Shaqra University (SU) (Arabic: جامعة شقراء) is located in Sahqra, Saudi Arabia, 190 km (120 mi) northwest of Riyadh). It comes under the supervision of the Ministry of Education. The university has twenty-four colleges in various disciplines. It is headquartered in Shaqra with its various colleges being geographically spread in the largest area of the Kingdom of Saudi Arabia covering several governorates and sub-governorates lying in the west of Riyadh.
The incorporation of artificial intelligence (AI) and machine learning (ML) into microalgal research is transforming biomass generation, biofuel synthesis, and wastewater remediation strategies. Sophisticated ML techniques, such as artificial neural networks (ANN), support vector machines (SVM), and genetic algorithms (GA), facilitate precise simulation and forecasting of highly intricate microalgal systems. Although constraints related to data accessibility and model scalability persist, ML-based methodologies are increasingly demonstrating their value in enhancing the sustainability and operational efficiency of microalgal processes. Simultaneously, technoeconomic analysis (TEA) has become an indispensable framework for assessing biorefinery viability through systematic evaluation of life-cycle environmental burdens. Recent progress in TEA methodologies has strengthened iterative design optimization, uncertainty quantification, and user accessibility via open-source computational platforms. Broader systems boundaries now account for policy mechanisms, performance during end-use phase, and international market dynamics, thereby reinforcing TEA’s contribution to sustainable bioeconomic advancement. Collectively, these computational and analytical innovations are expediting the deployment of scalable and economically feasible microalgal technologies. Highlights
Bacillus cereus is a spore-forming foodborne pathogen responsible for diarrheal and emetic syndromes, as well as severe opportunistic infections. Its persistence in diverse environments and intrinsic resistance mechanisms highlight the urgent need for effective preventive strategies, particularly in the absence of licensed vaccines. In this study, an integrated computational and immunoinformatics approach was employed to identify conserved antigenic targets and design a multi-epitope vaccine candidate against Bacillus cereus. Pan-genome analysis enabled the identification of conserved antigenic proteins, followed by epitope prediction and construction of a multi-epitope vaccine. The designed construct exhibited strong antigenicity, stability, and solubility, with 97.3% of residues located in the favored regions of the Ramachandran plot and a ProSA-web Z-score within the range of native proteins. Molecular docking analysis demonstrated a favorable binding affinity for Toll-like receptor 2 (TLR2), while molecular dynamics simulations confirmed the structural stability of the complex. Codon optimization enhanced expression potential by reducing the GC content from 70.10% to 56.80%. Immune simulations predicted robust IgG responses, Th1-skewed cytokine production, and the development of immunological memory. Overall, the designed multi-epitope vaccine demonstrates strong in silico immunogenic potential, broad global population coverage (96.98%), and favorable expression feasibility. These findings support the proposed construct as a promising vaccine candidate; however, experimental validation through in vitro and in vivo studies is required to confirm its efficacy.
Chitosan (CTS), a naturally derived polysaccharide from chitin, exhibits intrinsic but relatively moderate antimicrobial activity, typically requiring high concentrations to achieve significant inhibition. To overcome this limitation, recent research has focused on Zn(II) complexation and nanoengineering approaches, which have demonstrated substantial improvements in antimicrobial efficacy. Reported minimum inhibitory concentration (MIC) values for CTS-Zn systems are frequently reduced compared to CTS, with enhanced activity observed against both Gram-positive and Gram-negative pathogens. This enhancement is attributed to synergistic mechanisms, including improved membrane permeability, sustained Zn2+ ion release, and increased surface area in nanostructured forms. This review presents a comparative analysis of CTS-Zn(II) complexes and CTS-based Zn nanomaterials, examining the impact of coordination modes, particle size, and surface charge on antimicrobial activity. Unlike previous descriptive reviews, this work systematically correlates physicochemical modifications with antimicrobial performance, highlighting that variations in synthesis strategies and material design influence the antimicrobial activity. Special emphasis is placed on Zn(II) complexation and nanoengineering approaches to enhance the antimicrobial efficacy of CTS.
Schiff bases are privileged azomethine pharmacophores with diverse bioactivity. In this report, two new hydrazine-derived Schiff bases, L1 and L2, were synthesized. The structures were confirmed by experimental approaches involving IR, UV-vis, 1H, and 1 3C NMR spectroscopy and showed strong correlation with theoretical computational predictions. Molecular reactivity was explored using DFT (B3LYP/6-311G(d,p), SMD/water) through frontier orbitals, electrostatic potential, and global reactivity descriptors. Antibacterial evaluation against Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa showed strain-selective potency: L1 was most active against S. aureus and E. coli (MIC 4.9 and 6.5 & micro;g/mL), while L2 displayed moderate activity against P. aeruginosa (MIC 8.4 & micro;g/mL). Ciprofloxacin remained the strongest reference (MIC 0.049-0.521 & micro;g/mL). Docking with DNA gyrase (PDB ID 6F86) indicated favorable binding for L1 (-6.4 kcal/mol) and L2 (-6.0 kcal/mol) vs. ciprofloxacin (-7.0 kcal/mol). ADMET predictions supported acceptable drug-likeness and low toxicity. These results highlight hydrazine-based Schiff bases as promising leads for antibacterial design against multidrug resistance.
The green synthesis of copper nanoparticles (Cu NPs) using mushroom biomass is an eco-friendly and sustainable alternative to conventional chemical and physical methods. The bioactive compounds in the mushroom biomass act as reducing and stabilizing agents, facilitating the nanoparticle synthesis without the need for toxic reagents. This study explores the biosynthesis of Cu NPs, an aqueous mushroom extract from S. ostrea. The development of a dark ocher color confirmed the formation of SO-Cu (II) NPs. Various parameters were optimized, including concentration of CuSO4.5H2O, extract level, and procurement period to achieve enhanced nanoparticle yield. The optimal concentration of CuSO4.5H2O was 1 M, the optimal extract level was 1%, while the procurement period determined was 45 min. The synthesized Cu NPs were characterized using UV-vis spectroscopy, FTIR, XRD, and SEM. UV-vis spectroscopy showed a distinct surface plasmon resonance (SPR) peak in the range of similar to 290 nm. SEM showed the structure, FTIR revealed distinct functional groups, and the crystalline nature of myco-synthesized Cu NPs was confirmed by XRD analysis. Concentration-dependent antimicrobial activity was observed, as 10 & micro;L produced better results than 5 & micro;L. This mushroom-mediated synthesis approach aligns with green chemistry principles, offering a low-cost, nontoxic, and scalable method with promising implications in medicine.
