Systematic Review and Meta-Analysis of the Occurrence of ESKAPE Bacteria Group in Dogs, and the Related Zoonotic Risk in Animal-Assisted Therapy, and in Animal-Assisted Activity in the Health Context

Animal-assisted interventions are widely implemented in different contexts worldwide. Particularly, animal-assisted therapies and animal-assisted activities are often implemented in hospitals, rehabilitation centers, and other health facilities. These interventions bring several benefits to patients but can also expose them to the risk of infection with potentially zoonotic agents. The dog is the main animal species involved used in these interventions. Therefore, we aimed at collecting data regarding the occurrence of the pathogens ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp.) in dogs, in order to draft guidelines concerning the possible monitoring of dogs involved in animal-assisted therapies and animal-assisted activities in healthcare facilities. We performed a literature search using the PRISMA guidelines to examine three databases: PubMed, Web of Science, and Scopus. Out of 2604 records found, 52 papers were identified as eligible for inclusion in the review/meta-analysis. Sixteen papers reported data on E. faecium; 16 on S. aureus; nine on K. pneumoniae; four on A. baumannii; eight on P. aeruginosa; and six on Enterobacter spp. This work will contribute to increased awareness to the potential zoonotic risks posed by the involvement of dogs in animal-assisted therapies, and animal-assisted activities in healthcare facilities

A Pipelined Tracer-Aware Approach for Lesion Segmentation in Breast DCE-MRI

The recent spread of Deep Learning (DL) in medical imaging is pushing researchers to explore its suitability for lesion segmentation in Dynamic Contrast-Enhanced Magnetic-Resonance Imaging (DCE-MRI), a complementary imaging procedure increasingly used in breast-cancer analysis. Despite some promising proposed solutions, we argue that a “naive” use of DL may have limited effectiveness as the presence of a contrast agent results in the acquisition of multimodal 4D images requiring thorough processing before training a DL model. We thus propose a pipelined approach where each stage is intended to deal with or to leverage a peculiar characteristic of breast DCE-MRI data: the use of a breast-masking pre-processing to remove non-breast tissues; the use of Three-Time-Points (3TP) slices to effectively highlight contrast agent time course; the application of a motion-correction technique to deal with patient involuntary movements; the leverage of a modified U-Net architecture tailored on the problem; and the introduction of a new “Eras/Epochs” training strategy to handle the unbalanced dataset while performing a strong data augmentation. We compared our pipelined solution against some literature works. The results show that our approach outperforms the competitors by a large margin (+9.13% over our previous solution) while also showing a higher generalization ability

Setting up of a theoretical-experimental approach for the measurement of the vibrational impact on cultural heritages due to vehicular traffic

During the last years, the constant increase of the request for transport by road and by rail, connected to the economic and social development process, has provoked the birth of the phenomenon of the vibrations induced by the means of transports, with the result of stimulating the resident population's curiosity and the technical personnel's interest. Into the historic centres of the towns, it is possible to trace a series of concomitant factors that tend further to increase this process: the road superstructure mostly used in the historical centres are often stony, and their usage is due mainly to aesthetic reasons; the no-measurement or the lack of maintenance are often the causes of high levels of roughness; the buildings set into the historical centres have got a historical-art value of primary importance and some part of them (such as mosaics, pictures, etc.) are very sensitive to vibrations, being sticking to the outside walls; According to what said above, the noisy and vibrations provoked by the road traffic are becoming even more one of the main cause of the environmental damage

Students’ Entrepreneurial Orientation in Italy: do digital and coding skills matter?

The literature presents several papers regarding students’ entrepreneurial intention. However, only a few papers have recently analyzed student entrepreneurship. This paper aims at improving our understanding on this by testing if digital and coding skills matter for entrepreneurial orientation and student entrepreneurship. Adopting a Human Capital and Social Capital Theory perspective, we hypnotized that these individual skills may have a statistically and positive impact on entrepreneurial orientation and student entrepreneurship. Based on Logit and Probit regression analyses on more than 2000 Italian university students, we confirmed our hypotheses

Understanding Shape Memory Alloy Torsional Actuators: From the Conceptual to the Preliminary Design

Shape memory alloy actuators have been studied for more than thirty years. Many experimental tests have been performed, and several patents have been registered. However, designing such devices is still a challenging task. On the one hand, models are not yet able to provide the accuracy required to replace a substantial portion of the experimental tests; on the other hand, it seems that a gap exists in the literature between the main ideas behind SMA torsional actuators and their actual implementation. This work is a systematic effort to fill this gap, helping researchers and designers in developing SMA torsional actuators with a particular focus on aeronautical applications. This paper reports all the steps toward the preliminary design of such devices, using a state-of-theart, commercially available FEM software. Moreover, the SMA rods’ behaviour under mechanical and thermal loading is thoroughly examined, looking at monitoring stress, temperature, torque and martensite evolution simultaneously, and thus providing a holistic vision of the macroscopic phenomena involved during phase transformations. Simple aerodynamic load predictions are also performed, using Xfoil for three classes of aircraft (medium size UAV, Four-Seat Aircraft and Regional Transport Aircraft

Measurement of intervertebral cervical motion by means of dynamic X-ray image processing and data interpolation

Accurate measurement of intervertebral kinematics of the cervical spine can support the diagnosis of widespread diseases related to neck pain, such chronic whiplash dysfunction, arthritis, segmental degeneration. The natural inaccessibility of the spine, its complex anatomy and the small range of motion only permit concise measurement in-vivo. Low dose X-ray fluoroscopy allows time-continuous screening of cervical spine during patient’s spontaneous motion. To obtain accurate motion measurements, each vertebra was tracked by means of image processing along a sequence of radiographic images. To obtain a time-continuous representation of motion and to reduce noise in the experimental data, smoothing spline interpolation was used. Estimation of intervertebral motion for cervical segments was obtained by processing patient’s fluoroscopic sequence: intervertebral angle and displacement and the instantaneous centre of rotation were computed. The RMS value of fitting errors resulted about 0.2 degree for rotation and 0.2 mm for displacement

One special question to start with: can HIF/NFkB be a target in inflammation?

Hypoxia and Inflammation are strictly interconnected with important consequences at clinical and therapeutic level. While cell and tissue damage due to acute hypoxia mostly leads to cell necrosis, in chronic hypoxia, cells that are located closer to vessels are able to survive adapting their phenotype through the expression of a number of genes, including proinflammatory receptors for alarmins. These receptors are activated by alarmins released by necrotic cells and generate signals for master transcription factors such as NFkB, AP1, etc. which control hundreds of genes for innate immunity and damage repair. Clinical consequences of chronic inflammatory reparative response activation include cell and tissue remodeling, damage in the primary site and, the systemic involvement of distant organs and tissues. Thus every time a tissue environment becomes stably hypoxic, inflammation can be activated followed by chronic damage and cell death or repair with vessel proliferation and fibrosis. This pathway can occur in cancer, myocardial infarction and stroke, diabetes, obesity, neurodegenerative diseases, chronic and autoimmune diseases and age-related diseases. Interestingly, proinflammatory gene expression can be observed earlier in hypoxic tissue cells and, in addition, in activated resident or recruited leukocytes. Herewith, the reciprocal relationships between hypoxia and inflammation will be shortly reviewed to underline the possible therapeutic targets to control hypoxia-related inflammation in a number of epidemiologically important human diseases and conditions

Measurement of intervertebral cervical motion by means of dynamic X-ray image processing and data interpolation

Accurate measurement of intervertebral kinematics of the cervical spine can support the diagnosis of widespread diseases related to neck pain, such as chronic whiplash dysfunction, arthritis, and segmental degeneration. The natural inaccessibility of the spine, its complex anatomy, and the small range of motion only permit concise measurement in vivo. Low dose X-ray fluoroscopy allows time-continuous screening of cervical spine during patient's spontaneous motion. To obtain accurate motion measurements, each vertebra was tracked by means of image processing along a sequence of radiographic images. To obtain a time-continuous representation of motion and to reduce noise in the experimental data, smoothing spline interpolation was used. Estimation of intervertebral motion for cervical segments was obtained by processing patient's fluoroscopic sequence; intervertebral angle and displacement and the instantaneous centre of rotation were computed. The RMS value of fitting errors resulted in about 0.2 degree for rotation and 0.2 mm for displacements

Bioremediation of Crude Oil by Haematococcus Pluvialis: A Preliminary Study

Nowadays, oil pollution is one of the main environmental problems. The current methods for recovering spills mainly involve chemical agents, but scientific research has focused on more natural and less harmful techniques for the environment, including a consortium of bacteria and microalgae to clean up water contaminated by hydrocarbons. The purpose of this preliminary study was to evaluate the ability of a microalga belonging to Chlorophyceae to grow in the presence of crude oil and remove the principal contaminants. H. pluvialis, which is usually used for nutraceutical purposes, thanks to the production of astaxanthin, was able to grow in anaerobic conditions, varying its metabolism from autotrophic to heterotrophic, exploiting the carbon present in the solution deriving from the presence of 1% of crude oil. Furthermore, the results of bioremediation showed a relevant reduction in chemical pollutants such as nitrate, fluoride, sulfate, and phosphate. The most important aspect of the study was the reduction after 160 days in the hydrocarbon concentration inside not only the culture medium (−32%) but also the algal biomass (−80.25%), demonstrating an optimized degradation rather than a simple absorption inside the alga

HOLMeS: eHealth in the Big Data and Deep Learning Era

Now, data collection and analysis are becoming more and more important in a variety of application domains, as long as novel technologies advance. At the same time, we are experiencing a growing need for human–machine interaction with expert systems, pushing research toward new knowledge representation models and interaction paradigms. In particular, in the last few years, eHealth—which usually indicates all the healthcare practices supported by electronic elaboration and remote communications—calls for the availability of a smart environment and big computational resources able to offer more and more advanced analytics and new human–computer interaction paradigms. The aim of this paper is to introduce the HOLMeS (health online medical suggestions) system: A particular big data platform aiming at supporting several eHealth applications. As its main novelty/functionality, HOLMeS exploits a machine learning algorithm, deployed on a cluster-computing environment, in order to provide medical suggestions via both chat-bot and web-app modules, especially for prevention aims. The chat-bot, opportunely trained by leveraging a deep learning approach, helps to overcome the limitations of a cold interaction between users and software, exhibiting a more human-like behavior. The obtained results demonstrate the effectiveness of the machine learning algorithms, showing an area under ROC (receiver operating characteristic) curve (AUC) of 74.65% when some first-level features are used to assess the occurrence of different chronic diseases within specific prevention pathways. When disease-specific features are added, HOLMeS shows an AUC of 86.78%, achieving a greater effectiveness in supporting clinical decisions