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1. Numerical evaluation of two-loop QCD helicity amplitudes for $gg\to t \bar{t} g$ at leading colour

Author

Badger, Simon, Becchetti, Matteo, Brancaccio, Colomba, Hartanto, Heribertus Bayu, and Zoia, Simone

Subjects
High Energy Physics - Phenomenology, High Energy Physics - Theory
Abstract

We present the first benchmark evaluation of the two-loop finite remainders for the production of a top-quark pair in association with a jet at hadron colliders in the gluon channel. We work in the leading colour approximation, and perform the numerical evaluation in the physical phase space. To achieve this result, we develop a new method for expressing the master integrals in terms of a (over-complete) basis of special functions that enables the infrared and ultraviolet poles to be cancelled analytically despite the presence of elliptic Feynman integrals. The special function basis makes it manifest that the elliptic functions appear solely in the finite remainder, and can be evaluated numerically through generalised series expansions. The helicity amplitudes are constructed using four dimensional projectors combined with finite-field techniques to perform integration-by-parts reduction, mapping to special functions and Laurent expansion in the dimensional regularisation parameter., Comment: 22 pages, 6 figures, 3 tables, ancillary files at https://zenodo.org/records/14473301

Published
2024

2. Towards two-loop QCD corrections to $ \mathbf{pp \to t \bar{t} j}$

Author

Brancaccio, Colomba

Subjects
High Energy Physics - Phenomenology
Abstract

I discuss the status of the computation of the two-loop QCD corrections to top-quark pair production associated with a jet at hadron colliders. This amplitude is a missing ingredient for next-to-next-to-leading order (NNLO) QCD predictions. I briefly present computational techniques to tackle the algebraic and analytic complexities of two-loop multi-scale amplitudes, in particular where massive propagators give rise to elliptic Feynman integrals. I then describe how a special function basis for the helicity amplitudes is obtained and present first numerical evaluations for the finite remainders of the $gg\to t\bar{t}g$ channel, after the infrared and ultraviolet poles have been identified analytically., Comment: Talk at the 17th International Workshop on Top Quark Physics (Top2024), 22-27 September 2024

Published
2024

3. Chromophore Optimization in Organometallic Au(III) Cys Arylation of Peptides and Proteins for 266 nm Photoactivation.

Author

Silzel, Jacob, Chen, Chengwei, Sanchez-Marsetti, Colomba, Farias, Phillip, Carta, Veronica, Harman, William, and Julian, Ryan

Subjects
Cysteine, Peptides, Gold, Photochemical Processes, Proteins, Organogold Compounds, Humans
Abstract

Cysteine is the most reactive naturally occurring amino acid due to the presence of a free thiol, presenting a tantalizing handle for covalent modification of peptides/proteins. Although many mass spectrometry experiments could benefit from site-specific modification of Cys, the utility of direct arylation has not been thoroughly explored. Recently, Spokoyny and co-workers reported a Au(III) organometallic reagent that robustly arylates Cys and tolerates a wide variety of solvents and conditions. Given the chromophoric nature of aryl groups and the known susceptibility of carbon-sulfur bonds to photodissociation, we set out to identify an aryl group that could efficiently cleave Cys carbon-sulfur bonds at 266 nm. A streamlined workflow was developed to facilitate rapid examination of a large number of aryls with minimal sample using a simple test peptide, RAAACGVLK. We were able to identify several aryl groups that yield abundant homolytic photodissociation of the adjacent Cys carbon-sulfur bonds with short activation times (

Published
2024

4. Level Up Your Tutorials: VLMs for Game Tutorials Quality Assessment

Author

Cambrin, Daniele Rege, Militone, Gabriele Scaffidi, Colomba, Luca, Malnati, Giovanni, Apiletti, Daniele, and Garza, Paolo

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language
Abstract

Designing effective game tutorials is crucial for a smooth learning curve for new players, especially in games with many rules and complex core mechanics. Evaluating the effectiveness of these tutorials usually requires multiple iterations with testers who have no prior knowledge of the game. Recent Vision-Language Models (VLMs) have demonstrated significant capabilities in understanding and interpreting visual content. VLMs can analyze images, provide detailed insights, and answer questions about their content. They can recognize objects, actions, and contexts in visual data, making them valuable tools for various applications, including automated game testing. In this work, we propose an automated game-testing solution to evaluate the quality of game tutorials. Our approach leverages VLMs to analyze frames from video game tutorials, answer relevant questions to simulate human perception, and provide feedback. This feedback is compared with expected results to identify confusing or problematic scenes and highlight potential errors for developers. In addition, we publish complete tutorial videos and annotated frames from different game versions used in our tests. This solution reduces the need for extensive manual testing, especially by speeding up and simplifying the initial development stages of the tutorial to improve the final game experience., Comment: Accepted at ECCV 2024 CV2 Workshop

Published
2024

6. Bacterial-like inflammatory response in children with adenovirus leads to inappropriate antibiotic use: a multicenter cohort study

Author

Moracas, Cristina, Poeta, Marco, Grieco, Francesca, Tamborino, Agnese, Moriondo, Maria, Stracuzzi, Marta, Diana, Alfredo, Petrarca, Laura, Marra, Simona, Licari, Amelia, Linsalata, Stefano, Albano, Chiara, Condemi, Anna, Del Tufo, Ester, Di Fraia, Teresa, Punzi, Liana, Ardia, Eleonora, Lo Vecchio, Andrea, Bruzzese, Eugenia, Colomba, Claudia, Giacomet, Vania, Midulla, Fabio, Marseglia, Gian Luigi, Galli, Luisa, and Guarino, Alfredo

Published
2024
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9. ViGEO: an Assessment of Vision GNNs in Earth Observation

Author

Colomba, Luca and Garza, Paolo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Satellite missions and Earth Observation (EO) systems represent fundamental assets for environmental monitoring and the timely identification of catastrophic events, long-term monitoring of both natural resources and human-made assets, such as vegetation, water bodies, forests as well as buildings. Different EO missions enables the collection of information on several spectral bandwidths, such as MODIS, Sentinel-1 and Sentinel-2. Thus, given the recent advances of machine learning, computer vision and the availability of labeled data, researchers demonstrated the feasibility and the precision of land-use monitoring systems and remote sensing image classification through the use of deep neural networks. Such systems may help domain experts and governments in constant environmental monitoring, enabling timely intervention in case of catastrophic events (e.g., forest wildfire in a remote area). Despite the recent advances in the field of computer vision, many works limit their analysis on Convolutional Neural Networks (CNNs) and, more recently, to vision transformers (ViTs). Given the recent successes of Graph Neural Networks (GNNs) on non-graph data, such as time-series and images, we investigate the performances of a recent Vision GNN architecture (ViG) applied to the task of land cover classification. The experimental results show that ViG achieves state-of-the-art performances in multiclass and multilabel classification contexts, surpassing both ViT and ResNet on large-scale benchmarks., Comment: Accepted at SSTDM 2023 workshop, held in conjunction with ICDM 2023 conference

Published
2024
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10. CaBuAr: California Burned Areas dataset for delineation

Author

Cambrin, Daniele Rege, Colomba, Luca, and Garza, Paolo

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

Forest wildfires represent one of the catastrophic events that, over the last decades, caused huge environmental and humanitarian damages. In addition to a significant amount of carbon dioxide emission, they are a source of risk to society in both short-term (e.g., temporary city evacuation due to fire) and long-term (e.g., higher risks of landslides) cases. Consequently, the availability of tools to support local authorities in automatically identifying burned areas plays an important role in the continuous monitoring requirement to alleviate the aftereffects of such catastrophic events. The great availability of satellite acquisitions coupled with computer vision techniques represents an important step in developing such tools. This paper introduces a novel open dataset that tackles the burned area delineation problem, a binary segmentation problem applied to satellite imagery. The presented resource consists of pre- and post-fire Sentinel-2 L2A acquisitions of California forest fires that took place starting in 2015. Raster annotations were generated from the data released by California's Department of Forestry and Fire Protection. Moreover, in conjunction with the dataset, we release three different baselines based on spectral indexes analyses, SegFormer, and U-Net models., Comment: Accepted at the IEEE Geoscience and Remote Sensing Magazine

Published
2024
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12. Explainable prediction model for the human papillomavirus status in patients with oropharyngeal squamous cell carcinoma using CNN on CT images

Author

Fanizzi, Annarita, Comes, Maria Colomba, Bove, Samantha, Cavalera, Elisa, de Franco, Paola, Di Rito, Alessia, Errico, Angelo, Lioce, Marco, Pati, Francesca, Portaluri, Maurizio, Saponaro, Concetta, Scognamillo, Giovanni, Troiano, Ippolito, Troiano, Michele, Zito, Francesco Alfredo, and Massafra, Raffaella

Published
2024
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14. ITALIC: An Italian Intent Classification Dataset

Author

Koudounas, Alkis, La Quatra, Moreno, Vaiani, Lorenzo, Colomba, Luca, Attanasio, Giuseppe, Pastor, Eliana, Cagliero, Luca, and Baralis, Elena

Subjects
Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Recent large-scale Spoken Language Understanding datasets focus predominantly on English and do not account for language-specific phenomena such as particular phonemes or words in different lects. We introduce ITALIC, the first large-scale speech dataset designed for intent classification in Italian. The dataset comprises 16,521 crowdsourced audio samples recorded by 70 speakers from various Italian regions and annotated with intent labels and additional metadata. We explore the versatility of ITALIC by evaluating current state-of-the-art speech and text models. Results on intent classification suggest that increasing scale and running language adaptation yield better speech models, monolingual text models outscore multilingual ones, and that speech recognition on ITALIC is more challenging than on existing Italian benchmarks. We release both the dataset and the annotation scheme to streamline the development of new Italian SLU models and language-specific datasets., Comment: Accepted at INTERSPEECH 2023. Data and code at https://github.com/RiTA-nlp/ITALIC

Published
2023
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15. An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients

Author

Maria Colomba Comes, Livia Fucci, Sabino Strippoli, Samantha Bove, Gerardo Cazzato, Carmen Colangiuli, Ivana De Risi, Ileana De Roma, Annarita Fanizzi, Fabio Mele, Maurizio Ressa, Concetta Saponaro, Clara Soranno, Rosita Tinelli, Michele Guida, Alfredo Zito, and Raffaella Massafra

Subjects
Melanoma, Artificial intelligence, Recurrence risk prediction, Digital pathology, Medicine
Abstract

Abstract Background Risk stratification and treatment benefit prediction models are urgent to improve negative sentinel lymph node (SLN-) melanoma patient selection, thus avoiding costly and toxic treatments in patients at low risk of recurrence. To this end, the application of artificial intelligence (AI) could help clinicians to better calculate the recurrence risk and choose whether to perform adjuvant therapy. Methods We made use of AI to predict recurrence-free status (RFS) within 2-years from diagnosis in 94 SLN- melanoma patients. In detail, we detected quantitative imaging information from H&E slides of a cohort of 71 SLN- melanoma patients, who registered at Istituto Tumori “Giovanni Paolo II” in Bari, Italy (investigational cohort, IC). For each slide, two expert pathologists firstly annotated two Regions of Interest (ROIs) containing tumor cells alone (TUMOR ROI) or with infiltrating cells (TUMOR + INF ROI). In correspondence of the two kinds of ROIs, two AI-based models were developed to extract information directly from the tiles in which each ROI was automatically divided. This information was then used to predict RFS. Performances of the models were computed according to a 5-fold cross validation scheme. We further validated the prediction power of the two models on an independent external validation cohort of 23 SLN- melanoma patients (validation cohort, VC). Results The TUMOR ROIs have revealed more informative than the TUMOR + INF ROIs. An Area Under the Curve (AUC) value of 79.1% and 62.3%, a sensitivity value of 81.2% and 76.9%, a specificity value of 70.0% and 43.3%, an accuracy value of 73.2% and 53.4%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the IC cohort, respectively. An AUC value of 76.5% and 65.2%, a sensitivity value of 66.7% and 41.6%, a specificity value of 70.0% and 55.9%, an accuracy value of 70.0% and 56.5%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the VC cohort, respectively. Conclusions Our approach represents a first effort to develop a non-invasive prognostic method to better define the recurrence risk and improve the management of SLN- melanoma patients.

Published
2024
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17. CausalXtract, a flexible pipeline to extract causal effects from live-cell time-lapse imaging data

Author

Franck Simon, Maria Colomba Comes, Tiziana Tocci, Louise Dupuis, Vincent Cabeli, Nikita Lagrange, Arianna Mencattini, Maria Carla Parrini, Eugenio Martinelli, and Herve Isambert

Subjects
causal inference, time-lapse image analysis, live-cell imaging, tumor on chip, causal discovery, granger causality, Medicine, Science, Biology (General), QH301-705.5
Abstract

Live-cell microscopy routinely provides massive amounts of time-lapse images of complex cellular systems under various physiological or therapeutic conditions. However, this wealth of data remains difficult to interpret in terms of causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers causal and possibly time-lagged effects from morphodynamic features and cell–cell interactions in live-cell imaging data. CausalXtract methodology combines network-based and information-based frameworks, which is shown to discover causal effects overlooked by classical Granger and Schreiber causality approaches. We showcase the use of CausalXtract to uncover novel causal effects in a tumor-on-chip cellular ecosystem under therapeutically relevant conditions. In particular, we find that cancer-associated fibroblasts directly inhibit cancer cell apoptosis, independently from anticancer treatment. CausalXtract uncovers also multiple antagonistic effects at different time delays. Hence, CausalXtract provides a unique computational tool to interpret live-cell imaging data for a range of fundamental and translational research applications.

Published
2025
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18. Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers‐Based Model

Author

Maria Colomba Comes, Annarita Fanizzi, Samantha Bove, Luca Boldrini, Agnese Latorre, Deniz Can Guven, Serena Iacovelli, Tiziana Talienti, Alessandro Rizzo, Francesco Alfredo Zito, and Raffaella Massafra

Subjects
breast cancer, ensemble model, Neoadjuvant chemotherapy, pathological complete response, vision transformers, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

ABSTRACT Background Morphological and vascular characteristics of breast cancer can change during neoadjuvant chemotherapy (NAC). Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI)‐acquired pre‐ and mid‐treatment quantitatively capture information about tumor heterogeneity as potential earlier indicators of pathological complete response (pCR) to NAC in breast cancer. Aims This study aimed to develop an ensemble deep learning‐based model, exploiting a Vision Transformer (ViT) architecture, which merges features automatically extracted from five segmented slices of both pre‐ and mid‐treatment exams containing the maximum tumor area, to predict and monitor pCR to NAC. Materials and Methods Imaging data analyzed in this study referred to a cohort of 86 breast cancer patients, randomly split into training and test sets at a ratio of 8:2, who underwent NAC and for which information regarding the pCR status was available (37.2% of patients achieved pCR). We further validated our model using a subset of 20 patients selected from the publicly available I‐SPY2 trial dataset (independent test). Results The performances of the proposed model were assessed using standard evaluation metrics, and promising results were achieved: area under the curve (AUC) value of 91.4%, accuracy value of 82.4%, a specificity value of 80.0%, a sensitivity value of 85.7%, precision value of 75.0%, F‐score value of 80.0%, and G‐mean value of 82.8%. The results obtained from the independent test show an AUC of 81.3%, an accuracy of 80.0%, a specificity value of 76.9%, a sensitivity of 85.0%, a precision of 66.7%, an F‐score of 75.0%, and a G‐mean of 81.2%. Discussion As far as we know, our research is the first proposal using ViTs on DCE‐MRI exams to monitor pCR over time during NAC. Conclusion Finally, the changes in DCE‐MRI at pre‐ and mid‐treatment could affect the accuracy of pCR prediction to NAC.

Published
2024
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19. Explainable machine learning for predicting recurrence-free survival in endometrial carcinosarcoma patients

Author

Samantha Bove, Francesca Arezzo, Gennaro Cormio, Erica Silvestris, Alessia Cafforio, Maria Colomba Comes, Annarita Fanizzi, Giuseppe Accogli, Gerardo Cazzato, Giorgio De Nunzio, Brigida Maiorano, Emanuele Naglieri, Andrea Lupo, Elsa Vitale, Vera Loizzi, and Raffaella Massafra

Subjects
endometrial carcinosarcoma, recurrence-free survival, machine learning, explainable artificial intelligence, personalized medicine, Electronic computers. Computer science, QA75.5-76.95
Abstract

ObjectivesEndometrial carcinosarcoma is a rare, aggressive high-grade endometrial cancer, accounting for about 5% of all uterine cancers and 15% of deaths from uterine cancers. The treatment can be complex, and the prognosis is poor. Its increasing incidence underscores the urgent requirement for personalized approaches in managing such challenging diseases.MethodIn this work, we designed an explainable machine learning approach to predict recurrence-free survival in patients affected by endometrial carcinosarcoma. For this purpose, we exploited the predictive power of clinical and histopathological data, as well as chemotherapy and surgical information collected for a cohort of 80 patients monitored over time. Among these patients, 32.5% have experienced the appearance of a recurrence.ResultsThe designed model was able to well describe the observed sequence of events, providing a reliable ranking of the survival times based on the individual risk scores, and achieving a C-index equals to 70.00% (95% CI, 59.38–84.74).ConclusionAccordingly, machine learning methods could support clinicians in discriminating between endometrial carcinosarcoma patients at low-risk or high-risk of recurrence, in a non-invasive and inexpensive way. To the best of our knowledge, this is the first study proposing a preliminary approach addressing this task.

Published
2024
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22. In vitro anti-inflammatory activity of Malus × domestica var. Mela Rosa Marchigiana pulp callus extract contrasting high glucose conditions

Author

Leila Benayada, Federica Gubitosa, Daniele Fraternale, Silvia Carloni, Liana Cerioni, Giulia Matacchione, Fabiola Olivieri, Lucia Potenza, Roberta De Bellis, Laura Chiarantini, Carla Roselli, Laura Valentini, Pietro Gobbi, Walter Balduini, Noemi Pappagallo, Natascia Ventura, George E.N. Kass, Mariastella Colomba, and Maria Cristina Albertini

Subjects
Apple, Ethanolic extract, Secondary metabolites, Pentacyclic triterpenic acids, Nutrition. Foods and food supply, TX341-641
Abstract

The anti-inflammatory activity of the “Mela Rosa Marchigiana” (MRM) pulp callus ethanol Extract (MRME) was tested in different cellular models including (i) LPS-treated RAW 264.7; (ii) HUVEC exposed to short-term high Glucose (HG, 45 mM) or normal glucose (NG, 5 mM) concentrations; and (iii) HG (30 mN) and LPS-treated U937. To evaluate the anti-inflammatory effect of the extract, Nitric Oxide (NO) production was measured in RAW 264.6 cells, while IL-8, IL-1ß and MCP-1 expressions, along with modulation of some inflammation- or senescence-associated miRNAs (miR-21, miR-126, miR-17 and miR-217) were assessed in HUVECs and/or U937. MRME treatment reduced pro-inflammatory markers amount, suggesting a decreased generalised inflammatory response. Present findings indicate that MRME can contrast senescence- or HG-induced inflammation. Moreover, in HG- and LPS-induced inflammation, MRME reduced U937 monocyte activation by inhibiting mitochondrial respiration and inflammatory markers. Finally, the inhibitory potential of MRME on the digestive enzymes α-glucosidase, α-amylase and lipase activity was investigated. Our results support the idea that MRME has a positive effect on inhibition of endothelial/macrophage dysfunction under HG/senescence inflammatory conditions. Furthermore, the obtained results showed a modest inhibitory power of the extract (IC50 values: 2.98 ± 0.24, 1.77 ± 0.15, and 2.06 ± 0.31 mg/ml, respectively), which, however, could be of some help in decreasing the absorption of glucose and triglycerides.

Published
2024
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23. Transfer learning approach in pre-treatment CT images to predict therapeutic response in advanced malignant pleural mesothelioma

Author

Annarita Fanizzi, Annamaria Catino, Samantha Bove, Maria Colomba Comes, Michele Montrone, Angela Sicolo, Rahel Signorile, Pia Perrotti, Pamela Pizzutilo, Domenico Galetta, and Raffaella Massafra

Subjects
malignant pleural mesothelioma, convolutional neural network, machine learning, mRECIST, CT images, response to therapy, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

IntroductionMalignant pleural mesothelioma (MPM) is a poor-prognosis disease. Owing to the recent availability of new therapeutic options, there is a need to better assess prognosis. The initial clinical response could represent a useful parameter.MethodsWe proposed a transfer learning approach to predict an initial treatment response starting from baseline CT scans of patients with advanced/unresectable MPM undergoing first-line systemic therapy. The therapeutic response has been assessed according to the mRECIST criteria by CT scan at baseline and after two to three treatment cycles. We used three slices of baseline CT scan as input to the pre-trained convolutional neural network as a radiomic feature extractor. We identified a feature subset through a double feature selection procedure to train a binary SVM classifier to discriminate responders (partial response) from non-responders (stable or disease progression).ResultsThe performance of the prediction classifiers was evaluated with an 80:20 hold-out validation scheme. We have evaluated how the developed model was robust to variations in the slices selected by the radiologist. In our dataset, 25 patients showed an initial partial response, whereas 13 patients showed progressive or stable disease. On the independent test, the proposed model achieved a median AUC and accuracy of 86.67% and 87.50%, respectively.ConclusionsThe proposed model has shown high performance even by varying the reference slices. Novel tools could help to improve the prognostic assessment of patients with MPM and to better identify subgroups of patients with different therapeutic responsiveness.

Published
2024
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28. Severe Panton–Valentine-Leukocidin-Positive Staphylococcus aureus Infections in Pediatric Age: A Case Report and a Literature Review

Author

Valeria Garbo, Laura Venuti, Giovanni Boncori, Chiara Albano, Anna Condemi, Giuseppe Natoli, Valentina Frasca Polara, Sebastiano Billone, Laura Antonella Canduscio, Antonio Cascio, and Claudia Colomba

Subjects
Panton–Valentine leukocidin (PVL), Staphylococcus aureus (S. aureus), methicillin-resistant Staphylococcus aureus (MRSA), pediatric infections, necrotizing pneumonia, osteomyelitis, Therapeutics. Pharmacology, RM1-950
Abstract

Background: Infections caused by S. aureus strains encoding Panton–Valentine leukocidin (PVL-SA) have become increasingly relevant in community settings and can cause severe conditions in pediatric populations. We present the pediatric case of an invasive disease caused by PVL-SA and provide a literature review of severe manifestations caused by these strains in children. Methods: A PubMed search (February 2024) found studies that included relevant clinical outcomes, diagnostics, and treatments, excluding cases of asymptomatic infection or in adult populations. A logistical multivariate analysis was used to find predictors of the need for intensive care. Results: A 10-year-old boy came to the attention of our Pediatric Infectious Diseases Unit with fever, chest pain, and tachypnea. A rapid worsening of his clinical conditions was observed, with the development of necrotizing pneumonia, osteomyelitis, deep vein thrombosis (DVT), and multiple abscesses. Blood cultures confirmed the presence of PVL-producing methicillin-resistant S. aureus (MRSA). The initial treatment included linezolid and ceftaroline and was later adjusted to clindamycin, daptomycin, and fosfomycin, with clinical improvement. Discussion: Our review collected 36 articles, including 156 pediatric cases of severe PVL-SA infection. Bacteremia was present in 49% of cases, lung infection in 47%, and osteomyelitis in 37%. The presence of pulmonary localization was predictive of the need for intensive care, O.R. 25.35 (7.46–86.09; p < 0.001). Anti-toxin molecules were used in about half the cases where information on treatment was reported. Our report highlights the capacity of PVL-SA to cause life-threatening complications in children, while also discussing the full range of its clinical spectrum and the most effective therapeutic approaches.

Published
2024
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33. Immune predictors of response to immune checkpoint inhibitors in mismatch repair-deficient endometrial cancer

Author

Etienne Rouleau, Ana Oaknin, Christophe Klein, Alexandra Leary, Sebastien Gouy, Patricia Pautier, Catherine Genestie, Amandine Maulard, Judith Michels, Marco de Bruyn, Emeline Colomba-Blameble, Annechien Plat, Juan Francisco Grau Bejar, Elisa Yaniz Galende, Qinghe Zeng, Audrey Le Formal, Elodie Edmond, and Maëva Moreau

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Background Patients with mismatch repair-deficient (MMRd) endometrial cancer (EC) can derive great benefit from immune checkpoint inhibitors (ICI). However not all responses and predictors of primary resistance are lacking.Methods We compared the immune tumor microenvironment of MMRd EC ICI-responders (Rs) and ICI non-responders (NRs), using spatial multiplexed immune profiling and unsupervised hierarchical clustering analysis.Results Overall, NRs exhibited drastically lower CD8+, absent terminally differentiated T cells, lack of mature tertiary lymphoid structures and dendritic cells, as well as loss of human leukocyte antigen class I. However, no single marker could predict R versus NR with confidence. Clustering analysis identified a combination of four immune features that demonstrated that accurately predicted ICI response, with a discriminative power of 92%. Finally, 80% of NRs lacked programmed death-ligand 1, however, 60% exhibited another actionable immune checkpoint (T-cell immunoglobulin and mucin containing protein-3, indoleamine 2,3-dioxygenase 1, or lymphocyte activation gene 3).Conclusions These findings underscore the potential of immune tumor microenvironment features for identifying patients with MMRd EC and primary resistance to ICI who should be oriented towards trials testing novel immunotherapeutic combinations.

Published
2024
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34. An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicators

Author

Annarita Fanizzi, Francesca Arezzo, Gennaro Cormio, Maria Colomba Comes, Gerardo Cazzato, Luca Boldrini, Samantha Bove, Michele Bollino, Anila Kardhashi, Erica Silvestris, Pietro Quarto, Michele Mongelli, Emanuele Naglieri, Rahel Signorile, Vera Loizzi, and Raffaella Massafra

Subjects
gynecological ultrasound, machine learning, ovarian cancer, precision medicine, solid adnexal masses, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background Accurate characterization of newly diagnosed a solid adnexal lesion is a key step in defining the most appropriate therapeutic approach. Despite guidance from the International Ovarian Tumor Analyzes Panel, the evaluation of these lesions can be challenging. Recent studies have demonstrated how machine learning techniques can be applied to clinical data to solve this diagnostic problem. However, ML models can often consider as black‐boxes due to the difficulty of understanding the decision‐making process used by the algorithm to obtain a specific result. Aims For this purpose, we propose an Explainable Artificial Intelligence model trained on clinical characteristics and qualitative ultrasound indicators to predict solid adnexal masses diagnosis. Materials & Methods Since the diagnostic task was a three‐class problem (benign tumor, invasive cancer, or ovarian metastasis), we proposed a waterfall classification model: a first model was trained and validated to discriminate benign versus malignant, a second model was trained to distinguish nonmetastatic versus metastatic malignant lesion which occurs when a patient is predicted to be malignant by the first model. Firstly, a stepwise feature selection procedure was implemented. The classification performances were validated on Leave One Out scheme. Results The accuracy of the three‐class model reaches an overall accuracy of 86.36%, and the precision per‐class of the benign, nonmetastatic malignant, and metastatic malignant classes were 86.96%, 87.27%, and 77.78%, respectively. Discussion: SHapley Additive exPlanations were performed to visually show how the machine learning model made a specific decision. For each patient, the SHAP values expressed how each characteristic contributed to the classification result. Such information represents an added value for the clinical usability of a diagnostic system. Conclusions This is the first work that attempts to design an explainable machine‐learning tool for the histological diagnosis of solid masses of the ovary.

Published
2024
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36. Studio preliminare delle ceramiche di impasto grossolano celtiche dall’area dell’anfiteatro romano di Milano

Author

Beatrice Bin, Maria Teresa Colomba, and Francesca Roncoroni

Subjects
north of italy, milan, la tène, celtic pottery, coarse ceramic, roman amphitheater, celtic sanctuary, ollae, Archaeology, CC1-960
Abstract

Preliminary study of Celtic coarse ware pottery from the area of the Roman amphitheatre in Milan The proposed study presents some ceramics from the area of the Roman amphitheatre in Milan, mostly from US 298 (excavation 2020), a pit filled exclusively with La Tène pottery dating from between the 4th and 3rd centuries BC. The ollae, in particular, are abundant and complete, whereas in other sites in the Insubri region they are only known from a few fragments. Their abundance makes it possible to understand that this type of vase was produced in different sizes, probably to contain solid food of different types. Some bowls and lids, as well as a jug, imitate fine ware shapes. The coarse pottery is itself of good quality, wheel made and fired at a low temperature. It is probably the continuation of a local, Golasecca culture, production.

Published
2024

37. Development of Novel Models of Aggressive Variants of Castration-resistant Prostate Cancer

Author

Bigot, Ludovic, Sabio, Jonathan, Poiraudeau, Loic, Annereau, Maxime, Menssouri, Naoual, Helissey, Carole, Déas, Olivier, Aglave, Marine, Ibrahim, Tony, Pobel, Cédric, Nobre, Catline, Nicotra, Claudio, Ngo-Camus, Maud, Lacroix, Ludovic, Rouleau, Etienne, Tselikas, Lambros, Judde, Jean-Gabriel, Chauchereau, Anne, Bernard-Tessier, Alice, Patrikidou, Anna, Naoun, Natacha, Flippot, Ronan, Colomba, Emeline, Fuerea, Alina, Albiges, Laurence, Lavaud, Pernelle, Massard, Christophe, Friboulet, Luc, Fizazi, Karim, Besse, Benjamin, Scoazec, Jean-Yves, and Loriot, Yohann

Published
2024
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38. DeepCEL0 for 2D Single Molecule Localization in Fluorescence Microscopy

Author

Cascarano, Pasquale, Comes, Maria Colomba, Sebastiani, Andrea, Mencattini, Arianna, Piccolomini, Elena Loli, and Martinelli, Eugenio

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Mathematics - Numerical Analysis, 65Z05, 65K10, 65R30, 68U10, 68T07, 65F22, G.1, I.2, I.4, J.3
Abstract

In fluorescence microscopy, Single Molecule Localization Microscopy (SMLM) techniques aim at localizing with high precision high density fluorescent molecules by stochastically activating and imaging small subsets of blinking emitters. Super Resolution (SR) plays an important role in this field since it allows to go beyond the intrinsic light diffraction limit. In this work, we propose a deep learning-based algorithm for precise molecule localization of high density frames acquired by SMLM techniques whose $\ell_{2}$-based loss function is regularized by positivity and $\ell_{0}$-based constraints. The $\ell_{0}$ is relaxed through its Continuous Exact $\ell_{0}$ (CEL0) counterpart. The arising approach, named DeepCEL0, is parameter-free, more flexible, faster and provides more precise molecule localization maps if compared to the other state-of-the-art methods. We validate our approach on both simulated and real fluorescence microscopy data.

Published
2021

39. Higgs-boson production in top-quark fragmentation

Author

Brancaccio, Colomba, Czakon, Michał, Generet, Terry, and Krämer, Michael

Subjects
High Energy Physics - Phenomenology
Abstract

We compute the fragmentation functions for the production of a Higgs boson at $\mathcal{O}(y_t^2\alpha_s)$. As part of this calculation, the relevant splitting functions are also derived at the same perturbative order. Our results can be used to compute differential cross sections with arbitrary top-quark and Higgs-boson masses from massless calculations. They can also be used to resum logarithms of the form $\ln(p_T/m)$ at large transverse momentum $p_T$ to next-to-leading-logarithmic accuracy by solving the DGLAP equations., Comment: 49 pages, 8 figures

Published
2021
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40. Investigating Osteomyelitis as a Rare Adverse Effect of Vaccination in the Pediatric Population

Author

Valeria Garbo, Laura Venuti, Chiara Albano, Costanza Caruana, Alessandra Cuccia, Anna Condemi, Giovanni Boncori, Valentina Frasca Polara, Antonio Cascio, Sergio Salerno, and Claudia Colomba

Subjects
pediatric osteomyelitis, vaccination adverse effects, vaccine-associated infections, Staphylococcus aureus, immunization procedures, skin preparation, Medicine
Abstract

Immunization is a preventive measure of crucial importance. As with any other medication, side effects are a possibility and include the rare occurrence of severe infections, such as osteomyelitis. We report an unusual case of pediatric osteomyelitis following vaccination and provide a review of similar reports submitted to the Vaccine Adverse Event Report System (VAERS), aiming to explore the association between the vaccination procedure and the occurrence of osteomyelitis in childhood. A previously healthy infant, with no history of trauma or infection, presented with hyperpyrexia, swelling, and functional impairment in the left leg and was eventually diagnosed with osteomyelitis of the left femur. An edema was noted at the site of the injection that he received days before for immunization purposes. The infection required surgical drainage and a four-week-long intravenous antibiotic treatment, and the patient was discharged upon showing improved clinical conditions. Forty-seven reports of similar cases submitted to VAERS between 1994 and 2023 were collected, and several cases from the literature, including a case of femoral osteomyelitis in a newborn vaccinated against Hepatitis B, attributed to improper injection technique. Another case was reported in a 15-year-old girl, which aligned with six similar cases of osteomyelitis in adolescents following HPV vaccines collected from VAERS. Despite the small sample number, the findings that in 77% of cases the infection was localized in the vaccinated limb and that symptoms appeared on average 4.3 days (IQR 1.0–5.7 days) post-vaccination suggest a possible link to the injection procedure and highlight the need to adhere to recommendations regarding skin preparation and the selection of the appropriate needle length and injection site.

Published
2024
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43. Explainable 3D CNN based on baseline breast DCE-MRI to give an early prediction of pathological complete response to neoadjuvant chemotherapy

Author

Comes, Maria Colomba, Fanizzi, Annarita, Bove, Samantha, Didonna, Vittorio, Diotiaiuti, Sergio, Fadda, Federico, La Forgia, Daniele, Giotta, Francesco, Latorre, Agnese, Nardone, Annalisa, Palmiotti, Gennaro, Ressa, Cosmo Maurizio, Rinaldi, Lucia, Rizzo, Alessandro, Talienti, Tiziana, Tamborra, Pasquale, Zito, Alfredo, Lorusso, Vito, and Massafra, Raffaella

Published
2024
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44. Immunomodulators for immunocompromised patients hospitalized for COVID-19: a meta-analysis of randomized controlled trials

Author

Hermine, Olivier, Mariette, Xavier, Ravaud, Philippe, Bureau, Serge, Dougados, Maxime, Resche-Rigon, Matthieu, Tharaux, Pierre-Louis, Tibi, Annick, Azoulay, Elie, Cadranel, Jacques, Emmerich, Joseph, Fartoukh, Muriel, Guidet, Bertrand, Humbert, Marc, Lacombe, Karine, Mahevas, Matthieu, Pene, Frédéric, Porcher, Raphaël, Pourchet-Martinez, Valerie, Schlemmer, Frédéric, Yazdanpanah, Yazdan, Baron, Gabriel, Perrodeau, Elodie, Vanhoye, Damien, Kedzia, Cécile, Demerville, Lauren, Gysembergh-Houal, Anne, Bourgoin, Alexandre, Raked, Nabil, Mameri, Lakhdar, Montlahuc, Claire, Biard, Lucie, Alary, St.phanie, Hamiria, Samir, Bariz, Thinhinane, Semri, Hala, Hai, Dhiaa Meriem, Benafla, Moustafa, Belloul, Mohamed, Vauboin, Pernelle, Flamand, Saskia, Pacheco, Claire, Walter-Petrich, Anouk, Stan, Emilia, Benarab, Souad, Nyanou, Corine, Charreteur, Robin, Dupre, Céline, Cardet, Kévin, Lehmann, Blandine, Baghli, Kamyl, Madelaine, Claire, D'Ortenzio, Eric, Puéchal, Oriane, Semaille, Caroline, Savale, Laurent, Harrois, Anatole, Figueiredo, Samy, Duranteau, Jacques, Anguel, Nadia, Pavot, Arthur, Monnet, Xavier, Richard, Christian, Teboul, Jean-Louis, Durand, Philippe, Tissieres, Pierre, Jevnikar, Mitja, Montani, David, Pavy, Stephan, Nocturne, Gaétane, Bitoun, Samuel, Noel, Nicolas, Lambotte, Olivier, Escaut, Lelia, Jauréguiberry, Stephane, Baudry, Elodie, Verny, Christiane, Lefevre, Edouard, Zaidan, Mohamad, Molinari, Domitille, Leprun, Gaël, Fourreau, Alain, Cylly, Laurent, Grimaldi, Lamiae, Virlouvet, Myriam, Meftali, Ramdane, Fabre, Soléne, Licois, Marion, Mamoune, Asmaa, Boudali, Yacine, Le Tiec, Clotilde, Verstuyft, Céline, Roques, Anne-Marie, Georgin-Lavialle, Sophie, Senet, Patricia, Pialoux, Gilles, Soria, Angele, Parrot, Antoine, François, Helene, Rozensztajn, Nathalie, Blin, Emmanuelle, Choinier, Pascaline, Camuset, Juliette, Rech, Jean-Simon, Canellas, Antony, Rolland-Debord, Camille, Lemarié, Nadege, Belaube, Nicolas, Nadal, Marine, Siguier, Martin, Petit-Hoang, Camille, Chas, Julie, Drouet, Elodie, Lemoine, Matthieu, Phibel, Audrey, Aunay, Lucie, Bertrand, Eliane, Ravato, Sylviane, Vayssettes, Marie, Adda, Anne, Wilpotte, Celine, Thibaut, Pélagie, Fillon, Julie, Debrix, Isabelle, Fellahi, Soraya, Bastard, Jean-Philippe, Lefévre, Guillaume, Gottenberg, Jacques-Eric, Hansmann, Yves, Blanc, Frédéric, Ohlmann-Caillard, Sophie, Castelain, Vincent, Chatelus, Emmanuel, Chatron, Eva, Collange, Olivier, Danion, François, De Blay, Frédéric, Diemunsch, Pierre, Diemunsch, Sophie, Felten, Renaud, Goichot, Bernard, Greigert, Valentin, Guffroy, Aurelien, Heger, Bob, Kaeuffer, Charlotte, Kassegne, Loic, Korganow, Anne Sophie, Le Borgne, Pierrick, Lefebvre, Nicolas, Mertes, Paul-Michel, Noll, Eric, Oberlin, Mathieu, Poindron, Vincent, Pottecher, Julien, Ruch, Yvon, Weill, François, Meyer, Nicolas, Andres, Emmanuel, Demonsant, Eric, Tayebi, Hakim, Nisand, Gabriel, Brin, Stéphane, Sublon, Cédric, Becker, Guillaume, Hutt, Anne, Martin, Tristan, Bayer, Sophie, Metzger, Catherine, Mekinian, Arsene, Abisror, Noémie, Adedjouma, Amir, Bollens, Diane, Bonneton, Marion, Bourcicaux, Nathalie, Bourrier, Anne, Thibault Chiarabiani, Maria Chauchard, Chopin, Doroth.e, Cohen, Jonathan, Devred, Ines, Donadille, Bruno, Fain, Olivier, Hariri, Geoffrey, Jachiet, Vincent, Ingliz, Patrick, Garnier, Marc, Gatfosse, Marc, Ghrenassia, Etienne, Gobert, Delphine, Krause le Garrec, Jessica, Landman, Cecilia, Lavillegrand, Jean Remy, Lefebvre, Benedicte, Mahevas, Thibault, Mazerand, Sandie, Meynard, Jean Luc, Morgand, Marjolaine, Ouaz.ne, Zineb, Pacanowski, Jerome, Riviere, S.bastien, Seksik, Philippe, Sokol, Harry, Soliman, Heithem, Valin, Nadia, Urbina, Thomas, McAvoy, Chloé, Miranda, Maria Pereira, Aratus, Gladys, Berard, Laurence, Simon, Tabassome, Nguyen, Anne Daguenel, Girault, Elise, Mayala-Kanda, Cl.mentine, Antignac, Marie, Leplay, Céline, Arlet, Jean-Benoit, Diehl, Jean-Luc, Bellenfant, Florence, Blanchard, Anne, Buffet, Alexandre, Cholley, Bernard, Fayol, Antoine, Flamarion, Edouard, Godier, Anne, Gorget, Thomas, Hamada, Sophie-Rym, Hauw-Berlemont, Caroline, Hulot, Jean-Sébastien, Lebeaux, David, Livrozet, Marine, Michon, Adrien, Neuschwander, Arthur, Pennet, Marie-Aude, Planquette, Benjamin, Ranque, Brigitte, Sanchez, Olivier, Volle, Geoffroy, Briois, Sandrine, Cornic, Mathias, Elisee, Virginie, Denis, Jesuthasan, Djadi-Prat, Juliette, Jouany, Pauline, Junquera, Ramon, Henriques, Mickael, Kebir, Amina, Lehir, Isabelle, Meunier, Jeanne, Patin, Florence, Paquet, Val.rie, Tréhan, Anne, Vigna, Véronique, Sabatier, Brigitte, Bergerot, Damien, Jouve, Charléne, Knosp, Camille, Lenoir, Olivia, Mahtal, Nassim, Resmini, Léa, Lescure, Xavier, Ghosn, Jade, Bachelard, Antoine, Rachline, Anne, Isernia, Valentina, Bao-chau, Phung, Vallois, Dorothée, Sautereau, Aurelie, Neukrich, Catherine, Dossier, Antoine, Borie, Raphaël, Crestani, Bruno, Ducrocq, Gregory, Steg, Philippe Gabriel, Dieude, Philippe, Papo, Thomas, Marcault, Estelle, Chaudhry, Marhaba, Da Silveira, Charléne, Metois, Annabelle, Mahenni, Ismahan, Meziani, Meriam, Nilusmas, Cyndie, Le Gac, Sylvie, Ndiaye, Awa, Louni, Fran.oise, Chansombat, Malikhone, Julia, Zelie, Chalal, Solaya, Chalal, Lynda, Kramer, Laura, Le Grand, Jeniffer, Ouifiya, Kafif, Piquard, Valentine, Tubiana, Sarah, Nguyen, Yann, Honsel, Vasco, Weiss, Emmanuel, Codorniu, Anais, Zarrouk, Virginie, de Lastours, Victoire, Uzzan, Matthieu, Gamany, Naura, Claveirole, Agathe, Navid, Alexandre, Fouque, Tiffanie, Cohen, Yonathan, Lupo, Maya, Gilles, Constance, Rahli, Roza, Louis, Zeina, Boutboul, David, Galicier, Lionel, Amara, Yaël, Archer, Gabrielle, Benattia, Amira, Bergeron, Anne, Bondeelle, Louise, de Castro, Nathalie, Clément, Melissa, Darmon, Michaël, Denis, Blandine, Dupin, Clairelyne, Feredj, Elsa, Feyeux, Delphine, Joseph, Adrien, Lenglin, Etienne, Le Guen, Pierre, Liégeon, Geoffroy, Lorillon, Gwenaël, Mabrouki, Asma, Mariotte, Eric, Martin de Frémont, Grégoire, Mirouse, Adrien, Molina, Jean-Michel, Peffault de Latour, Régis, Oksenhendler, Eric, Saussereau, Julien, Tazi, Abdellatif, Tudesq, Jean-Jacques, Zafrani, Lara, Brindele, Isabelle, Bugnet, Emmanuelle, Lebras, Karine Celli, Chabert, Julien, Djaghout, Lamia, Fauvaux, Catherine, Jegu, Anne Lise, Kozakiewicz, Ewa, Meunier, Martine, Tremorin, Marie-Thérèse, Davoine, Claire, Madelaine, Isabelle, Caillat-Zucman, Sophie, Delaugerre, Constance, Morin, Florence, Sène, Damien, Burlacu, Ruxandra, Chousterman, Benjamin, Mégarbanne, Bruno, Richette, Pascal, Riveline, Jean-Pierre, Frazier, Aline, Vicaut, Eric, Berton, Laure, Hadjam, Tassadit, Vazquez-Ibarra, Miguel Alejandro, Jourdaine, Clément, Tran, Olivia, Jouis, Véronique, Jacob, Aude, Smati, Julie, Renaud, Stéphane, Pernin, Claire, Suarez, Lydia, Semerano, Luca, Abad, Sébastien, nainous, Ruben B., Bonnet, Nicolas, Comparon, Celine, Cohen, Yves, Cordel, Hugues, Dhote, Robin, Dournon, Nathalie, Duchemann, Boris, Ebstein, Nathan, Gille, Thomas, Giroux-Leprieur, Benedicte, Goupil de Bouille, Jeanne, Nunes, Hilario, Oziel, Johanna, Roulot, Dominique, Sese, Lucile, ClaireTantet, Uzunhan, Yurdagul, Bloch-Queyrat, Coralie, Levy, Vincent, Messani, Fadhila, Rahaoui, Mohammed, Petit, Myléne, Brahmi, Sabrina, Rathoin, Vanessa, Rigal, Marthe, Costedoat-Chalumeau, Nathalie, Luong, Liem Binh, Hamou, Zakaria Ait, Benghanem, Sarah, Blanche, Philippe, Carlier, Nicolas, Chaigne, Benjamin, Gauzit, Remy, Joumaa, Hassan, Jozwiak, Mathieu, Lachétre, Marie, Lafoeste, Hélène, Launay, Odie, Legendre, Paul, Marey, Jonathan, Morbieu, Caroline, Palmieri, Lola-Jade, Szwebel, Tali-Anne, Abdoul, Hendy, Bruneau, Alexandra, Beclin-Clabaux, Audrey, Larrieu, Charly, Montanari, Pierre, Dufour, Eric, Clarke, Ada, Le Bourlout, Catherine, Marin, Nathalie, Menage, Nathalie, Saleh-Mghir, Samira, Cisse, Mamadou Salif, Cheref, Kahina, Guerin, Corinne, Zerbit, Jérémie, Michel, Marc, Gallien, Sébastien, Crickx, Etienne, Le Vavasseur, Benjamin, Kempf, Emmanuelle, Jaffal, Karim, Vindrios, William, Oniszczuk, Julie, Guillaud, Constance, Lim, Pascal, Fois, Elena, Melica, Giovanna, Matignon, Marie, Jalabert, Maud, Lelièvre, Jean-Daniel, Schmitz, David, Bourhis, Marion, Belazouz, Sylia, Languille, Laetitia, Boucle, Caroline, Cita, Nelly, Didier, Agnés, Froura, Fahem, Ledudal, Katia, Sadaoui, Thiziri, Thiemele, Alaki, Le Febvre De Bailly, Delphine, Verlinde, Muriel Carvhalo, Mayaux, Julien, Cacoub, Patrice, Saadoun, David, Vautier, Mathieu, Bugaut, Héléne, Benveniste, Olivier, Allenbach, Yves, Leroux, Gaëlle, Rigolet, Aude, Guillaume-Jugnot, Perrine, Domont, Fanny, Desbois, Anne Claire, Comarmond, Chloé, Champtiaux, Nicolas, Toquet, Segolene, Ghembaza, Amine, Vieira, Matheus, Maalouf, Georgina, Boleto, Goncalo, Ferfar, Yasmina, Corvol, Jean-Christophe, Louapre, C.line, Sambin, Sara, Mariani, Louise-Laure, Karachi, Carine, Tubach, Florence, Estellat, Candice, Gimeno, Linda, Martin, Karine, Bah, Aicha, Keo, Vixra, Ouamri, Sabrine, Messaoudi, Yasmine, Yelles, Nessima, Faye, Pierre, Cavelot, Sebastien, Larcheveque, Cecile, Annonay, Laurence, Benhida, Jaouad, Zahrate-Ghoul, Aida, Hammal, Soumeya, Belilita, Ridha, Charbonnier, Fanny, Aguilar, Claire, Alby-Laurent, Fanny, Burger, Carole, Campos-Vega, Clara, Chavarot, Nathalie, Fournier, Benjamin, Rouzaud, Claire, Vimpére, Damien, Elie, Caroline, Bakouboula, Prissile, Choupeaux, Laure, Granville, Sophie, Issorat, Elodie, Broissand, Christine, Alyanakian, Marie-Alexandra, Geri, Guillaume, Derridj, Nawal, Sguiouar, Naima, Meddah, Hakim, Djadel, Mourad, Chambrin-Lauvray, Héléne, Duclos-vallée, Jean-Charles, Saliba, Faouzi, Sacleux, Sophie-Caroline, Kounis, Ilias, Tamazirt, Sonia, Rudant, Eric, Michot, Jean-Marie, Stoclin, Annabelle, Colomba, Emeline, Pommeret, Fanny, Willekens, Christophe, Da Silva, Rosa, Dejean, Valérie, Mekid, Yasmina, Ben-Mabrouk, Ines, Netzer, Florence, Pradon, Caroline, Drouard, Laurence, Camara-Clayette, Valérie, Morel, Alexandre, Garcia, Gilles, Mohebbi, Abolfazl, Berbour, Férial, Dehais, Mélanie, Pouliquen, Anne-Lise, Klasen, Alison, Soyez-Herkert, Loren, London, Jonathan, Keroumi, Younes, Guillot, Emmanuelle, Grailles, Guillaume, El amine, Younes, Defrancq, Fanny, Fodil, Hanane, Bouras, Chaouki, Dautel, Dominique, Gambier, Nicolas, Dieye, Thierno, Bienvenu, Boris, Lancon, Victor, Lecomte, Laurence, Beziriganyan, Kristina, Asselate, Belkacem, Allanic, Laure, Kiouris, Elena, Legros, Marie-Héléne, Lemagner, Christine, Martel, Pascal, Provitolo, Vincent, Ackermann, Félix, Le Marchand, Mathilde, Chan Hew Wai, Aurélie, Fremont, Dimitri, Coupez, Elisabeth, Adda, Mireille, Duée, Frédéric, Bernard, Lise, Gros, Antoine, Henry, Estelle, Courtin, Claire, Pattyn, Anne, Guinot, Pierre-Grégoire, Bardou, Marc, Maurer, Agnes, Jambon, Julie, Cransac, Amélie, Pernot, Corinne, Mourvillier, Bruno, Marquis, Eric, Benoit, Philippe, Roux, Damien, Gernez, Coralie, Yelnik, Cécile, Poissy, Julien, Nizard, Mandy, Denies, Fanette, Gros, Helene, Mourad, Jean-Jacques, Sacco, Emmanuelle, Renet, Sophie, Ader, F., Yazdanpanah, Y., Mentre, F., Peiffer-Smadja, N., Lescure, F.X., Poissy, J., Bouadma, L., Timsit, J.F., Lina, B., Morfin-Sherpa, F., Bouscambert, M., Gaymard, A., Peytavin, G., Abel, L., Guedj, J., Andrejak, C., Burdet, C., Laouenan, C., Belhadi, D., Dupont, A., Alfaiate, T., Basli, B., Chair, A., Laribi, S., Level, J., Schneider, M., Tellier, M.C., Dechanet, A., Costagliola, D., Terrier, B., Ohana, M., Couffin-Cadiergues, S., Esperou, H., Delmas, C., Saillard, J., Fougerou, C., Moinot, L., Wittkop, L., Cagnot, C., Le Mestre, S., Lebrasseur-Longuet, D., Petrov-Sanchez, V., Diallo, A., Mercier, N., Icard, V., Leveau, B., Tubiana, S., Hamze, B., Gelley, A., Noret, M., D’Ortenzio, E., Puechal, O., Semaille, C., Welte, T., Paiva, J.A., Halanova, M., Kieny, M.P., Balssa, E., Birkle, C., Gibowski, S., Landry, E., Le Goff, A., Moachon, L., Moins, C., Wadouachi, L., Paul, C., Levier, A., Bougon, D., Djossou, F., Epelboin, L., Dellamonica, J., Marquette, C.H., Robert, C., Gibot, S., Senneville, E., Jean-Michel, V., Zerbib, Y., Chirouze, C., Boyer, A., Cazanave, C., Gruson, D., Malvy, D., Andreu, P., Quenot, J.P., Terzi, N., Faure, K., Chabartier, C., Le Moing, V., Klouche, K., Ferry, T., F, Valour, Gaborit, B., Canet, E., Le Turnier, P., Boutoille, D., Bani-Sadr, F., Benezit, F., Revest, M., Cameli, C., Caro, A., Um Tegue, MJ Ngo, Le Tulzo, Y., Laviolle, B., Laine, F., Thiery, G., Meziani, F., Hansmann, Y., Oulehri, W., Tacquard, C., Vardon-Bounes, F., Riu-Poulenc, B., Murris-Espin, M., Bernard, L., Garot, D., Hinschberger, O., Martinot, M., Bruel, C., Pilmis, B., Bouchaud, O., Loubet, P., Roger, C., Monnet, X., Figueiredo, S., Godard, V., Mira, J.P., Lachatre, M., Kerneis, S., Aboab, J., Sayre, N., Crockett, F., Lebeaux, D., Buffet, A., Diehl, J.L., Fayol, A., Hulot, J.S., Livrozet, M., Dessap, A Mekontso, Ficko, C., Stefan, F., Le Pavec, J., Mayaux, J., Ait-Oufella, H., Molina, J.M., Pialoux, G., Fartoukh, M., Textoris, J., Brossard, M., Essat, A., Netzer, E., Riault, Y., Ghislain, M., Beniguel, L., Genin, M., Gouichiche, L., Betard, C., Belkhir, L., Altdorfer, A., Centro, V Fraipont, Braz, S., Ribeiro, JM Ferreira, Alburqueque, R Roncon, Berna, M., Alexandre, M., Lamprecht, B., Egle, A., Greil, R., Joannidis, M., Patterson, Thomas F., Ponce, Philip O., Taylor, Barbara S., Patterson, Jan E., Bowling, Jason E., Javeri, Heta, Kalil, Andre C., Larson, LuAnn, Hewlett, Angela, Mehta, Aneesh K., Rouphael, Nadine G., Saklawi, Youssef, Scanlon, Nicholas, Traenkner, Jessica J., Trible, Ronald P., Jr., Walter, Emmanuel B., Ivey, Noel, Holland, Thomas L., Ruiz-Palacios, Guillermo M., Ponce de León, Alfredo, Rajme, Sandra, Hsieh, Lanny, Amin, Alpesh N., Watanabe, Miki, Lee, Helen S., Kline, Susan, Billings, Joanne, Noren, Brooke, Kim, Hyun, Bold, Tyler D., Tapson, Victor, Grein, Jonathan, Sutterwala, Fayyaz, Iovine, Nicole, Beattie, Lars K., Wakeman, Rebecca Murray, Shaw, Matthew, Jain, Mamta K., Mocherla, Satish, Meisner, Jessica, Luque, Amneris, Sweeney, Daniel A., Benson, Constance A., Ali, Farhana, Atmar, Robert L., El Sahly, Hana M., Whitaker, Jennifer, Falsey, Ann R., Branche, Angela R., Rozario, Cheryl, Pineda, Justino Regalado, Martinez-Orozco, José Arturo, Lye, David Chien, Ong, Sean WX., Chia, Po Ying, Young, Barnaby E., Sandkovsky, Uriel, Berhe, Mezgebe, Haley, Clinton, Dishner, Emma, Cantos, Valeria D., Kelley, Colleen F., Rebolledo Esteinou, Paulina A., Kandiah, Sheetal, Doernberg, Sarah B., Crouch, Pierre-Cedric B., Jang, Hannah, Luetkemeyer, Anne F., Dwyer, Jay, Cohen, Stuart H., Thompson, George R., 3rd, Nguyen, Hien H., Finberg, Robert W., Wang, Jennifer P., Perez-Velazquez, Juan, Wessolossky, Mireya, Jackson, Patrick E.H., Bell, Taison D., West, Miranda J., Taiwo, Babafemi, Krueger, Karen, Perez, Johnny, Pearson, Triniece, Paules, Catharine I., Julian, Kathleen G., Ahmad, Danish, Hajduczok, Alexander G., Arguinchona, Henry, Arguinchona, Christa, Erdmann, Nathaniel, Goepfert, Paul, Ahuja, Neera, Frank, Maria G., Wyles, David, Young, Heather, Oh, Myoung-don, Park, Wan Beom, Kang, Chang Kyung, Marconi, Vincent, Moanna, Abeer, Cribbs, Sushma, Harrison, Telisha, Kim, Eu Suk, Jung, Jongtak, Song, Kyoung-Ho, Kim, Hong Bin, Tan, Seow Yen, Shafi, Humaira, Chien, Jaime, Fong, Raymond KC., Murray, Daniel D., Lundgren, Jens, Nielsen, Henrik, Jensen, Tomas, Zingman, Barry S., Grossberg, Robert, Riska, Paul F., Yang, Otto O., Ahn, Jenny, Arias, Rubi, Rapaka, Rekha R., Hauser, Naomi, Campbell, James D., Short, William R., Tebas, Pablo, Baron, Jillian T., McLellan, Susan L.F., Blanton, Lucas S., Seashore, Justin B., Creech, C. Buddy, Rice, Todd W., Walker, Shannon, Thomsen, Isaac P., Lopez de Castilla, Diego, Van Winkle, Jason W., Riedo, Francis X., Pada, Surinder Kaur, Wang, Alvin DY., Lin, Li, Harkins, Michelle, Mertz, Gregory, Sosa, Nestor, Ann Chai, Louis Yi, Tambyah, Paul Anantharajah, Tham, Sai Meng, Archuleta, Sophia, Yan, Gabriel, Lindholm, David A., Markelz, Ana Elizabeth, Mende, Katrin, Mularski, Richard, Hohmann, Elizabeth, Torres-Soto, Mariam, Jilg, Nikolaus, Maves, Ryan C., Utz, Gregory C., George, Sarah L., Hoft, Daniel F., Brien, James D., Paredes, Roger, Mateu, Lourdes, Loste, Cora, Kumar, Princy, Thornton, Sarah, Mohanraj, Sharmila, Hynes, Noreen A., Sauer, Lauren M., Colombo, Christopher J., Schofield, Christina, Colombo, Rhonda E., Chambers, Susan E., Novak, Richard M., Wendrow, Andrea, Gupta, Samir K., Lee, Tida, Lalani, Tahaniyat, Holodniy, Mark, Chary, Aarthi, Huprikar, Nikhil, Ganesan, Anuradha, Ohmagari, Norio, Mikami, Ayako, Price, D. Ashley, Duncan, Christopher J.A., Dierberg, Kerry, Neumann, Henry J., Taylor, Stephanie N., Lacour, Alisha, Masri, Najy, Swiatlo, Edwin, Widmer, Kyle, Neaton, James D., Bessesen, Mary, Stephens, David S., Burgess, Timothy H., Uyeki, Timothy M., Walker, Robert, Marks, G. Lynn, Osinusi, Anu, Cao, Huyen, Cardoso, Anabela, de Bono, Stephanie, Schlichting, Douglas E., Chung, Kevin K., Ferreira, Jennifer L., Green, Michelle, Makowski, Mat, Wierzbicki, Michael R., Conrad, Tom M., El-Khorazaty, Jill Ann, Hill, Heather, Bonnett, Tyler, Gettinger, Nikki, Engel, Theresa, Lewis, Teri, Wang, Jing, Beigel, John H., Tomashek, Kay M., Ghazaryan, Varduhi, Beresnev, Tatiana, Nayak, Seema, Dodd, Lori E., Dempsey, Walla, Nomicos, Effie, Lee, Marina, Pikaart-Tautges, Rhonda, Elsafy, Mohamed, Jurao, Robert, Koo, Hyung, Proschan, Michael, Yokum, Tammy, Arega, Janice, Florese, Ruth, Voell, Jocelyn D., Davey, Richard, Serrano, Ruth C., Wiley, Zanthia, Phadke, Varun K., Goepfert, Paul A., Gomez, Carlos A., Sofarelli, Theresa A., Certain, Laura, Imlay, Hannah N., Wolfe, Cameron R., Ko, Emily R., Engemann, John J., Felix, Nora Bautista, Wan, Claire R., Elmor, Sammy T., Bristow, Laurel R., Harkins, Michelle S., Iovine, Nicole M., Elie-Turenne, Marie-Carmelle, Tapson, Victor F., Choe, Pyoeng Gyun, Mularski, Richard A., Rhie, Kevin S., Hussein, Rezhan H., Ince, Dilek, Winokur, Patricia L., Takasaki, Jin, Saito, Sho, McConnell, Kimberly, Wyles, David L., Sarcone, Ellen, Grimes, Kevin A., Perez, Katherine, Janak, Charles, Whitaker, Jennifer A., Rebolledo, Paulina A., Gharbin, John, Lambert, Allison A., Zea, Diego F., Bainbridge, Emma, Hostler, David C., Hostler, Jordanna M., Shahan, Brian T., Ling, Evelyn, Go, Minjoung, Hubbard, Fleesie A., Chakrabarty, Melony, Laguio-Vila, Maryrose, Walsh, Edward E., Guirgis, Faheem, Marconi, Vincent C., Madar, Christian, Borgetti, Scott A., Levine, Corri, Nock, Joy, Candiotti, Keith, Rozman, Julia, Dangond, Fernando, Hyvert, Yann, Seitzinger, Andrea, Cross, Kaitlyn, Pettibone, Stephanie, Nayak, Seema U., Deye, Gregory A., Siempos, Ilias I., Belhadi, Drifa, Veiga, Viviane Cordeiro, Cavalcanti, Alexandre Biasi, Branch-Elliman, Westyn, Papoutsi, Eleni, Gkirgkiris, Konstantinos, Xixi, Nikoleta A., and Kotanidou, Anastasia

Published
2024
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45. Long-lasting bioactive compounds production from Malus x domestica Borkh var. Mela Rosa Marchigiana pulp callus culture with antioxidant activity: In vitro, ex vivo and in vivo validation

Author

Gubitosa, Federica, Benayada, Leila, Fraternale, Daniele, De Bellis, Roberta, Carloni, Silvia, Potenza, Lucia, Chiarantini, Laura, Gorassini, Andrea, Verardo, Giancarlo, Roselli, Carla, Valentini, Laura, Gobbi, Pietro, Balduini, Walter, Ventura, Natascia, Giannaccini, Francesca, Kass, George E.N., Colomba, Mariastella, and Albertini, Maria Cristina

Published
2024
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46. A Randomized, Open-label, Cross-over Phase 2 Trial of Darolutamide and Enzalutamide in Men with Asymptomatic or Mildly Symptomatic Metastatic Castrate-resistant Prostate Cancer: Patient Preference and Cognitive Function in ODENZA

Author

Colomba, Emeline, Jonas, Sarah Flora, Eymard, Jean-Christophe, Delva, Rémi, Brachet, Pierre Emmanuel, Neuzillet, Yann, Penel, Nicolas, Roubaud, Guilhem, Bompas, Emmanuelle, Mahammedi, Hakim, Longo, Raffaelle, Helissey, Carole, Barthélemy, Philippe, Borchiellini, Delphine, Hasbini, Ali, Priou, Franck, Saldana, Carolina, Voog, Eric, Narcisso, Bérangère, Ladoire, Sylvain, Berdah, Jean-François, Aisenfarb, Jean-Baptiste, Foulon, Stéphanie, and Fizazi, Karim

Published
2024
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47. Causes of hospitalization and predictors of in-hospital mortality among people living with HIV in Sicily-Italy between 2010 and 2021

Author

Luca Pipitò, Giuseppe Zinna, Marcello Trizzino, Claudia Gioè, Manlio Tolomeo, Paola Di Carlo, Claudia Colomba, Lidia Gibaldi, Chiara Iaria, Piero Almasio, and Antonio Cascio

Subjects
Hospital admissions, HIV, AIDS, Italians, Migrants, Africans, Infectious and parasitic diseases, RC109-216, Public aspects of medicine, RA1-1270
Abstract

Background: Despite the rising number of people living with human immunodeficiency virus (HIV), there is a lack of knowledge about the factors that lead to PLWHs being hospitalized in worldwide literature. Our study aimed to investigate PLWH admissions in Sicily (Italy) between January 2010 and September 2021 and to analyze the characteristics and risk factors for in-hospital mortality and differences between Italians and foreigners. Methods: Data from the hospital discharge forms of all people living with HIV (PLWH) hospitalized in Sicilian hospitals were retrospectively collected. Age, sex, nationality, length of stay, acquired immunodeficiency syndrome (AIDS), and non-AIDS-related diseases were evaluated using univariate analysis according to in-hospital mortality rates. The factors associated with mortality were included in the logistic regression model. Results: In total, 5281 admissions from 2726 PLWHs occurred, most of which were related to non-AIDS diseases. Approximately 20 % regarded foreign patients, mainly from Africa. Logistic regression analysis revealed an association between in-hospital mortality and some AIDS- and non-AIDS-related diseases (wasting syndrome, lymphomas, Kaposi sarcomas, progressive multifocal leukoencephalopathy, cryptococcosis, abscesses, sepsis, cardiovascular disease, nephropathy, and respiratory diseases). African patient admissions were significantly associated with tuberculosis, toxoplasmosis, Burkitt lymphoma, and hepatitis B diagnoses. Conclusions: Our study showed that most hospitalizations were related to non-AIDS-defining diseases, with differences between Italian and foreign patients, mainly from Africa.

Published
2023
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48. Diagnosing Fabry nephropathy: the challenge of multiple kidney disease

Author

Pasquale Esposito, Carmela Caputo, Monica Repetto, Alberto Somaschini, Bellone Pietro, Paolo Colomba, Carmela Zizzo, Angelica Parodi, Valentina Zanetti, Marco Canepa, Virginia Eustachi, Francesca Sanguineri, Paola Mandich, and Francesca Viazzi

Subjects
Fabry disease, Fabry nephropathy, Kidney biopsy, Chronic kidney disease, Glomerulonephritis, ADPKD, Diseases of the genitourinary system. Urology, RC870-923
Abstract

Abstract Fabry disease (FD) is an X-linked inherited lysosomal disorder due to a deficiency of the enzyme alpha-galactosidase A (α-gla) due to mutations in the GLA gene. These mutations result in plasma and lysosome accumulation of glycosphingolipids, leading to progressive organ damage and reduced life expectancy. Due to the availability of specific disease-modifying treatments, proper and timely diagnosis and therapy are essential to prevent irreversible complications. However, diagnosis of FD is often delayed because of the wide clinical heterogeneity of the disease and multiple organ involvement developing in variable temporal sequences. This observation is also valid for renal involvement, which may manifest with non-specific signs, such as proteinuria and chronic kidney disease, which are also common in many other nephropathies. Moreover, an additional confounding factor is the possibility of the coexistence of FD with other kidney disorders. Thus, suspecting and diagnosing FD nephropathy in patients with signs of kidney disease may be challenging for the clinical nephrologist. Herein, also through the presentation of a unique case of co-occurrence of autosomal dominant polycystic kidney disease and FD, we review the available literature on cases of coexistence of FD and other renal diseases and discuss the implications of these conditions. Moreover, we highlight the clinical, laboratory, and histological elements that may suggest clinical suspicion and address a proper diagnosis of Fabry nephropathy.

Published
2023
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49. Comparison between vision transformers and convolutional neural networks to predict non-small lung cancer recurrence

Author

Annarita Fanizzi, Federico Fadda, Maria Colomba Comes, Samantha Bove, Annamaria Catino, Erika Di Benedetto, Angelo Milella, Michele Montrone, Annalisa Nardone, Clara Soranno, Alessandro Rizzo, Deniz Can Guven, Domenico Galetta, and Raffaella Massafra

Subjects
Medicine, Science
Abstract

Abstract Non-Small cell lung cancer (NSCLC) is one of the most dangerous cancers, with 85% of all new lung cancer diagnoses and a 30–55% of recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients during diagnosis could be essential to drive targeted therapies preventing either overtreatment or undertreatment of cancer patients. The radiomic analysis of CT images has already shown great potential in solving this task; specifically, Convolutional Neural Networks (CNNs) have already been proposed providing good performances. Recently, Vision Transformers (ViTs) have been introduced, reaching comparable and even better performances than traditional CNNs in image classification. The aim of the proposed paper was to compare the performances of different state-of-the-art deep learning algorithms to predict cancer recurrence in NSCLC patients. In this work, using a public database of 144 patients, we implemented a transfer learning approach, involving different Transformers architectures like pre-trained ViTs, pre-trained Pyramid Vision Transformers, and pre-trained Swin Transformers to predict the recurrence of NSCLC patients from CT images, comparing their performances with state-of-the-art CNNs. Although, the best performances in this study are reached via CNNs with AUC, Accuracy, Sensitivity, Specificity, and Precision equal to 0.91, 0.89, 0.85, 0.90, and 0.78, respectively, Transformer architectures reach comparable ones with AUC, Accuracy, Sensitivity, Specificity, and Precision equal to 0.90, 0.86, 0.81, 0.89, and 0.75, respectively. Based on our preliminary experimental results, it appears that Transformers architectures do not add improvements in terms of predictive performance to the addressed problem.

Published
2023
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50. Prognostic power assessment of clinical parameters to predict neoadjuvant response therapy in HER2‐positive breast cancer patients: A machine learning approach

Author

Annarita Fanizzi, Agnese Latorre, Domenica Antonia Bavaro, Samantha Bove, Maria Colomba Comes, Erika Francesca Di Benedetto, Federico Fadda, Daniele La Forgia, Francesco Giotta, Gennaro Palmiotti, Nicole Petruzzellis, Lucia Rinaldi, Alessandro Rizzo, Vito Lorusso, and Raffaella Massafra

Subjects
bioinformatics, breast cancer, neoadjuvant therapy, prognostic factor, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background About 15%–20% of breast cancer (BC) cases is classified as Human Epidermal growth factor Receptor type 2 (HER2) positive. The Neoadjuvant chemotherapy (NAC) was initially introduced for locally advanced and inflammatory BC patients to allow a less extensive surgical resection, whereas now it represents the current standard for early‐stage and operable BC. However, only 20%–40% of patients achieve pathologic complete response (pCR). According to the results of practice‐changing clinical trials, the addition of trastuzumab to NAC brings improvements to pCR, and recently, the use of pertuzumab plus trastuzumab has registered further statistically significant and clinically meaningful improvements in terms of pCR. The goal of our work is to propose a machine learning model to predict the pCR to NAC in HER2‐positive patients based on a subset of clinical features. Method First, we evaluated the significant association of clinical features with pCR on the retrospectively collected data referred to 67 patients afferent to Istituto Tumori “Giovanni Paolo II.” Then, we performed a feature selection procedure to identify a subset of features to be used for training a machine learning‐based classification algorithm. As a result, pCR to NAC was associated with ER status, Pgr status, and HER2 score. Results The machine learning model trained on a subgroup of essential features reached an AUC of 73.27% (72.44%–73.66%) and an accuracy of 71.67% (71.64%–73.13%). According to our results, the clinical features alone are not enough to define a support system useful for clinical pathway. Conclusion Our results seem worthy of further investigation in large validation studies and this work could be the basis of future study that will also involve radiomics analysis of biomedical images.

Published
2023
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