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173 results on '"de Marinis, P."'

1. Stability of neural ODEs by a control over the expansivity of their flows

Author

De Marinis, Arturo, Guglielmi, Nicola, Sicilia, Stefano, and Tudisco, Francesco

Subjects
Mathematics - Numerical Analysis, Mathematics - Optimization and Control
Abstract

We propose a method to enhance the stability of a neural ordinary differential equation (neural ODE) by means of a control over the Lipschitz constant $C$ of its flow. Since it is known that $C$ depends on the logarithmic norm of the Jacobian matrix associated with the neural ODE, we tune this parameter at our convenience by suitably perturbing the Jacobian matrix with a perturbation as small as possible in Frobenius norm. We do so by introducing an optimization problem for which we propose a nested two-level algorithm. For a given perturbation size, the inner level computes the optimal perturbation with a fixed Frobenius norm, while the outer level tunes the perturbation amplitude. We embed the proposed algorithm in the training of the neural ODE to improve its stability. Numerical experiments on the MNIST and FashionMNIST datasets show that an image classifier including a neural ODE in its architecture trained according to our strategy is more stable than the same classifier trained in the classical way, and therefore, it is more robust and less vulnerable to adversarial attacks., Comment: 22 pages, 3 figures, 2 tables

Published
2025

2. RoWeeder: Unsupervised Weed Mapping through Crop-Row Detection

Author

De Marinis, Pasquale, Vessio, Gennaro, and Castellano, Giovanna

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Precision agriculture relies heavily on effective weed management to ensure robust crop yields. This study presents RoWeeder, an innovative framework for unsupervised weed mapping that combines crop-row detection with a noise-resilient deep learning model. By leveraging crop-row information to create a pseudo-ground truth, our method trains a lightweight deep learning model capable of distinguishing between crops and weeds, even in the presence of noisy data. Evaluated on the WeedMap dataset, RoWeeder achieves an F1 score of 75.3, outperforming several baselines. Comprehensive ablation studies further validated the model's performance. By integrating RoWeeder with drone technology, farmers can conduct real-time aerial surveys, enabling precise weed management across large fields. The code is available at: \url{https://github.com/pasqualedem/RoWeeder}., Comment: Computer Vision for Plant Phenotyping and Agriculture (CVPPA) workshop at ECCV 2024

Published
2024

3. Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts

Author

De Marinis, Pasquale, Fanelli, Nicola, Scaringi, Raffaele, Colonna, Emanuele, Fiameni, Giuseppe, Vessio, Gennaro, and Castellano, Giovanna

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We present Label Anything, an innovative neural network architecture designed for few-shot semantic segmentation (FSS) that demonstrates remarkable generalizability across multiple classes with minimal examples required per class. Diverging from traditional FSS methods that predominantly rely on masks for annotating support images, Label Anything introduces varied visual prompts -- points, bounding boxes, and masks -- thereby enhancing the framework's versatility and adaptability. Unique to our approach, Label Anything is engineered for end-to-end training across multi-class FSS scenarios, efficiently learning from diverse support set configurations without retraining. This approach enables a "universal" application to various FSS challenges, ranging from $1$-way $1$-shot to complex $N$-way $K$-shot configurations while remaining agnostic to the specific number of class examples. This innovative training strategy reduces computational requirements and substantially improves the model's adaptability and generalization across diverse segmentation tasks. Our comprehensive experimental validation, particularly achieving state-of-the-art results on the COCO-$20^i$ benchmark, underscores Label Anything's robust generalization and flexibility. The source code is publicly available at: https://github.com/pasqualedem/LabelAnything.

Published
2024

4. A Silicon Photonic 32-Input Coherent Combiner for Turbulence Mitigation in Free Space Optics Links

Author

De Marinis, Lorenzo, Kincaid, Peter Seigo, Lucas, Yann, Krafft, Lea, Michau, Vincent, Cherchi, Matteo, Karppinen, Mikko, and Contestabile, Giampiero

Subjects
Physics - Optics, Electrical Engineering and Systems Science - Signal Processing
Abstract

A photonic integrated circuit (PIC) for the coherent combination of 32 input optical signals into a single output fiber is reported. The PIC was fabricated using a low-loss thick silicon-on-insulator (SOI) process and packaged with 32 input and 1 output fibers. The basic building block is a 2x2 Mach-Zehnder interferometer (MZI) with an external (to the MZI branches) and an internal thermal phase shifter, and a bandwidth in excess of 80 kHz. The PIC monolithically integrates 31 MZIs and 31 germanium photodetectors, and is suitable in principle for turbulence mitigation in LEO-ground and horizontal free space optics links. Improvements to the device for the coherent combination of 64 inputs and for the reduction of insertion losses are also discussed

Published
2024

5. Contractivity of neural ODEs: an eigenvalue optimization problem

Author

Guglielmi, Nicola, De Marinis, Arturo, Savostianov, Anton, and Tudisco, Francesco

Subjects
Mathematics - Numerical Analysis, Mathematics - Optimization and Control
Abstract

We propose a novel methodology to solve a key eigenvalue optimization problem which arises in the contractivity analysis of neural ODEs. When looking at contractivity properties of a one layer weight-tied neural ODE $\dot{u}(t)=\sigma(Au(t)+b)$ (with $u,b \in {\mathbb R}^n$, $A$ is a given $n \times n$ matrix, $\sigma : {\mathbb R} \to {\mathbb R}$ denotes an activation function and for a vector $z \in {\mathbb R}^n$, $\sigma(z) \in {\mathbb R}^n$ has to be interpreted entry-wise), we are led to study the logarithmic norm of a set of products of type $D A$, where $D$ is a diagonal matrix such that ${\mathrm{diag}}(D) \in \sigma'({\mathbb R}^n)$. Specifically, given a real number $c$ (usually $c=0$), the problem consists in finding the largest positive interval $\text{I}\subseteq \mathbb [0,\infty)$ such that the logarithmic norm $\mu(DA) \le c$ for all diagonal matrices $D$ with $D_{ii}\in \text{I}$. We propose a two-level nested methodology: an inner level where, for a given $\text{I}$, we compute an optimizer $D^\star(\text{I})$ by a gradient system approach, and an outer level where we tune $\text{I}$ so that the value $c$ is reached by $\mu(D^\star(\text{I})A)$. We extend the proposed two-level approach to the general multilayer, and possibly time-dependent, case $\dot{u}(t) = \sigma( A_k(t) \ldots \sigma ( A_{1}(t) u(t) + b_{1}(t) ) \ldots + b_{k}(t) )$ and we propose several numerical examples to illustrate its behaviour, including its stabilizing performance on a one-layer neural ODE applied to the classification of the MNIST handwritten digits dataset., Comment: 26 pages, 6 figures, 4 tables

Published
2024

6. The novel SMYD3 inhibitor EM127 impairs DNA repair response to chemotherapy-induced DNA damage and reverses cancer chemoresistance

Author

Sanese, Paola, De Marco, Katia, Lepore Signorile, Martina, La Rocca, Francesca, Forte, Giovanna, Latrofa, Marialaura, Fasano, Candida, Disciglio, Vittoria, Di Nicola, Elisabetta, Pantaleo, Antonino, Bianco, Giusy, Spilotro, Vito, Ferroni, Claudia, Tubertini, Matilde, Labarile, Nicoletta, De Marinis, Lucia, Armentano, Raffaele, Gigante, Gianluigi, Lantone, Valerio, Lantone, Giuliano, Naldi, Marina, Bartolini, Manuela, Varchi, Greta, Del Rio, Alberto, Grossi, Valentina, and Simone, Cristiano

Published
2024
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8. Application of the nnU-Net for automatic segmentation of lung lesion on CT images, and implication on radiomic models

Author

Ferrante, Matteo, Rinaldi, Lisa, Botta, Francesca, Hu, Xiaobin, Dolp, Andreas, Minotti, Marta, De Piano, Francesca, Funicelli, Gianluigi, Volpe, Stefania, Bellerba, Federica, De Marco, Paolo, Raimondi, Sara, Rizzo, Stefania, Shi, Kuangyu, Cremonesi, Marta, Jereczek-Fossa, Barbara A., Spaggiari, Lorenzo, De Marinis, Filippo, Orecchia, Roberto, and Origgi, Daniela

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

Lesion segmentation is a crucial step of the radiomic workflow. Manual segmentation requires long execution time and is prone to variability, impairing the realisation of radiomic studies and their robustness. In this study, a deep-learning automatic segmentation method was applied on computed tomography images of non-small-cell lung cancer patients. The use of manual vs automatic segmentation in the performance of survival radiomic models was assessed, as well. METHODS A total of 899 NSCLC patients were included (2 proprietary: A and B, 1 public datasets: C). Automatic segmentation of lung lesions was performed by training a previously developed architecture, the nnU-Net, including 2D, 3D and cascade approaches. The quality of automatic segmentation was evaluated with DICE coefficient, considering manual contours as reference. The impact of automatic segmentation on the performance of a radiomic model for patient survival was explored by extracting radiomic hand-crafted and deep-learning features from manual and automatic contours of dataset A, and feeding different machine learning algorithms to classify survival above/below median. Models' accuracies were assessed and compared. RESULTS The best agreement between automatic and manual contours with DICE=0.78 +(0.12) was achieved by averaging predictions from 2D and 3D models, and applying a post-processing technique to extract the maximum connected component. No statistical differences were observed in the performances of survival models when using manual or automatic contours, hand-crafted, or deep features. The best classifier showed an accuracy between 0.65 and 0.78. CONCLUSION The promising role of nnU-Net for automatic segmentation of lung lesions was confirmed, dramatically reducing the time-consuming physicians' workload without impairing the accuracy of survival predictive models based on radiomics.

Published
2022

10. Dostarlimab or pembrolizumab plus chemotherapy in previously untreated metastatic non-squamous non-small cell lung cancer: the randomized PERLA phase II trial

Author

Lim, Sun Min, Peters, Solange, Ortega Granados, Ana Laura, Pinto, Gustavo dix Junqueira, Fuentes, Christian Sebastián, Lo Russo, Giuseppe, Schenker, Michael, Ahn, Jin Seok, Reck, Martin, Szijgyarto, Zsolt, Huseinovic, Neda, Zografos, Eleftherios, Buss, Elena, Stjepanovic, Neda, O’Donnell, Sean, and de Marinis, Filippo

Published
2023
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12. Author Correction: BIM and mTOR expression levels predict outcome to erlotinib in EGFR-mutant non-small-cell lung cancer

Author

Karachaliou, Niki, Codony-Servat, Jordi, Teixidó, Cristina, Pilotto, Sara, Drozdowskyj, Ana, Codony-Servat, Carles, Giménez-Capitán, Ana, Molina-Vila, Miguel Angel, Bertrán-Alamillo, Jordi, Gervais, Radj, Massuti, Bartomeu, Morán, Teresa, Majem, Margarita, Felip, Enriqueta, Carcereny, Enric, García-Campelo, Rosario, Viteri, Santiago, González-Cao, María, Morales-Espinosa, Daniela, Verlicchi, Alberto, Crisetti, Elisabetta, Chaib, Imane, Santarpia, Mariacarmela, Luis Ramírez, José, Bosch-Barrera, Joaquim, Felipe Cardona, Andrés, de Marinis, Filippo, López-Vivanco, Guillermo, Miguel Sánchez, José, Vergnenegre, Alain, Sánchez Hernández, José Javier, Sperduti, Isabella, Bria, Emilio, and Rosell, Rafael

Published
2023
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18. An immersed boundary method for fluid-structure interaction based on overlapping domain decomposition

Author

Nestola, Maria Giuseppina Chiara, Becsek, Barna, Zolfaghari, Hadi, Zulian, Patrick, De Marinis, Dario, Krause, Rolf, and Obrist, Dominik

Subjects
Physics - Computational Physics
Abstract

We present a novel framework inspired by the Immersed Boundary Method for predicting the fluid-structure interaction of complex structures immersed in flows with moderate to high Reynolds numbers. The main novelties of the proposed fluid-structure interaction framework are 1) the use of elastodynamics equations for the structure, 2) the use of a high-order Navier-Stokes solver for the flow, and 3) the variational transfer (L2-projection) for coupling the solid and fluid subproblems. The dynamic behavior of a deformable structure is simulated in a finite element framework by adopting a fully implicit scheme for its temporal integration. It allows for mechanical constitutive laws including nonhomogeneous and fiber-reinforced materials. The Navier-Stokes equations for the incompressible flow are discretized with high-order finite differences which allow for the direct numerical simulation of laminar, transitional and turbulent flows. The structure and the flow solvers are coupled by using an L2-projection method for the transfer of velocities and forces between the fluid grid and the solid mesh. This strategy allows for the numerical solution of coupled large scale problems based on nonconforming structured and unstructured grids. The framework is validated with the Turek-Hron benchmark and a newly proposed benchmark modelling the flow-induced oscillation of an inert plate. A three-dimensional simulation of an elastic beam in transitional flow is provided to show the solver's capability of coping with anisotropic elastic structures immersed in complex fluid flow., Comment: submitted Journal of Computational Physics (28th February 2018)

Published
2018

20. Correction to: Chronic Non-cancer Pain Management in a Tertiary Pain Clinic Network: a Retrospective Study

Author

Latina, Roberto, Varrassi, Giustino, Di Biagio, Ettore, Giannarelli, Diana, Gravante, Francesco, Paladini, Antonella, D’Angelo, Daniela, Iacorossi, Laura, Martella, Cristina, Alvaro, Rosaria, Ivziku, Dhurata, Veronese, Nicola, Barbagallo, Mario, Marchetti, Anna, Notaro, Paolo, Terrenato, Irene, Tarsitani, Gianfranco, and De Marinis, Maria Grazia

Published
2023
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23. Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomised controlled trial

Author

Rittmeyer, Achim, Barlesi, Fabrice, Waterkamp, Daniel, Park, Keunchil, Ciardiello, Fortunato, von Pawel, Joachim, Gadgeel, Shirish M, Hida, Toyoaki, Kowalski, Dariusz M, Dols, Manuel Cobo, Cortinovis, Diego L, Leach, Joseph, Polikoff, Jonathan, Barrios, Carlos, Kabbinavar, Fairooz, Frontera, Osvaldo Arén, De Marinis, Filippo, Turna, Hande, Lee, Jong-Seok, Ballinger, Marcus, Kowanetz, Marcin, He, Pei, Chen, Daniel S, Sandler, Alan, Gandara, David R, and Group, OAK Study

Subjects
Clinical Research, Cancer, Clinical Trials and Supportive Activities, Lung, Adult, Aged, Aged, 80 and over, Antibodies, Monoclonal, Antibodies, Monoclonal, Humanized, Antineoplastic Agents, Carcinoma, Non-Small-Cell Lung, Disease-Free Survival, Docetaxel, Drug Administration Schedule, Female, Humans, Infusions, Intravenous, Kaplan-Meier Estimate, Lung Neoplasms, Male, Middle Aged, Taxoids, Treatment Outcome, OAK Study Group, Medical and Health Sciences, General & Internal Medicine
Abstract

BackgroundAtezolizumab is a humanised antiprogrammed death-ligand 1 (PD-L1) monoclonal antibody that inhibits PD-L1 and programmed death-1 (PD-1) and PD-L1 and B7-1 interactions, reinvigorating anticancer immunity. We assessed its efficacy and safety versus docetaxel in previously treated patients with non-small-cell lung cancer.MethodsWe did a randomised, open-label, phase 3 trial (OAK) in 194 academic or community oncology centres in 31 countries. We enrolled patients who had squamous or non-squamous non-small-cell lung cancer, were 18 years or older, had measurable disease per Response Evaluation Criteria in Solid Tumors, and had an Eastern Cooperative Oncology Group performance status of 0 or 1. Patients had received one to two previous cytotoxic chemotherapy regimens (one or more platinum based combination therapies) for stage IIIB or IV non-small-cell lung cancer. Patients with a history of autoimmune disease and those who had received previous treatments with docetaxel, CD137 agonists, anti-CTLA4, or therapies targeting the PD-L1 and PD-1 pathway were excluded. Patients were randomly assigned (1:1) to intravenously receive either atezolizumab 1200 mg or docetaxel 75 mg/m2 every 3 weeks by permuted block randomisation (block size of eight) via an interactive voice or web response system. Coprimary endpoints were overall survival in the intention-to-treat (ITT) and PD-L1-expression population TC1/2/3 or IC1/2/3 (≥1% PD-L1 on tumour cells or tumour-infiltrating immune cells). The primary efficacy analysis was done in the first 850 of 1225 enrolled patients. This study is registered with ClinicalTrials.gov, number NCT02008227.FindingsBetween March 11, 2014, and April 29, 2015, 1225 patients were recruited. In the primary population, 425 patients were randomly assigned to receive atezolizumab and 425 patients were assigned to receive docetaxel. Overall survival was significantly longer with atezolizumab in the ITT and PD-L1-expression populations. In the ITT population, overall survival was improved with atezolizumab compared with docetaxel (median overall survival was 13·8 months [95% CI 11·8-15·7] vs 9·6 months [8·6-11·2]; hazard ratio [HR] 0·73 [95% CI 0·62-0·87], p=0·0003). Overall survival in the TC1/2/3 or IC1/2/3 population was improved with atezolizumab (n=241) compared with docetaxel (n=222; median overall survival was 15·7 months [95% CI 12·6-18·0] with atezolizumab vs 10·3 months [8·8-12·0] with docetaxel; HR 0·74 [95% CI 0·58-0·93]; p=0·0102). Patients in the PD-L1 low or undetectable subgroup (TC0 and IC0) also had improved survival with atezolizumab (median overall survival 12·6 months vs 8·9 months; HR 0·75 [95% CI 0·59-0·96]). Overall survival improvement was similar in patients with squamous (HR 0·73 [95% CI 0·54-0·98]; n=112 in the atezolizumab group and n=110 in the docetaxel group) or non-squamous (0·73 [0·60-0·89]; n=313 and n=315) histology. Fewer patients had treatment-related grade 3 or 4 adverse events with atezolizumab (90 [15%] of 609 patients) versus docetaxel (247 [43%] of 578 patients). One treatment-related death from a respiratory tract infection was reported in the docetaxel group.InterpretationTo our knowledge, OAK is the first randomised phase 3 study to report results of a PD-L1-targeted therapy, with atezolizumab treatment resulting in a clinically relevant improvement of overall survival versus docetaxel in previously treated non-small-cell lung cancer, regardless of PD-L1 expression or histology, with a favourable safety profile.FundingF. Hoffmann-La Roche Ltd, Genentech, Inc.

Published
2017

27. Elevated circulating follistatin associates with an increased risk of type 2 diabetes

Author

Wu, Chuanyan, Borné, Yan, Gao, Rui, López Rodriguez, Maykel, Roell, William C., Wilson, Jonathan M., Regmi, Ajit, Luan, Cheng, Aly, Dina Mansour, Peter, Andreas, Machann, Jürgen, Staiger, Harald, Fritsche, Andreas, Birkenfeld, Andreas L., Tao, Rongya, Wagner, Robert, Canouil, Mickaël, Hong, Mun-Gwan, Schwenk, Jochen M., Ahlqvist, Emma, Kaikkonen, Minna U., Nilsson, Peter, Shore, Angela C., Khan, Faisel, Natali, Andrea, Melander, Olle, Orho-Melander, Marju, Nilsson, Jan, Häring, Hans-Ulrich, Renström, Erik, Wollheim, Claes B., Engström, Gunnar, Weng, Jianping, Pearson, Ewan R., Franks, Paul W., White, Morris F., Duffin, Kevin L., Vaag, Allan Arthur, Laakso, Markku, Stefan, Norbert, Groop, Leif, and De Marinis, Yang

Published
2021
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37. Exploraciones en teoría social : Ensayos de imaginación metodológica

Author

de Marinis, Pablo, Coordinador, Ruiz, Fermín Álvarez, Alvaro, Daniel, Bialakowsky, Alejandro, Blanco, Ana, De Marinis, Pablo, Fraga, Eugenia, Grondona, Ana, Haidar, Victoria, Sasín, Mariano, Torterola, Emiliano, Trovero, Juan Ignacio, de Marinis, Pablo, Ruiz, Fermín Álvarez, Alvaro, Daniel, Bialakowsky, Alejandro, Blanco, Ana, De Marinis, Pablo, Fraga, Eugenia, Grondona, Ana, Haidar, Victoria, Sasín, Mariano, Torterola, Emiliano, and Trovero, Juan Ignacio

Published
2019
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38. Management of intracranial hypertension following traumatic brain injury: a best clinical practice adoption proposal for intracranial pressure monitoring and decompressive craniectomy. Joint statements by the Traumatic Brain Injury Section of the Italian Society of Neurosurgery (SINch) and the Neuroanesthesia and Neurocritical Care Study Group of the Italian Society of Anesthesia, Analgesia, Resuscitation and Intensive Care (SIAARTI)

Author

Iaccarino C., Lippa L., Munari M., Castioni C. A., Robba C., Caricato A., Pompucci A., Signoretti S., Zona G., Rasulo F. A., Aimar E., Amato S., Angileri F. F., Anile C., Assietti R., Baratto V., Barbanera A., Basile L., Battaglia R., Bellocchi S., Bertuccio A., Blanco S., Bolognini A., Boniferro B., Bordi L., Bortolotti C., Brandini V., Broger M., Brollo M., Caffarella D. D., Caggiano C., Cantisani P. L., Capone C., Cappelletto B., Capuano C., Carangelo B., Caruselli G., Chessa M. A., Chiara M., Chibbaro S., Cioffi V., Ciprianocecchi P., Colistra D., Conti C., Contratti F., Costella G. B., Cuoci A., D'Avella D., D'Ercole M., Deangelis M., Defalco R., de Luca G., de Marinis P., Del Vecchio C., Delfinis C., Denaro L., Deodato F., Desogus N., Disomma A., Domenicucci M., Dones F., Fina M., Fiori L., Fricia M., Gaetani P., Gazzeri R., Gentile M., Germano A., Ghadirpour R., Gianfreda C. D., Gigante N., Gigli R., Giorgetti J., Giusa M., Gravina U. G., Grippi L., Guida F., Guizzardi G., Iannuzzo G., Kropp M., Lattanzi L., Lucantoni D., Maffei L., Magliulo M., Marconi F., Marruzzo D., Martellotta N., Marton E., Maugeri R., Mauro G., Meli F., Menniti A., Merciadri P., Milanese L., Nardacci B., Nasi D., Orvieto P., Pacca P., Pansini G., Panzarasa G., Passanisi M., Pavesi G., Pizzoni C., Pulera F., Rapana A., Ricci A., Rispoli R., Rotondo M., Russo N., Santilli S., Scarano E., Schwarz A., Servadei F., Simonetti G., Stefini R., Talamonti G., Turrisi A., Valente V. M., Villa A., Vindigni M., Visocchi M., Vitali M., Wierzbicki V., Zambon G., Zanotti B., Zenga F., Alampi D., Alessandri F., Aloj F., Amigoni A., Aspide R., Bertuetti R., Betti V., Bilotta F., Bonato V., Bosco E., Brita M., Buscema G., Cafiero T., Cappuccio D., Caradonna M., Caria C. G., Casartelliliviero M., Ciritella P., Cirrincione S., Citerio G., Colelli S., Coletta F., Concordia L., Congedo E., Covotta M., Crimella F., Dall'Acqua G., De Cassai A., Defulviis S., Deperi E., Deana C., Delgaudio A., Denittis N., Dicolandrea S., Divezza F., Ferri F., Flocco R., Fontana C., Forastierimolinari A., Frangiosa A., Fumagalli P., Fuselli E., Garbarino M. M., Gelormini D., Geraci C., Geraldini F., Giacomucci A., Giampaoli V., Giorgetti D., Gritti P., Gualdani S., Iacovazzo C., Iermano C., Latronico N., Lugari S., Lusenti F., Maglione C., Magnoni S., Maiarota F., Malla M., Marchesi M., Martino C., Matteotti I., Mazzeo A. T., Morello G., Nardiello I., Paticchio F., Pegoli M., Perotti V., Piazzolla M., Picciafuochi F., Rachedi N., Radolovich D. K., Recchia A., Riccardi S., Romagnoli S., Sala S., Scafuro M. A., Sgarlata P., Soragni A., Stefani F., Stival E., Stofella G., Terranova F., Tinturini R., Togni T., Toto R., Trapani D., Tringali E., Tullo L., Valente A., Valeo T., Varelli G., Villani R., Zamacavicchi F., Zanello M., Zarrillo N., Zugni N., Iaccarino, C, Lippa, L, Munari, M, Castioni, C, Robba, C, Caricato, A, Pompucci, A, Signoretti, S, Zona, G, Rasulo, F, Aimar, E, Amato, S, Angileri, F, Anile, C, Assietti, R, Baratto, V, Barbanera, A, Basile, L, Battaglia, R, Bellocchi, S, Bertuccio, A, Blanco, S, Bolognini, A, Boniferro, B, Bordi, L, Bortolotti, C, Brandini, V, Broger, M, Brollo, M, Caffarella, D, Caggiano, C, Cantisani, P, Capone, C, Cappelletto, B, Capuano, C, Carangelo, B, Caruselli, G, Chessa, M, Chiara, M, Chibbaro, S, Cioffi, V, Ciprianocecchi, P, Colistra, D, Conti, C, Contratti, F, Costella, G, Cuoci, A, D'Avella, D, D'Ercole, M, Deangelis, M, Defalco, R, de Luca, G, de Marinis, P, Del Vecchio, C, Delfinis, C, Denaro, L, Deodato, F, Desogus, N, Disomma, A, Domenicucci, M, Dones, F, Fina, M, Fiori, L, Fricia, M, Gaetani, P, Gazzeri, R, Gentile, M, Germano, A, Ghadirpour, R, Gianfreda, C, Gigante, N, Gigli, R, Giorgetti, J, Giusa, M, Gravina, U, Grippi, L, Guida, F, Guizzardi, G, Iannuzzo, G, Kropp, M, Lattanzi, L, Lucantoni, D, Maffei, L, Magliulo, M, Marconi, F, Marruzzo, D, Martellotta, N, Marton, E, Maugeri, R, Mauro, G, Meli, F, Menniti, A, Merciadri, P, Milanese, L, Nardacci, B, Nasi, D, Orvieto, P, Pacca, P, Pansini, G, Panzarasa, G, Passanisi, M, Pavesi, G, Pizzoni, C, Pulera, F, Rapana, A, Ricci, A, Rispoli, R, Rotondo, M, Russo, N, Santilli, S, Scarano, E, Schwarz, A, Servadei, F, Simonetti, G, Stefini, R, Talamonti, G, Turrisi, A, Valente, V, Villa, A, Vindigni, M, Visocchi, M, Vitali, M, Wierzbicki, V, Zambon, G, Zanotti, B, Zenga, F, Alampi, D, Alessandri, F, Aloj, F, Amigoni, A, Aspide, R, Bertuetti, R, Betti, V, Bilotta, F, Bonato, V, Bosco, E, Brita, M, Buscema, G, Cafiero, T, Cappuccio, D, Caradonna, M, Caria, C, Casartelliliviero, M, Ciritella, P, Cirrincione, S, Citerio, G, Colelli, S, Coletta, F, Concordia, L, Congedo, E, Covotta, M, Crimella, F, Dall'Acqua, G, De Cassai, A, Defulviis, S, Deperi, E, Deana, C, Delgaudio, A, Denittis, N, Dicolandrea, S, Divezza, F, Ferri, F, Flocco, R, Fontana, C, Forastierimolinari, A, Frangiosa, A, Fumagalli, P, Fuselli, E, Garbarino, M, Gelormini, D, Geraci, C, Geraldini, F, Giacomucci, A, Giampaoli, V, Giorgetti, D, Gritti, P, Gualdani, S, Iacovazzo, C, Iermano, C, Latronico, N, Lugari, S, Lusenti, F, Maglione, C, Magnoni, S, Maiarota, F, Malla, M, Marchesi, M, Martino, C, Matteotti, I, Mazzeo, A, Morello, G, Nardiello, I, Paticchio, F, Pegoli, M, Perotti, V, Piazzolla, M, Picciafuochi, F, Rachedi, N, Radolovich, D, Recchia, A, Riccardi, S, Romagnoli, S, Sala, S, Scafuro, M, Sgarlata, P, Soragni, A, Stefani, F, Stival, E, Stofella, G, Terranova, F, Tinturini, R, Togni, T, Toto, R, Trapani, D, Tringali, E, Tullo, L, Valente, A, Valeo, T, Varelli, G, Villani, R, Zamacavicchi, F, Zanello, M, Zarrillo, N, Zugni, N, Iaccarino, C., Lippa, L., Munari, M., Castioni, C. A., Robba, C., Caricato, A., Pompucci, A., Signoretti, S., Zona, G., Rasulo, F. A., Aimar, E., Amato, S., Angileri, F. F., Anile, C., Assietti, R., Baratto, V., Barbanera, A., Basile, L., Battaglia, R., Bellocchi, S., Bertuccio, A., Blanco, S., Bolognini, A., Boniferro, B., Bordi, L., Bortolotti, C., Brandini, V., Broger, M., Brollo, M., Caffarella, D. D., Caggiano, C., Cantisani, P. L., Capone, C., Cappelletto, B., Capuano, C., Carangelo, B., Caruselli, G., Chessa, M. A., Chiara, M., Chibbaro, S., Cioffi, V., Ciprianocecchi, P., Colistra, D., Conti, C., Contratti, F., Costella, G. B., Cuoci, A., D'Avella, D., D'Ercole, M., Deangelis, M., Defalco, R., de Luca, G., de Marinis, P., Del Vecchio, C., Delfinis, C., Denaro, L., Deodato, F., Desogus, N., Disomma, A., Domenicucci, M., Dones, F., Fina, M., Fiori, L., Fricia, M., Gaetani, P., Gazzeri, R., Gentile, M., Germano, A., Ghadirpour, R., Gianfreda, C. D., Gigante, N., Gigli, R., Giorgetti, J., Giusa, M., Gravina, U. G., Grippi, L., Guida, F., Guizzardi, G., Iannuzzo, G., Kropp, M., Lattanzi, L., Lucantoni, D., Maffei, L., Magliulo, M., Marconi, F., Marruzzo, D., Martellotta, N., Marton, E., Maugeri, R., Mauro, G., Meli, F., Menniti, A., Merciadri, P., Milanese, L., Nardacci, B., Nasi, D., Orvieto, P., Pacca, P., Pansini, G., Panzarasa, G., Passanisi, M., Pavesi, G., Pizzoni, C., Pulera, F., Rapana, A., Ricci, A., Rispoli, R., Rotondo, M., Russo, N., Santilli, S., Scarano, E., Schwarz, A., Servadei, F., Simonetti, G., Stefini, R., Talamonti, G., Turrisi, A., Valente, V. M., Villa, A., Vindigni, M., Visocchi, M., Vitali, M., Wierzbicki, V., Zambon, G., Zanotti, B., Zenga, F., Alampi, D., Alessandri, F., Aloj, F., Amigoni, A., Aspide, R., Bertuetti, R., Betti, V., Bilotta, F., Bonato, V., Bosco, E., Brita, M., Buscema, G., Cafiero, T., Cappuccio, D., Caradonna, M., Caria, C. G., Casartelliliviero, M., Ciritella, P., Cirrincione, S., Citerio, G., Colelli, S., Coletta, F., Concordia, L., Congedo, E., Covotta, M., Crimella, F., Dall'Acqua, G., De Cassai, A., Defulviis, S., Deperi, E., Deana, C., Delgaudio, A., Denittis, N., Dicolandrea, S., Divezza, F., Ferri, F., Flocco, R., Fontana, C., Forastierimolinari, A., Frangiosa, A., Fumagalli, P., Fuselli, E., Garbarino, M. M., Gelormini, D., Geraci, C., Geraldini, F., Giacomucci, A., Giampaoli, V., Giorgetti, D., Gritti, P., Gualdani, S., Iacovazzo, C., Iermano, C., Latronico, N., Lugari, S., Lusenti, F., Maglione, C., Magnoni, S., Maiarota, F., Malla, M., Marchesi, M., Martino, C., Matteotti, I., Mazzeo, A. T., Morello, G., Nardiello, I., Paticchio, F., Pegoli, M., Perotti, V., Piazzolla, M., Picciafuochi, F., Rachedi, N., Radolovich, D. K., Recchia, A., Riccardi, S., Romagnoli, S., Sala, S., Scafuro, M. A., Sgarlata, P., Soragni, A., Stefani, F., Stival, E., Stofella, G., Terranova, F., Tinturini, R., Togni, T., Toto, R., Trapani, D., Tringali, E., Tullo, L., Valente, A., Valeo, T., Varelli, G., Villani, R., Zamacavicchi, F., Zanello, M., Zarrillo, N., and Zugni, N.

Subjects
medicine.medical_specialty, Decompressive Craniectomy, Consensus development conference, Decompressive craniectomy, Guideline, Traumatic brain injuries, Critical Care, Intracranial Pressure, medicine.medical_treatment, MEDLINE, Neurosurgery, Traumatic brain injurie, law.invention, 03 medical and health sciences, 0302 clinical medicine, Randomized controlled trial, law, Intensive care, Settore MED/41 - ANESTESIOLOGIA, Brain Injuries, Traumatic, medicine, Humans, Anesthesia, Intensive care medicine, Randomized Controlled Trials as Topic, business.industry, Neurointensive care, Evidence-based medicine, Italy, 030220 oncology & carcinogenesis, Intracranial pressure monitoring, Surgery, Neurology (clinical), Analgesia, Intracranial Hypertension, business, 030217 neurology & neurosurgery, Human
Abstract

No robust evidence is provided by literature regarding the management of intracranial hypertension following severe traumatic brain injury (TBI). This is mostly due to the lack of prospective randomized controlled trials (RCTs), the presence of studies containing extreme heterogeneously collected populations and controversial considerations about chosen outcome. A scientific society should provide guidelines for care management and scientific support for those areas for which evidence-based medicine has not been identified. However, RCTs in severe TBI have failed to establish intervention effectiveness, arising the need to make greater use of tools such as Consensus Conferences between experts, which have the advantage of providing recommendations based on experience, on the analysis of updated literature data and on the direct comparison of different logistic realities. The Italian scientific societies should provide guidelines following the national laws ruling the best medical practice. However, many limitations do not allow the collection of data supporting high levels of evidence for intracranial pressure (ICP) monitoring and decompressive craniectomy (DC) in patients with severe TBI. This intersociety document proposes best practice guidelines for this subsetting of patients to be adopted on a national Italian level, along with joint statements from "TBI Section" of the Italian Society of Neurosurgery (SINch) endorsed by the Neuroanesthesia and Neurocritical Care Study Group of the Italian Society of Anesthesia, Analgesia, Resuscitation and Intensive Care (SIAARTI). Presented here is a recap of recommendations on management of ICP and DC supported a high level of available evidence and rate of agreement expressed by the assemblies during the more recent consensus conferences, where members of both groups have had a role of active participants and supporters. The listed recommendations have been sent to a panel of experts consisting of the 107 members of the "TBI Section" of the SINch and the 111 members of the Neuroanesthesia and Neurocritical Care Study Group of the SIAARTI. The aim of the survey was to test a preliminary evaluation of the grade of predictable future adherence of the recommendations following this intersociety proposal. The following recommendations are suggested as representing best clinical practice, nevertheless, adoption of local multidisciplinary protocols regarding thresholds of ICP values, drug therapies, hemostasis management and perioperative care of decompressed patients is strongly recommended to improve treatment efficiency, to increase the quality of data collection and to provide more powerful evidence with future studies. Thus, for this future perspective a rapid overview of the role of the multimodal neuromonitoring in the optimal severe TBI management is also provided in this document. It is reasonable to assume that the recommendations reported in this paper will in future be updated by new observations arising from future trials. They are not binding, and this document should be offered as a guidance for clinical practice through an intersociety agreement, taking in consideration the low level of evidence.

Published
2021

40. Nutritional Assessment in Stroke Patients: A Review on Comprehensive Evaluations Across Disease Phases

Author

Sguanci, Marco, Mancin, Stefano, Piredda, Michela, and De Marinis, Maria Grazia

Abstract

Stroke is a prevalent acute neurological condition causing significant morbidity and mortality worldwide. Upon hospital admission, approximately 20% of stroke victims display signs of malnutrition and dysphagia. For these patients, the evaluation of nutritional status is pivotal, leading to tailored nutritional strategies. This study aims to outline nutritional evaluations during both the immediate and extended phases post-stroke.

Published
2023
Full Text
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42. Management of intracranial hypertension following traumatic brain injury: A best clinical practice adoption proposal for intracranial pressure monitoring and decompressive craniectomy: Joint statements by the Traumatic Brain Injury Section of the Italian Society of Neurosurgery (SINch) and the Neuroanesthesia and Neurocritical Care Study Group of the Italian Society of Anesthesia, Analgesia, Resuscitation and Intensive Care (SIAARTI)

Author

Iaccarino, C, Lippa, L, Munari, M, Castioni, C, Robba, C, Caricato, A, Pompucci, A, Signoretti, S, Zona, G, Rasulo, F, Aimar, E, Amato, S, Angileri, F, Anile, C, Assietti, R, Baratto, V, Barbanera, A, Basile, L, Battaglia, R, Bellocchi, S, Bertuccio, A, Blanco, S, Bolognini, A, Boniferro, B, Bordi, L, Bortolotti, C, Brandini, V, Broger, M, Brollo, M, Caffarella, D, Caggiano, C, Cantisani, P, Capone, C, Cappelletto, B, Capuano, C, Carangelo, B, Caruselli, G, Chessa, M, Chiara, M, Chibbaro, S, Cioffi, V, Ciprianocecchi, P, Colistra, D, Conti, C, Contratti, F, Costella, G, Cuoci, A, D'Avella, D, D'Ercole, M, Deangelis, M, Defalco, R, de Luca, G, de Marinis, P, Del Vecchio, C, Delfinis, C, Denaro, L, Deodato, F, Desogus, N, Disomma, A, Domenicucci, M, Dones, F, Fina, M, Fiori, L, Fricia, M, Gaetani, P, Gazzeri, R, Gentile, M, Germano, A, Ghadirpour, R, Gianfreda, C, Gigante, N, Gigli, R, Giorgetti, J, Giusa, M, Gravina, U, Grippi, L, Guida, F, Guizzardi, G, Iannuzzo, G, Kropp, M, Lattanzi, L, Lucantoni, D, Maffei, L, Magliulo, M, Marconi, F, Marruzzo, D, Martellotta, N, Marton, E, Maugeri, R, Mauro, G, Meli, F, Menniti, A, Merciadri, P, Milanese, L, Nardacci, B, Nasi, D, Orvieto, P, Pacca, P, Pansini, G, Panzarasa, G, Passanisi, M, Pavesi, G, Pizzoni, C, Pulera, F, Rapana, A, Ricci, A, Rispoli, R, Rotondo, M, Russo, N, Santilli, S, Scarano, E, Schwarz, A, Servadei, F, Simonetti, G, Stefini, R, Talamonti, G, Turrisi, A, Valente, V, Villa, A, Vindigni, M, Visocchi, M, Vitali, M, Wierzbicki, V, Zambon, G, Zanotti, B, Zenga, F, Alampi, D, Alessandri, F, Aloj, F, Amigoni, A, Aspide, R, Bertuetti, R, Betti, V, Bilotta, F, Bonato, V, Bosco, E, Brita, M, Buscema, G, Cafiero, T, Cappuccio, D, Caradonna, M, Caria, C, Casartelliliviero, M, Ciritella, P, Cirrincione, S, Citerio, G, Colelli, S, Coletta, F, Concordia, L, Congedo, E, Covotta, M, Crimella, F, Dall'Acqua, G, De Cassai, A, Defulviis, S, Deperi, E, Deana, C, Delgaudio, A, Denittis, N, Dicolandrea, S, Divezza, F, Ferri, F, Flocco, R, Fontana, C, Forastierimolinari, A, Frangiosa, A, Fumagalli, P, Fuselli, E, Garbarino, M, Gelormini, D, Geraci, C, Geraldini, F, Giacomucci, A, Giampaoli, V, Giorgetti, D, Gritti, P, Gualdani, S, Iacovazzo, C, Iermano, C, Latronico, N, Lugari, S, Lusenti, F, Maglione, C, Magnoni, S, Maiarota, F, Malla, M, Marchesi, M, Martino, C, Matteotti, I, Mazzeo, A, Morello, G, Nardiello, I, Paticchio, F, Pegoli, M, Perotti, V, Piazzolla, M, Picciafuochi, F, Rachedi, N, Radolovich, D, Recchia, A, Riccardi, S, Romagnoli, S, Sala, S, Scafuro, M, Sgarlata, P, Soragni, A, Stefani, F, Stival, E, Stofella, G, Terranova, F, Tinturini, R, Togni, T, Toto, R, Trapani, D, Tringali, E, Tullo, L, Valente, A, Valeo, T, Varelli, G, Villani, R, Zamacavicchi, F, Zanello, M, Zarrillo, N, Zugni, N, Iaccarino C., Lippa L., Munari M., Castioni C. A., Robba C., Caricato A., Pompucci A., Signoretti S., Zona G., Rasulo F. A., Aimar E., Amato S., Angileri F. F., Anile C., Assietti R., Baratto V., Barbanera A., Basile L., Battaglia R., Bellocchi S., Bertuccio A., Blanco S., Bolognini A., Boniferro B., Bordi L., Bortolotti C., Brandini V., Broger M., Brollo M., Caffarella D. D., Caggiano C., Cantisani P. L., Capone C., Cappelletto B., Capuano C., Carangelo B., Caruselli G., Chessa M. A., Chiara M., Chibbaro S., Cioffi V., Ciprianocecchi P., Colistra D., Conti C., Contratti F., Costella G. B., Cuoci A., D'Avella D., D'Ercole M., Deangelis M., Defalco R., de Luca G., de Marinis P., Del Vecchio C., Delfinis C., Denaro L., Deodato F., Desogus N., Disomma A., Domenicucci M., Dones F., Fina M., Fiori L., Fricia M., Gaetani P., Gazzeri R., Gentile M., Germano A., Ghadirpour R., Gianfreda C. D., Gigante N., Gigli R., Giorgetti J., Giusa M., Gravina U. G., Grippi L., Guida F., Guizzardi G., Iannuzzo G., Kropp M., Lattanzi L., Lucantoni D., Maffei L., Magliulo M., Marconi F., Marruzzo D., Martellotta N., Marton E., Maugeri R., Mauro G., Meli F., Menniti A., Merciadri P., Milanese L., Nardacci B., Nasi D., Orvieto P., Pacca P., Pansini G., Panzarasa G., Passanisi M., Pavesi G., Pizzoni C., Pulera F., Rapana A., Ricci A., Rispoli R., Rotondo M., Russo N., Santilli S., Scarano E., Schwarz A., Servadei F., Simonetti G., Stefini R., Talamonti G., Turrisi A., Valente V. M., Villa A., Vindigni M., Visocchi M., Vitali M., Wierzbicki V., Zambon G., Zanotti B., Zenga F., Alampi D., Alessandri F., Aloj F., Amigoni A., Aspide R., Bertuetti R., Betti V., Bilotta F., Bonato V., Bosco E., Brita M., Buscema G., Cafiero T., Cappuccio D., Caradonna M., Caria C. G., Casartelliliviero M., Ciritella P., Cirrincione S., Citerio G., Colelli S., Coletta F., Concordia L., Congedo E., Covotta M., Crimella F., Dall'Acqua G., De Cassai A., Defulviis S., Deperi E., Deana C., Delgaudio A., Denittis N., Dicolandrea S., Divezza F., Ferri F., Flocco R., Fontana C., Forastierimolinari A., Frangiosa A., Fumagalli P., Fuselli E., Garbarino M. M., Gelormini D., Geraci C., Geraldini F., Giacomucci A., Giampaoli V., Giorgetti D., Gritti P., Gualdani S., Iacovazzo C., Iermano C., Latronico N., Lugari S., Lusenti F., Maglione C., Magnoni S., Maiarota F., Malla M., Marchesi M., Martino C., Matteotti I., Mazzeo A. T., Morello G., Nardiello I., Paticchio F., Pegoli M., Perotti V., Piazzolla M., Picciafuochi F., Rachedi N., Radolovich D. K., Recchia A., Riccardi S., Romagnoli S., Sala S., Scafuro M. A., Sgarlata P., Soragni A., Stefani F., Stival E., Stofella G., Terranova F., Tinturini R., Togni T., Toto R., Trapani D., Tringali E., Tullo L., Valente A., Valeo T., Varelli G., Villani R., Zamacavicchi F., Zanello M., Zarrillo N., Zugni N., Iaccarino, C, Lippa, L, Munari, M, Castioni, C, Robba, C, Caricato, A, Pompucci, A, Signoretti, S, Zona, G, Rasulo, F, Aimar, E, Amato, S, Angileri, F, Anile, C, Assietti, R, Baratto, V, Barbanera, A, Basile, L, Battaglia, R, Bellocchi, S, Bertuccio, A, Blanco, S, Bolognini, A, Boniferro, B, Bordi, L, Bortolotti, C, Brandini, V, Broger, M, Brollo, M, Caffarella, D, Caggiano, C, Cantisani, P, Capone, C, Cappelletto, B, Capuano, C, Carangelo, B, Caruselli, G, Chessa, M, Chiara, M, Chibbaro, S, Cioffi, V, Ciprianocecchi, P, Colistra, D, Conti, C, Contratti, F, Costella, G, Cuoci, A, D'Avella, D, D'Ercole, M, Deangelis, M, Defalco, R, de Luca, G, de Marinis, P, Del Vecchio, C, Delfinis, C, Denaro, L, Deodato, F, Desogus, N, Disomma, A, Domenicucci, M, Dones, F, Fina, M, Fiori, L, Fricia, M, Gaetani, P, Gazzeri, R, Gentile, M, Germano, A, Ghadirpour, R, Gianfreda, C, Gigante, N, Gigli, R, Giorgetti, J, Giusa, M, Gravina, U, Grippi, L, Guida, F, Guizzardi, G, Iannuzzo, G, Kropp, M, Lattanzi, L, Lucantoni, D, Maffei, L, Magliulo, M, Marconi, F, Marruzzo, D, Martellotta, N, Marton, E, Maugeri, R, Mauro, G, Meli, F, Menniti, A, Merciadri, P, Milanese, L, Nardacci, B, Nasi, D, Orvieto, P, Pacca, P, Pansini, G, Panzarasa, G, Passanisi, M, Pavesi, G, Pizzoni, C, Pulera, F, Rapana, A, Ricci, A, Rispoli, R, Rotondo, M, Russo, N, Santilli, S, Scarano, E, Schwarz, A, Servadei, F, Simonetti, G, Stefini, R, Talamonti, G, Turrisi, A, Valente, V, Villa, A, Vindigni, M, Visocchi, M, Vitali, M, Wierzbicki, V, Zambon, G, Zanotti, B, Zenga, F, Alampi, D, Alessandri, F, Aloj, F, Amigoni, A, Aspide, R, Bertuetti, R, Betti, V, Bilotta, F, Bonato, V, Bosco, E, Brita, M, Buscema, G, Cafiero, T, Cappuccio, D, Caradonna, M, Caria, C, Casartelliliviero, M, Ciritella, P, Cirrincione, S, Citerio, G, Colelli, S, Coletta, F, Concordia, L, Congedo, E, Covotta, M, Crimella, F, Dall'Acqua, G, De Cassai, A, Defulviis, S, Deperi, E, Deana, C, Delgaudio, A, Denittis, N, Dicolandrea, S, Divezza, F, Ferri, F, Flocco, R, Fontana, C, Forastierimolinari, A, Frangiosa, A, Fumagalli, P, Fuselli, E, Garbarino, M, Gelormini, D, Geraci, C, Geraldini, F, Giacomucci, A, Giampaoli, V, Giorgetti, D, Gritti, P, Gualdani, S, Iacovazzo, C, Iermano, C, Latronico, N, Lugari, S, Lusenti, F, Maglione, C, Magnoni, S, Maiarota, F, Malla, M, Marchesi, M, Martino, C, Matteotti, I, Mazzeo, A, Morello, G, Nardiello, I, Paticchio, F, Pegoli, M, Perotti, V, Piazzolla, M, Picciafuochi, F, Rachedi, N, Radolovich, D, Recchia, A, Riccardi, S, Romagnoli, S, Sala, S, Scafuro, M, Sgarlata, P, Soragni, A, Stefani, F, Stival, E, Stofella, G, Terranova, F, Tinturini, R, Togni, T, Toto, R, Trapani, D, Tringali, E, Tullo, L, Valente, A, Valeo, T, Varelli, G, Villani, R, Zamacavicchi, F, Zanello, M, Zarrillo, N, Zugni, N, Iaccarino C., Lippa L., Munari M., Castioni C. A., Robba C., Caricato A., Pompucci A., Signoretti S., Zona G., Rasulo F. A., Aimar E., Amato S., Angileri F. F., Anile C., Assietti R., Baratto V., Barbanera A., Basile L., Battaglia R., Bellocchi S., Bertuccio A., Blanco S., Bolognini A., Boniferro B., Bordi L., Bortolotti C., Brandini V., Broger M., Brollo M., Caffarella D. D., Caggiano C., Cantisani P. L., Capone C., Cappelletto B., Capuano C., Carangelo B., Caruselli G., Chessa M. A., Chiara M., Chibbaro S., Cioffi V., Ciprianocecchi P., Colistra D., Conti C., Contratti F., Costella G. B., Cuoci A., D'Avella D., D'Ercole M., Deangelis M., Defalco R., de Luca G., de Marinis P., Del Vecchio C., Delfinis C., Denaro L., Deodato F., Desogus N., Disomma A., Domenicucci M., Dones F., Fina M., Fiori L., Fricia M., Gaetani P., Gazzeri R., Gentile M., Germano A., Ghadirpour R., Gianfreda C. D., Gigante N., Gigli R., Giorgetti J., Giusa M., Gravina U. G., Grippi L., Guida F., Guizzardi G., Iannuzzo G., Kropp M., Lattanzi L., Lucantoni D., Maffei L., Magliulo M., Marconi F., Marruzzo D., Martellotta N., Marton E., Maugeri R., Mauro G., Meli F., Menniti A., Merciadri P., Milanese L., Nardacci B., Nasi D., Orvieto P., Pacca P., Pansini G., Panzarasa G., Passanisi M., Pavesi G., Pizzoni C., Pulera F., Rapana A., Ricci A., Rispoli R., Rotondo M., Russo N., Santilli S., Scarano E., Schwarz A., Servadei F., Simonetti G., Stefini R., Talamonti G., Turrisi A., Valente V. M., Villa A., Vindigni M., Visocchi M., Vitali M., Wierzbicki V., Zambon G., Zanotti B., Zenga F., Alampi D., Alessandri F., Aloj F., Amigoni A., Aspide R., Bertuetti R., Betti V., Bilotta F., Bonato V., Bosco E., Brita M., Buscema G., Cafiero T., Cappuccio D., Caradonna M., Caria C. G., Casartelliliviero M., Ciritella P., Cirrincione S., Citerio G., Colelli S., Coletta F., Concordia L., Congedo E., Covotta M., Crimella F., Dall'Acqua G., De Cassai A., Defulviis S., Deperi E., Deana C., Delgaudio A., Denittis N., Dicolandrea S., Divezza F., Ferri F., Flocco R., Fontana C., Forastierimolinari A., Frangiosa A., Fumagalli P., Fuselli E., Garbarino M. M., Gelormini D., Geraci C., Geraldini F., Giacomucci A., Giampaoli V., Giorgetti D., Gritti P., Gualdani S., Iacovazzo C., Iermano C., Latronico N., Lugari S., Lusenti F., Maglione C., Magnoni S., Maiarota F., Malla M., Marchesi M., Martino C., Matteotti I., Mazzeo A. T., Morello G., Nardiello I., Paticchio F., Pegoli M., Perotti V., Piazzolla M., Picciafuochi F., Rachedi N., Radolovich D. K., Recchia A., Riccardi S., Romagnoli S., Sala S., Scafuro M. A., Sgarlata P., Soragni A., Stefani F., Stival E., Stofella G., Terranova F., Tinturini R., Togni T., Toto R., Trapani D., Tringali E., Tullo L., Valente A., Valeo T., Varelli G., Villani R., Zamacavicchi F., Zanello M., Zarrillo N., and Zugni N.

Abstract

No robust evidence is provided by literature regarding the management of intracranial hypertension following severe traumatic brain injury (TBI). This is mostly due to the lack of prospective randomized controlled trials (RCTs), the presence of studies containing extreme heterogeneously collected populations and controversial considerations about chosen outcome. A scientific society should provide guidelines for care management and scientific support for those areas for which evidence-based medicine has not been identified. However, RCTs in severe TBI have failed to establish intervention effectiveness, arising the need to make greater use of tools such as Consensus Conferences between experts, which have the advantage of providing recommendations based on experience, on the analysis of updated literature data and on the direct comparison of different logistic realities. The Italian scientific societies should provide guidelines following the national laws ruling the best medical practice. However, many limitations do not allow the collection of data supporting high levels of evidence for intracranial pressure (ICP) monitoring and decompressive craniectomy (DC) in patients with severe TBI. This intersociety document proposes best practice guidelines for this subsetting of patients to be adopted on a national Italian level, along with joint statements from “TBI Section” of the Italian Society of Neurosurgery (SINch) endorsed by the Neuroanesthesia and Neurocritical Care Study Group of the Italian Society of Anesthesia, Analgesia, Resuscitation and Intensive Care (SIAARTI). Presented here is a recap of recommendations on management of ICP and DC supported a high level of available evidence and rate of agreement expressed by the assemblies during the more recent consensus conferences, where members of both groups have had a role of active participants and supporters. The listed recommendations have been sent to a panel of experts consisting of the 107 members of the “T

Published
2021

43. Advanced non-small cell lung cancer management in patients progressing after first-line treatment: results of the cross-sectional phase of the Italian LIFE observational study

Author

Gridelli, Cesare, de Marinis, Filippo, Ardizzoni, Andrea, Novello, Silvia, Fontanini, Gabriella, Cappuzzo, Federico, Grossi, Francesco, Santo, Antonio, Cortinovis, Diego, Favaretto, Adolfo, Lorusso, Vito, Galetta, Domenico, Siena, Salvatore, Bettini, Anna, Iurlaro, Monica, Caprioli, Alberto, and on behalf of LIFE study team

Published
2014
Full Text
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44. GCNPMDA: Human microbe-disease association prediction by hierarchical graph convolutional network with layer attention.

Author

Wu, Chuanyan, Lin, Bentao, Zhang, Huanghe, Xu, Da, Gao, Rui, Song, Rui, Liu, Zhi-Ping, and De Marinis, Yang

Subjects
INFLAMMATORY bowel diseases, TYPE 2 diabetes, RHEUMATOID arthritis, DEEP learning, RECEIVER operating characteristic curves
Abstract

Microorganisms play a crucial role in various physiological processes, including metabolism, immune defense, nutrition absorption, defense against cancer, and protection against pathogen colonization. Changes in microbial communities serve as potential biomarkers for diseases, offering significant insights into disease treatment and diagnosis. However, the association between microorganisms and diseases is still unclear, and more computational methods are needed to predict potential associations. In this paper, we introduce a novel computational model, the Graph Convolutional Network to Predict Microbe-Disease Associations (GCNPMDA), which employs layer attention mechanisms (see Figure 1). GCNPMDA integrates known microbe-disease associations, microbe–microbe similarities, and disease–disease similarities into a heterogeneous network. The model utilizes a Graph Convolutional Network (GCN) to learn embeddings for diseases and microbes. To enhance attribute information, microbe–microbe similarities are computed using Cosine similarity, Jaccard similarity, Gaussian kernel, and functional information, while disease–disease similarities are computed using Cosine similarity, Jaccard similarity, Gaussian kernel, and symptom information. Additionally, attention mechanisms are applied to combine embeddings from multiple graph convolution layers. The model's predictive effectiveness is evaluated on Human Microbe-Disease Association Database (HMDAD). Leave-one-out cross-validation (LOOCV) was conducted. The Area Under ROC Curve (AUC) of LOOCV is 0.98. The 5-fold cross-validation (5-fold CV) on HMDAD yields average AUC of 0.98 ± 0.009. Furthermore, we carried out a case study of type 2 diabetes (T2D), inflammatory bowel disease (IBD), and rheumatoid arthritis. Based on existing literature evidence, it was confirmed that 6, 7, and 7 of the top-10 inferred microbes have established associations with T2D, IBD, and rheumatoid arthritis, respectively. GCNPMDA demonstrates potential efficacy in identifying disease-related microbes, offering a promising tool to uncover the intricate relationship between microorganisms and their human hosts. [Display omitted] • We propose a novel model named GCNPMDA to forecast microbe-disease associations. • GCNPMDA uses multilayer graph convolution to capture diverse features from microbes and diseases. • We use an attention mechanism to combine embeddings from different convolution layers for microbes and diseases. [ABSTRACT FROM AUTHOR]

Published
2025
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47. BIM and mTOR expression levels predict outcome to erlotinib in EGFR-mutant non-small-cell lung cancer

Author

Karachaliou, Niki, Codony-Servat, Jordi, Teixidó, Cristina, Pilotto, Sara, Drozdowskyj, Ana, Codony-Servat, Carles, Giménez-Capitán, Ana, Molina-Vila, Miguel Angel, Bertrán-Alamillo, Jordi, Gervais, Radj, Massuti, Bartomeu, Morán, Teresa, Majem, Margarita, Felip, Enriqueta, Carcereny, Enric, García-Campelo, Rosario, Viteri, Santiago, González-Cao, María, Morales-Espinosa, Daniela, Verlicchi, Alberto, Crisetti, Elisabetta, Chaib, Imane, Santarpia, Mariacarmela, Luis Ramírez, José, Bosch-Barrera, Joaquim, Felipe Cardona, Andrés, de Marinis, Filippo, López-Vivanco, Guillermo, Miguel Sánchez, José, Vergnenegre, Alain, Sánchez Hernández, José Javier, Sperduti, Isabella, Bria, Emilio, and Rosell, Rafael

Published
2015
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49. Management of intracranial hypertension following traumatic brain injury: A best clinical practice adoption proposal for intracranial pressure monitoring and decompressive craniectomy: Joint statements by the Traumatic Brain Injury Section of the Italian Society of Neurosurgery (SINch) and the Neuroanesthesia and Neurocritical Care Study Group of the Italian Society of Anesthesia, Analgesia, Resuscitation and Intensive Care (SIAARTI)

Author

Iaccarino, C., Lippa, L., Munari, M., Castioni, C. A., Robba, C., Caricato, Anselmo, Pompucci, Angelo, Signoretti, S., Zona, G., Rasulo, F. A., Aimar, E., Amato, S., Angileri, F. F., Anile, Carmelo, Assietti, R., Baratto, V., Barbanera, A., Basile, L., Battaglia, R., Bellocchi, S., Bertuccio, A., Blanco, S., Bolognini, A., Boniferro, B., Bordi, L., Bortolotti, C., Brandini, V., Broger, Maximilian, Brollo, M., Caffarella, D. D., Caggiano, Cinzia, Cantisani, P. L., Capone, C., Cappelletto, B., Capuano, C., Carangelo, B., Caruselli, G., Chessa, M. A., Chiara, M., Chibbaro, S., Cioffi, V., Ciprianocecchi, P., Colistra, D., Conti, C., Contratti, F., Costella, G. B., Cuoci, A., D'Avella, D., D'Ercole, Manuela, Deangelis, M., Defalco, R., de Luca, G., de Marinis, P., Del Vecchio, C., Delfinis, C., Denaro, Luca, Deodato, F., Desogus, N., Disomma, A., Domenicucci, M., Dones, F., Fina, M., Fiori, L., Fricia, M., Gaetani, P., Gazzeri, R., Gentile, M., Germano, A., Ghadirpour, R., Gianfreda, C. D., Gigante, N., Gigli, R., Giorgetti, J., Giusa, M., Gravina, U. G., Grippi, L., Guida, F., Guizzardi, G., Iannuzzo, G., Kropp, M., Lattanzi, L., Lucantoni, D., Maffei, L., Magliulo, M., Marconi, F., Marruzzo, D., Martellotta, N., Marton, E., Maugeri, R., Mauro, G., Meli, F., Menniti, A., Merciadri, P., Milanese, L., Nardacci, B., Nasi, D., Orvieto, P., Pacca, P., Pansini, G., Panzarasa, G., Passanisi, M., Pavesi, G., Pizzoni, C., Pulera, F., Rapana, A., Ricci, A., Rispoli, R., Rotondo, M., Russo, N., Santilli, S., Scarano, E., Schwarz, A., Servadei, Franco, Simonetti, G., Stefini, R., Talamonti, G., Turrisi, A., Valente, V. M., Villa, A., Vindigni, M., Visocchi, Massimiliano, Vitali, M., Wierzbicki, V., Zambon, G., Zanotti, B., Zenga, F., Alampi, D., Alessandri, F., Aloj, F., Amigoni, A., Aspide, R., Bertuetti, R., Betti, V., Bilotta, F., Bonato, V., Bosco, E., Brita, M., Buscema, G., Cafiero, T., Cappuccio, D., Caradonna, M., Caria, C. G., Casartelliliviero, M., Ciritella, P., Cirrincione, S., Citerio, G., Colelli, S., Coletta, F., Concordia, L., Congedo, E., Covotta, M., Crimella, F., Dall'Acqua, G., De Cassai, A., Defulviis, S., Deperi, E., Deana, C., Delgaudio, A., Denittis, N., Dicolandrea, S., Divezza, F., Ferri, F., Flocco, R., Fontana, C., Forastierimolinari, A., Frangiosa, A., Fumagalli, P., Fuselli, E., Garbarino, M. M., Gelormini, D., Geraci, C., Geraldini, F., Giacomucci, A., Giampaoli, V., Giorgetti, D., Gritti, P., Gualdani, S., Iacovazzo, C., Iermano, C., Latronico, N., Lugari, S., Lusenti, F., Maglione, C., Magnoni, S., Maiarota, F., Malla, M., Marchesi, M., Martino, C., Matteotti, I., Mazzeo, A. T., Morello, G., Nardiello, I., Paticchio, F., Pegoli, M., Perotti, Valerio, Piazzolla, M., Picciafuochi, F., Rachedi, N., Radolovich, D. K., Recchia, A., Riccardi, S., Romagnoli, S., Sala, S., Scafuro, M. A., Sgarlata, P., Soragni, A., Stefani, F., Stival, Eleonora, Stofella, G., Terranova, F., Tinturini, R., Togni, T., Toto, R., Trapani, D., Tringali, E., Tullo, L., Valente, A., Valeo, T., Varelli, G., Villani, R., Zamacavicchi, F., Zanello, M., Zarrillo, N., Zugni, N., Caricato A. (ORCID:0000-0001-5929-120X), Pompucci A. (ORCID:0000-0002-5427-9719), Anile C. (ORCID:0000-0002-0481-9713), Broger M., Caggiano C., D'Ercole M., Denaro L., Servadei F., Visocchi M. (ORCID:0000-0003-1087-0491), Perotti V. (ORCID:0000-0001-9461-2101), Stival E., Iaccarino, C., Lippa, L., Munari, M., Castioni, C. A., Robba, C., Caricato, Anselmo, Pompucci, Angelo, Signoretti, S., Zona, G., Rasulo, F. A., Aimar, E., Amato, S., Angileri, F. F., Anile, Carmelo, Assietti, R., Baratto, V., Barbanera, A., Basile, L., Battaglia, R., Bellocchi, S., Bertuccio, A., Blanco, S., Bolognini, A., Boniferro, B., Bordi, L., Bortolotti, C., Brandini, V., Broger, Maximilian, Brollo, M., Caffarella, D. D., Caggiano, Cinzia, Cantisani, P. L., Capone, C., Cappelletto, B., Capuano, C., Carangelo, B., Caruselli, G., Chessa, M. A., Chiara, M., Chibbaro, S., Cioffi, V., Ciprianocecchi, P., Colistra, D., Conti, C., Contratti, F., Costella, G. B., Cuoci, A., D'Avella, D., D'Ercole, Manuela, Deangelis, M., Defalco, R., de Luca, G., de Marinis, P., Del Vecchio, C., Delfinis, C., Denaro, Luca, Deodato, F., Desogus, N., Disomma, A., Domenicucci, M., Dones, F., Fina, M., Fiori, L., Fricia, M., Gaetani, P., Gazzeri, R., Gentile, M., Germano, A., Ghadirpour, R., Gianfreda, C. D., Gigante, N., Gigli, R., Giorgetti, J., Giusa, M., Gravina, U. G., Grippi, L., Guida, F., Guizzardi, G., Iannuzzo, G., Kropp, M., Lattanzi, L., Lucantoni, D., Maffei, L., Magliulo, M., Marconi, F., Marruzzo, D., Martellotta, N., Marton, E., Maugeri, R., Mauro, G., Meli, F., Menniti, A., Merciadri, P., Milanese, L., Nardacci, B., Nasi, D., Orvieto, P., Pacca, P., Pansini, G., Panzarasa, G., Passanisi, M., Pavesi, G., Pizzoni, C., Pulera, F., Rapana, A., Ricci, A., Rispoli, R., Rotondo, M., Russo, N., Santilli, S., Scarano, E., Schwarz, A., Servadei, Franco, Simonetti, G., Stefini, R., Talamonti, G., Turrisi, A., Valente, V. M., Villa, A., Vindigni, M., Visocchi, Massimiliano, Vitali, M., Wierzbicki, V., Zambon, G., Zanotti, B., Zenga, F., Alampi, D., Alessandri, F., Aloj, F., Amigoni, A., Aspide, R., Bertuetti, R., Betti, V., Bilotta, F., Bonato, V., Bosco, E., Brita, M., Buscema, G., Cafiero, T., Cappuccio, D., Caradonna, M., Caria, C. G., Casartelliliviero, M., Ciritella, P., Cirrincione, S., Citerio, G., Colelli, S., Coletta, F., Concordia, L., Congedo, E., Covotta, M., Crimella, F., Dall'Acqua, G., De Cassai, A., Defulviis, S., Deperi, E., Deana, C., Delgaudio, A., Denittis, N., Dicolandrea, S., Divezza, F., Ferri, F., Flocco, R., Fontana, C., Forastierimolinari, A., Frangiosa, A., Fumagalli, P., Fuselli, E., Garbarino, M. M., Gelormini, D., Geraci, C., Geraldini, F., Giacomucci, A., Giampaoli, V., Giorgetti, D., Gritti, P., Gualdani, S., Iacovazzo, C., Iermano, C., Latronico, N., Lugari, S., Lusenti, F., Maglione, C., Magnoni, S., Maiarota, F., Malla, M., Marchesi, M., Martino, C., Matteotti, I., Mazzeo, A. T., Morello, G., Nardiello, I., Paticchio, F., Pegoli, M., Perotti, Valerio, Piazzolla, M., Picciafuochi, F., Rachedi, N., Radolovich, D. K., Recchia, A., Riccardi, S., Romagnoli, S., Sala, S., Scafuro, M. A., Sgarlata, P., Soragni, A., Stefani, F., Stival, Eleonora, Stofella, G., Terranova, F., Tinturini, R., Togni, T., Toto, R., Trapani, D., Tringali, E., Tullo, L., Valente, A., Valeo, T., Varelli, G., Villani, R., Zamacavicchi, F., Zanello, M., Zarrillo, N., Zugni, N., Caricato A. (ORCID:0000-0001-5929-120X), Pompucci A. (ORCID:0000-0002-5427-9719), Anile C. (ORCID:0000-0002-0481-9713), Broger M., Caggiano C., D'Ercole M., Denaro L., Servadei F., Visocchi M. (ORCID:0000-0003-1087-0491), Perotti V. (ORCID:0000-0001-9461-2101), and Stival E.

Abstract

No robust evidence is provided by literature regarding the management of intracranial hypertension following severe traumatic brain injury (TBI). This is mostly due to the lack of prospective randomized controlled trials (RCTs), the presence of studies containing extreme heterogeneously collected populations and controversial considerations about chosen outcome. A scientific society should provide guidelines for care management and scientific support for those areas for which evidence-based medicine has not been identified. However, RCTs in severe TBI have failed to establish intervention effectiveness, arising the need to make greater use of tools such as Consensus Conferences between experts, which have the advantage of providing recommendations based on experience, on the analysis of updated literature data and on the direct comparison of different logistic realities. The Italian scientific societies should provide guidelines following the national laws ruling the best medical practice. However, many limitations do not allow the collection of data supporting high levels of evidence for intracranial pressure (ICP) monitoring and decompressive craniectomy (DC) in patients with severe TBI. This intersociety document proposes best practice guidelines for this subsetting of patients to be adopted on a national Italian level, along with joint statements from “TBI Section” of the Italian Society of Neurosurgery (SINch) endorsed by the Neuroanesthesia and Neurocritical Care Study Group of the Italian Society of Anesthesia, Analgesia, Resuscitation and Intensive Care (SIAARTI). Presented here is a recap of recommendations on management of ICP and DC supported a high level of available evidence and rate of agreement expressed by the assemblies during the more recent consensus conferences, where members of both groups have had a role of active participants and supporters. The listed recommendations have been sent to a panel of experts consisting of the 107 members of the “T

Published
2021

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