Preparation and characterisation of chicken skin gelatin as an alternative to mammalian gelatin

The aims of this study were to report for the first time, the extraction and physico-chemical properties of chicken skin gelatin compared to bovine gelatin. Extracted chicken skin gelatin 6.67% (w/v) had a higher bloom value (355 ± 1.48 g) than bovine gelatin (259 ± 0.71 g). The dynamic viscoelastic profile of chicken gelatin exhibited higher viscous and elastic modulus values compared to bovine gelatin for a range of concentrations and frequencies. Thermal properties studied by differential scanning calorimetry (DSC) showed that the melting temperature of 6.67%, chicken skin gelatin was significantly greater (p < 0.05) than that of bovine gelatin, indicating lower stability of bovine gelatin compared to chicken skin gelatin. Results obtained in this study showed that Gly (33.70%), Pro (13.42%), H.Pro (12.13%) and Ala (10.08%) were the most dominant amino acids in chicken skin gelatin which contributed to the higher gel strength and stability. Raman spectra of chicken skin and bovine gelatin were similar and displayed typical protein spectra. Chicken gelatin showed strong hydrogen bonding compared to bovine gelatin as the tyrosine doublet ratio (I₈₅₅/I₈₃₀) of chicken gelatin was significantly lower than that of bovine gelatin. Significantly, the alpha helix and β-sheet type structures were higher for chicken skin gelatin compared with bovine gelatin. The average molecular weight of chicken gelatin was 285,000 Da. These findings, obtained for the first time for chicken skin gelatin, show that it has high potential for application as an alternative to commercial gelatin.     

Residual Interactions of LL-37 with POPC and POPE:POPG Bilayer Model Studied by All-Atom Molecular Dynamics Simulation

LL-37 is a membrane-active antimicrobial peptide (AMP) that could disrupt the integrity of bacterial membranes due to its inherent cationic and amphipathic nature. Developing a shorter derivative of a long peptide such as LL-37 is of great interest, as it can reduce production costs and cytotoxicity. However, more detailed information about the residual interaction between LL-37 and the membrane is required for further optimization. Previously, molecular dynamics simulation using mixed all-atom and united-atom force fields showed that LL-37 could penetrate the bilayer membrane. This study aimed to perform all-atom molecular dynamics simulations, highlighting the residual interaction of LL-37 with the simplest model of the bacterial membrane, POPE:POPG (2:1), and compare its interaction with the POPC, which represents the eukaryotic membrane. The result showed leucine–leucine as the leading residues of LL-37 that first contact the membrane surface. Then, the cationic peptide of LL-37 started to penetrate the membrane by developing salt bridges between positively charged amino acids, Lys–Arg, and the exposed phosphate group of POPE:POPG, which is shielded in POPC. Residues 18 to 29 are suggested as the core region of LL-37, as they actively interact with the POPE:POPG membrane, not POPC. These results could provide a basis for modifying the amino acid sequence of LL-37 and developing a more efficient design for LL-37 derivatives.     

Effect of microstructural evolution from nano to micron grain size regime towards structural,magnetic, electrical and microwave properties of gadolinium iron garnet (Gd3Fe5O12)

Journal of Materials Science: Materials in Electronics - The influence of microstructural changes from nano to micron grain size regime towards their structural, magnetic, electrical and microwave...     

MDEV Model: A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images

Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful; thus, catching it early is crucial. Medical physicians’ time is limited in outdoor situations due to many patients; therefore, automated systems can be a rescue. The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’ experience. Therefore, radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays. In medical classifications, deep convolution neural networks are commonly used. This research aims to use deep pre-trained transfer learning models to accurately categorize CXR images into binary classes, i.e., Normal and Pneumonia. The MDEV is a proposed novel ensemble approach that concatenates four heterogeneous transfer learning models: MobileNet, DenseNet-201, EfficientNet-B0, and VGG-16, which have been finetuned and trained on 5,856 CXR images. The evaluation matrices used in this research to contrast different deep transfer learning architectures include precision, accuracy, recall, AUC-roc, and f1-score. The model effectively decreases training loss while increasing accuracy. The findings conclude that the proposed MDEV model outperformed cutting-edge deep transfer learning models and obtains an overall precision of 92.26%, an accuracy of 92.15%, a recall of 90.90%, an auc-roc score of 90.9%, and f-score of 91.49% with minimal data pre-processing, data augmentation, finetuning and hyperparameter adjustment in classifying Normal and Pneumonia chests.     

Energy enhancement of routing protocol with hidden Markov model in wireless sensor networks

Neural Computing and Applications - Energy enhancement is a crucial factor while designing routing models in wireless sensor networks (WSNs). Many energy efficiency routing schemes are implemented...