A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score

Background: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling.Objective: We aimed to develop a machine learning-based score-the Piacenza score-for 30-day mortality prediction in patients with COVID-19 pneumonia.Methods: The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naive Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO2/FiO(2) ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naive Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naive Bayes algorithm with 14 features chosen a priori.Results: The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naive Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively.Conclusions: Our findings demonstrated that a customizable machine learning-based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia

Commuting holomorphic maps in strongly convex domains

Consiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro,7, Rome / CNR - Consiglio Nazionale delle RichercheSIGLEITItal

Preference-Based Optimization of a Human-Robot Collaborative Controller

Preference-based optimization is a powerful tool to improve the performance of a system in an intuitive way. Such a methodology allows for solving optimization problems in which the decision-maker cannot evaluate the objective function related to the target problem, but rather can only express a preference such as “this is better than that” between two candidate decision vectors. In such a way, the target cost function can be easily defined and implemented, avoiding complex formulations and the use of external sources of data. Such a methodology is nowadays deeply investigated to reduce the complexity related to systems/applications tuning in real applications, providing the user with a natural procedure capable to exploit his/her knowledge and preferences. Physical human-robot collaboration (HRC) is an important field of application considering the preference-based optimization topic. Physical HRC is increasingly important in many different domains. Properly tuning the robot behavior is important to achieve satisfactory performance from the human's perspective. However, a common tuning for all the possible subjects is not feasible due to their different skills, different backgrounds, and different expected performance. However, ad hoc tuning of the robot behavior is not trivial. To address this issue, the here presented contribution applies preference-based optimization to the tuning of a physical HRC controller. In such a way, an ad hoc tuning of the robot behavior based on the perceived interaction is achieved for each subject. Experimental results show the capabilities of the method, being able to optimize the robot behavior in limited optimization trials

In vivo transcriptomic profiling using cell encapsulation identifies effector pathways of systemic aging

Sustained exposure to a young systemic environment rejuvenates aged organisms and promotes cellular function. However, due to the intrinsic complexity of tissues it remains challenging to pinpoint niche-independent effects of circulating factors on specific cell populations. Here, we describe a method for the encapsulation of human and mouse skeletal muscle progenitors in diffusible polyethersulfone hollow fiber capsules that can be used to profile systemic aging in vivo independent of heterogeneous short-range tissue interactions. We observed that circulating long-range signaling factors in the old systemic environment lead to an activation of Myc and E2F transcription factors, induce senescence, and suppress myogenic differentiation. Importantly, in vitro profiling using young and old serum in 2D culture does not capture all pathways deregulated in encapsulated cells in aged mice. Thus, in vivo transcriptomic profiling using cell encapsulation allows for the characterization of effector pathways of systemic aging with unparalleled accuracy.We thank Phoukham Phothirath and Oliver Rizzo of the preclinical investigations group of Nestlé Research for expert advice and support with in vivo experiments. OM, JNF, and CFB are supported by the Fondation Suisse de Recherche sur les Maladies Musculaires (FSRMM). CFB is supported by the Canadian Institutes of Health Research (CIHR, PJT-162442), the Natural Sciences and Engineering Research Council of Canada (NSERC, RGPIN-2017-05490), the Fonds de Recherche du Québec – Santé (FRQS, Dossiers 296357, 34813, and 36789), the ThéCell Network (supported by the FRQS), the Canadian Stem Cell Network, and a research chair of the Centre de Recherche Médicale de l’Université de Sherbrooke (CRMUS). PMC is supported by ERC-2016-AdG-741966, La Caixa-HEALTH-HR17-00040, MDA, AFM, MWRF, UPGRADE-H2020-825825, RTI2018-096068-B-I00, a María de Maeztu Unit of Excellence award to UPF (MDM-2014-0370), and a Severo Ochoa Center of Excellence award to the CNIC (SEV-2015-0505). XH is recipient of a Severo Ochoa FPI (SEV-2015-0505-17-1) predoctoral fellowship

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field