CLOSANTEL AS A POTENTIAL LIPOPOLYSACCHARIDE BIOSYNTHESIS INHIBITOR IN SHIGELLA SONNEI 4303

Shigella spp. are Gram-negative intracellular pathogenic bacteria belonging to the family Enterobacteriaceae. The pathophysiological impact of the bacteria is highly related to the composition and structural variability of lipopolysaccharides. Serum sensitivity and biofilm forming ability are correlated with the length of these molecules, while bacteria with truncated lipopolysaccharides are more sensitive to hydrophobic antibiotics. Inhibitors of lipopolysaccharide biosynthesis have the potential to develop new antimicrobial agents or antibiotic adjuvants. Bacterial two-component systems enable bacteria to sense and to respond to the changes in different environmental conditions. This study focuses on the inhibition of the rfaD gene encoding the ADP-L-glycero-D-mannoheptose-6-epimerase, which is involved in the lipopolysaccharide biosynthesis. Although there are some inhibitors presumed for bacterial two-component systems like Closantel, their impact on lipopolysaccharide biosynthesis has not been examined previously. The Shigella sonnei 4303 strain was involved in the experiments with known lipopolysaccharide structure. The effect of Closantel on lipopolysaccharide biosynthesis and the limitations of its use are presented

A survey of assistive technologies and applications for blind users on mobile platforms: a review and foundation for research

This paper summarizes recent developments in audio and tactile feedback based assistive technologies targeting the blind community. Current technology allows applications to be efficiently distributed and run on mobile and handheld devices, even in cases where computational requirements are significant. As a result, electronic travel aids, navigational assistance modules, text-to-speech applications, as well as virtual audio displays which combine audio with haptic channels are becoming integrated into standard mobile devices. This trend, combined with the appearance of increasingly user- friendly interfaces and modes of interaction has opened a variety of new perspectives for the rehabilitation and training of users with visual impairments. The goal of this paper is to provide an overview of these developments based on recent advances in basic research and application development. Using this overview as a foundation, an agenda is outlined for future research in mobile interaction design with respect to users with special needs, as well as ultimately in relation to sensor-bridging applications in genera

Improving the Audio Game-Playing Performances of People with Visual Impairments Through Multimodal Training

As the number of people with visual impairments (that is, those who are blind or have low vision) is continuously increasing, rehabilitation and engineering researchers have identified the need to design sensorysubstitution devices that would offer assistance and guidance to these people for performing navigational tasks. Auditory and haptic cues have been shown to be an effective approach towards creating a rich spatial representation of the environment, so they are considered for inclusion in the development of assistive tools that would enable people with visual impairments to acquire knowledge of the surrounding space in a way close to the visually based perception of sighted individuals. However, achieving efficiency through a sensory substitution device requires extensive training for visually impaired users to learn how to process the artificial auditory cues and convert them into spatial information. Methods: Considering all the potential advantages gamebased learning can provide, we propose a new method for training sound localization and virtual navigational skills of visually impaired people in a 3D audio game with hierarchical levels of difficulty. The training procedure is focused on a multimodal (auditory and haptic) learning approach in which the subjects have been asked to listen to 3D sounds while simultaneously perceiving a series of vibrations on a haptic headband that corresponds to the direction of the sound source in space. Results: The results we obtained in a sound-localization experiment with 10 visually impaired people showed that the proposed training strategy resulted in significant improvements in auditory performance and navigation skills of the subjects, thus ensuring behavioral gains in the spatial perception of the environment.Sound of Vision, Horizon 2020 nr. 643636Peer Reviewe

A fotonszámláló detektoros CT működési alapelve, előnyei és jelentősége a klinikai gyakorlatban = Photoncounting-detector CT: Basic principles, advantages and implications in clinical practice

Az elmúlt évtizedben fizikai és preklinikai vizsgálatokkal igazolták az alapjaiban új típusú, fotonszámláló komputertomográfiás (CT) detektor kiváló képalkotási tulajdonságait, míg napjainkban a páréves klinikai felhasználás egyre szélesebb körű tapasztalatait veszik számba. A klinikai gyakorlatban elterjedt, hagyományos CT-berendezésekben energiaintegráló detektorok (EID) találhatók, melyek indirekt konverziós technológiával alakítják át a röntgenfotonok energiáját elektromos jellé. Ezzel ellentétben a fotonszámláló CT detektorai (PCD) közvetlenül és magasabb hatásfokkal képesek elektromos jellé alakítani a röntgenfotonok energiáját, megszámlálni az egyes röntgenfotonok által létrehozott töltéseket és mérni azok energiaszintjét. Az új PCD-technológia számos előnyt nyújt a hagyományos EID-technológiával összevetve: egyrészt kisebb sugárterhelés mellett jobb térbeli felbontású, kedvezőbb jel/zaj arányú, kevesebb sugárkeményedési („beam-hardening”) műterméket tartalmazó és alacsonyabb elektronikus zajjal terhelt CT-képeket hoz létre, másrészt lehetővé teszi a spektrális képalkotást, valamint csökkentett dózisú kontrasztanyag alkalmazására is lehetőséget ad. Összefoglaló közleményünk a PCD-CT műszaki és fizikai alapelveit ismerteti, valamint áttekintést nyújt annak előnyeiről és a klinikai gyakorlatban való felhasználásáról. | Over the last decade, an esentially new type of computed tomography (CT) detector, namely the photoncounting detector has demonstrated its superior capabilities over traditional CT detectors in both physical and preclinical evaluations, while is now at the stage of early clinical experiences. Conventional CT scanners available today for routine clinical practice use energy integrated detectors (EID) which rely on indirect conversion technology. In contrary, the newly-introduced photon-counting detectors (PCD) utilize a direct conversion method allowing to count the number of x-ray photons and carry detailed information about the energy level of each individual x-ray photon. Due to the fundamental changes in the physical mechanisms responsible for photon detection and signal creation, PCDs have several benefits over traditional CT detectors. In comparison to current CT technology, PCDCT can produce better spatial resolution, reduced electronic noise with a higher contrast-to-noise ratio, reduced beam-hardening and metal artifacts. Furthermore, from the spectral information, this new technology is capable to reconstruct virtual monoenergetic images and optimize iv. contrast agent dose. In our current review article, technical principles and physics of PCDs and, in addition, early clinical experiences with their applications are summarized

EASY-APP : An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis

Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed.The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit-learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross-validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross-validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP).The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy-to-use web application in the Streamlit Python-based framework (http://easy-app.org/).The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model