Lebanese French University

This 2024comprehensive review examines the crucial functions of lipids in neurological health, highlighting their vital contributions to brain structure, function, and pathology. The intricate lipid composition of the brain, comprising phospholipids, sphingolipids, cholesterol, glycolipids, and polyunsaturated fatty acids, supports membrane integrity, synaptic transmission, and myelination. Lipid production, metabolism, and transport in the central nervous system are meticulously controlled, necessitating specialised interactions among neurones, glial cells, and the blood-brain barrier. Lipid homeostasis dysregulation is widely acknowledged as playing a critical role in the aetiology of neurodegenerative diseases such as Alzheimer’s and Parkinson’s, multiple sclerosis, and neuropsychiatric disorders like schizophrenia and depression. These disruptions result in compromised synapse function, neuroinflammation, oxidative stress, and neuronal injury. The review emphasises bioactive lipids, particularly specialised pro-resolving mediators originating from polyunsaturated fatty acids, which regulate neuroinflammation and enhance neuroprotection. Progress in lipidomics has enabled the discovery of new lipid biomarkers and therapeutic targets, presenting intriguing opportunities for disease diagnosis, prognosis, and therapy. This paper highlights the significance of lipid biology in maintaining brain health and the therapeutic potential of targeting lipid pathways to mitigate the progression of neurological diseases, integrating contemporary lipidomic discoveries and mechanistic knowledge.

Colon cancer remains a leading global health challenge driven by substantial molecular heterogeneity, complex carcinogenic pathways, and the persistent emergence of therapeutic resistance. This review provides a comprehensive and integrative synthesis of contemporary treatment strategies spanning conventional chemotherapy, molecularly targeted agents, and immunotherapy while contextualizing them within the biological mechanisms that shape therapeutic response. We dissect the mechanistic underpinnings and clinical performance of foundational regimens such as FOLFOX, FOLFIRI, and CAPOX, and analyze how key driver alterations, including RAS/RAF mutations, HER2 amplification, MSI/MMR status, and VEGF-mediated angiogenesis, influence disease progression and therapeutic selection. In addition, we analyze shared resistance pathways and the mechanistic rationale supporting rational combination strategies, including BRAF/EGFR/MEK blockade, HER2-directed dual targeting, and PD-1/PD-L1-based combinations aimed at overcoming immune exclusion in MSS tumors. Emerging advances such as KRAS G12C inhibitors, multi-kinase angiogenesis modulators, antibody-drug conjugates, ribosome biogenesis inhibitors, AI-guided therapeutic algorithms, and ctDNA-based monitoring are also discussed. By integrating mechanistic insights with clinical evidence, this review offers a structured framework to better understand current treatment paradigms and future directions in biomarker-driven precision therapy for colon cancer.

Zolpidem (ZLP), a non-benzodiazepine hypnotic of the imidazopyridine class, is widely prescribed for the short-term management of insomnia owing to its rapid onset of action and minimal residual effects. However, its extensive metabolism, low plasma concentration, and instability in pharmaceutical formulations, biological matrices, and environmental samples create significant analytical challenges. This review provides a comprehensive overview of analytical strategies for the accurate, sensitive, and selective quantification of ZLP and its metabolites across various matrices. Articles were retrieved from major scientific databases, including Scopus, Web of Science, PubMed, ScienceDirect, and Google Scholar. Chromatographic methods such as HPLC, liquid chromatographic-tandem mass spectrometry (LC-MS/MS), and ultra-high-performance liquid chromatography (UHPLC)-MS/MS demonstrate high sensitivity, precision, and selectivity, while spectrophotometric and electrochemical approaches provide faster, simpler, and more economical alternatives suitable for quality control and routine screening. While LC-MS/MS and UHPLC-MS/MS offer superior sensitivity and selectivity for trace-level analysis, their high cost and operational complexity limit routine use, whereas HPLC-UV, spectroscopic, and electrochemical methods remain valuable for quality control and screening despite lower sensitivity. The incorporation of nanomaterials, molecularly imprinted polymers (MIPs), and biosensor-based systems has markedly enhanced analytical sensitivity and selectivity and enabled miniaturization. Additionally, adopting green analytical chemistry (GAC) principles, eco-friendly solvents, and microextraction techniques has improved method sustainability and environmental compatibility. Looking ahead, the integration of artificial intelligence (AI), machine learning (ML), and lab-on-a-chip (LOC) technologies is expected to enhance ZLP determination by enabling automation, real-time monitoring, and predictive analysis. These innovative, eco-conscious approaches will yield robust, intelligent, and sustainable platforms for ZLP detection in pharmaceutical, biological, and environmental samples.

This paper presents a novel feed advance neural network domain using the intelligent Bayesian regularized scheme to generate a numerical solution of the effect of Brownian and thermophoresis diffusion influence on Marangoni convection flow of Biological nanofluid along a sheet with thermophoretic particle deposition and induced magnetic field. The proposed model has significant implications for biological and technical applications based on heat and mass transfer. It enhances synovial nanofluid performance under produced magnetic fields, making it useful for drug delivery systems, targeted therapy, and artificial joint lubrication. Furthermore, the addition of thermophoretic particle deposition, Brownian motion, and Marangoni convection makes it perfect for microfluidic devices, cancer hyperthermia treatment, and advanced cooling systems that demand precise control over nanoparticle transport and thermal management. The numerical outcomes are presented as tables and graphs using the Homotopy analysis method (HAM). Variations in flow characteristics include velocity, temperature, solutal field profiles. The results show that the thermal field expands as the magnetic factor rises and the velocity profile declines.

Carbonation-induced degradation is one of the leading causes of durability loss in concrete structures. Despite advances in conventional concrete carbonation models, predictive models for fiber-reinforced ultra-high-performance concrete (FR-UHPC) remain scarce, given its complex, multiscale behavior. This study presents a new and data-driven analytical framework for predicting the carbonation depth of FR-UHPC using advanced machine learning techniques, including neural operators for modeling physical systems (NOMPS), artificial intelligence-based pipeline search for regression (AIPSR), quantum machine learning (QML), and explainable AI using quantum shapley values (EAIQSV). Analysis of 800 experimental data points identified curing time, temperature, and silica fume content as key determinants of carbonation depth. The models were validated through rigorous statistical analysis and 5-fold cross-validation, with AIPSR outperforming the other models in terms of prediction accuracy (R² = 0.83) and consistency. This framework provides a robust and repeatable method for predicting carbonation in FR-UHPC, while improving interpretability and incorporating quantum-inspired machine learning techniques.