Multiple teeth replacement with endosseous one-piece yttrium-stabilized zirconia dental implants

Objectives: The purpose of this study is to clinically and radiographically evaluate survival and success rate of multiple zirconia dental implants positioned in each patient during a follow-up period of at least 12 months up to 48 months. Study Design: Eight patients were treated for multiple edentulism with 29 zirconia dental implants. All implants received immediate temporary restorations and 6 months after surgery were definitively restored. 6 months to 4 years after implant insertion, a clinical-radiographic evaluation was performed in order to estimate peri-implant tissues health and peri-implant marginal bone loss. Results: Survival rate within follow-up period was therefore 100%. The average marginal bone loss (MBL) from baseline to 6 months was +1.375±0.388 mm; from 6 months to 1 year was +0.22±0.598 mm; from 1 year to 2 years was -0.368±0.387 mm; from 2 years to 3 years was -0.0669±0.425 mm; from 3 years to 4 years +0.048±0.262 mm. The mean marginal bone loss at 4 years from the implants insertion was +1.208 mm. Conclusions: According to several studies, when using a radiographic criterion for implant success, marginal bone loss below 0.9-1.6 mm during the first year in function can be considered acceptable. In our work, radiographic measurements of MBL showed values not exceeding 1.6 mm during the first year of loading and also 1 year up to 4 years after surgery further marginal bone loss was minimal and not significant. This peri-implant bone preservation may be associated to the absence of micro-gap between fixture and abutment since zirconia dental implants are one-piece implant. Moreover, zirconia is characterized by high biocompatibility and it accumulates significantly fewer bacteria than titanium

Gut microbiota and artificial intelligence approaches: A scoping review

This article aims to provide a thorough overview of the use of Artificial Intelligence (AI) techniques in studying the gut microbiota and its role in the diagnosis and treatment of some important diseases. The association between microbiota and diseases, together with its clinical relevance, is still difficult to interpret. The advances in AI techniques, such as Machine Learning (ML) and Deep Learning (DL), can help clinicians in processing and interpreting these massive data sets. Two research groups have been involved in this Scoping Review, working in two different areas of Europe: Florence and Sarajevo. The papers included in the review describe the use of ML or DL methods applied to the study of human gut microbiota. In total, 1109 papers were considered in this study. After elimination, a final set of 16 articles was considered in the scoping review. Different AI techniques were applied in the reviewed papers. Some papers applied ML, while others applied DL techniques. 11 papers evaluated just different ML algorithms (ranging from one to eight algorithms applied to one dataset). The remaining five papers examined both ML and DL algorithms. The most applied ML algorithm was Random Forest and it also exhibited the best performances

Unraveling pedestrian mobility on a road network using ICTs data during great tourist events

Tourist flows in historical cities are continuously growing in a globalized world and adequate governance processes, politics and tools are necessary in order to reduce impacts on the urban livability and to guarantee the preservation of cultural heritage. The ICTs offer the possibility of collecting large amount of data that can point out and quantify some statistical and dynamic properties of human mobility emerging from the individual behavior and referring to a whole road network. In this paper we analyze a new dataset that has been collected by the Italian mobile phone company TIM, which contains the GPS positions of a relevant sample of mobile devices when they actively connected to the cell phone network. Our aim is to propose innovative tools allowing to study properties of pedestrian mobility on the whole road network. Venice is a paradigmatic example for the impact of tourist flows on the resident life quality and on the preservation of cultural heritage. The GPS data provide anonymized georeferenced information on the displacements of the devices. After a filtering procedure, we develop specific algorithms able to reconstruct the daily mobility paths on the whole Venice road network. The statistical analysis of the mobility paths suggests the existence of a travel time budget for the mobility and points out the role of the rest times in the empirical relation between the mobility time and the corresponding path length. We succeed to highlight two connected mobility subnetworks extracted from the whole road network, that are able to explain the majority of the observed mobility. Our approach shows the existence of characteristic mobility paths in Venice for the tourists and for the residents. Moreover the data analysis highlights the different mobility features of the considered case studies and it allows to detect the mobility paths associated to different points of interest. Finally we have disaggregated the Italian and foreigner categories to study their different mobility behaviors

A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy

The application of machine learning (ML) techniques could facilitate the identification of predictive biomarkers of somatostatin analog (SSA) efficacy in patients with neuroendocrine tumors (NETs). We collected data from 74 patients with a pancreatic or gastrointestinal NET who received SSA as first-line therapy. We developed three classification models to predict whether the patient would experience a progressive disease (PD) after 12 or 18 months based on clinic-pathological factors at the baseline. The dataset included 70 samples and 15 features. We initially developed three classification models with accuracy ranging from 55% to 70%. We then compared ten different ML algorithms. In all but one case, the performance of the Multinomial Naive Bayes algorithm (80%) was the highest. The support vector machine classifier (SVC) had a higher performance for the recall metric of the progression-free outcome (97% vs. 94%). Overall, for the first time, we documented that the factors that mainly influenced progression-free survival (PFS) included age, the number of metastatic sites and the primary site. In addition, the following factors were also isolated as important: adverse events G3-G4, sex, Ki67, metastatic site (liver), functioning NET, the primary site and the stage. In patients with advanced NETs, ML provides a predictive model that could potentially be used to differentiate prognostic groups and to identify patients for whom SSA therapy as a single agent may not be sufficient to achieve a long-lasting PFS

The Nutraceutical Dehydrozingerone and Its Dimer Counteract Inflammation- and Oxidative Stress-Induced Dysfunction of In Vitro

Atherosclerosis is characterized by endothelial dysfunction, mainly induced by inflammation and oxidative stress. Increased reactive oxygen species (ROS) production together with increased adhesion molecules and thrombogenic tissue factor (TF) expression on endothelial cells has a key role in proatherogenic mechanisms. Therefore downmodulation of these molecules could be useful for reducing the severity of inflammation and atherosclerosis progression. Dehydrozingerone (DHZ) is a nutraceutical compound with anti-inflammatory and antioxidant activities. In this study we evaluated the ability of DHZ and its symmetric dimer to modulate hydrogen peroxide- (H2O2-) induced ROS production in human umbilical vein endothelial cells (HUVEC). We also evaluated intercellular adhesion molecule- (ICAM-) 1, vascular cell adhesion molecule- (VCAM-) 1, and TF expression in HUVEC activated by tumor necrosis factor- (TNF-) α. HUVEC pretreatment with DHZ and DHZ dimer reduced H2O2-induced ROS production and inhibited adhesion molecule expression and secretion. Of note, only DHZ dimer was able to reduce TF expression. DHZ effects were in part mediated by the inhibition of the nuclear factor- (NF-) κB activation. Overall, our findings demonstrate that the DHZ dimer exerts a potent anti-inflammatory, antioxidant, and antithrombotic activity on endothelial cells and suggest potential usefulness of this compound to contrast the pathogenic mechanisms involved in atherosclerosis progression

A collaborative RESTful cloud-based tool for management of chromatic pupillometry in a clinical trial

Chromatic Pupillometry represents a novel approach for the assessment of Inherited Retinal Diseases. A multi-centric pilot study with a sample of 40 paediatric patients has been designed, involving physicians and engineers. In this paper, the Electronic Medical Record, named ORÁO and specifically developed to collect ophthalmologic and pupillometric data, is presented. The platform is a cloud- based application, with a RESTful and three-tier architecture. These features make it available via web for the ophthalmologists involved in the project and working in two different University centres. The platform has been designed by the whole team and developed by the Department of Information Engineering of the University of Florence. The interfaces of the medical record have been evaluated in term of Usability, according to standards. An Heuristic Evaluation has been performed in the first stage of the design of the platform and the main severe usability issues have been addressed. The outcome of the project is a customized software solution. Moreover, the physicians have an excellent attitude toward the use of ORÁO and they perceive it as a useful tool to gather the data they collect with the aim of evaluating the overall progression of the pilot study

A Model of Pupillometric Signals for Studying Inherited Retinal Diseases in Childhood Population

A cutting-edge method to assess the status of photoreceptors is represented by chromatic pupillometry, which consists in stimulating the eyes of the patients with light of different wavelengths and intensities. The signals of the dynamics of the pupil after the optical stimulations can be approximated by a 2nd-order linear model. Fitting parameters are established by a weighted least-squares algorithm, in order to achieve the correct estimate of the shape of the pupillometric data. Results presented in this work indicate that our model is able to capture the dynamics of pupillometric signals

Machine learning for analysis of gene expression data in fast- and slow-progressing amyotrophic lateral sclerosis murine models

Amyotrophic lateral sclerosis is a fatal motor neuron disease characterised by degenerative changes in both upper and lower motor neurons. Current treatment options in the general cohort of ALS patients have only a minimal impact on survival. Only two approved medications are available today, just addressing the management of symptoms and supporting the respiration. In this work, gene expression data from genetically modified murine motor neurons have been analysed with machine learning techniques, with the scope of distinguishing between mice developing a fast progression of the disease, and mice showing a slower progression. Results showed high accuracy (above 80%) in all tasks, with peaks of accuracy for specific ones – such as distinguishing between fast and slow progression. In the above mentioned task the best performing algorithm reached an accuracy of 100%. This research group is currently working on three more investigations on data from mice, using similar approaches and methodology, focusing on thoracic and lumbar metabolomic data as well as microbiome data. We believe that, based on the findings in the murine models, machine learning could be used to discover ALS progression markers in humans by looking at features related to the immune response. This could pave the path for the discovery of druggable targets and disease biomarkers for homogeneous ALS patient subgroups

Biomimetic Tendrils by Four Dimensional Printing Bimorph Springs with Torsion and Contraction Properties Based on Bio‐Compatible Graphene/Silk Fibroin and Poly(3-Hydroxybutyrate‐co‐3‐Hydroxyvalerate)

Taking inspiration from plant tendril geometry, in this study, 4D bimorph coiled structures with an internal core of graphene nanoplatelets-modified regenerated silk and an external shell of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) are fabricated by 4D printing. Finite element simulations and experimental tests demonstrate that integrating these biomaterials with different coefficients of thermal expansion results in the temperature induced self-compression and torsion of the structure. The bimorph spring also exhibits reversible contractive actuation after exposure to water environment that paves its exploitation in regenerative medicine, since core materials also have been proven to be biocompatible. Finally, the authors validate their findings with experimental measurements using such springs for temperature-mediated lengthening of an artificial intestine. © 2021 The Authors. Advanced Functional Materials published by Wiley-VCH Gmb

Professione infermieristica: stati dell’identità e soddisfazione lavorativa

Assumendo la prospettiva della teoria degli stati d’identità di Marcia, l’obiettivo del presente studio è di analizzare i processi di ridefinizione dell’identità professionale di un gruppo di infermieri della Regione Emilia Romagna esaminandone la connessione da un lato con alcune variabili organizzative (percezione del cambiamento normativo), dall’altro con la motivazione e con la soddisfazione lavorativa. Attraverso uno studio correlazionale su un campione di 838 infermieri reclutati da 5 ospedali della Regione Emilia Romagna e realizzato mediante un questionario, sono stati misurati: 1) gli stati d’identità professionale, 2) le motivazioni alla professione, 3) il grado di soddisfazione lavorativa, 4) la percezione dell’importanza del cambiamento normativo. I risultati mostrano che sono gli infermieri in stato di identità acquisita a mostrare maggior soddisfazione lavorativa, maggiore motivazione e a considerare più importanti i cambiamenti normativi. Dai risultati si evince che le organizzazioni sanitarie dovrebbero promuovere azioni formative orientate non solo alle conoscenze (knowing) e al "saper fare" (doing), ma anche al miglioramento della pratica professionale intesa come "saper essere" (being)