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Hyaluronic acid (HA) is a key component of the extracellular matrix of the lungs. A unique attribute of HA is its water-retaining properties, so HA has a major role in the regulation of fluid balance in the lung interstitium. Hyaluronic acid has been widely used in the treatment of eyes, ears, joints and skin disorders, but in the last years, it has been also proposed in the treatment of certain lung diseases, including airway diseases, due to its anti-inflammatory and water-binding capacities. Hyaluronic acid aerosol decreases the severity of elastase-induced emphysema in murine models, prevents bronchoconstriction in asthmatics and improves some functional parameters in chronic obstructive pulmonary disease (COPD) patients. Due to the protection of HA against bronchoconstriction and its hydration properties, inhaled HA would increase the volume of airway surface liquid, resulting in mucus hydration, increased mucous transport and less mucous plugging of the airways. read more In addition, it has been seen in human studies that the treatment with nebulised HA improves the tolerability of nebulised hypertonic saline (even at 6% or 7% of concentration), which has been demonstrated to be an effective treatment in bronchial secretion management in patients with cystic fibrosis and bronchiectasis. Our objective is to review the role of HA treatment in the management of chronic airway diseases.For screening excellent lactic acid bacteria (LAB) strains to inhibit enterotoxigenic Escherichia coli (ETEC) K88, inhibitory activities of more than 1100 LAB strains isolated from different materials, and kept in the lab, were evaluated in this study. Nine strains with inhibition zones, at least 22.00 mm (including that of a hole puncher, 10.00 mm), and good physiological and biochemical characteristics identified by 16S DNA gene sequencing and recA gene multiple detection, were assigned to Lactobacillus (L.) plantarum subsp. plantarum (5), L. fermentum (1), L. reuteri (1), Weissella cibaria (1) and Enterococcus faecalis (1), respectively. As investigated for their tolerance abilities and safety, only strain ZA3 possessed high hydrophobicity and auto-aggregation abilities, had high survival rate in low pH, bile salt environment, and gastrointestinal (GI) fluids, was sensitive to ampicillin, and resistant to norfloxacin and amikacin, without hemolytic activity, and did not carry antibiotic resistance genes, but exhibited broad spectrum activity against a wide range of microorganisms. Antibacterial substance may attribute to organic acids, especially lactic acid and acetic acid. The results indicated that the selected strain L. plantarum subsp. plantarum ZA3 could be considered a potential probiotic to inhibit ETEC K88 in weaned piglets for further research.Novel water-soluble multifunctional pillar[5]arenes containing amide-ammonium-amino acid moiety were synthesized. The compounds demonstrated a superior ability to bind (1S)-(+)-10-camphorsulfonic acid (S-CSA) and methyl orange dye depending on the nature of the substituent, resulting in the formation one-to-one complexes with both guests. The formation of host-guest complexes was confirmed by ultraviolet (UV), circular dichroism (CD) and 1H NMR spectroscopy. This work demonstrates the first case of using S-CSA as a chiral template for the non-covalent self-assembly of architectures based on pillar[5]arenes. It was shown that pillar[5]arenes with glycine or L-alanine fragments formed aggregates with average hydrodynamic diameters (d) of 165 and 238 nm, respectively. It was established that the addition of S-CSA to the L-alanine-containing derivative led to the formation of micron-sized aggregates with d of 713 nm. This study may advance the design novel stereoselective catalysts and transmembrane amino acid channels.Two randomized complete block design experiments were conducted to evaluate the effect of bedding use in confined beef steers. Experiment 1 used Simmental × Angus steers (n = 240; initial body weight (BW) = 365 ± 22.5 kg). Experiment 2 used newly weaned Charolais × Red Angus steers (n = 162; initial BW = 278 ± 13.4 kg). Steers were allotted to one of two treatments (1) no bedding (NO), or (2) 1.8 kg (Experiment 1) or 1.0 kg (Experiment 2) of wheat straw (as-is basis) bedding/steer·d-1 (BED). In Experiment 1, applying bedding improved (p ≤ 0.01) dry matter intake (DMI), kg of gain to kg of feed (GF), and average daily gain (ADG). Bedding reduced (p = 0.01) the estimated maintenance coefficient (MQ). Dressing percentage, rib fat, marbling, and yield grade were increased (p ≤ 0.03) in NO. Bedding resulted in an increase (p = 0.01) in serum insulin-like growth factor I (IGF-I). In Experiment 2, a tendency (p = 0.06) for increased DMI for NO was noted. Bedding improved GF (p = 0.01). MQ was elevated (p = 0.03) for NO and NO had an increase (p = 0.02) in serum concentration of urea-N (SUN). An increase (p = 0.01) in serum non-esterified fatty acid was noted for NO. These data indicate that bedding application should be considered to improve growth performance and feed efficiency by reducing maintenance energy requirements in beef steers during the feedlot receiving and finishing phase.Metallography is the study of the structure of metals and alloys. Metallographic analysis can be regarded as a detection tool to assist in identifying a metal or alloy, to evaluate whether an alloy is processed correctly, to inspect multiple phases within a material, to locate and characterize imperfections such as voids or impurities, or to find the damaged areas of metallographic images. However, the defect detection of metallography is evaluated by human experts, and its automatic identification is still a challenge in almost every real solution. Deep learning has been applied to different problems in computer vision since the proposal of AlexNet in 2012. In this study, we propose a novel convolutional neural network architecture for metallographic analysis based on a modified residual neural network (ResNet). Multi-scale ResNet (M-ResNet), the modified method, improves efficiency by utilizing multi-scale operations for the accurate detection of objects of various sizes, especially small objects. The experimental results show that the proposed method yields an accuracy of 85.