Deep eutectic solvents (DESs) have emerged as sustainable and tunable alternatives to conventional solvents for the extraction of polysaccharides. This review presents a structure-informed framework linking DES composition to polysaccharide solubility, emphasizing the differential responsiveness of amorphous, interfacial, and crystalline domains. Amorphous polysaccharides are efficiently extracted under mild DES conditions, while crystalline polymers often require stronger hydrogen bond acceptors or thermal/mechanical activation. Beyond dissolution, DESs modulate key properties of the extracted polysaccharides, including molecular weight, monomer composition, and bioactivity. Comparative analysis highlights how acidic, basic, or metal-coordinating DESs selectively target distinct polymer classes. Emerging innovations, such as in situ DES formation, mechanochemical systems, and switchable solvents, enhance efficiency and reduce downstream processing demands. Furthermore, the integration of machine learning and COSMO-RS modeling enables predictive solvent design, reducing reliance on empirical screening. By combining mechanistic insight, compositional tailoring, and computational tools, this review provides a scientifically grounded perspective for advancing DES-mediated extraction processes and enabling structure-preserving, application-oriented recovery of polysaccharides in food, pharmaceutical, and biorefinery domains.

Suicide remains a leading cause of death globally, with nearly 800,000 deaths annually, particularly among young adults in regions like Europe, Australia, and the Middle East, highlighting the urgent need for innovative intervention strategies beyond conventional methods. This review aims to explore the transformative role of artificial intelligence (AI) and machine learning (ML) in enhancing suicide risk prediction and developing effective prevention strategies, examining how these technologies integrate complex risk factors, including psychiatric, socio-economic, dietary, and environmental influences. A comprehensive review of literature from databases such as PubMed and Web of Science was conducted, focusing on studies that utilize AI and ML technologies. The review assessed the efficacy of various models, including Random Forest, neural networks, and others, in analyzing data from electronic health records, social media, and digital behaviors. Additionally, it evaluated a broad spectrum of dietary factors and their influence on suicidal behaviors, as well as the impact of environmental contaminants like lithium, arsenic, fluoride, mercury, and organophosphorus pesticides. AI and ML are revolutionizing suicide prevention strategies, with models achieving nearly 90 % predictive accuracy by integrating diverse data sources. Our findings highlight the need for geographically and demographically tailored public health interventions and comprehensive AI models that address the multifactorial nature of suicide risk. However, the deployment of these technologies must address critical ethical and privacy concerns, ensuring compliance with regulations and the development of transparent, ethically guided AI systems. AI-driven tools, such as virtual therapists and chatbots, are essential for immediate support, particularly in underserved regions.

Background/Objectives: This review examines the evolution of lyotropic liquid crystals (LLCs) in ocular drug delivery, focusing on their ability to address the challenges associated with traditional ophthalmic formulations. This study aims to underscore the enhanced bioavailability, prolonged retention, and controlled release properties of LLCs that significantly improve therapeutic outcomes. Methods: This review synthesizes data from various studies on both bulk-forming LLCs and liquid crystal nanoparticles (LCNPs). It also considers advanced analytical techniques, including the use of machine learning and AI-driven predictive modeling, to forecast the phase behavior and molecular structuring of LLC systems. Emerging technologies in biosensing and real-time diagnostics are discussed to illustrate the broader applicability of LLCs in ocular health. Results: LLCs are identified as pivotal in promoting targeted drug delivery across different regions of the eye, with specific emphasis on the tailored optimization of LCNPs. This review highlights principal categories of LLCs used in ocular applications, each facilitating unique interactions with physiological systems to enhance drug efficacy and safety. Additionally, novel applications in biosensing demonstrate LLCs’ capacity to improve diagnostic processes. Conclusions: Lyotropic liquid crystals offer transformative potential in ocular drug delivery by overcoming significant limitations of conventional delivery methods. The integration of predictive technologies and biosensing applications further enriches the utility of LLCs, indicating a promising future for their use in clinical settings. This review points to continued advancements and encourages further research in LLC technology to maximize its therapeutic benefits.

Deep eutectic solvents (DESs) have emerged as sustainable and tunable alternatives to conventional solvents for the extraction polysaccharides. This review presents a struc-ture-informed framework linking DES composition to polysaccharide solubility, em-phasizing the differential responsiveness of amorphous, interfacial, and crystalline domains. Amorphous polysaccharides are efficiently extracted under mild DES condi-tions, while crystalline polymers often require stronger hydrogen bond acceptors or thermal/mechanical activation. Beyond dissolution, DESs modulate key properties of the extracted polysaccharides, including molecular weight, monomer composition, and bioactivity. Comparative analysis highlights how acidic, basic, or metal-coordinating DESs selectively target distinct polymer classes. Emerging innovations, such as in situ DES formation, mechanochemical systems, and switchable solvents, enhance efficiency and reduce downstream processing demands. Furthermore, the integration of machine learning and COSMO-RS modeling enables predictive solvent design, reducing reliance on empirical screening. By combining mechanistic insight, compositional tailoring, and computational tools, this review provides a scientifically grounded perspective for ad-vancing DES-mediated extraction processes and enabling structure-preserving, appli-cation-oriented recovery of polysaccharides in food, pharmaceutical, and biorefinery domains.