Nanospider Technology for the Production of Nylon-6 Nanofibers for Biomedical Applications

Nylon-6 nanofiber mat incorporated with 5,5-dimethyl hydantoin (DMH) as an antimicrobial drug was electrospun from formic acid. The morphology of the nanofiber mat using scanning electron microscope (SEM) showed that the obtained fiber had an average diameter of around 15–328 nm. The nanofiber was characterized by FTIR spectra, TGA, and DSC. The nanofiber containing drug showed initial fast release. It released about 55% of its drug content within the first two hours. Moreover, the antimicrobial activity of the electrospun nylon-6 nanofiber containing drug was examined against Escherichia coli, Pseudomonas aeruginosa, Aspergillus niger, and Aspergillus flavus. The nylon-6 nanofiber exhibited high inhibitory effects against the microbes. The results clearly indicate that the antimicrobial activity of the electrospun nylon-6 nanofiber containing drug varies with the species of the organisms used. Thus, the study ascertains the value of the use of electrospun nanofiber, which could be of considerable interest to the development of new antimicrobial materials. The microbes, examined by SEM, were totally deformed and exhibited severe destruction. Abnormal cell division was observed at high frequencies among cells that tried to divide in the presence of the nanofiber. Many cells were enlarged, elongated, empty ghosts, or fragmented, consistent with the extremely low viability

Controlled Release of 5-Aminosalicylic Acid (5-ASA) from New Biodegradable Polyurethanes

Segmented polyurethanes containing azo aromatic groups in the main chain were synthesized by reaction of 3,3'-azobis(6-hydroxybenzoic acid) (ABHB), 5-[4-(hydroxyphenyl)azo] salicylic acid (HPAS), and 5-[1-hydroxynaphthyl)azo] salicylic acid (HNAS) with hexamethylenediisocyanate (HDI). All synthesized monomers and polymers were characterized by elemental analysis, FTIR, 1H-NMR spectra, TGA and DSC analysis. All the synthesized azo polymers showed good thermal stability and the onset decomposition temperature of all these polymers was found to be above 195 ºC under nitrogen atmosphere.The release of 5-ASA under physiological conditions (pH = 7.8 and pH = 1.5) was investigated at body temperature (37 ºC). The release rate of 5-ASA increased with increasing pH (i.e., 7.8 > 1.5)

Synthesis and characterization of laminated Si/SiC composites

Laminated Si/SiC ceramics were synthesized from porous preforms of biogenous carbon impregnated with Si slurry at a temperature of 1500 °C for 2 h. Due to the capillarity infiltration with Si, both intrinsic micro- and macrostructure in the carbon preform were retained within the final ceramics. The SEM micrographs indicate that the final material exhibits a distinguished laminar structure with successive Si/SiC layers. The produced composites show weight gain of ≈5% after heat treatment in air at 1300 °C for 50 h. The produced bodies could be used as high temperature gas filters as indicated from the permeability results

Classification of Monkeypox Images Based on Transfer Learning and the Al-Biruni Earth Radius Optimization Algorithm

The world is still trying to recover from the devastation caused by the wide spread of COVID-19, and now the monkeypox virus threatens becoming a worldwide pandemic. Although the monkeypox virus is not as lethal or infectious as COVID-19, numerous countries report new cases daily. Thus, it is not surprising that necessary precautions have not been taken, and it will not be surprising if another worldwide pandemic occurs. Machine learning has recently shown tremendous promise in image-based diagnosis, including cancer detection, tumor cell identification, and COVID-19 patient detection. Therefore, a similar application may be implemented to diagnose monkeypox as it invades the human skin. An image can be acquired and utilized to further diagnose the condition. In this paper, two algorithms are proposed for improving the classification accuracy of monkeypox images. The proposed algorithms are based on transfer learning for feature extraction and meta-heuristic optimization for feature selection and optimization of the parameters of a multi-layer neural network. The GoogleNet deep network is adopted for feature extraction, and the utilized meta-heuristic optimization algorithms are the Al-Biruni Earth radius algorithm, the sine cosine algorithm, and the particle swarm optimization algorithm. Based on these algorithms, a new binary hybrid algorithm is proposed for feature selection, along with a new hybrid algorithm for optimizing the parameters of the neural network. To evaluate the proposed algorithms, a publicly available dataset is employed. The assessment of the proposed optimization of feature selection for monkeypox classification was performed in terms of ten evaluation criteria. In addition, a set of statistical tests was conducted to measure the effectiveness, significance, and robustness of the proposed algorithms. The results achieved confirm the superiority and effectiveness of the proposed methods compared to other optimization methods. The average classification accuracy was 98.8%

Value-chain analysis — An assessment methodology to estimate Egyptian aquaculture sector performance

Egypt's aquaculture production (705,490 tonnes in 2009) is by far the largest of any African country and places it 11th in terms of global aquaculture production. The aquaculture sector in Egypt is now a mature one having developed over a period of more than 30 years, but the financial performance of the sector is not well understood or documented, even though value-chain analysis provides a methodological tool to do so. To provide a better understanding of the sector, a WorldFish Center study completed in September 2011 and funded by the Swiss Agency for Development and Cooperation, conducted a value-chain analysis of the pond fish farming sector. The sector concentrates on the production of tilapia with additional production of mullet, catfish and carp from earthen ponds. The study mapped the value-chain and showed that there is no processing and virtually no export of farmed fish, a short time-period from harvest to final consumption by the consumer (typically around one day) due to the live/fresh nature of all sales, and very low rates (< 1%) of post-harvest losses. Quantitative data were collected for each link in the value-chain on operational and financial performance (e.g. gross output values, variable and fixed costs, operational and net profit margins, value-added generation), and on employment creation (by gender, age and full-time/part-time). The results showed that the industry generates a combined LE 4619 ($775) of value-added (i.e. profits plus wages/earnings) for farmers, traders and retailers for each tonne of fish produced. Employment generation is also significant with around 14 full-time equivalent jobs generated for every 100 tonnes of fish produced. However, the sector as a whole is under increasing financial pressure. Critical factors impacting on the performance of the value-chain relate to inputs (most importantly to rising feed costs and the poor quality of fry), to production (most importantly to poor practices with regard to feed management, farm design and construction, fish health management, and stocking densities), and to the marketing, transportation and sale of product (most importantly to declining fish prices in real terms, consumer preference for wild fish and a distrust of filleted/processed products, fluctuating seasonal prices, poor hygiene and handling practices, the lack of value-addition through processing, and the lack of exports). This paper highlights the benefits of value-chain analysis as a useful tool to understand sector performance and argues for its wider use in identifying critical factors and actions to support aquaculture sector improvements