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Royal jelly fatty acids as emerging anticancer agents:integrative mechanisms and translational perspectives 认领 引用
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作者 Jie Yi Isack Ibrahim Mrisho +3 位作者 Elshan Musazade Wei Li Jiahong Han Enbo Cai 《Food Science and Human Wellness》 SCIE CAS CSCD 2026年第7期3052-3068,共17页
Royal jelly(RJ),a nutrient-rich secretion from the hypopharyngeal and mandibular glands of worker bees(Apis mellifera),has gained significant attention as a natural source of bioactive compounds with therapeutic poten... Royal jelly(RJ),a nutrient-rich secretion from the hypopharyngeal and mandibular glands of worker bees(Apis mellifera),has gained significant attention as a natural source of bioactive compounds with therapeutic potential.Among its various constituents,medium-chain fatty acids(MCFAs),notably 10-hydroxy-2-decenoic acid(10-HDA),10-hydroxydecanoic acid(10-HDAA),and sebacic acid(SA),exhibit potent antioxidant,antiinflammatory,immunomodulatory,and antimicrobial properties.In recent years,10-HDA has emerged as the primary anticancer agent in RJ,demonstrating the ability to modulate key oncogenic pathways,including the induction of apoptosis,cell cycle arrest,and inhibition of angiogenesis and metastasis.This review provides a comprehensive analysis of the chemical composition,biosynthetic origin,and extraction techniques of RJ fatty acids(RJFAs),with a critical focus on their molecular mechanisms of anticancer action,including modulation of cell signaling pathways,DNA repair mechanisms,and immune response.We further explore their metabolic stability,bioavailability,and synergistic potential with conventional chemotherapeutics in models of lung cancer,breast cancer,liver cancer,and melanoma.Although promising,current limitations,including challenges in standardization,pharmacokinetics,and formulation,warrant further investigation.Future directions emphasize omics-driven approaches,such as genomics,proteomics,and metabolomics,as well as translational research to optimize the clinical applicability of RJFAs.Collectively,these insights position RJFAs,particularly 10-HDA,as compelling candidates for adjunctive cancer therapy and integrative oncology. 展开更多
关键词 Royal jelly fatty acids 10-Hydroxy-2-decenoic acid 10-Hydroxydecanoic acid Anticancer mechanisms Integrative oncology Natural bioactive compounds
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Integration of LLMs and VLMs in plant stress phenotyping:From trait recognition to decision support 认领 引用
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作者 Elshan Musazade Isack Ibrahim Mrisho +1 位作者 Jinshan Gao Xianzhong Feng 《Plant Phenomics》 SCIE EI CSCD 2026年第1期416-432,共17页
The integration of Large Language Models(LLMs)with Vision-Language Models(VLMs)holds transformative potential for plant stress phenotyping,enhancing high-throughput crop monitoring,trait identification,and decision su... The integration of Large Language Models(LLMs)with Vision-Language Models(VLMs)holds transformative potential for plant stress phenotyping,enhancing high-throughput crop monitoring,trait identification,and decision support.Traditional phenotyping methods,often reliant on manual assessments and task-specific Ma-chine Learning(ML)models,face persistent limitations in scalability,adaptability,and contextual interpretation,especially under complex and overlapping stress conditions.VLMs address these challenges by combining deep visual recognition with contextual reasoning,enabling real-time analysis of multimodal inputs such as high-resolution imagery,agronomic text data,and environmental sensor readings.Complementarily,LLMs contribute to text mining,semantic annotation of trait descriptors,and the integration of external knowledge via Retrieval-Augmented Generation(RAG),thereby enhancing the interpretability and adaptability of phenotyping workflows.This review critically evaluates the emerging role of integrating LLMs with VLMs in plant stress phenotyping,highlighting their applications in visual trait recognition,knowledge extraction,and autonomous decision-making.We synthesize current advances and identify key challenges,including data quality,domain-specific generalization,model transparency,and equitable access to AI technologies.As one of the first comprehensive reviews on this topic,we propose a forward-looking framework that integrates LLMs,VLMs,and RAG systems to enable scalable,explainable,and user-centric phenotyping solutions.This interdisciplinary convergence offers a promising pathway toward sustainable and resilient AI-driven agriculture. 展开更多
关键词 Large language models Vision-language models Plant stress detection Precision agriculture Artificial intelligence in phenotyping
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