Dr Mohammed Mahmoud Youssif

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.

Alzheimer's disease (AD) is among the top mortality causing diseases worldwide. The presence of extracellular β-amyloidosis, as well as intraneuronal neurofibrillary aggregates of the abnormally hyperphosphorylated tau protein are two major characteristics of AD. Targeting protein kinases that are involved in the disease pathways has been a common approach in the fight against AD. Unfortunately, most kinase inhibitors currently available target the adenosine triphosphate (ATP)- binding site, which has proven unsuccessful due to issues with selectivity and resistance. As a result, a pressing need to find other alternative sites beyond the ATP- binding site has profoundly evolved. In this review, we will showcase some case studies of inhibitors of tau protein kinases acting beyond ATP binding site which have shown promising results in alleviating AD.

There is an emerging wide use of nanotechnology in the medical fields. The information regarding distribution and clearance of gold nanoparticles (AuNPs) in the ocular tissue is insufficient. We investigated the cumulative effect of AuNPs on rat lens structure and their effect on the redox state and aquaporin-0 (AQP0) expression. Thirty-six male rats were distributed as follow: control, AuNPs-200 (200 μg/kg/rat for 4-weeks) and AuNPs-500 (500 μg/kg/rat for 4-weeks) groups. Rats were euthanized after 4-weeks, and the eye lenses were investigated for histological studies, transmission and scanning electron microscopic studies, immunohistochemistry for AQP0 and morphometric measures. Lens homogenates were investigated for tumour necrosis factor-alpha (TNF-α) and total reactive oxygen species levels by ELISA and for p-c-SRC by western-blot. AuNPs administration induced morphological and ultrastructural changes in rat lens. Degenerative changes in the lens epithelium, cytoplasmic vacuoles, distorted separated cortical lens fibers and loss of ball-and-socket junctions were observed. A significant reduction of AQP0-immune-staining with a significant elevation of TNF-α, total ROS and p-c-SRC content in rat lens homogenates were detected as compared to the control group. Repetitive spherical 20 nm-sized AuNPs administration, especially at 500 μg/kg/rat, induced structural changes in lens fibers of rats and increased oxidative stress level in the lens tissue.

Recently, cold and cough dosage forms have gained significant attention due to their use in the supportive protocols for managing COVID-19 symptoms. In this study, a pharmaceutical formulation containing Paracetamol (PAR), Guaifenesin (GUA), and Phenylephrine hydrochloride (PHE) was investigated for spectral resolution and quantification using advanced spectrophotometric methods. The spectra of these components were significantly overlapped and present in their combined tablet in a challenging ratio of 250:100:5 for PAR, GUA, and PHE, respectively. The established approaches were employed for the simultaneous determination of these drugs in their pharmaceutical formulation without interference from matrix excipients. The study involved various manipulation steps, allowing each component in the combination to be analyzed by more than one approach. Integrating these methods with smart mathematical techniques, the issue of spectral data overlap was resolved without the need for preliminary separation steps. The developed methods are dual wavelength, first derivative, derivative ratio, ratio difference, constant center coupled with spectrum subtraction, and constant multiplication paired with spectrum subtraction. The proposed methods were linear over the concentration range of 3.0-35.0 μg/mL for GUA and 3.0-30.0 μg/mL for PHE. While the PAR ranges for the first derivative and constant multiplication methods were 2.5-35.0 μg/mL and 2.5-25.0 μg/mL, respectively. Excellent linearity of the suggested methods was demonstrated by the high correlation coefficients (R2), ≥ 0.9998 for all the tested compounds. These methodologies were validated according to ICH guidelines. Validation results demonstrated excellent accuracy, with recovery percentages ranging from 98 to 102 %, and precision, with RSD values less than 2 %. The obtained results were statistically compared with the official ones using F-test, Student's t-test, and one-way ANOVA, revealing no significant differences. The proposed methods are accurate, green, smart, fast, and cost-effective. Their compliance with Green Analytical Chemistry principles was evaluated and compared to a published method using various tools to enable a more holistic evaluation from different perspectives. The promising results revealed that the investigated methods are superior green alternatives for routine analysis of the cited drugs in laboratories with limited resources and without access to expensive instruments.

A validated technique has been developed for the sequential determination of pholcodine, paracetamol, and pseudoephedrine. This method allows for their separation either in different ratios within a ternary mixture or in their fixed-dose combination (FDC) using HPLC. The chromatographic conditions involved the utilization of an X-select C18 column (5µ, 4.6 × 150 mm) and a mobile phase consisting of 0.05 M KH2PO4: Methanol (75:25, v/v), with orthophosphoric acid added to adjust the pH to 2.6. The separation was conducted at a 1.2 ml/min flow rate, while a UV detector was set at a wavelength of 206 nm to monitor the process. The three drugs were successfully eluted with retention times of 0.8, 1.9, and 3.0 min for pholcodine, paracetamol, and pseudoephedrine respectively providing fast analysis times. The method exhibited linearity for pholcodine, paracetamol, and pseudoephedrine over concentration ranges of 12–200 µg/ml, 5–100 µg/ml, and 30–200 µg/ml, respectively. It was accurate, reliable, and precise (RSD < 2 %). This proposed methodology was effectively implemented to quantify the concentration of pholcodine, pseudoephedrine, and paracetamol in freshly prepared mixtures and pharmaceutical dosage forms. Furthermore, the method's environmental impact was assessed using five different tools, demonstrating low hazard levels to the environment, and promoting its environmental friendliness compared to the reported method.