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As the immune system seems to be the key player in the cross-talk between diet, gut microbiota and the lungs, involved immune interactions are discussed. There are key nutrients that, when present in our diet, help in gut homeostasis and lead to a healthier lifestyle, even ameliorating chronic diseases. Thus, with this review we hope to incite the scientific community interest to use diet as a valuable non-pharmacological addition to lung diseases management. First, we talk about the intestinal microbiota and interactions through the intestinal barrier for a better understanding of the following sections, which are the main focus of this article the way diet impacts the intestinal microbiota and the immune interactions of the gut-lung axis that can explain the impact of diet, a key modifiable factor influencing the gut microbiota in several lung diseases.Inertial sensor-based step length estimation has become increasingly important with the emergence of pedestrian-dead-reckoning-based (PDR-based) indoor positioning. So far, many refined step length estimation models have been proposed to overcome the inaccuracy in estimating distance walked. Both the kinematics associated with the human body during walking and actual step lengths are rarely used in their derivation. Our paper presents a new step length estimation model that utilizes acceleration magnitude. To the best of our knowledge, we are the first to employ principal component analysis (PCA) to characterize the experimental data for the derivation of the model. These data were collected from anatomical landmarks on the human body during walking using a highly accurate optical measurement system. We evaluated the performance of the proposed model for four typical smartphone positions for long-term human walking and obtained promising results the proposed model outperformed all acceleration-based models selected for the comparison producing an overall mean absolute stride length estimation error of 6.44 cm. The proposed model was also least affected by walking speed and smartphone position among acceleration-based models and is unaffected by smartphone orientation. Therefore, the proposed model can be used in the PDR-based indoor positioning with an important advantage that no special care regarding orientation is needed in attaching the smartphone to a particular body segment. All the sensory data acquired by smartphones that we utilized for evaluation are publicly available and include more than 10 h of walking measurements.Incidence of Clostridioides difficile infection (CDI) has been increasing in recent decades due to different factors, namely (i) extended use of broad-spectrum antibiotics, (ii) transmission within asymptomatic and susceptible patients, and (iii) unbalanced gastrointestinal microbiome and collateral diseases that favor C. difficile gastrointestinal domination and toxin production. Although antibiotic therapies have resulted in successful control of CDI in the last 20 years, the development of novel strategies is urged in order to combat the capability of C. difficile to generate and acquire resistance to conventional treatments and its consequent proliferation. In this regard, vegetable and marine bioactives have emerged as alternative and effective molecules to fight against this concerning pathogen. The present review examines the effectiveness of natural antimicrobials from vegetable and algae origin that have been used experimentally in in vitro and in vivo settings to prevent and combat CDI. The aim of the present work is to contribute to accurately describe the prospective use of emerging antimicrobials as future nutraceuticals and preventive therapies, namely (i) as dietary supplement to prevent CDI and reduce CDI recurrence by means of microbiota modulation and (ii) administering them complementarily to other treatments requiring antibiotics to prevent C. difficile gut invasion and infection progression.Insufficient blood levels of the neurohormone vitamin D are associated with increased risk of COVID-19 severity and mortality. Despite the global rollout of vaccinations and promising preliminary results, the focus remains on additional preventive measures to manage COVID-19. Results conflict on vitamin D's plausible role in preventing and treating COVID-19. We examined the relation between vitamin D status and COVID-19 severity and mortality among the multiethnic population of the United Arab Emirates. Our observational study used data for 522 participants who tested positive for SARS-CoV-2 at one of the main hospitals in Abu Dhabi and Dubai. Only 464 of those patients were included for data analysis. Demographic and clinical data were retrospectively analyzed. Serum samples immediately drawn at the first hospital visit were used to measure serum 25-hydroxyvitamin D [25(OH)D] concentrations through automated electrochemiluminescence. Levels less then 12 ng/mL were significantly associated with higher risk of severe COVID-19 infection and of death. Age was the only other independent risk factor, whereas comorbidities and smoking did not contribute to the outcomes upon adjustment. Sex of patients was not an important predictor for severity or death. Our study is the first conducted in the UAE to measure 25(OH)D levels in SARS-CoV-2-positive patients and confirm the association of levels less then 12 ng/mL with COVID-19 severity and mortality.The risk of pollen-induced allergies can be determined and predicted based on data derived from pollen monitoring. Hirst-type samplers are sensors that allow airborne pollen grains to be detected and their number to be determined. Airborne pollen grains are deposited on adhesive-coated tape, and slides are then prepared, which require further analysis by specialized personnel. Deep learning can be used to recognize pollen taxa based on microscopic images. This paper presents a method for recognizing a taxon based on microscopic images of pollen grains, allowing the pollen monitoring process to be automated. In this research, a deep CNN (convolutional neural network) model was built from scratch. Publicly available deep neural network models, pre-trained on image data (not including microscopic pictures), were also used. The results show that even a simple deep learning model produces quite good results when the classification of pollen grain taxa is performed directly from the images. The best deep learning model achieved 97.88% accuracy in the difficult task of recognizing three types of pollen grains (birch, alder, and hazel) with similar structures. The derived models can be used to build a system to support pollen monitoring experts in their work.Recycling of plastics is hindered by their important variety and strong incompatibility. However, sorting technologies bear costs and meet limits. Very high purities ( less then 2 wt%) are difficult to reach. Yet, such rates may be detrimental to functional properties. In this work, an ABS matrix (major plastic in Waste of Electrical and Electronic Equipments) was filled with 4 wt% of PP to mimic impurities in ABS after recycling. PP-g-MA was introduced in the blend to improve the compatibility. A finite element model was developed from the mechanical behavior of each component. ABS and PP were individually characterized from tensile tests instrumented with photomechanics and their behaviors were modelled through a set of numerical parameters (elasto-visco-plasticity with a Gurson's criterion behavior). Comparison between the determinist model results and the experimental data (strength, volumetric variation) shows that this type of modelling could be a predictive tool in order to anticipate composite mechanical properties and to understand micromechanisms of deformation (damage, cavitation). The main result is that PP introduced at 4 wt% into ABS does not alter the static mechanical properties despite polymers incompatibility. The addition of PP-g-MA modifies the local properties and possibly conduct to a premature breakage of the polymer blend.COVID-19, predominantly a mild disease, is associated with more severe clinical manifestation upon pulmonary involvement. Virion-laden aerosols and droplets target different anatomical sites for deposition. Compared to droplets, aerosols more readily advance into the peripheral lung. We performed in silico modeling to confirm the secondary pulmonary lobules as the primary site of disease initiation. By taking different anatomical aerosol origins into consideration and reflecting aerosols from exhalation maneuvers breathing and vocalization, the physicochemical properties of generated respiratory aerosol particles were defined upon conversion to droplet nuclei by evaporation at ambient air. To provide detailed, spatially-resolved information on particle deposition in the thoracic region of the lung, a top-down refinement approach was employed. Our study presents evidence for hot spots of aerosol deposition in lung generations beyond the terminal bronchiole, with a maximum in the secondary pulmonary lobules and a high preference to the lower lobes of both lungs. In vivo, initial chest CT anomalies, the ground glass opacities, resulting from partial alveolar filling and interstitial thickening in the secondary pulmonary lobules, are likewise localized in these lung generations, with the highest frequency in both lower lobes and in the early stage of disease. Hence, our results suggest a disease initiation right there upon inhalation of virion-laden respiratory aerosols, linking the aerosol transmission route to pathogenesis associated with higher disease burden and identifying aerosol transmission as a new independent risk factor for developing a pulmonary phase with a severe outcome.Sophorolipids (SLs) are glycolipids that consist of a hydrophilic sophorose head group covalently linked to a hydrophobic fatty acid tail. They are produced by fermentation of non-pathogenic yeasts such as Candida Bombicola. The fermentation products predominantly consist of the diacetylated lactonic form that coexists with the open-chain acidic form. A systematic series of modified SLs were prepared by ring opening of natural lactonic SL with n-alkanols of varying chain length under alkaline conditions and lipase-selective acetylation of sophorose primary hydroxyl groups. The antimicrobial activity of modified SLs against Gram-positive human pathogens was a function of the n-alkanol length, as well as the degree of sophorose acetylation at the primary hydroxyl sites. Modified SLs were identified with promising antimicrobial activities against Gram-positive human pathogens with moderate selectivity (therapeutic index, TI = EC50/MICB. cereus = 6-33). SL-butyl ester exhibited the best antimicrobial activity (MIC = 12 μM) and selectivity (TI = 33) among all SLs tested. Kinetic studies revealed that SL-ester derivatives kill B. cereus in a time-dependent manner resulting in greater than a 3-log reduction in cell number within 1 h at 2×MIC. In contrast, lactonic SL required 3 h to achieve the same efficiency.Cannabidiol (CBD) is part of a group of phytocannabinoids derived from Cannabissativa. Initial work on CBD presumed the compound was inactive, but it was later found to exhibit antipsychotic, anti-depressive, anxiolytic, and antiepileptic effects. In recent decades, evidence has indicated a role for CBD in the modulation of mitochondrial processes, including respiration and bioenergetics, mitochondrial DNA epigenetics, intrinsic apoptosis, the regulation of mitochondrial and intracellular calcium concentrations, mitochondrial fission, fusion and biogenesis, and mitochondrial ferritin concentration and mitochondrial monoamine oxidase activity regulation. Despite these advances, current data demonstrate contradictory findings with regard to not only the magnitude of effects mediated by CBD, but also to the direction of effects. For example, there are data indicating that CBD treatment can increase, decrease, or have no significant effect on intrinsic apoptosis. [https://www.selleckchem.com/products/xst-14.html click here] Differences between studies in cell type, cell-specific response to CBD, and, in some cases, dose of CBD may help to explain differences in outcomes.