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Article ; Online: CD80 expression is upregulated by TP53 activation in human cancer epithelial cells.

Scarpa, Melania / Marchiori, Chiara / Scarpa, Marco / Castagliuolo, Ignazio

2021 Volume 10, Issue 1, Page(s) 1907912

Abstract: CD80 is recognized as one of the most potent costimulatory molecules by which immune cells limit cancer progression; however, the current understanding of the regulation of its expression on human tumor cells is limited. The TP53 tumor suppressor plays a ...

Abstract	CD80 is recognized as one of the most potent costimulatory molecules by which immune cells limit cancer progression; however, the current understanding of the regulation of its expression on human tumor cells is limited. The TP53 tumor suppressor plays a critical role in cancer and its significant role in the control of immune responses is emerging. Here, we evaluated the role of TP53 as a regulator of CD80 expression in human cancer cells. A set of well-known TP53-reactivating compounds were used on TP53-wild-type, TP53-deficient, TP53-mutated and TP53-knockdown cancer cell lines to determine if TP53 can regulate CD80. CD80 expression was analyzed in samples from patients with TP53-active vs TP53-inactive Colon Adenocarcinomas (COAD) from TCGA panCancer Atlas. We report that the pharmacological activation of TP53 can stimulate the expression of CD80 in human tumor cells of epithelial origin. We also provide evidence that CD80 expression exhibits a strong correlation with TP53 activation in a subgroup of colon tumors with better overall survival. These results confirm the link between TP53 and immune surveillance in human cancer and provide the possibility that conventional TP53-activation approaches for tumoricidal effects may be repurposed for immunotherapy strategies.
MeSH term(s)	B7-1 Antigen/genetics ; Cell Adhesion Molecules ; Colonic Neoplasms/genetics ; Epithelial Cells ; Humans ; Immunotherapy ; Tumor Suppressor Protein p53/genetics
Chemical Substances	B7-1 Antigen ; Cell Adhesion Molecules ; TP53 protein, human ; Tumor Suppressor Protein p53
Language	English
Publishing date	2021-04-05
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2645309-5
ISSN	2162-402X ; 2162-4011
ISSN (online)	2162-402X
ISSN	2162-4011
DOI	10.1080/2162402X.2021.1907912
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Fooling Explanations in Text Classifiers

Ivankay, Adam / Girardi, Ivan / Marchiori, Chiara / Frossard, Pascal

2022

Abstract: State-of-the-art text classification models are becoming increasingly reliant on deep neural networks (DNNs). Due to their black-box nature, faithful and robust explanation methods need to accompany classifiers for deployment in real-life scenarios. ... ...

Abstract	State-of-the-art text classification models are becoming increasingly reliant on deep neural networks (DNNs). Due to their black-box nature, faithful and robust explanation methods need to accompany classifiers for deployment in real-life scenarios. However, it has been shown in vision applications that explanation methods are susceptible to local, imperceptible perturbations that can significantly alter the explanations without changing the predicted classes. We show here that the existence of such perturbations extends to text classifiers as well. Specifically, we introduceTextExplanationFooler (TEF), a novel explanation attack algorithm that alters text input samples imperceptibly so that the outcome of widely-used explanation methods changes considerably while leaving classifier predictions unchanged. We evaluate the performance of the attribution robustness estimation performance in TEF on five sequence classification datasets, utilizing three DNN architectures and three transformer architectures for each dataset. TEF can significantly decrease the correlation between unchanged and perturbed input attributions, which shows that all models and explanation methods are susceptible to TEF perturbations. Moreover, we evaluate how the perturbations transfer to other model architectures and attribution methods, and show that TEF perturbations are also effective in scenarios where the target model and explanation method are unknown. Finally, we introduce a semi-universal attack that is able to compute fast, computationally light perturbations with no knowledge of the attacked classifier nor explanation method. Overall, our work shows that explanations in text classifiers are very fragile and users need to carefully address their robustness before relying on them in critical applications.
Keywords	Computer Science - Machine Learning ; Computer Science - Computation and Language ; Computer Science - Cryptography and Security
Subject code	006
Publishing date	2022-06-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Disparities in access to COVID-19 vaccine in Verona, Italy: a cohort study using local health immunization data.

Benoni, Roberto / Sartorello, Anna / Moretti, Francesca / Marchiori, Francesco / Accordini, Luciana / Postiglione, Chiara / Coffele, Viviana / Tardivo, Stefano

Frontiers in public health

2023 Volume 11, Page(s) 1167414

Abstract: Introduction: Migrant populations worldwide were disproportionately impacted by the COVID-19 pandemic. Although substantial resources have been invested in scaling COVID-19 vaccination campaigns, globally vaccine rate and uptake remained low among ... ...

Abstract	Introduction: Migrant populations worldwide were disproportionately impacted by the COVID-19 pandemic. Although substantial resources have been invested in scaling COVID-19 vaccination campaigns, globally vaccine rate and uptake remained low among migrants from across many countries. This study aimed to explore the country of birth as a factor influencing access to the COVID-19 vaccine. Methods: This retrospective cohort study included adults vaccinated against SARS-CoV-2 receiving at least one dose in the Verona province between 27 December 2020 and 31 December 2021. Time-to-vaccination was estimated as the difference between the actual date of each person's first dose of COVID-19 vaccination and the date in which the local health authorities opened vaccination reservations for the corresponding age group. The birth country was classified based on both the World Health Organization regions and the World Bank country-level economic classification. Results were reported as the average marginal effect (AME) with corresponding 0.95 confidence intervals (CI). Results: During the study period, 7,54,004 first doses were administered and 5,06,734 (F = 2,46,399, 48.6%) were included after applying the exclusion criteria, with a mean age of 51.2 years (SD 19.4). Migrants were 85,989 (17.0%, F = 40,277, 46.8%), with a mean age of 42.4 years (SD 13.3). The mean time-to-vaccination for the whole sample was 46.9 days (SD 45.9), 41.8 days (SD 43.5) in the Italian population, and 71.6 days (SD 49.1) in the migrant one (p < 0.001). The AME of the time-to-vaccination compared to the Italian population was higher by 27.6 [0.95 CI 25.4-29.8], 24.5 [0.95 CI 24.0-24.9], 30.5 [0.95 CI 30.1-31.0] and 7.3 [0.95 CI 6.2-8.3] days for migrants from low-, low-middle-, upper-middle- and high-income countries, respectively. Considering the WHO region, the AME of the time-to-vaccination compared to the Italian group was higher by 31.5 [0.95 CI 30.6-32.5], 31.1 [0.95 CI 30.6-31.5], and 29.2 [0.95 CI 28.5-29.9] days for migrants from African, European, and East-Mediterranean regions, respectively. Overall, time-to-vaccination decreased with increasing age (p < 0.001). Although both migrants and Italians mainly used hub centers (>90%), migrants also used pharmacies and local health units as alternative sites (2.9% and 1.5%, respectively), while Italians (3.3%) and migrants from the European region (4.2%) relied more on family doctors. Conclusion: The birth country of migrants influenced access to COVID-19 vaccine both in terms of time-to-vaccination and vaccination points used, especially for the LIC migrant group. Public health authorities should take socio-cultural and economic factors into consideration for tailored communication to people from migrant communities and for planning a mass vaccination campaign.
MeSH term(s)	Adult ; Humans ; Middle Aged ; COVID-19 Vaccines ; Cohort Studies ; Retrospective Studies ; Pandemics/prevention & control ; COVID-19/epidemiology ; COVID-19/prevention & control ; SARS-CoV-2 ; Vaccination ; Italy/epidemiology
Chemical Substances	COVID-19 Vaccines
Language	English
Publishing date	2023-06-15
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2711781-9
ISSN	2296-2565 ; 2296-2565
ISSN (online)	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2023.1167414
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Estimating the Adversarial Robustness of Attributions in Text with Transformers

Ivankay, Adam / Rigotti, Mattia / Girardi, Ivan / Marchiori, Chiara / Frossard, Pascal

2022

Abstract: Explanations are crucial parts of deep neural network (DNN) classifiers. In high stakes applications, faithful and robust explanations are important to understand and gain trust in DNN classifiers. However, recent work has shown that state-of-the-art ... ...

Abstract	Explanations are crucial parts of deep neural network (DNN) classifiers. In high stakes applications, faithful and robust explanations are important to understand and gain trust in DNN classifiers. However, recent work has shown that state-of-the-art attribution methods in text classifiers are susceptible to imperceptible adversarial perturbations that alter explanations significantly while maintaining the correct prediction outcome. If undetected, this can critically mislead the users of DNNs. Thus, it is crucial to understand the influence of such adversarial perturbations on the networks' explanations and their perceptibility. In this work, we establish a novel definition of attribution robustness (AR) in text classification, based on Lipschitz continuity. Crucially, it reflects both attribution change induced by adversarial input alterations and perceptibility of such alterations. Moreover, we introduce a wide set of text similarity measures to effectively capture locality between two text samples and imperceptibility of adversarial perturbations in text. We then propose our novel TransformerExplanationAttack (TEA), a strong adversary that provides a tight estimation for attribution robustness in text classification. TEA uses state-of-the-art language models to extract word substitutions that result in fluent, contextual adversarial samples. Finally, with experiments on several text classification architectures, we show that TEA consistently outperforms current state-of-the-art AR estimators, yielding perturbations that alter explanations to a greater extent while being more fluent and less perceptible.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2022-12-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Disparities in access to COVID-19 vaccine in Verona, Italy

Roberto Benoni / Anna Sartorello / Francesca Moretti / Francesco Marchiori / Luciana Accordini / Chiara Postiglione / Viviana Coffele / Stefano Tardivo

Frontiers in Public Health, Vol

a cohort study using local health immunization data

2023 Volume 11

Abstract: IntroductionMigrant populations worldwide were disproportionately impacted by the COVID-19 pandemic. Although substantial resources have been invested in scaling COVID-19 vaccination campaigns, globally vaccine rate and uptake remained low among migrants ...

Abstract	IntroductionMigrant populations worldwide were disproportionately impacted by the COVID-19 pandemic. Although substantial resources have been invested in scaling COVID-19 vaccination campaigns, globally vaccine rate and uptake remained low among migrants from across many countries. This study aimed to explore the country of birth as a factor influencing access to the COVID-19 vaccine.MethodsThis retrospective cohort study included adults vaccinated against SARS-CoV-2 receiving at least one dose in the Verona province between 27 December 2020 and 31 December 2021. Time-to-vaccination was estimated as the difference between the actual date of each person's first dose of COVID-19 vaccination and the date in which the local health authorities opened vaccination reservations for the corresponding age group. The birth country was classified based on both the World Health Organization regions and the World Bank country-level economic classification. Results were reported as the average marginal effect (AME) with corresponding 0.95 confidence intervals (CI).ResultsDuring the study period, 7,54,004 first doses were administered and 5,06,734 (F = 2,46,399, 48.6%) were included after applying the exclusion criteria, with a mean age of 51.2 years (SD 19.4). Migrants were 85,989 (17.0%, F = 40,277, 46.8%), with a mean age of 42.4 years (SD 13.3). The mean time-to-vaccination for the whole sample was 46.9 days (SD 45.9), 41.8 days (SD 43.5) in the Italian population, and 71.6 days (SD 49.1) in the migrant one (p < 0.001). The AME of the time-to-vaccination compared to the Italian population was higher by 27.6 [0.95 CI 25.4–29.8], 24.5 [0.95 CI 24.0–24.9], 30.5 [0.95 CI 30.1–31.0] and 7.3 [0.95 CI 6.2–8.3] days for migrants from low-, low-middle-, upper-middle- and high-income countries, respectively. Considering the WHO region, the AME of the time-to-vaccination compared to the Italian group was higher by 31.5 [0.95 CI 30.6–32.5], 31.1 [0.95 CI 30.6–31.5], and 29.2 [0.95 CI 28.5–29.9] days for migrants from African, ...
Keywords	COVID-19 ; health inequities ; migrants ; SARS-CoV-2 ; healthcare access ; COVID-19 vaccine ; Public aspects of medicine ; RA1-1270
Subject code	306
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Determination of the Spatial Anisotropy of the Surface MicroStructures of Different Implant Materials: An Atomic Force Microscopy Study.

Gambardella, Alessandro / Marchiori, Gregorio / Maglio, Melania / Russo, Alessandro / Rossi, Chiara / Visani, Andrea / Fini, Milena

Materials (Basel, Switzerland)

2021 Volume 14, Issue 17

Abstract: Many biomaterials' surfaces exhibit directional properties, i.e., possess spatial anisotropy on a range of spatial scales spanning from the domain of the naked eye to the sub-micrometer level. Spatial anisotropy of surface can influence the mechanical, ... ...

Abstract	Many biomaterials' surfaces exhibit directional properties, i.e., possess spatial anisotropy on a range of spatial scales spanning from the domain of the naked eye to the sub-micrometer level. Spatial anisotropy of surface can influence the mechanical, physicochemical, and morphological characteristics of the biomaterial, thus affecting its functional behavior in relation, for example, to the host tissue response in regenerative processes, or to the efficacy of spatially organized surface patterns in avoiding bacterial attachment. Despite the importance of the availability of quantitative data, a comprehensive characterization of anisotropic topographies is generally a hard task due to the proliferation of parameters and inherent formal complications. This fact has led so far to excessive simplification that has often prevented researchers from having comparable results. In an attempt to overcome these issues, in this work a systematic and multiscale approach to spatial anisotropy is adopted, based on the determination of only two statistical parameters of surface, namely the texture aspect ratio
Language	English
Publishing date	2021-08-24
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2487261-1
ISSN	1996-1944
ISSN	1996-1944
DOI	10.3390/ma14174803
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: FAR

Ivankay, Adam / Girardi, Ivan / Marchiori, Chiara / Frossard, Pascal

A General Framework for Attributional Robustness

2020

Abstract: Attribution maps are popular tools for explaining neural networks predictions. By assigning an importance value to each input dimension that represents its impact towards the outcome, they give an intuitive explanation of the decision process. However, ... ...

Abstract	Attribution maps are popular tools for explaining neural networks predictions. By assigning an importance value to each input dimension that represents its impact towards the outcome, they give an intuitive explanation of the decision process. However, recent work has discovered vulnerability of these maps to imperceptible adversarial changes, which can prove critical in safety-relevant domains such as healthcare. Therefore, we define a novel generic framework for attributional robustness (FAR) as general problem formulation for training models with robust attributions. This framework consist of a generic regularization term and training objective that minimize the maximal dissimilarity of attribution maps in a local neighbourhood of the input. We show that FAR is a generalized, less constrained formulation of currently existing training methods. We then propose two new instantiations of this framework, AAT and AdvAAT, that directly optimize for both robust attributions and predictions. Experiments performed on widely used vision datasets show that our methods perform better or comparably to current ones in terms of attributional robustness while being more generally applicable. We finally show that our methods mitigate undesired dependencies between attributional robustness and some training and estimation parameters, which seem to critically affect other competitor methods.
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2020-10-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Learning Analytics Applied to Clinical Diagnostic Reasoning Using a Natural Language Processing-Based Virtual Patient Simulator: Case Study.

Furlan, Raffaello / Gatti, Mauro / Mene, Roberto / Shiffer, Dana / Marchiori, Chiara / Giaj Levra, Alessandro / Saturnino, Vincenzo / Brunetta, Enrico / Dipaola, Franca

JMIR medical education

2022 Volume 8, Issue 1, Page(s) e24372

Abstract: Background: Virtual patient simulators (VPSs) log all users' actions, thereby enabling the creation of a multidimensional representation of students' medical knowledge. This representation can be used to create metrics providing teachers with valuable ... ...

Abstract	Background: Virtual patient simulators (VPSs) log all users' actions, thereby enabling the creation of a multidimensional representation of students' medical knowledge. This representation can be used to create metrics providing teachers with valuable learning information. Objective: The aim of this study is to describe the metrics we developed to analyze the clinical diagnostic reasoning of medical students, provide examples of their application, and preliminarily validate these metrics on a class of undergraduate medical students. The metrics are computed from the data obtained through a novel VPS embedding natural language processing techniques. Methods: A total of 2 clinical case simulations (tests) were created to test our metrics. During each simulation, the students' step-by-step actions were logged into the program database for offline analysis. The students' performance was divided into seven dimensions: the identification of relevant information in the given clinical scenario, history taking, physical examination, medical test ordering, diagnostic hypothesis setting, binary analysis fulfillment, and final diagnosis setting. Sensitivity (percentage of relevant information found) and precision (percentage of correct actions performed) metrics were computed for each issue and combined into a harmonic mean (F Results: The mean overall scores were consistent between test 1 (mean 0.59, SD 0.05) and test 2 (mean 0.54, SD 0.12). For each student, the overall performance was achieved through a different contribution in collecting and analyzing information. Methodological scores highlighted discordances between the reference diagnostic pattern previously set by the teacher and the one pursued by the student. No significant correlation was found between the VPS scores and hematology examination scores. Conclusions: Different components of the students' diagnostic process may be disentangled and quantified by appropriate metrics applied to students' actions recorded while addressing a virtual case. Such an approach may help teachers provide students with individualized feedback aimed at filling competence drawbacks and methodological inconsistencies. There was no correlation between the hematology curricular examination score and any of the proposed scores as these scores address different aspects of students' medical knowledge.
Language	English
Publishing date	2022-03-03
Publishing country	Canada
Document type	Journal Article
ISSN	2369-3762
ISSN	2369-3762
DOI	10.2196/24372
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Article: Probable airborne transmission in a nosocomial Sars-CoV-2 outbreak with an high attack rate.

Anello, Paola / Boscolo Cegion, Riccardo / Basso, Andrea / Cabbia, Chiara / Azzolini, Francesca / Crimi, Luciano / Ruggiero, Myriam / Carraro, Mara / Mazzon, Stefano / Marchiori, Milvia

Igiene e sanita pubblica

2023 Volume 80, Issue 5, Page(s) 110–117

Abstract: Throughout the current COVID-19 pandemic, preventing nosocomial COVID-19 outbreaks has been a significant challenge for hospitals. It is essential to understand the ways in which SARS-CoV-2 spreads in healthcare settings to apply proper infection ... ...

Abstract	Throughout the current COVID-19 pandemic, preventing nosocomial COVID-19 outbreaks has been a significant challenge for hospitals. It is essential to understand the ways in which SARS-CoV-2 spreads in healthcare settings to apply proper infection prevention and control (IPC) measures. The objectives of this study are to report on the hospital's response to a COVID-19 cluster and the transmission dynamics in a hospital ward of Geriatrics, Rehabilitation and Long term care. The study will focus specifically on how insufficient air replacement and directional airflow in indoor settings may have contributed to the transmission of the virus.
MeSH term(s)	Humans ; SARS-CoV-2 ; COVID-19 ; Cross Infection/epidemiology ; Cross Infection/prevention & control ; Incidence ; Pandemics ; Respiratory Aerosols and Droplets ; Disease Outbreaks ; Hospitals
Language	English
Publishing date	2023-12-15
Publishing country	Italy
Document type	Journal Article
ZDB-ID	3031485-9
ISSN	0019-1639
ISSN	0019-1639
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Article ; Online: Artificial Intelligence Decision Support for Medical Triage.

Marchiori, Chiara / Dykeman, Douglas / Girardi, Ivan / Ivankay, Adam / Thandiackal, Kevin / Zusag, Mario / Giovannini, Andrea / Karpati, Daniel / Saenz, Henri

AMIA ... Annual Symposium proceedings. AMIA Symposium

2021 Volume 2020, Page(s) 793–802

Abstract: Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider. The system ... ...

Abstract	Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider. The system evaluates care alternatives through interactions with patients via a mobile application. Reasoning on an initial set of provided symptoms, the triage application generates AI-powered, personalized questions to better characterize the problem and recommends the most appropriate point of care and time frame for a consultation. The underlying technology was developed to meet the needs for performance, transparency, user acceptance and ease of use, central aspects to the adoption of AI-based decision support systems. Providing such remote guidance at the beginning of the chain of care has significant potential for improving cost efficiency, patient experience and outcomes. Being remote, always available and highly scalable, this service is fundamental in high demand situations, such as the current COVID-19 outbreak.
MeSH term(s)	Algorithms ; Artificial Intelligence ; COVID-19/epidemiology ; COVID-19/prevention & control ; Decision Support Systems, Management ; Expert Systems ; Humans ; Remote Consultation ; SARS-CoV-2 ; Telemedicine ; Triage
Language	English
Publishing date	2021-01-25
Publishing country	United States
Document type	Journal Article
ISSN	1942-597X
ISSN (online)	1942-597X
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