Keratin: dissolution, extraction and biomedical application

Keratinous materials such as wool, feathers and hooves are tough unique biological co-products that usually have high sulfur and protein contents. A high cystine content (7–13%) differentiates keratins from other structural proteins, such as collagen and elastin. Dissolution and extraction of keratin is a difficult process compared to other natural polymers, such as chitosan, starch, collagen, and a large-scale use of keratin depends on employing a relatively fast, cost-effective and time efficient extraction method. Keratin has some inherent ability to facilitate cell adhesion, proliferation, and regeneration of the tissue, therefore keratin biomaterials can provide a biocompatible matrix for regrowth and regeneration of the defective tissue. Additionally, due to its amino acid constituents, keratin can be tailored and finely tuned to meet the exact requirement of degradation, drug release or incorporation of different hydrophobic or hydrophilic tails. This review discusses the various methods available for the dissolution and extraction of keratin with emphasis on their advantages and limitations. The impacts of various methods and chemicals used on the structure and the properties of keratin are discussed with the aim of highlighting options available toward commercial keratin production. This review also reports the properties of various keratinbased biomaterials and critically examines how these materials are influenced by the keratin extraction procedure, discussing the features that make them effective as biomedical applications, as well as some of the mechanisms of action and physiological roles of keratin. Particular attention is given to the practical application of keratin biomaterials, namely addressing the advantages and limitations on the use of keratin films, 3D composite scaffolds and keratin hydrogels for tissue engineering, wound healing, hemostatic and controlled drug release.info:eu-repo/semantics/publishedVersio

Innovative foods : the future food supply, nutrition and health

In the coming decades, feeding the growing world population is going to become a global food-supply challenge for our existing food systems. At present, the global food-supply chain has been severely affected due to disruptions caused by the COVID-19 pandemic, climate change, and political conflicts. These disruptions have led to substantial increases in food prices (e.g., FAO cereal Price Index increased by about 25 points and vegetable oil Price Index increased by more than 60 points in March 2022, https://www. statista.com/chart/20165/un-global-food-price-index/, accessed on 22 February 2023). The global food-production crisis and lack of sustenance affordability can create further regional food-security and political disruptions and trigger further socioeconomical injustice among various nations. Innovations to reshape global food systems through improving local food-production capabilities, enabling infrastructure for agricultural innovation, and facilitating knowledge flow as well as technology dissemination are necessary to curb food shortages and security. Developing and applying new and emerging technologies, including synthetic biology and artificial intelligence, to modernize food production and processing would strengthen efforts to overcome supply challenges in the future

Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models.

Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the predictive model is. Predicting marine data such as sea surface temperature (SST) and Significant Wave Height (SWH) is a vital task in a variety of disciplines, including marine activities, deep-sea, and marine biodiversity monitoring. The literature has efforts to forecast such marine data; these efforts can be classified into three classes: machine learning, deep learning, and statistical predictive models. To the best of the authors' knowledge, no study compared the performance of these three approaches on a real dataset. This paper focuses on the prediction of two critical marine features: the SST and SWH. In this work, we proposed implementing statistical, deep learning, and machine learning models for predicting the SST and SWH on a real dataset obtained from the Korea Hydrographic and Oceanographic Agency. Then, we proposed comparing these three predictive approaches on four different evaluation metrics. Experimental results have revealed that the deep learning model slightly outperformed the machine learning models for overall performance, and both of these approaches greatly outperformed the statistical predictive model

A Robust UWSN Handover Prediction System Using Ensemble Learning.

The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical

Fungal and bacterial proteases: Characteristics, and opportunities for the processing of plant proteins

The enzymatic hydrolysis of proteins has been successful in improving their functional and bioactive properties. Extensive work has been conducted in applying plant, fungal, and bacterial proteases to animal protein sources (Ryder et al., 2015; Ha et al., 2012). Less work has been conducted examining the effects of fungal and bacterial proteases on plant proteins. This study characterised a range of bacterial and fungal proteases and evaluated the effects of selected bacterial and fungal proteases on a plant protein substrate assay. Commercially available proprietary fungal (FPII, F31K, and F60K), bacterial (HT, 4000 P, BS Conc), and plant (papain) proteases were screened for activity using the BODIPY-FL-Casein substrate. The soluble protein concentration of the protease powders was also assessed using the Bradford method. Based on the results of the screening, one fungal protease (F31K) and one bacterial protease (HT) were selected for further experiments. The selected proteases were then used to hydrolyse brown rice protein powder. The hydrolysis was conducted for up to 120 min at optimal conditions for each respective protease (pH 8.5 at 45 ⁰C, and pH 6 at 65 °C for F31K and HT, respectively). The resulting hydrolsates were evaluated for their soluble protein content using the Bradford method. The breakdown of protein was also visualised using SDS-PAGE. The mean enzyme activities ranged from 3.55×104 to 39.5×104 Δfluo.min-1.mg soluble protein-1 (for FPII and 4000 P, respectively). Both HT and F31K significantly increased (p < 0.05) the soluble protein concentration of the brown rice protein powder (from 0.586 to 2.21 and 3.12 mg/mL for HT and F31K, respectively). SDS-PAGE showed substantially different hydrolysis patterns for each protease over time. This study provides insights into how proteases from non-gut origin may overcome some of the challenges currently faced in the production of alternative proteins

Plackett–Burman randomization method for Bacterial Ghosts preparation form E. coli JM109

AbstractPlackett–Burman randomization method is a conventional tool for variables randomization aiming at optimization. Bacterial Ghosts (BGs) preparation has been recently established using methods other than the E lysis gene. The protocol has been based mainly on using critical concentrations from chemical compounds able to convert viable cells to BGs. The Minimum Inhibition Concentration (MIC) and the Minimum Growth Concentration (MGC) were the main guide for the BGs preparation. In this study, Escherichia coli JM109 DEC has been used to produce the BGs following the original protocol. The study contained a detail protocol for BGs preparation that could be used as a guide

Virtual Communities and Achieving Electronic Institutional Excellence in the Kingdom of Saudi Arabia - University of Hail as a Model

This study aims to examine the reality of virtual communities and institutional excellence in the Kingdom of Saudi Arabia, using the University of Hail as a case study. It aims also to investigate the extent to which mechanisms for virtual communities and institutional excellence are available at Hail University. the achievement of the goals of electronic institutional excellence, and the major obstacles that stand in the way of achieving the goals and electronic institutional excellence. This study employs a random sample and a descriptive analysis. Social survey method is descriptive and analytical studies. 245 students who got help were studied. To gain data, a sample was given a questionnaire. The study's spatial and human limitations were Hail University teachers and students. Finalizing the research will take 12 months. After analyzing the study's underlying assumptions, the first and third hypotheses were approved as the college's electronic information networks, academic communication, and information sources. Due to limited electronic collaboration, the second theory was partially accepted. Due to lack of experience, the report proposed building rehabilitation and training programs for "virtual communities." One researcher’ biggest issues was not knowing how to use virtual communities to attain greatness. Main results The most important results were a high institutional level under the coronavirus pandemic (3.82), followed by an average of 3.81 for academic processes. The results highlight the prospects for effective application of the COVID-19 crisis responses by offering a secure electronic educational environment with expanded virtual capabilities. This highlights the University's role in handling the crisis, establishing institutional excellence, and addressing education.Scientific Research Deanship at University of Ha'il- Saudi Arabia RG-2019

Prediction of lamb tenderness using combined quality parameters and meat surface characteristics

The objectives of the present study were: to investigate the predictability of cooked lamb tenderness from textural parameters extracted from lamb chops images using GLRM and GLDM techniques. To study the combined effects of texture features, marbling and ultimate pH on the prediction models

Electron spin resonance as a tool to monitor the influence of novel processing technologies on food properties

Nowadays, electron spin resonance (ESR) is widely used as a powerful, non-destructive and very sensitive technique for the detection of free radicals in food systems. It can be applied for the direct identification of highly reactive oxygen species, organic and inorganic paramagnetic species and screening of food for potential toxicity. Its applications cover investigating food oxidative stability and properties of irradiated foods including fruits and vegetables, meats and fishes, spices, cereal grains, and oil seeds.publishe

Inhibition of growth of Leishmania donovani promastigotes by newly synthesized 1,3,4-thiadiazole analogs

AbstractLeishmania donovani, the causative agent of visceral leishmaniasis, is transmitted by sand flies and replicates intracellularly in their mammalian host cells. The emergence of drug-resistant strains has hampered efforts to control the spread of the disease worldwide. Forty-four 1,3,4-thiadiazole derivatives and related compounds were tested in vitro for possible anti-leishmanial activity against the promastigotes of L. donovani. Micromolar concentrations of these agents were used to study the inhibition of multiplication of L. donovani promastigotes. Seven compounds were identified with potential antigrowth agents of the parasite. Compound 4a was the most active at 50μM followed by compound 3a. These compounds could prove useful as a future alternative for the control of visceral leishmaniasis