Dr Mohammed Mahmoud Youssif

Acute otitis media (AOM) is a prevalent middle ear infection, affecting nearly one-third of children in the U.S. This study focused on enhancing non-invasive trans-tympanic delivery of ciprofloxacin (CFX) by encapsulating it within Brijosomes, a novel nano-vesicular system for improved ototopical treatment. Brijosomes were formulated using the ethanol injection technique and optimized through a 23 full factorial design, investigating the effects of surfactant: limonene ratio, cholesterol: drug ratio, and Brij 92 concentration. Key parameters evaluated included entrapment efficiency (EE%), particle size (PS), polydispersity index (PDI), and zeta potential (ZP). The optimized formulation, selected via Design Expert® software, demonstrated a high desirability value of 0.979, with EE% of 91.68 %, PS of 196.85 nm, and ZP of −25.45 mV. Characterization studies revealed that the optimized Brijosomal formula had a spherical morphology, exhibited a bi-phasic in vitro release profile, and remained stable for three months at 5 ± 3 °C. Ex vivo permeation studies demonstrated superior penetration and flux compared to CFX solution, with a 2.15-fold enhancement ratio. Microbiological assessments confirmed improved antibacterial and antibiofilm activities of the optimized formulation. Additionally, confocal laser scanning microscopy (CLSM) validated deeper penetration, consistent with the ex vivo findings. Histopathological examination confirmed the formulation's safety for ototopical application. These findings collectively emphasize the potential of CFX-loaded Brijosomes as an effective, non-invasive drug delivery system for AOM treatment.

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Edible insect farming presents significant potential as a sustainable solution to meet the increasing demand for protein and biowaste recycling. However, microbial contamination in rearing substrates poses a serious threat to insect populations. This study investigates the pathogenicity of Serratia marcescens YSMM-S1, isolated from dead Protaetia brevitarsis larvae, and explores its interaction with Trichoderma xixiacum, a fungus co-isolated from the same rearing environment. The identified S. marcescens YSMM-S1 was confirmed as a highly virulent strain, causing 100 % larval mortality within 24 h. In addition, T. xixiacum identified via extensive molecular and morphological characterization, is reported here for the first time in Korea. Co-cultivation and mycophagy assays revealed a significant increase in S. marcescens proliferation in the presence of T. xixiacum, likely through fungal mycelium consumption. Moreover, preinoculation with T. xixiacum in oak-fermented sawdust significantly enhanced S. marcescens population growth and colonization. In vivo assays further demonstrated that co-inoculation of the substrate with both microorganisms resulted in significantly higher larval mortality compared to single inoculations. This study provides novel insights into the complex microbial dynamics in insect-rearing systems, emphasizing the role of T. xixiacum in facilitating bacterial pathogenicity. The findings highlight the need for careful microbial management to prevent outbreaks in mass-rearing environments. This work also contributes to the understanding of fungal-bacterial interactions in insect farming and offers new perspectives on the ecological roles of Trichoderma species in rearing substrates.

This study develops fuzzy-probabilistic models to investigate the dynamic instability (i.e., the first instability point) of corroded buried pipes conveying fluids. The models are developed via a novel hybrid of random fields and fuzzy logic methods to capture both aleatoric uncertainty, stemming from the stochastic nature of pipeline, fluid, and soil parameters, and epistemic uncertainty, arising from corrosion inspection challenges, especially in urban areas. Key features of the models include: (1) applicability to various pipe geometries, fluid types, and soil stiffness distributions; (2) Pipe Condition Index, on 0–100 scale, updatable from field inspection to reflect corrosion levels; and (3) uncertainty quantifiers including the random field significance level and fuzzy model coefficients. Results showcase the corrosion’s significant influence on dynamic instability. Furthermore, non-uniform soil stiffness distribution, with minimum stiffness at mid-span, increases the susceptibility to dynamic instability compared to the uniform soil case. The developed models achieve a high goodness-of-fit, with R2 within 0.91–0.96, underscoring their accuracy in predicting the dynamic instability of corroded pipelines conveying fluids.

OBJECTIVE: The past ten years have seen an increase in gabapentin (GBP) overuse and abuse in Egypt after pregabalin scheduling. Numerous studies have demonstrated the detrimental effects of pregabalin; nonetheless, GBP`s effects are minimal. The objective of this investigation is to study GBP-induced neurotoxicity in rats and the protective benefits of alpha-tocopherol (vitamin E `Vit E`).

MATERIAL AND METHODS: Forty (40) adult male albino rats were randomly split into four groups: (10 rats each): Group I, which was subdivided into group Ia (5 rats), received a regular diet as a negative control; group Ib (5 rats) received corn oil as a positive control; group II received alpha-tocopherol; group III (GBP misuse); and group IV received GBP + alpha-tocopherol. The corresponding medicines were administered to every rat for fifty days. Neurobehavioral tests were performed on the day of scarification. Hippocampal tissues were collected for immunohistochemical and histological analysis.

RESULTS: Weight gain rose considerably by the end of the research in the drug-treated groups. In neurobehavioral tests, controls performed better and had higher locomotor indices. The group that misused GBP showed more deteriorated cells and more negative effects on hippocampal tissues. These histological alterations dramatically decreased with alpha-tocopherol therapy.

CONCLUSION: GBP in high doses had neurotoxic effects, disrupted hippocampal tissues, and increased the number of degenerated cells. Alpha-tocopherol treatment significantly attenuated the deleterious effects induced by GBP.

The Egyptian Nubian (Zaraibi) goat has significant potential to contribute to animal production and rural livelihoods in Egypt. This article explores the breed's unique genetic makeup and potential for improvement through examining its genetic parameters, population structure, genetic diversity, and candidate genes associated with economically important traits. The reported potential candidate genes include caseins, alpha-lactalbumin (α-LA), beta-lactoglobulin (β-LG), prolactin receptor (PRLR), Fec gene, GDF9, and growth hormone. These genes are associated with milk yield and composition, litter size, and growth performance. Although most candidate gene studies on Zaraibi goats have limitations, their findings can be utilized in genomic evaluation to improve perdition accuracy. Despite challenges such as small herd size, experimental design constraints, epigenetic influence, and potential trade-off between productivity and heat tolerance, there are significant opportunities to enhance the breed's productivity and resilience. The studies highlight advantages such as high genetic diversity, positive genetic gain, adaptation to hot dry environment and clear genetic distinction from other local goat breeds. Prospects for improving Zaraibi goats include recognizing their socioeconomic role in rural communities, supporting selective breeding programs, integrating genomic information into selection strategies, implementing an open nucleus breeding scheme, and expanding the specialized goat cheese market. Like other promising subtropical breeds, utilizing their potential while addressing existing challenges is essential for ensuring sustainable production and continued contributions to the rural economy.

Sustainable agriculture approaches necessitate a concerted effort from researchers to establish paths that meet global population needs without compromising environmental resources. Goats are unique among ruminants because of their ability to adapt to some of the harshest environments around the world. Growth Hormone (GH) gene is a major regulator of muscle mass growth. Therefore, our study aims to analyze the whole sequence of GH gene in order to identify various single nucleotide polymorphisms (SNPs) in Damascus and Black Bengal goats (BBG) and to predict the effect of mutant residues on the protein's structure and function. Additionally, this study examined the effects of these SNPs on growth characteristics in Damascus goats. The study was designed to test 22 male goats. To obtain high-quality amplification of the GH gene, we utilized the nested PCR technique and performed paired-end sequencing for each fragment. Sequences alignment in the GH gene of Damascus and BBG goats unveiled 25 SNPs with different frequencies within the two breeds. Seven SNPs identified in coding regions comprised four synonymous variants (719 C → T, 731 G → A, 1610 C → T and 1625 C → T) and three non-synonymous variants (1112 G → A, 1459 C → T and 1470 C → T). The SNP 1112 G → A led to the substitution of Glutamic acid with Lysine (E/K) at amino acid position 137. The SNP 1459 C → T led to the substitution of Alanine with Valine (A/V) at amino acid position 160. Meanwhile, the SNP 1470 C → T led to the substitution of Leucine with Phenylalanine(L/F) at amino acid position 164. The SNP 1112 G → A was predicted to have a deleterious effect on the protein's structure and function. The SNPs 914 G → A and 1112 G → A showed significant differences (P < 0.05) between genotypes in pre-slaughter weight and almost carcass cuts in Damascus goats, indicating their possible use in breeding programs as a potential genetic marker for weight/size in goats as well as in assessing and choosing members of this breed for meat production.

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Genetic variability in livestock driven by selection leaves distinct signatures within the genome. However, the comprehensive landscape of the selection responses for milk production traits in the Chinese buffalo population remains elusive. This study employed an integrated haplotype score (iHS) and runs of homozygosity (ROH) analyses of whole-genome sequence data from 100 Chinese buffaloes to decipher selection signatures. Using iHS and ROH, we identified 1 046 and 1 045 significant genomic regions, containing 717 and 263 candidate genes, respectively. The integration of iHS and ROH revealed 258 candidate regions and 108 overlapping genes, representing true selection signatures. Additionally, 94 candidate regions overlapped with 672 previously reported quantitative trait loci associated with key economically important traits. Annotation of the genomic regions highlighted candidate genes linked to milk production traits, including SNORD42, COX18, ANKRD17, ALB, RASSF6, CXCL8, TMEM232, ARHGAP26, and NR3C1. Transcriptome-wide association analysis supported ANKRD17 and CEP41 as potential candidates for affecting milk traits. This study unveils a comprehensive selection signature profile for the Chinese buffalo population by integrating iHS and ROH methods. The findings have broad implications for improving milk production traits in buffalo populations globally, contributing to more sustainable livestock systems. The identified candidate genes shed light on the selection response for milk production traits, offering crucial insights into optimising the breeding strategies for Chinese buffaloes.

This study presents a smart analytical chemistry approach that strategically integrates artificial intelligence (AI), green chemistry principles, and white analytical chemistry frameworks for pharmaceutical analysis. Smart analytical chemistry, the synergy of greenness, analytical performance, and computational intelligence, was applied to develop smart spectrophotometric methods for the simultaneous determination of solifenacin succinate (SOF) and mirabegron (MIR) used in overactive bladder therapy. AI-optimized signal difference strategies were used to address spectral overlap challenges between the compounds. Two complementary smart resolution approaches were developed: (1) a zero-window scenario using absorbance resolution (AR) for MIR and first derivative (D1) for SOF, and (2) a ratio-window scenario with ratio extraction (RE) for MIR followed by absorbance variation (AV) for SOF. SOF showed linearity from 2.5 to 25.0 μg/mL at 222.0 nm using D1 and AV at ΔA(217.0–238.0) nm, while MIR exhibited linearity at 249.0 nm from 1.5 to 15.0 μg/mL. A comparative evaluation assessed both strategies for sensitivity and specificity. A cumulative validation score (CVS) was computed to serve as an indicator in the risk analysis. The method's smart profile was assessed using Analytical Green Star Area (AGSA) and the RGB12 algorithm, confirming balanced environmental, analytical, and practical performance aligned with Sustainable Development Goals (SDGs). Furthermore, the GLANCE tool (Graphical Layout for Analytical Chemistry Evaluation) provided a visual summary of twelve structured method attributes. This work exemplifies the application of smart analytical chemistry to deliver sustainable, accurate, and eco-friendly alternatives for pharmaceutical quality control.

This study presents a smart analytical chemistry approach that strategically integrates artificial intelligence (AI), green chemistry principles, and white analytical chemistry frameworks for pharmaceutical analysis. Smart analytical chemistry, the synergy of greenness, analytical performance, and computational intelligence, was applied to develop smart spectrophotometric methods for the simultaneous determination of solifenacin succinate (SOF) and mirabegron (MIR) used in overactive bladder therapy. AI-optimized signal difference strategies were used to address spectral overlap challenges between the compounds. Two complementary smart resolution approaches were developed: (1) a zero-window scenario using absorbance resolution (AR) for MIR and first derivative (D1) for SOF, and (2) a ratio-window scenario with ratio extraction (RE) for MIR followed by absorbance variation (AV) for SOF. SOF showed linearity from 2.5 to 25.0 μg/mL at 222.0 nm using D1 and AV at ΔA(217.0–238.0) nm, while MIR exhibited linearity at 249.0 nm from 1.5 to 15.0 μg/mL. A comparative evaluation assessed both strategies for sensitivity and specificity. A cumulative validation score (CVS) was computed to serve as an indicator in the risk analysis. The method's smart profile was assessed using Analytical Green Star Area (AGSA) and the RGB12 algorithm, confirming balanced environmental, analytical, and practical performance aligned with Sustainable Development Goals (SDGs). Furthermore, the GLANCE tool (Graphical Layout for Analytical Chemistry Evaluation) provided a visual summary of twelve structured method attributes. This work exemplifies the application of smart analytical chemistry to deliver sustainable, accurate, and eco-friendly alternatives for pharmaceutical quality control.