Multiple Antimicrobial Effects of Hybrid Peptides Synthesized Based on the Sequence of Ribosomal S1 Protein from Staphylococcus aureus

The need to develop new antimicrobial peptides is due to the high resistance of pathogenic bacteria to traditional antibiotics now and in the future. The creation of synthetic peptide constructs is a common and successful approach to the development of new antimicrobial peptides. In this work, we use a simple, flexible, and scalable technique to create hybrid antimicrobial peptides containing amyloidogenic regions of the ribosomal S1 protein from Staphylococcus aureus. While the cell-penetrating peptide allows the peptide to enter the bacterial cell, the amyloidogenic site provides an antimicrobial effect by coaggregating with functional bacterial proteins. We have demonstrated the antimicrobial effects of the R23F, R23DI, and R23EI hybrid peptides against Staphylococcus aureus, methicillin-resistant S. aureus (MRSA), Pseudomonas aeruginosa, Escherichia coli, and Bacillus cereus. R23F, R23DI, and R23EI can be used as antimicrobial peptides against Gram-positive and Gram-negative bacteria resistant to traditional antibiotics.     

Quasi-Diamond Platelet-Shaped Zinc Oxide Nanostructures Display Enhanced Antibacterial Activity

The current study compares the antibacterial activity of zinc oxide nanostructures (neZnO). For this purpose, two bacterial strains, Escherichia coli (ATCC 4157) and Staphylococcus aureus (ATCC 29213) were challenged in room light conditions with the aforementioned materials. Colloidal and hydrothermal methods were used to obtain the quasi-round and quasi-diamond platelet-shape nanostructures. Thus, the oxygen vacancy (V_O) effects on the surface of neZnO are also considered to assess its effects on antibacterial activity. The neZnO characterization was achieved by X-ray diffraction (XRD), a selected area electron diffraction (SAED) and Raman spectroscopy. The microstructural effects were monitored by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Furthermore, optical absorption ultraviolet visible spectrophotometry (UV-Vis) and X-ray photoelectron spectroscopy (XPS) analyses complement the physical characterization of these nanostructures; neZnO caused 50 % inhibition (IC₅₀) at concentrations from 0.064 to 0.072 mg/mL for S. aureus and from 0.083 to 0.104 mg/mL for E. coli, indicating an increase in activity against S. aureus compared to E. coli. Consequently, quasi-diamond platelet-shaped nanostructures (average particle size of 377.6±10 nm) showed enhanced antibacterial activity compared to quasi-round agglomerated particles (average size of 442.8±12 nm), regardless of Vo presence or absence.     

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological investigations, computer-aided diagnostic tools are implemented to reduce intra- and inter-observer variability. Using lately industrialized Artificial Intelligence (AI) algorithms and radiological techniques to diagnose and classify disease is advantageous. The current study develops an automatic identification and classification model for CXR pictures using Gaussian Filtering based Optimized Synergic Deep Learning using Remora Optimization Algorithm (GF-OSDL-ROA). This method is inclusive of preprocessing and classification based on optimization. The data is preprocessed using Gaussian filtering (GF) to remove any extraneous noise from the image’s edges. Then, the OSDL model is applied to classify the CXRs under different severity levels based on CXR data. The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work. OSDL model, applied in this study, was validated using the COVID-19 dataset. The experiments were conducted upon the proposed OSDL model, which achieved a classification accuracy of 99.83%, while the current Convolutional Neural Network achieved less classification accuracy, i.e., 98.14%.     

Physical–chemical,thermal, and functional properties of achira (Canna indica L.) flour and starch from different geographical origin

Achira (Canna indica L.) is a plant native to the Andes in South America, a starchy source, and its cultivation has expanded to different tropical countries, like Brazil. In order to evaluate the potential of this species, starch and flours with different particle size were obtained from Brazilian achira rhizomes. Proximal analyses, size distribution, SEM, swelling power, solubility, DSC, XRD analysis, and FTIR were performed for characterization of these materials. Flours showed high dietary fiber content (16.5–32.2% db) and high concentration of starch in the case of the smaller particle size fraction. Significant differences in protein and starch content, swelling power, solubility, and thermal properties were observed between the Brazilian and the Colombian starch. All the studied materials displayed the B‐type XRD pattern with relative crystallinity of 20.1% for the flour and between 27.0 and 28.0% for the starches. Results showed that the starch and flour produced from achira rhizomes have great technological potential for use as functional ingredient in the food industry.     

Probing Water Diffusion Inside Crystals of AlPO-5 by PFG NMR and IRM for Heat Storage Applications

Diffusion of water in the aluminophosphates AlPO-5 is studied using a combination of pulsed field gradient NMR and IR microimaging. The concentration profiles measured by the latter allowed to visualize the process of water sorption and propagation into the crystals of AlPO-5. The self-diffusion coefficients obtained by NMR were one order of magnitude higher values compared to corrected ones from the IR microimaging study. The concentration profiles allowed to visualize the process of water sorption and propagation into the crystals of AlPO-5. The obtained results are compared with experimental and simulation data for the water/AlPO-5 system for the literature and discussed in the context of its potential use for heat storage applications.