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A concentration equation links nanoparticle concentration to blood components via diffusion, convection, and reaction, and hydrogen peroxide is treated as a oxidative-stress biomarker. The study integrates machine learning to optimize delivery inside the human cardiovascular system, using magnetic fields to guide nanoparticles through complex fluid dynamics. 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nature.com/scientificreports)  \nOPEN  \nMachine learning-a new paradigm in nanoparticle-mediated drug delivery to cancerous tissues through the human cardiovascular system enhanced by magnetic field  \nYasmeen Akhtar1, Shabbir Ahmad2,10􀀍, Fareeha Khalid3, Moin-ud-Din Junjua4,11􀀍, YasharAryanfar5,6,12, Ahmed S. Hendy7,9, Mehdi Tlija8 & Ahmed T. Soliman8  \nNanoparticle-mediated drug delivery offers a promising approach to targeted cancer therapy, leveraging the ability of nanoparticles to deliver therapeutic agents directly to cancerous tissues with minimal impact on surrounding healthy cells. The presence of these nanoparticles is governed by a concentration equation, which accounts for the diffusion, convection, and reaction of the nanoparticles with the blood components. It is well-known that whenever a disease or infection occurs in a human, in 80% of cases a rise in the concentration of hydrogen peroxide in the blood occurs. This is the reason why blood is assumed to contain hydrogen peroxide (in the present study), which is a biomarker of oxidative stress and inflammation. This study explores the integration of machine learning (ML) techniques into the optimization of drug delivery processes within the human cardiovascular system, focusing on the enhancement of these processes through the application of magnetic fields. By employing ML algorithms, we analyze and predict the behavior of nanoparticles as they navigate the complex fluid dynamics ofthe cardiovascular system, particularly under the influence of an external magnetic field. The predictive power of ML models enables the precise control of nanoparticle trajectories, optimizing their accumulation in cancerous tissues and improving the efficacy of the drug delivery system. The findings ofthis study demonstrate that ML-enhanced magnetic targeting can significantly enhance the precision and effectiveness of nanoparticle-mediated drug delivery, offering anew paradigm in cancer treatment strategies. This approach has the potential to revolutionize the field by providing personalized and highly efficient therapeutic solutions for cancer patients.  \nKeywords Machine learning, Drug delivery, Magnetic field, Cancer tissues, Cardiovascular system  \nDrug delivery is a critical aspect of modern medical treatment, particularly in oncology, where the precise targeting of therapeutic agents to malignant tissues is essential for effective outcomes. The complication of carrying drugs to precise spots within the body is prejudiced by many factors, together with the chemical,  \n1Institute of Pharmaceutics, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China. 2Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, Hubei, China.  \n3Khawaja Fareed University of Engineering and Technology, RahimYar Khan, Pakistan. 4School of Mathematical Sciences, Zhejiang Normal University, Jinhua 321004, Zhejiang, China. 5State Key Laboratory of Hydrology-Water Recourses and Hydraulic Engineering, College of Mechanics and Materials, Hohai University, Nanjing 210098, Jiangsu, China. 6Department of Electric Engineering and Computation, Autonomous University of Ciudad Juárez, Av. Del Charro 450 Norte, Col. Partido Romero, Juárez, Chihuahua, Mexico. 7Department of Computational Mathematics and Computer Science, Institute of Natural Sciences and Mathematics, Ural Federal University, 19 Mira St., Yekaterinburg 620002, Russia. 8Department of Industrial Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia. 9Department of Mechanics and Mathematics, Western Caspian University, Baku, 1001, Azerbaijan. 10School of Ocean Engineering, Federal University of Rio Grande (FURG), Rio Grande - RS, 96203900, Brazil. 11Department of Mathematics, Ghazi University, Dera Ghazi Khan, 32200, Pakistan. 12Thermo-Fluids research group, Khazar University, Baku, Azerbaijan. 􀀍 email:","cbCaijvQaj99hDsB","https://ap.wps.com/l/cbCaijvQaj99hDsB","pdf",5009548,21,"English","# Abstract\n## Drug delivery modeling and biomarkers\n## ML-enabled optimization with magnetic targeting\n## Reported impact on precision and efficacy","[{\"question\":\"What problem does the study address in cancer drug delivery?\",\"answer\":\"It targets improved delivery of therapeutic agents to cancerous tissues with reduced impact on surrounding healthy cells by guiding nanoparticle behavior in the cardiovascular system.\"},{\"question\":\"How is the cardiovascular delivery process modeled in the study?\",\"answer\":\"The study uses a concentration equation describing diffusion, convection, and reaction of nanoparticles with blood components, and it assumes hydrogen peroxide in blood as a biomarker of oxidative stress and inflammation.\"},{\"question\":\"What role do machine learning and magnetic fields play?\",\"answer\":\"Machine learning algorithms analyze and predict nanoparticle behavior under cardiovascular fluid dynamics, while magnetic fields enhance the delivery process by enabling more precise control of nanoparticle trajectories and accumulation in cancerous tissues.\"}]","Machine learning-a new paradigm in nanoparticle-mediated drug delivery to cancerous tissues through the human cardiovascular system enhanced by magnetic field | PDF",1789982229,53]