Veronica, Chaudhry, Asghar, Carbonell, Juan, Gadh, Rajdeep, Fernandez, Victor, Kassem, Mohamad, Jacob, Radu, Wilder, Karen, Newsome, Britt, Klamm, Kathryn, Suyumova, Irina, Kooienga, Laura Ann, Janko, Catherine, Rizk, Dana, Julian, Bruce, Caster, Dawn, Perez, Erika, Garg, Gunjan, Gowda, Nayan, Udani, Suneel, Mandayam, Sreedhar, Workeneh, Biruh, Assefi, Ali, Greco, Barbara, Germain, Michael, Patel, Jusmin, Quinn, Sarah, Sullivan, James, Glaze, Jeffrey, Madonia, Phillip, McMahon, Kellyn, Giles, Harold, Adler, Sharon, Dai, Tiane, Heerspink, Hiddo J L, Alpers, Charles E, Bieler, Stewart, Diva, Ulysses, Inrig, Jula, Komers, Radko, Mercer, Alex, Noronha, Irene L, Rheault, Michelle N, Rote, William, Trachtman, Howard, Trimarchi, Hernán, and Perkovic, Vlado

Published
2023
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28. Long-term oncological outcomes after haemorrhagic apoplexy in pituitary adenoma managed operatively and non-operatively

Author

Budohoski, Karol P., Khawari, Sogha, Cavalli, Andrea, Quah, Boon L., Kolias, Angelos, Waqar, Mueez, Krishnan, Prahlad G., Lawes, Indu, Cains, Fiona, Arwyn-Jones, James, Su, Zhangjie, Gurnell, Mark, Powlson, Andrew, Donnelly, Neil, Tysome, James, Sharma, Rishi, Muthusamy, Brinda, Kearney, Tara, Robinson, Adam, Marcus, Hani J., Gnanalingham, Kanna, Karabatsou, Konstantina, Pathmanaban, Omar N., Sinha, Saurabh, Santarius, Thomas, Mannion, Richard, and Kirollos, Ramez W.

Published
2022
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30. Reply to: Hultström et al., Genetic determinants of mannose-binding lectin activity predispose to thromboembolic complications in critical COVID-19. Mannose-binding lectin genetics in COVID-19

Author

Asselta, Rosanna, Paraboschi, Elvezia Maria, Stravalaci, Matteo, Invernizzi, Pietro, Bonfanti, Paolo, Biondi, Andrea, Pagani, Isabel, Pedotti, Mattia, Doni, Andrea, Scavello, Francesco, Mapelli, Sarah N., Sironi, Marina, Perucchini, Chiara, Varani, Luca, Matkovic, Milos, Cavalli, Andrea, Cesana, Daniela, Gallina, Pierangela, Pedemonte, Nicoletta, Capurro, Valeria, Clementi, Nicola, Mancini, Nicasio, Bayarri-Olmos, Rafael, Garred, Peter, Rappuoli, Rino, Duga, Stefano, Bottazzi, Barbara, Uguccioni, Mariagrazia, Vicenzi, Elisa, Mantovani, Alberto, and Garlanda, Cecilia

Published
2022
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31. Recognition and inhibition of SARS-CoV-2 by humoral innate immunity pattern recognition molecules

Author

Stravalaci, Matteo, Pagani, Isabel, Paraboschi, Elvezia Maria, Pedotti, Mattia, Doni, Andrea, Scavello, Francesco, Mapelli, Sarah N., Sironi, Marina, Perucchini, Chiara, Varani, Luca, Matkovic, Milos, Cavalli, Andrea, Cesana, Daniela, Gallina, Pierangela, Pedemonte, Nicoletta, Capurro, Valeria, Clementi, Nicola, Mancini, Nicasio, Invernizzi, Pietro, Bayarri-Olmos, Rafael, Garred, Peter, Rappuoli, Rino, Duga, Stefano, Bottazzi, Barbara, Uguccioni, Mariagrazia, Asselta, Rosanna, Vicenzi, Elisa, Mantovani, Alberto, and Garlanda, Cecilia

Published
2022
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33. Influence of the Ion Coordination Number on Cation Exchange Reactions with Copper Telluride Nanocrystals

Author

Tu, Renyong, Xie, Yi, Bertoni, Giovanni, Lak, Aidin, Gaspari, Roberto, Rapallo, Arnaldo, Cavalli, Andrea, De Trizio, Luca, and Manna, Liberato

Subjects
Physics - Chemical Physics
Abstract

Cu2-xTe nanocubes were used as starting seeds to access metal telluride nanocrystals by cation exchanges at room temperature. The coordination number of the entering cations was found to play an important role in dictating the reaction pathways. The exchanges with tetrahedrally coordinated cations (i.e. with coordination number 4), such as Cd2+ or Hg2+, yielded monocrystalline CdTe or HgTe nanocrystals with Cu2-xTe/CdTe or Cu2-xTe/HgTe Janus-like heterostructures as intermediates. The formation of Janus-like architectures was attributed to the high diffusion rate of the relatively small tetrahedrally coordinated cations, which could rapidly diffuse in the Cu2-xTe NCs and nucleate the CdTe (or HgTe) phase in a preferred region of the host structure. Also, with both Cd2+ and Hg2+ ions the exchange led to wurtzite CdTe and HgTe phases rather than the more stable zinc-blende ones, indicating that the anion framework of the starting Cu2- xTe particles could be more easily deformed to match the anion framework of the metastable wurtzite structures. As hexagonal HgTe had never been reported to date, this represents another case of metastable new phases that can only be accessed by cation exchange. On the other hand, the exchanges involving octahedrally coordinated ions (i.e. with coordination number 6), such as Pb2+ or Sn2+, yielded rock-salt polycrystalline PbTe or SnTe nanocrystals with Cu2-xTe@PbTe or Cu2-xTe@SnTe core@shell architectures at the early stages of the exchange process. In this case, the octahedrally coordinated ions are probably too large to diffuse easily through the Cu2-xTe structure: their limited diffusion rate restricts their initial reaction to the surface of the nanocrystals, where cation exchange is initiated unselectively, leading to core@shell architectures., Comment: 11 pages, 7 figures in J. Am. Chem. Soc, 13 May 2016

Published
2016
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34. Studies on the Enantioselective Iminium Ion Trapping of Radicals Triggered by an Electron-Relay Mechanism

Author

Bahamonde, Ana, Murphy, John J, Savarese, Marika, Brémond, Éric, Cavalli, Andrea, and Melchiorre, Paolo

Subjects
Chemical Sciences, General Chemistry
Abstract

A combination of electrochemical, spectroscopic, computational, and kinetic studies has been used to elucidate the key mechanistic aspects of the previously reported enantioselective iminium ion trapping of photochemically generated carbon-centered radicals. The process, which provides a direct way to forge quaternary stereocenters with high fidelity, relies on the interplay of two distinct catalytic cycles: the aminocatalytic electron-relay system, which triggers the stereoselective radical trap upon iminium ion formation, and the photoredox cycle, which generates radicals under mild conditions. Critical to reaction development was the use of a chiral amine catalyst, bearing a redox-active carbazole unit, which could rapidly reduce the highly reactive and unstable intermediate generated upon radical interception. The carbazole unit, however, is also involved in another step of the electron-relay mechanism: the transiently generated carbazole radical cation acts as an oxidant to return the photocatalyst into the original state. By means of kinetic and spectroscopic studies, we have identified the last redox event as being the turnover-limiting step of the overall process. This mechanistic framework is corroborated by the linear correlation between the reaction rate and the reduction potential of the carbazole unit tethered to the aminocatalyst. The redox properties of the carbazole unit can thus be rationally tuned to improve catalytic activity. This knowledge may open a path for the mechanistically driven design of the next generation of electron-relay catalysts.

Published
2017

35. From the identification of a draggable target for the Olanzapine-induced Metabolic Syndrome to the development of a new therapeutic approach using CRISPR/Cas7-11S.

Author

Cavalli, Andrea, Veneziani, Federica <1988>, Cavalli, Andrea, and Veneziani, Federica <1988>

Abstract

Metabolic Syndrome (MS) is a clinical condition characterized by multiple risk factors for cardiovascular diseases. Component features of MS include obesity, dyslipidemia, diabetes, and high blood pressure. Unbalanced diets, alcohol consumption, and reduced physical activity are the main risk factors for metabolic dysfunction. Moreover, MS represents one of the major side effects of commonly prescribed drugs like neuropsychiatric medications (antidepressants, antipsychotics, and mood stabilizers). Previously, using an unbiased approach, we identified the involvement of Histamine Receptor 1 (H1R) and Cannabinoid receptor 1 (CB1R) in the MS signature in hypothalamic gene expression in female mouse models of both diet and drug-induced MS. Here, we uncover the interplay between these two GPCRs in the pathophysiology of Olanzapine (OL) induced MS. Then, using the CRISPR-Cas9 approach, we demonstrate that the CB1R knockout in hypothalamic neurons is sufficient to reverse OL-induced MS while avoiding the side effects observed with Rimonabant treatment. This result underlines the need to develop a site-specific CB1R targeting therapy for MS. However, the stable changes in the genome using Cas9 as a therapeutic strategy in humans raise health concerns. To overcome this significant limitation and ensure a cell- and site-specific target of CB1R, the new RNA targeting CRISPR-Cas seems promising. Here, we developed a CRISPR-Cas7-11S toolbox for both in vitro and in vivo RNA targeting while designing and cloning spacer RNA sequences against CB1R RNA that would work for both human and mouse transcriptomes. Then, we screened the efficiency of CRISPR-Cas7-11S and single and multiple spacer RNAs compared with a non-targeting guide using the neuroblastoma cell lines. To provide a safety switch-off in case of undesired side effects of the CRISPR/Cas7-11S therapeutic system, the study aims to identify and test in vitro new ligands acting as cleavage breakers or complex formation inhibit

Published
2024

36. Assessing the current state of adiabatic quantum computers in solving challenging computational problems

Author

Cavalli, Andrea, Rocutto, Lorenzo <1995>, Cavalli, Andrea, and Rocutto, Lorenzo <1995>

Abstract

The present Thesis aims to achieve three main objectives. The first is to present and comment the experimental results obtained during my PhD, which show that performances of modern AQCs are steadily improving thanks to both hardware and software advancements, and are getting close to practical utility. The second objective is to provide the reader with an extensive literature review regarding the design, the use, the capabilities, and the possible applications of AQCs. Indeed, there is currently no document or book on AQCs that contains the comprehensive amount of commented and ordered references presented in this Thesis. Thus, I hope this work can serve as an introductory compendium to a good portion of the modern knowledge regarding this topic. The third objective is to provide the reader with a collection of the most popular and effective ways to manipulate an AQC at the software (middelware) level in order to enhance its performances.

Published
2024

37. Unconventional computing paradigm methods with application to computational chemistry

Author

Cavalli, Andrea, Maronese, Marco <1995>, Cavalli, Andrea, and Maronese, Marco <1995>

Abstract

Over the last two decades, applied chemistry has experienced a significant shift, integrating theory and modeling as essential aspects of the discipline. This transformation has been driven by advancements in methodology, numerical techniques, and the increased capabilities of computer hardware and software. Notably, parallel Graphics Processing Units (GPUs) have become instrumental in computational chemistry, allowing for efficient simulations alongside traditional CPU-based computing. However, despite these advances, classical computing faces limitations, particularly in solving complex optimization and sampling problems inherent in computational chemistry. These challenges have prompted exploration into alternative computing paradigms, including quantum computing and memcomputing. Quantum computers leverage principles of quantum mechanics to surpass classical computing limits for specific tasks, while memcomputing exploits classical mechanics in a non-Turing manner, using the evolution of physical systems for computation. This thesis examines these alternative paradigms and their potential to address the limitations of classical computing in computational chemistry. It delves into the physical implementations of quantum and memcomputing systems and evaluates their performance through benchmarking against classical methods. Tests include assessing quantum amplitude estimation methods on trapped ion computers, evaluating quantum optimization for crystal structure prediction, and analyzing the effectiveness of memcomputing and adiabatic quantum computing on NP-hard optimization problems. While the results show promise for these alternative paradigms, they also highlight current limitations in hardware and implementation. Nonetheless, the findings suggest a hopeful outlook for the future development and application of quantum computing and memcomputing in computational chemistry.

Published
2024

38. Conversion of monoclonal IgG to dimeric and secretory IgA restores neutralizing ability and prevents Infection of Omicron lineages

Author

Marcotte, Harold, Cao, Yunlong, Zuo, Fanglei, Simonelli, Luca, Sammartino, Jose Camilla, Pedotti, Mattia, Sun, Rui, Cassaniti, Irene, Hagbom, Marie, Piralla, Antonio, Yang, Jinxuan, Du, Likun, Percivalle, Elena, Bertoglio, Federico, Schubert, Maren, Abolhassani, Hassan, Sherina, Natalia, Guerra, Concetta, Borte, Stephan, Rezaei, Nima, Kumagai-Braesch, Makiko, Xue, Yintong, Su, Chen, Yan, Qihong, He, Ping, Groenwall, Caroline, Klareskog, Lars, Calzolai, Luigi, Cavalli, Andrea, Wang, Qiao, Robbiani, Davide F., Hust, Michael, Shi, Zhengli, Feng, Liqiang, Svensson, Lennart, Chen, Ling, Bao, Linlin, Baldanti, Fausto, Xiao, Junyu, Qin, Chuan, Hammarstrom, Lennart, Yang, Xinglou, Varani, Luca, Xie, Xiaoliang Sunney, Pan-Hammarstrom, Qiang, Marcotte, Harold, Cao, Yunlong, Zuo, Fanglei, Simonelli, Luca, Sammartino, Jose Camilla, Pedotti, Mattia, Sun, Rui, Cassaniti, Irene, Hagbom, Marie, Piralla, Antonio, Yang, Jinxuan, Du, Likun, Percivalle, Elena, Bertoglio, Federico, Schubert, Maren, Abolhassani, Hassan, Sherina, Natalia, Guerra, Concetta, Borte, Stephan, Rezaei, Nima, Kumagai-Braesch, Makiko, Xue, Yintong, Su, Chen, Yan, Qihong, He, Ping, Groenwall, Caroline, Klareskog, Lars, Calzolai, Luigi, Cavalli, Andrea, Wang, Qiao, Robbiani, Davide F., Hust, Michael, Shi, Zhengli, Feng, Liqiang, Svensson, Lennart, Chen, Ling, Bao, Linlin, Baldanti, Fausto, Xiao, Junyu, Qin, Chuan, Hammarstrom, Lennart, Yang, Xinglou, Varani, Luca, Xie, Xiaoliang Sunney, and Pan-Hammarstrom, Qiang

Abstract

The emergence of Omicron lineages and descendent subvariants continues to present a severe threat to the effectiveness of vaccines and therapeutic antibodies. We have previ- ously suggested that an insufficient mucosal immunoglobulin A (IgA) response induced by the mRNA vaccines is associated with a surge in breakthrough infections. Here, we further show that the intramuscular mRNA and/or inactivated vaccines cannot suffi- ciently boost the mucosal secretory IgA response in uninfected individuals, particu- larly against the Omicron variant. We thus engineered and characterized recombinant monomeric, dimeric, and secretory IgAl antibodies derived from four neutralizing IgG monoclonal antibodies (mAbs 01A05, rmAb23, DXP-604, and XG014) targeting the receptor-binding domain of the spike protein. Compared to their parental IgG antibod- ies, dimeric and secretory IgAl antibodies showed a higher neutralizing activity against different variants of concern (VOCs), in part due to an increased avidity. Importantly, the dimeric or secretory IgAl form of the DXP-604 antibody significantly outperformed its parental IgG antibody, and neutralized the Omicron lineages BA.1, BA.2, and BA.4/5 with a 25- to 75-fold increase in potency. In human angiotensin converting enzyme 2 (ACE2) transgenic mice, a single intranasal dose of the dimeric IgA DXP-604 conferred prophylactic and therapeutic protection against Omicron BA.5. Thus, dimeric or secre- tory IgA delivered by nasal administration may potentially be exploited for the treatment Iand prevention of Omicron infection, thereby providing an alternative tool for combating immune evasion by the current circulating subvariants and, potentially, future VOCs.

Published
2024
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39. Metainference: A Bayesian Inference Method for Heterogeneous Systems

Author

Bonomi, Massimiliano, Camilloni, Carlo, Cavalli, Andrea, and Vendruscolo, Michele

Subjects
Physics - Computational Physics, Physics - Data Analysis, Statistics and Probability, Quantitative Biology - Quantitative Methods
Abstract

Modelling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model, and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system populates simultaneously an ensemble of different states and experimental data are measured as averages over such states. To address this problem we present a Bayesian inference method, called metainference, that is able to deal with errors in experimental measurements as well as with experimental measurements averaged over multiple states. To achieve this goal, metainference models a finite sample of the distribution of models using a replica approach, in the spirit of the replica-averaging modelling based on the maximum entropy principle. To illustrate the method we present its application to a heterogeneous model system and to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to model complex systems with heterogeneous components and interconverting between different states by taking into account all possible sources of errors., Comment: 29 pages, 10 figures

Published
2015

40. On the allosteric puzzle and pocket crosstalk through computational means.

Author

Aguti, Riccardo, Bernetti, Mattia, Bosio, Stefano, Decherchi, Sergio, and Cavalli, Andrea

Subjects
PROTEIN structure, LIGAND binding (Biochemistry), ANDROGEN receptors, PUZZLES, EPIDERMAL growth factor receptors
Abstract

Allostery is a constitutive, albeit often elusive, feature of biomolecular systems, which heavily determines their functioning. Its mechanical, entropic, long-range, ligand, and environment-dependent nature creates far from trivial interplays between residues and, in general, the secondary structure of proteins. This intricate scenario is mirrored in computational terms as different notions of "correlation" among residues and pockets can lead to different conclusions and outcomes. In this article, we put on a common ground and challenge three computational approaches for the correlation estimation task and apply them to three diverse targets of pharmaceutical interest: the androgen A2A receptor, the androgen receptor, and the EGFR kinase domain. Results show that partial results consensus can be attained, yet different notions lead to pointing the attention to different pockets and communications. [ABSTRACT FROM AUTHOR]

Published
2023
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43. Simple Learning with a Teacher via Biased Regularized Least Squares

Author

Decherchi, Sergio, Cavalli, Andrea, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Nicosia, Giuseppe, editor, Pardalos, Panos, editor, Giuffrida, Giovanni, editor, Umeton, Renato, editor, and Sciacca, Vincenzo, editor

Published
2019
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45. Identification of a 2,4-diaminopyrimidine scaffold targeting Trypanosoma brucei pteridine reductase 1 from the LIBRA compound library screening campaign

Author

Linciano, Pasquale, Cullia, Gregorio, Borsari, Chiara, Santucci, Matteo, Ferrari, Stefania, Witt, Gesa, Gul, Sheraz, Kuzikov, Maria, Ellinger, Bernhard, Santarém, Nuno, Cordeiro da Silva, Anabela, Conti, Paola, Bolognesi, Maria Laura, Roberti, Marinella, Prati, Federica, Bartoccini, Francesca, Retini, Michele, Piersanti, Giovanni, Cavalli, Andrea, Goldoni, Luca, Bertozzi, Sine Mandrup, Bertozzi, Fabio, Brambilla, Enzo, Rizzo, Vincenzo, Piomelli, Daniele, Pinto, Andrea, Bandiera, Tiziano, and Costi, Maria Paola

Published
2020
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47. Characterization of a novel humanized heavy chain antibody targeting endogenous retroviruses with anti-lymphoma activity

Author

Spriano, Filippo, primary, Cascione, Luciano, additional, Sgrignani, Jacopo, additional, Bendik, Nikolai, additional, Napoli, Sara, additional, Sartori, Giulio, additional, Cannas, Eleonora, additional, Gong, Tao, additional, Arribas, Alberto J., additional, Pizzi, Marco, additional, Rossi, Davide, additional, Robbiani, Davide F., additional, Cavalli, Andrea, additional, and Bertoni, Francesco, additional

Published
2024
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48. Conversion of monoclonal IgG to dimeric and secretory IgA restores neutralizing ability and prevents infection of Omicron lineages

Author

Marcotte, Harold, primary, Cao, Yunlong, additional, Zuo, Fanglei, additional, Simonelli, Luca, additional, Sammartino, Josè Camilla, additional, Pedotti, Mattia, additional, Sun, Rui, additional, Cassaniti, Irene, additional, Hagbom, Marie, additional, Piralla, Antonio, additional, Yang, Jinxuan, additional, Du, Likun, additional, Percivalle, Elena, additional, Bertoglio, Federico, additional, Schubert, Maren, additional, Abolhassani, Hassan, additional, Sherina, Natalia, additional, Guerra, Concetta, additional, Borte, Stephan, additional, Rezaei, Nima, additional, Kumagai-Braesch, Makiko, additional, Xue, Yintong, additional, Su, Chen, additional, Yan, Qihong, additional, He, Ping, additional, Grönwall, Caroline, additional, Klareskog, Lars, additional, Calzolai, Luigi, additional, Cavalli, Andrea, additional, Wang, Qiao, additional, Robbiani, Davide F., additional, Hust, Michael, additional, Shi, Zhengli, additional, Feng, Liqiang, additional, Svensson, Lennart, additional, Chen, Ling, additional, Bao, Linlin, additional, Baldanti, Fausto, additional, Xiao, Junyu, additional, Qin, Chuan, additional, Hammarström, Lennart, additional, Yang, Xinglou, additional, Varani, Luca, additional, Xie, Xiaoliang Sunney, additional, and Pan-Hammarström, Qiang, additional

Published
2024
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50. Neutralisation activity of mucosal IgA against XBB sublineages and BA.2.86

Author

Zuo, Fanglei, primary, Cao, Yunlong, additional, Sun, Rui, additional, Yisimayi, Ayijiang, additional, Du, Likun, additional, Bertoglio, Federico, additional, Schubert, Maren, additional, Guerra, Concetta, additional, Cavalli, Andrea, additional, Hust, Michael, additional, Robbiani, Davide F, additional, Abolhassani, Hassan, additional, Xie, Xiaoliang Sunney, additional, Hammarström, Lennart, additional, Marcotte, Harold, additional, and Pan-Hammarström, Qiang, additional

Published
2023
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